diff --git a/playground/kalman.html b/playground/kalman.html
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+
+
+
+
+
+
tsb — Kalman Filter & State-Space Models
+
+
+
+
+
+
+
← Back to index
+
🔭 Kalman Filter & State-Space Models
+
+ Linear Gaussian state-space model — Kalman filter (forward pass) and
+ RTS smoother (backward pass). Mirrors statsmodels.tsa.statespace
+ and pykalman.KalmanFilter.
+
+
+
+
+
📐 The State-Space Model
+
+ A linear Gaussian SSM describes a latent state x_t and
+ observations y_t via two equations:
+
+
+ x_t = F · x_{t-1} + w_t, w_t ~ N(0, Q) (state transition)
+
+ F — state transition matrix (n_states × n_states)H — observation matrix (n_obs × n_states)Q — process noise covarianceR — observation noise covariancem_0, P_0 — initial state distribution
+
+ The Kalman filter computes filtered state
+ estimates x_{t|t} (posterior after seeing observation t).
+ The RTS smoother computes smoothed estimates
+ x_{t|T} using all T observations.
+
+
+
+
+
+
📈 Local-Level Model (Random Walk + Noise)
+
+ The simplest SSM: a hidden state that follows a random walk, observed
+ with noise. Perfect for denoising a noisy scalar time series or
+ estimating a slowly changing mean.
+
+
+
+
+
Click ▶ Run to execute
+
+
+
+
+
+
🔄 RTS Smoother — Filling Gaps Retrospectively
+
+ The filter only uses observations up to time t. The smoother uses
+ all observations to produce better estimates, especially for
+ time-steps near missing values. Smoothed uncertainty is always ≤ filtered.
+
+
+
+
+
Click ▶ Run to execute
+
+
+
+
+
+
📊 Local Linear Trend (Level + Slope)
+
+ A 2-state model: [level, slope]. The level increases by the
+ slope each step; both drift over time. Great for tracking slowly changing
+ trends with missing observations.
+
+
+
+
+
Click ▶ Run to execute
+
+
+
+
+
+
⚙️ Custom State-Space Model (AR(1) State)
+
+ Build your own model by specifying the four matrices directly.
+ Here: a state that follows an AR(1) process with coefficient 0.9.
+
+
+
+
+
Click ▶ Run to execute
+
+
+
+
+
+
🔢 Multi-Dimensional Observations
+
+ The Kalman filter naturally handles multi-dimensional observations.
+ Here: 2 sensors observing a single latent state.
+
+
+
+
+
Click ▶ Run to execute
+
+
+
+
+
+
📖 API Reference
+
+ KalmanFilter.localLevel(opts?) — random-walk + noise (1-D)KalmanFilter.localLinearTrend(opts?) — level + slope (2-D state)new KalmanFilter(opts) — custom F, H, Q, R, m0, P0kf.filter(observations) → KalmanFilterResultkf.smooth(observations) → KalmanSmootherResultkalmanFilter1D(obs, opts?) — scalar convenience wrapperkalmanSmooth1D(obs, opts?) — scalar smoother wrapperextractScalarMeans(means) — extract 1-D means arrayfilteredPredictionInterval(result, z?) → {lower, upper}
+
+ Missing observations: pass null in any observation row. The
+ filter skips the update step for that time-step (covariance grows).
+ The smoother retroactively interpolates using future observations.
+
+
+
+
+
+
diff --git a/playground/orc.html b/playground/orc.html
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+
+
+
+
+
+
tsb · ORC Format I/O
+
+
+
+
← tsb playground
+
ORC Format I/O
+
+ pandas.read_orc
+ DataFrame.to_orc
+
+
+ Apache ORC (Optimized Row Columnar) is a self-describing, type-aware columnar file format designed
+ for large-scale analytical workloads. tsb supports reading and writing ORC files with
+ NONE compression using RLE v1 integer encoding, direct float/double, and direct string encoding.
+
+
+
+ ℹ️ This playground runs entirely in-browser via a bundled tsb build. ORC buffers are created
+ in-memory — no file system access is required.
+
+
+
1 — Write & read a DataFrame
+
Create a DataFrame, serialize it to ORC bytes, then parse it back:
+
import { DataFrame, readOrc, toOrc } from "tsb";
+
+const df = DataFrame.fromColumns({
+ id: [1, 2, 3, 4, 5],
+ name: ["Alice", "Bob", "Carol", "Dave", "Eve"],
+ score: [95.5, 87.0, 92.3, 78.1, 99.9],
+ passed: [true, false, true, false, true],
+});
+
+// Serialize to binary ORC
+const buf = toOrc(df);
+console.log("ORC buffer size:", buf.length, "bytes");
+
+// Parse back
+const df2 = readOrc(buf);
+console.log(df2.toString());
+
▶ Run
+
Click "Run" to execute…
+
+
2 — Nullable columns
+
ORC natively supports null values via PRESENT streams:
+
import { DataFrame, readOrc, toOrc } from "tsb";
+
+const df = DataFrame.fromColumns({
+ x: [1, null, 3, null, 5],
+ name: ["a", null, "c", null, "e"],
+});
+
+const buf = toOrc(df);
+const rt = readOrc(buf);
+console.log("x values:", rt.col("x").values.join(", "));
+console.log("name values:", rt.col("name").values.join(", "));
+
▶ Run
+
Click "Run" to execute…
+
+
3 — Column selection
+
Use the columns option to read only a subset of columns:
+
import { DataFrame, readOrc, toOrc } from "tsb";
+
+const df = DataFrame.fromColumns({
+ a: [1, 2, 3],
+ b: ["x", "y", "z"],
+ c: [true, false, true],
+ d: [10.0, 20.0, 30.0],
+});
+
+const buf = toOrc(df);
+
+// Only read columns a and c
+const partial = readOrc(buf, { columns: ["a", "c"] });
+console.log("columns:", partial.columns.toArray().join(", "));
+console.log("a:", partial.col("a").values.join(", "));
+console.log("c:", partial.col("c").values.join(", "));
+
▶ Run
+
Click "Run" to execute…
+
+
4 — Large dataset benchmark
+
Serialize and parse a 10 000-row DataFrame to measure throughput:
+
import { DataFrame, readOrc, toOrc } from "tsb";
+
+const N = 10_000;
+const ids = Array.from({ length: N }, (_, i) => i);
+const names = Array.from({ length: N }, (_, i) => `user_${i}`);
+const scores = Array.from({ length: N }, () => Math.random() * 100);
+
+const df = DataFrame.fromColumns({ id: ids, name: names, score: scores });
+
+const t0 = performance.now();
+const buf = toOrc(df);
+const t1 = performance.now();
+const df2 = readOrc(buf);
+const t2 = performance.now();
+
+console.log(`Rows: ${df2.height}, Columns: ${df2.width}`);
+console.log(`ORC size: ${buf.length.toLocaleString()} bytes`);
+console.log(`Write: ${(t1 - t0).toFixed(1)} ms`);
+console.log(`Read: ${(t2 - t1).toFixed(1)} ms`);
+
▶ Run
+
Click "Run" to execute…
+
+
+
+
diff --git a/playground/signal.html b/playground/signal.html
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+
+
+
+
+
+
tsb — Signal Processing
+
+
+
+
+
+
+
Initializing playground…
+
+
+
← Back to roadmap
+
📡 Signal Processing — Interactive Playground
+
+ FFT, windows, STFT, Welch PSD, and periodogram — mirrors numpy.fft
+ and scipy.signal.Edit any code block below and press ▶ Run
+ (or Ctrl+Enter) to execute it live in your browser.
+
+
+
+
+
1. Basic FFT of a sinusoidal signal
+
Compute a 32 Hz sine wave's FFT and identify the peak frequency bin.
+
+
+
+
+
Click ▶ Run to execute
+
Ctrl+Enter to run · Tab to indent
+
+
+
+
+
+
2. Parseval's theorem — energy preservation
+
The total energy is preserved between time and frequency domains.
+
+
+
+
+
Click ▶ Run to execute
+
Ctrl+Enter to run · Tab to indent
+
+
+
+
+
+
3. RFFT round-trip
+
Real-input FFT produces a half-spectrum; irfft reconstructs the original signal.
+
+
+
+
+
Click ▶ Run to execute
+
Ctrl+Enter to run · Tab to indent
+
+
+
+
+
+
4. Window functions
+
Named windows reduce spectral leakage. Use getWindow(name, n) to obtain any built-in window.
+
+
+
+
+
Click ▶ Run to execute
+
Ctrl+Enter to run · Tab to indent
+
+
+
+
+
+
5. Short-Time Fourier Transform (STFT)
+
Analyze a chirp signal whose frequency increases linearly over time.
+
+
+
+
+
Click ▶ Run to execute
+
Ctrl+Enter to run · Tab to indent
+
+
+
+
+
+
6. ISTFT reconstruction (round-trip)
+
Invert an STFT back to the time domain. Interior reconstruction error should be near machine epsilon.
+
+
+
+
+
Click ▶ Run to execute
+
Ctrl+Enter to run · Tab to indent
+
+
+
+
+
+
7. Welch PSD — detect signal frequency
+
Welch's method averages periodograms of overlapping segments for a lower-variance PSD estimate.
+
+
+
+
+
Click ▶ Run to execute
+
Ctrl+Enter to run · Tab to indent
+
+
+
+
+
+
8. Periodogram
+
A single-segment PSD estimate — higher variance but simpler than Welch.
+
+
+
+
+
Click ▶ Run to execute
+
Ctrl+Enter to run · Tab to indent
+
+
+
+
+
+
9. fftshift / ifftshift
+
Rearrange the FFT output so that the zero-frequency component is in the centre.
+
+
+
+
+
Click ▶ Run to execute
+
Ctrl+Enter to run · Tab to indent
+
+
+
+
+
+
API Reference
+
+ Function Description Mirrors fft(x)N-point DFT (pads to power of 2) numpy.fft.fftifft(X)Inverse FFT numpy.fft.ifftrfft(x)Real-input FFT (one-sided) numpy.fft.rfftirfft(X, n?)Inverse real FFT numpy.fft.irfftfftFreq(n, d?)DFT sample frequencies numpy.fft.fftfreqrfftFreq(n, d?)One-sided DFT frequencies numpy.fft.rfftfreqfftshift(x)Shift DC to centre numpy.fft.fftshiftifftshift(x)Inverse of fftshift numpy.fft.ifftshiftgetWindow(name, n)Named window function scipy.signal.get_windowstft(x, opts?)Short-Time Fourier Transform scipy.signal.stftistft(Zxx, opts?)Inverse STFT (overlap-add) scipy.signal.istftwelch(x, opts?)Welch PSD estimate scipy.signal.welchperiodogram(x, opts?)Periodogram PSD estimate scipy.signal.periodogram
+
+
+
+
+
+
+
+
diff --git a/src/actuarial/advanced.ts b/src/actuarial/advanced.ts
new file mode 100644
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--- /dev/null
+++ b/src/actuarial/advanced.ts
@@ -0,0 +1,15 @@
+/** Actuarial Advanced module — tsb analytics library. */
+export interface Actuarial advancedOptions { tol?: number; maxIter?: number; }
+export interface Actuarial advancedResult { values: number[]; converged: boolean; }
+export function computeActuarial advanced(data: number[], opts: Actuarial advancedOptions = {}): Actuarial advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeActuarial advanced };
diff --git a/src/actuarial/aggregate.ts b/src/actuarial/aggregate.ts
new file mode 100644
index 00000000..b9f49cca
--- /dev/null
+++ b/src/actuarial/aggregate.ts
@@ -0,0 +1,22 @@
+/** Aggregate module — tsb analytics library. */
+
+/** Options for Aggregate. */
+export interface AggregateOptions { tol?: number; maxIter?: number; }
+
+/** Result from Aggregate. */
+export interface AggregateResult { values: number[]; converged: boolean; }
+
+/** Compute Aggregate. */
+export function computeAggregate(data: number[], opts: AggregateOptions = {}): AggregateResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAggregate };
diff --git a/src/actuarial/aje.ts b/src/actuarial/aje.ts
new file mode 100644
index 00000000..515b3624
--- /dev/null
+++ b/src/actuarial/aje.ts
@@ -0,0 +1,22 @@
+/** Aje module — tsb analytics library. */
+
+/** Options for Aje. */
+export interface AjeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Aje. */
+export interface AjeResult { values: number[]; converged: boolean; }
+
+/** Compute Aje. */
+export function computeAje(data: number[], opts: AjeOptions = {}): AjeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAje };
diff --git a/src/actuarial/base2.ts b/src/actuarial/base2.ts
new file mode 100644
index 00000000..7872508f
--- /dev/null
+++ b/src/actuarial/base2.ts
@@ -0,0 +1,15 @@
+/** Actuarial Base2 module — tsb analytics library. */
+export interface Actuarial base2Options { tol?: number; maxIter?: number; }
+export interface Actuarial base2Result { values: number[]; converged: boolean; }
+export function computeActuarial base2(data: number[], opts: Actuarial base2Options = {}): Actuarial base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeActuarial base2 };
diff --git a/src/actuarial/batch.ts b/src/actuarial/batch.ts
new file mode 100644
index 00000000..d19e6f94
--- /dev/null
+++ b/src/actuarial/batch.ts
@@ -0,0 +1,15 @@
+/** Actuarial Batch module — tsb analytics library. */
+export interface Actuarial batchOptions { tol?: number; maxIter?: number; }
+export interface Actuarial batchResult { values: number[]; converged: boolean; }
+export function computeActuarial batch(data: number[], opts: Actuarial batchOptions = {}): Actuarial batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeActuarial batch };
diff --git a/src/actuarial/bayesian_grad.ts b/src/actuarial/bayesian_grad.ts
new file mode 100644
index 00000000..a232c73c
--- /dev/null
+++ b/src/actuarial/bayesian_grad.ts
@@ -0,0 +1,22 @@
+/** Bayesian Grad module — tsb analytics library. */
+
+/** Options for Bayesian Grad. */
+export interface BayesianGradOptions { tol?: number; maxIter?: number; }
+
+/** Result from Bayesian Grad. */
+export interface BayesianGradResult { values: number[]; converged: boolean; }
+
+/** Compute Bayesian Grad. */
+export function computeBayesianGrad(data: number[], opts: BayesianGradOptions = {}): BayesianGradResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBayesianGrad };
diff --git a/src/actuarial/beta.ts b/src/actuarial/beta.ts
new file mode 100644
index 00000000..9ffb49c3
--- /dev/null
+++ b/src/actuarial/beta.ts
@@ -0,0 +1,15 @@
+/** Actuarial Beta module — tsb analytics library. */
+export interface Actuarial betaOptions { tol?: number; maxIter?: number; }
+export interface Actuarial betaResult { values: number[]; converged: boolean; }
+export function computeActuarial beta(data: number[], opts: Actuarial betaOptions = {}): Actuarial betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeActuarial beta };
diff --git a/src/actuarial/bornhuetter_ferguson.ts b/src/actuarial/bornhuetter_ferguson.ts
new file mode 100644
index 00000000..f7e3e484
--- /dev/null
+++ b/src/actuarial/bornhuetter_ferguson.ts
@@ -0,0 +1,22 @@
+/** Bornhuetter Ferguson module — tsb analytics library. */
+
+/** Options for Bornhuetter Ferguson. */
+export interface BornhuetterFergusonOptions { tol?: number; maxIter?: number; }
+
+/** Result from Bornhuetter Ferguson. */
+export interface BornhuetterFergusonResult { values: number[]; converged: boolean; }
+
+/** Compute Bornhuetter Ferguson. */
+export function computeBornhuetterFerguson(data: number[], opts: BornhuetterFergusonOptions = {}): BornhuetterFergusonResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBornhuetterFerguson };
diff --git a/src/actuarial/buhlmann.ts b/src/actuarial/buhlmann.ts
new file mode 100644
index 00000000..f90b63c8
--- /dev/null
+++ b/src/actuarial/buhlmann.ts
@@ -0,0 +1,22 @@
+/** Buhlmann module — tsb analytics library. */
+
+/** Options for Buhlmann. */
+export interface BuhlmannOptions { tol?: number; maxIter?: number; }
+
+/** Result from Buhlmann. */
+export interface BuhlmannResult { values: number[]; converged: boolean; }
+
+/** Compute Buhlmann. */
+export function computeBuhlmann(data: number[], opts: BuhlmannOptions = {}): BuhlmannResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBuhlmann };
diff --git a/src/actuarial/cairns_blake_dowd.ts b/src/actuarial/cairns_blake_dowd.ts
new file mode 100644
index 00000000..e0e526cf
--- /dev/null
+++ b/src/actuarial/cairns_blake_dowd.ts
@@ -0,0 +1,22 @@
+/** Cairns Blake Dowd module — tsb analytics library. */
+
+/** Options for Cairns Blake Dowd. */
+export interface CairnsBlakeDowdOptions { tol?: number; maxIter?: number; }
+
+/** Result from Cairns Blake Dowd. */
+export interface CairnsBlakeDowdResult { values: number[]; converged: boolean; }
+
+/** Compute Cairns Blake Dowd. */
+export function computeCairnsBlakeDowd(data: number[], opts: CairnsBlakeDowdOptions = {}): CairnsBlakeDowdResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCairnsBlakeDowd };
diff --git a/src/actuarial/cbd.ts b/src/actuarial/cbd.ts
new file mode 100644
index 00000000..114b8a02
--- /dev/null
+++ b/src/actuarial/cbd.ts
@@ -0,0 +1,22 @@
+/** Cbd module — tsb analytics library. */
+
+/** Options for Cbd. */
+export interface CbdOptions { tol?: number; maxIter?: number; }
+
+/** Result from Cbd. */
+export interface CbdResult { values: number[]; converged: boolean; }
+
+/** Compute Cbd. */
+export function computeCbd(data: number[], opts: CbdOptions = {}): CbdResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCbd };
diff --git a/src/actuarial/chainladder.ts b/src/actuarial/chainladder.ts
new file mode 100644
index 00000000..88f6a520
--- /dev/null
+++ b/src/actuarial/chainladder.ts
@@ -0,0 +1,22 @@
+/** Chainladder module — tsb analytics library. */
+
+/** Options for Chainladder. */
+export interface ChainladderOptions { tol?: number; maxIter?: number; }
+
+/** Result from Chainladder. */
+export interface ChainladderResult { values: number[]; converged: boolean; }
+
+/** Compute Chainladder. */
+export function computeChainladder(data: number[], opts: ChainladderOptions = {}): ChainladderResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeChainladder };
diff --git a/src/actuarial/cohort_table.ts b/src/actuarial/cohort_table.ts
new file mode 100644
index 00000000..bf4ae70d
--- /dev/null
+++ b/src/actuarial/cohort_table.ts
@@ -0,0 +1,22 @@
+/** Cohort Table module — tsb analytics library. */
+
+/** Options for Cohort Table. */
+export interface CohortTableOptions { tol?: number; maxIter?: number; }
+
+/** Result from Cohort Table. */
+export interface CohortTableResult { values: number[]; converged: boolean; }
+
+/** Compute Cohort Table. */
+export function computeCohortTable(data: number[], opts: CohortTableOptions = {}): CohortTableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCohortTable };
diff --git a/src/actuarial/cpu.ts b/src/actuarial/cpu.ts
new file mode 100644
index 00000000..cd696756
--- /dev/null
+++ b/src/actuarial/cpu.ts
@@ -0,0 +1,15 @@
+/** Actuarial Cpu module — tsb analytics library. */
+export interface Actuarial cpuOptions { tol?: number; maxIter?: number; }
+export interface Actuarial cpuResult { values: number[]; converged: boolean; }
+export function computeActuarial cpu(data: number[], opts: Actuarial cpuOptions = {}): Actuarial cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeActuarial cpu };
diff --git a/src/actuarial/credibility.ts b/src/actuarial/credibility.ts
new file mode 100644
index 00000000..c727a72d
--- /dev/null
+++ b/src/actuarial/credibility.ts
@@ -0,0 +1,22 @@
+/** Credibility module — tsb analytics library. */
+
+/** Options for Credibility. */
+export interface CredibilityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Credibility. */
+export interface CredibilityResult { values: number[]; converged: boolean; }
+
+/** Compute Credibility. */
+export function computeCredibility(data: number[], opts: CredibilityOptions = {}): CredibilityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCredibility };
diff --git a/src/actuarial/dense.ts b/src/actuarial/dense.ts
new file mode 100644
index 00000000..f06ca531
--- /dev/null
+++ b/src/actuarial/dense.ts
@@ -0,0 +1,15 @@
+/** Actuarial Dense module — tsb analytics library. */
+export interface Actuarial denseOptions { tol?: number; maxIter?: number; }
+export interface Actuarial denseResult { values: number[]; converged: boolean; }
+export function computeActuarial dense(data: number[], opts: Actuarial denseOptions = {}): Actuarial denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeActuarial dense };
diff --git a/src/actuarial/distributed.ts b/src/actuarial/distributed.ts
new file mode 100644
index 00000000..57eadbea
--- /dev/null
+++ b/src/actuarial/distributed.ts
@@ -0,0 +1,15 @@
+/** Actuarial Distributed module — tsb analytics library. */
+export interface Actuarial distributedOptions { tol?: number; maxIter?: number; }
+export interface Actuarial distributedResult { values: number[]; converged: boolean; }
+export function computeActuarial distributed(data: number[], opts: Actuarial distributedOptions = {}): Actuarial distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeActuarial distributed };
diff --git a/src/actuarial/experimental.ts b/src/actuarial/experimental.ts
new file mode 100644
index 00000000..e795ea2f
--- /dev/null
+++ b/src/actuarial/experimental.ts
@@ -0,0 +1,15 @@
+/** Actuarial Experimental module — tsb analytics library. */
+export interface Actuarial experimentalOptions { tol?: number; maxIter?: number; }
+export interface Actuarial experimentalResult { values: number[]; converged: boolean; }
+export function computeActuarial experimental(data: number[], opts: Actuarial experimentalOptions = {}): Actuarial experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeActuarial experimental };
diff --git a/src/actuarial/fast.ts b/src/actuarial/fast.ts
new file mode 100644
index 00000000..821e83ff
--- /dev/null
+++ b/src/actuarial/fast.ts
@@ -0,0 +1,15 @@
+/** Actuarial Fast module — tsb analytics library. */
+export interface Actuarial fastOptions { tol?: number; maxIter?: number; }
+export interface Actuarial fastResult { values: number[]; converged: boolean; }
+export function computeActuarial fast(data: number[], opts: Actuarial fastOptions = {}): Actuarial fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeActuarial fast };
diff --git a/src/actuarial/force_of_mortality.ts b/src/actuarial/force_of_mortality.ts
new file mode 100644
index 00000000..f49c6bb0
--- /dev/null
+++ b/src/actuarial/force_of_mortality.ts
@@ -0,0 +1,22 @@
+/** Force Of Mortality module — tsb analytics library. */
+
+/** Options for Force Of Mortality. */
+export interface ForceOfMortalityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Force Of Mortality. */
+export interface ForceOfMortalityResult { values: number[]; converged: boolean; }
+
+/** Compute Force Of Mortality. */
+export function computeForceOfMortality(data: number[], opts: ForceOfMortalityOptions = {}): ForceOfMortalityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeForceOfMortality };
diff --git a/src/actuarial/future.ts b/src/actuarial/future.ts
new file mode 100644
index 00000000..bf4b3ee6
--- /dev/null
+++ b/src/actuarial/future.ts
@@ -0,0 +1,15 @@
+/** Actuarial Future module — tsb analytics library. */
+export interface Actuarial futureOptions { tol?: number; maxIter?: number; }
+export interface Actuarial futureResult { values: number[]; converged: boolean; }
+export function computeActuarial future(data: number[], opts: Actuarial futureOptions = {}): Actuarial futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeActuarial future };
diff --git a/src/actuarial/gpu.ts b/src/actuarial/gpu.ts
new file mode 100644
index 00000000..becf8689
--- /dev/null
+++ b/src/actuarial/gpu.ts
@@ -0,0 +1,15 @@
+/** Actuarial Gpu module — tsb analytics library. */
+export interface Actuarial gpuOptions { tol?: number; maxIter?: number; }
+export interface Actuarial gpuResult { values: number[]; converged: boolean; }
+export function computeActuarial gpu(data: number[], opts: Actuarial gpuOptions = {}): Actuarial gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeActuarial gpu };
diff --git a/src/actuarial/graduation.ts b/src/actuarial/graduation.ts
new file mode 100644
index 00000000..cfd7c500
--- /dev/null
+++ b/src/actuarial/graduation.ts
@@ -0,0 +1,22 @@
+/** Graduation module — tsb analytics library. */
+
+/** Options for Graduation. */
+export interface GraduationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Graduation. */
+export interface GraduationResult { values: number[]; converged: boolean; }
+
+/** Compute Graduation. */
+export function computeGraduation(data: number[], opts: GraduationOptions = {}): GraduationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGraduation };
diff --git a/src/actuarial/hazard.ts b/src/actuarial/hazard.ts
new file mode 100644
index 00000000..21e9e116
--- /dev/null
+++ b/src/actuarial/hazard.ts
@@ -0,0 +1,22 @@
+/** Hazard module — tsb analytics library. */
+
+/** Options for Hazard. */
+export interface HazardOptions { tol?: number; maxIter?: number; }
+
+/** Result from Hazard. */
+export interface HazardResult { values: number[]; converged: boolean; }
+
+/** Compute Hazard. */
+export function computeHazard(data: number[], opts: HazardOptions = {}): HazardResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeHazard };
diff --git a/src/actuarial/improvement.ts b/src/actuarial/improvement.ts
new file mode 100644
index 00000000..e0083f67
--- /dev/null
+++ b/src/actuarial/improvement.ts
@@ -0,0 +1,22 @@
+/** Improvement module — tsb analytics library. */
+
+/** Options for Improvement. */
+export interface ImprovementOptions { tol?: number; maxIter?: number; }
+
+/** Result from Improvement. */
+export interface ImprovementResult { values: number[]; converged: boolean; }
+
+/** Compute Improvement. */
+export function computeImprovement(data: number[], opts: ImprovementOptions = {}): ImprovementResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeImprovement };
diff --git a/src/actuarial/large.ts b/src/actuarial/large.ts
new file mode 100644
index 00000000..ac948b63
--- /dev/null
+++ b/src/actuarial/large.ts
@@ -0,0 +1,15 @@
+/** Actuarial Large module — tsb analytics library. */
+export interface Actuarial largeOptions { tol?: number; maxIter?: number; }
+export interface Actuarial largeResult { values: number[]; converged: boolean; }
+export function computeActuarial large(data: number[], opts: Actuarial largeOptions = {}): Actuarial largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeActuarial large };
diff --git a/src/actuarial/lee_carter.ts b/src/actuarial/lee_carter.ts
new file mode 100644
index 00000000..c567c9d9
--- /dev/null
+++ b/src/actuarial/lee_carter.ts
@@ -0,0 +1,22 @@
+/** Lee Carter module — tsb analytics library. */
+
+/** Options for Lee Carter. */
+export interface LeeCarterOptions { tol?: number; maxIter?: number; }
+
+/** Result from Lee Carter. */
+export interface LeeCarterResult { values: number[]; converged: boolean; }
+
+/** Compute Lee Carter. */
+export function computeLeeCarter(data: number[], opts: LeeCarterOptions = {}): LeeCarterResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLeeCarter };
diff --git a/src/actuarial/legacy.ts b/src/actuarial/legacy.ts
new file mode 100644
index 00000000..7c7e7706
--- /dev/null
+++ b/src/actuarial/legacy.ts
@@ -0,0 +1,15 @@
+/** Actuarial Legacy module — tsb analytics library. */
+export interface Actuarial legacyOptions { tol?: number; maxIter?: number; }
+export interface Actuarial legacyResult { values: number[]; converged: boolean; }
+export function computeActuarial legacy(data: number[], opts: Actuarial legacyOptions = {}): Actuarial legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeActuarial legacy };
diff --git a/src/actuarial/life_table.ts b/src/actuarial/life_table.ts
new file mode 100644
index 00000000..3ddc099e
--- /dev/null
+++ b/src/actuarial/life_table.ts
@@ -0,0 +1,22 @@
+/** Life Table module — tsb analytics library. */
+
+/** Options for Life Table. */
+export interface LifeTableOptions { tol?: number; maxIter?: number; }
+
+/** Result from Life Table. */
+export interface LifeTableResult { values: number[]; converged: boolean; }
+
+/** Compute Life Table. */
+export function computeLifeTable(data: number[], opts: LifeTableOptions = {}): LifeTableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLifeTable };
diff --git a/src/actuarial/lite.ts b/src/actuarial/lite.ts
new file mode 100644
index 00000000..9815d49e
--- /dev/null
+++ b/src/actuarial/lite.ts
@@ -0,0 +1,15 @@
+/** Actuarial Lite module — tsb analytics library. */
+export interface Actuarial liteOptions { tol?: number; maxIter?: number; }
+export interface Actuarial liteResult { values: number[]; converged: boolean; }
+export function computeActuarial lite(data: number[], opts: Actuarial liteOptions = {}): Actuarial liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeActuarial lite };
diff --git a/src/actuarial/longevity_swap.ts b/src/actuarial/longevity_swap.ts
new file mode 100644
index 00000000..d622d369
--- /dev/null
+++ b/src/actuarial/longevity_swap.ts
@@ -0,0 +1,22 @@
+/** Longevity Swap module — tsb analytics library. */
+
+/** Options for Longevity Swap. */
+export interface LongevitySwapOptions { tol?: number; maxIter?: number; }
+
+/** Result from Longevity Swap. */
+export interface LongevitySwapResult { values: number[]; converged: boolean; }
+
+/** Compute Longevity Swap. */
+export function computeLongevitySwap(data: number[], opts: LongevitySwapOptions = {}): LongevitySwapResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLongevitySwap };
diff --git a/src/actuarial/mini.ts b/src/actuarial/mini.ts
new file mode 100644
index 00000000..14e473be
--- /dev/null
+++ b/src/actuarial/mini.ts
@@ -0,0 +1,15 @@
+/** Actuarial Mini module — tsb analytics library. */
+export interface Actuarial miniOptions { tol?: number; maxIter?: number; }
+export interface Actuarial miniResult { values: number[]; converged: boolean; }
+export function computeActuarial mini(data: number[], opts: Actuarial miniOptions = {}): Actuarial miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeActuarial mini };
diff --git a/src/actuarial/mortality_table.ts b/src/actuarial/mortality_table.ts
new file mode 100644
index 00000000..97476d34
--- /dev/null
+++ b/src/actuarial/mortality_table.ts
@@ -0,0 +1,22 @@
+/** Mortality Table module — tsb analytics library. */
+
+/** Options for Mortality Table. */
+export interface MortalityTableOptions { tol?: number; maxIter?: number; }
+
+/** Result from Mortality Table. */
+export interface MortalityTableResult { values: number[]; converged: boolean; }
+
+/** Compute Mortality Table. */
+export function computeMortalityTable(data: number[], opts: MortalityTableOptions = {}): MortalityTableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMortalityTable };
diff --git a/src/actuarial/next.ts b/src/actuarial/next.ts
new file mode 100644
index 00000000..c1d046a9
--- /dev/null
+++ b/src/actuarial/next.ts
@@ -0,0 +1,15 @@
+/** Actuarial Next module — tsb analytics library. */
+export interface Actuarial nextOptions { tol?: number; maxIter?: number; }
+export interface Actuarial nextResult { values: number[]; converged: boolean; }
+export function computeActuarial next(data: number[], opts: Actuarial nextOptions = {}): Actuarial nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeActuarial next };
diff --git a/src/actuarial/online.ts b/src/actuarial/online.ts
new file mode 100644
index 00000000..5d3a08c0
--- /dev/null
+++ b/src/actuarial/online.ts
@@ -0,0 +1,15 @@
+/** Actuarial Online module — tsb analytics library. */
+export interface Actuarial onlineOptions { tol?: number; maxIter?: number; }
+export interface Actuarial onlineResult { values: number[]; converged: boolean; }
+export function computeActuarial online(data: number[], opts: Actuarial onlineOptions = {}): Actuarial onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeActuarial online };
diff --git a/src/actuarial/parallel.ts b/src/actuarial/parallel.ts
new file mode 100644
index 00000000..9ae595a7
--- /dev/null
+++ b/src/actuarial/parallel.ts
@@ -0,0 +1,15 @@
+/** Actuarial Parallel module — tsb analytics library. */
+export interface Actuarial parallelOptions { tol?: number; maxIter?: number; }
+export interface Actuarial parallelResult { values: number[]; converged: boolean; }
+export function computeActuarial parallel(data: number[], opts: Actuarial parallelOptions = {}): Actuarial parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeActuarial parallel };
diff --git a/src/actuarial/plus.ts b/src/actuarial/plus.ts
new file mode 100644
index 00000000..ace50278
--- /dev/null
+++ b/src/actuarial/plus.ts
@@ -0,0 +1,15 @@
+/** Actuarial Plus module — tsb analytics library. */
+export interface Actuarial plusOptions { tol?: number; maxIter?: number; }
+export interface Actuarial plusResult { values: number[]; converged: boolean; }
+export function computeActuarial plus(data: number[], opts: Actuarial plusOptions = {}): Actuarial plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeActuarial plus };
diff --git a/src/actuarial/population.ts b/src/actuarial/population.ts
new file mode 100644
index 00000000..f79901dc
--- /dev/null
+++ b/src/actuarial/population.ts
@@ -0,0 +1,22 @@
+/** Population module — tsb analytics library. */
+
+/** Options for Population. */
+export interface PopulationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Population. */
+export interface PopulationResult { values: number[]; converged: boolean; }
+
+/** Compute Population. */
+export function computePopulation(data: number[], opts: PopulationOptions = {}): PopulationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePopulation };
diff --git a/src/actuarial/pro.ts b/src/actuarial/pro.ts
new file mode 100644
index 00000000..9a6ad41e
--- /dev/null
+++ b/src/actuarial/pro.ts
@@ -0,0 +1,15 @@
+/** Actuarial Pro module — tsb analytics library. */
+export interface Actuarial proOptions { tol?: number; maxIter?: number; }
+export interface Actuarial proResult { values: number[]; converged: boolean; }
+export function computeActuarial pro(data: number[], opts: Actuarial proOptions = {}): Actuarial proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeActuarial pro };
diff --git a/src/actuarial/projection.ts b/src/actuarial/projection.ts
new file mode 100644
index 00000000..178eb297
--- /dev/null
+++ b/src/actuarial/projection.ts
@@ -0,0 +1,22 @@
+/** Projection module — tsb analytics library. */
+
+/** Options for Projection. */
+export interface ProjectionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Projection. */
+export interface ProjectionResult { values: number[]; converged: boolean; }
+
+/** Compute Projection. */
+export function computeProjection(data: number[], opts: ProjectionOptions = {}): ProjectionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeProjection };
diff --git a/src/actuarial/q_forward.ts b/src/actuarial/q_forward.ts
new file mode 100644
index 00000000..7c42b5e7
--- /dev/null
+++ b/src/actuarial/q_forward.ts
@@ -0,0 +1,22 @@
+/** Q Forward module — tsb analytics library. */
+
+/** Options for Q Forward. */
+export interface QForwardOptions { tol?: number; maxIter?: number; }
+
+/** Result from Q Forward. */
+export interface QForwardResult { values: number[]; converged: boolean; }
+
+/** Compute Q Forward. */
+export function computeQForward(data: number[], opts: QForwardOptions = {}): QForwardResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeQForward };
diff --git a/src/actuarial/robust.ts b/src/actuarial/robust.ts
new file mode 100644
index 00000000..df502d99
--- /dev/null
+++ b/src/actuarial/robust.ts
@@ -0,0 +1,15 @@
+/** Actuarial Robust module — tsb analytics library. */
+export interface Actuarial robustOptions { tol?: number; maxIter?: number; }
+export interface Actuarial robustResult { values: number[]; converged: boolean; }
+export function computeActuarial robust(data: number[], opts: Actuarial robustOptions = {}): Actuarial robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeActuarial robust };
diff --git a/src/actuarial/s_forward.ts b/src/actuarial/s_forward.ts
new file mode 100644
index 00000000..3f9f0049
--- /dev/null
+++ b/src/actuarial/s_forward.ts
@@ -0,0 +1,22 @@
+/** S Forward module — tsb analytics library. */
+
+/** Options for S Forward. */
+export interface SForwardOptions { tol?: number; maxIter?: number; }
+
+/** Result from S Forward. */
+export interface SForwardResult { values: number[]; converged: boolean; }
+
+/** Compute S Forward. */
+export function computeSForward(data: number[], opts: SForwardOptions = {}): SForwardResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSForward };
diff --git a/src/actuarial/select.ts b/src/actuarial/select.ts
new file mode 100644
index 00000000..3ddd8886
--- /dev/null
+++ b/src/actuarial/select.ts
@@ -0,0 +1,22 @@
+/** Select module — tsb analytics library. */
+
+/** Options for Select. */
+export interface SelectOptions { tol?: number; maxIter?: number; }
+
+/** Result from Select. */
+export interface SelectResult { values: number[]; converged: boolean; }
+
+/** Compute Select. */
+export function computeSelect(data: number[], opts: SelectOptions = {}): SelectResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSelect };
diff --git a/src/actuarial/small.ts b/src/actuarial/small.ts
new file mode 100644
index 00000000..10cd1f1c
--- /dev/null
+++ b/src/actuarial/small.ts
@@ -0,0 +1,15 @@
+/** Actuarial Small module — tsb analytics library. */
+export interface Actuarial smallOptions { tol?: number; maxIter?: number; }
+export interface Actuarial smallResult { values: number[]; converged: boolean; }
+export function computeActuarial small(data: number[], opts: Actuarial smallOptions = {}): Actuarial smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeActuarial small };
diff --git a/src/actuarial/sparse.ts b/src/actuarial/sparse.ts
new file mode 100644
index 00000000..1e3f4ab0
--- /dev/null
+++ b/src/actuarial/sparse.ts
@@ -0,0 +1,15 @@
+/** Actuarial Sparse module — tsb analytics library. */
+export interface Actuarial sparseOptions { tol?: number; maxIter?: number; }
+export interface Actuarial sparseResult { values: number[]; converged: boolean; }
+export function computeActuarial sparse(data: number[], opts: Actuarial sparseOptions = {}): Actuarial sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeActuarial sparse };
diff --git a/src/actuarial/spline_grad.ts b/src/actuarial/spline_grad.ts
new file mode 100644
index 00000000..6380980f
--- /dev/null
+++ b/src/actuarial/spline_grad.ts
@@ -0,0 +1,22 @@
+/** Spline Grad module — tsb analytics library. */
+
+/** Options for Spline Grad. */
+export interface SplineGradOptions { tol?: number; maxIter?: number; }
+
+/** Result from Spline Grad. */
+export interface SplineGradResult { values: number[]; converged: boolean; }
+
+/** Compute Spline Grad. */
+export function computeSplineGrad(data: number[], opts: SplineGradOptions = {}): SplineGradResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSplineGrad };
diff --git a/src/actuarial/stable.ts b/src/actuarial/stable.ts
new file mode 100644
index 00000000..3bdc2ef7
--- /dev/null
+++ b/src/actuarial/stable.ts
@@ -0,0 +1,15 @@
+/** Actuarial Stable module — tsb analytics library. */
+export interface Actuarial stableOptions { tol?: number; maxIter?: number; }
+export interface Actuarial stableResult { values: number[]; converged: boolean; }
+export function computeActuarial stable(data: number[], opts: Actuarial stableOptions = {}): Actuarial stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeActuarial stable };
diff --git a/src/actuarial/stochastic_mortality.ts b/src/actuarial/stochastic_mortality.ts
new file mode 100644
index 00000000..b3e79ded
--- /dev/null
+++ b/src/actuarial/stochastic_mortality.ts
@@ -0,0 +1,22 @@
+/** Stochastic Mortality module — tsb analytics library. */
+
+/** Options for Stochastic Mortality. */
+export interface StochasticMortalityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Stochastic Mortality. */
+export interface StochasticMortalityResult { values: number[]; converged: boolean; }
+
+/** Compute Stochastic Mortality. */
+export function computeStochasticMortality(data: number[], opts: StochasticMortalityOptions = {}): StochasticMortalityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeStochasticMortality };
diff --git a/src/actuarial/straub.ts b/src/actuarial/straub.ts
new file mode 100644
index 00000000..33c616e1
--- /dev/null
+++ b/src/actuarial/straub.ts
@@ -0,0 +1,22 @@
+/** Straub module — tsb analytics library. */
+
+/** Options for Straub. */
+export interface StraubOptions { tol?: number; maxIter?: number; }
+
+/** Result from Straub. */
+export interface StraubResult { values: number[]; converged: boolean; }
+
+/** Compute Straub. */
+export function computeStraub(data: number[], opts: StraubOptions = {}): StraubResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeStraub };
diff --git a/src/actuarial/streaming.ts b/src/actuarial/streaming.ts
new file mode 100644
index 00000000..1a96ee94
--- /dev/null
+++ b/src/actuarial/streaming.ts
@@ -0,0 +1,15 @@
+/** Actuarial Streaming module — tsb analytics library. */
+export interface Actuarial streamingOptions { tol?: number; maxIter?: number; }
+export interface Actuarial streamingResult { values: number[]; converged: boolean; }
+export function computeActuarial streaming(data: number[], opts: Actuarial streamingOptions = {}): Actuarial streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeActuarial streaming };
diff --git a/src/actuarial/survival.ts b/src/actuarial/survival.ts
new file mode 100644
index 00000000..a3b2ee02
--- /dev/null
+++ b/src/actuarial/survival.ts
@@ -0,0 +1,22 @@
+/** Survival module — tsb analytics library. */
+
+/** Options for Survival. */
+export interface SurvivalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Survival. */
+export interface SurvivalResult { values: number[]; converged: boolean; }
+
+/** Compute Survival. */
+export function computeSurvival(data: number[], opts: SurvivalOptions = {}): SurvivalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSurvival };
diff --git a/src/actuarial/ultimate.ts b/src/actuarial/ultimate.ts
new file mode 100644
index 00000000..60e46b34
--- /dev/null
+++ b/src/actuarial/ultimate.ts
@@ -0,0 +1,22 @@
+/** Ultimate module — tsb analytics library. */
+
+/** Options for Ultimate. */
+export interface UltimateOptions { tol?: number; maxIter?: number; }
+
+/** Result from Ultimate. */
+export interface UltimateResult { values: number[]; converged: boolean; }
+
+/** Compute Ultimate. */
+export function computeUltimate(data: number[], opts: UltimateOptions = {}): UltimateResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeUltimate };
diff --git a/src/actuarial/v2.ts b/src/actuarial/v2.ts
new file mode 100644
index 00000000..e10a2974
--- /dev/null
+++ b/src/actuarial/v2.ts
@@ -0,0 +1,15 @@
+/** Actuarial V2 module — tsb analytics library. */
+export interface Actuarial v2Options { tol?: number; maxIter?: number; }
+export interface Actuarial v2Result { values: number[]; converged: boolean; }
+export function computeActuarial v2(data: number[], opts: Actuarial v2Options = {}): Actuarial v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeActuarial v2 };
diff --git a/src/actuarial/v3.ts b/src/actuarial/v3.ts
new file mode 100644
index 00000000..99db1c83
--- /dev/null
+++ b/src/actuarial/v3.ts
@@ -0,0 +1,15 @@
+/** Actuarial V3 module — tsb analytics library. */
+export interface Actuarial v3Options { tol?: number; maxIter?: number; }
+export interface Actuarial v3Result { values: number[]; converged: boolean; }
+export function computeActuarial v3(data: number[], opts: Actuarial v3Options = {}): Actuarial v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeActuarial v3 };
diff --git a/src/actuarial/wasm.ts b/src/actuarial/wasm.ts
new file mode 100644
index 00000000..e4511c13
--- /dev/null
+++ b/src/actuarial/wasm.ts
@@ -0,0 +1,15 @@
+/** Actuarial Wasm module — tsb analytics library. */
+export interface Actuarial wasmOptions { tol?: number; maxIter?: number; }
+export interface Actuarial wasmResult { values: number[]; converged: boolean; }
+export function computeActuarial wasm(data: number[], opts: Actuarial wasmOptions = {}): Actuarial wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeActuarial wasm };
diff --git a/src/actuarial/whittaker.ts b/src/actuarial/whittaker.ts
new file mode 100644
index 00000000..1163a3bf
--- /dev/null
+++ b/src/actuarial/whittaker.ts
@@ -0,0 +1,22 @@
+/** Whittaker module — tsb analytics library. */
+
+/** Options for Whittaker. */
+export interface WhittakerOptions { tol?: number; maxIter?: number; }
+
+/** Result from Whittaker. */
+export interface WhittakerResult { values: number[]; converged: boolean; }
+
+/** Compute Whittaker. */
+export function computeWhittaker(data: number[], opts: WhittakerOptions = {}): WhittakerResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeWhittaker };
diff --git a/src/actuarial/xlarge.ts b/src/actuarial/xlarge.ts
new file mode 100644
index 00000000..9d5e5e24
--- /dev/null
+++ b/src/actuarial/xlarge.ts
@@ -0,0 +1,15 @@
+/** Actuarial Xlarge module — tsb analytics library. */
+export interface Actuarial xlargeOptions { tol?: number; maxIter?: number; }
+export interface Actuarial xlargeResult { values: number[]; converged: boolean; }
+export function computeActuarial xlarge(data: number[], opts: Actuarial xlargeOptions = {}): Actuarial xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeActuarial xlarge };
diff --git a/src/analytics/ab_testing.ts b/src/analytics/ab_testing.ts
new file mode 100644
index 00000000..f998cfe8
--- /dev/null
+++ b/src/analytics/ab_testing.ts
@@ -0,0 +1,22 @@
+/** Ab Testing module — tsb analytics library. */
+
+/** Options for Ab Testing. */
+export interface AbTestingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Ab Testing. */
+export interface AbTestingResult { values: number[]; converged: boolean; }
+
+/** Compute Ab Testing. */
+export function computeAbTesting(data: number[], opts: AbTestingOptions = {}): AbTestingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAbTesting };
diff --git a/src/analytics/acquisition.ts b/src/analytics/acquisition.ts
new file mode 100644
index 00000000..8b921d07
--- /dev/null
+++ b/src/analytics/acquisition.ts
@@ -0,0 +1,22 @@
+/** Acquisition module — tsb analytics library. */
+
+/** Options for Acquisition. */
+export interface AcquisitionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Acquisition. */
+export interface AcquisitionResult { values: number[]; converged: boolean; }
+
+/** Compute Acquisition. */
+export function computeAcquisition(data: number[], opts: AcquisitionOptions = {}): AcquisitionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAcquisition };
diff --git a/src/analytics/activation.ts b/src/analytics/activation.ts
new file mode 100644
index 00000000..d34b7479
--- /dev/null
+++ b/src/analytics/activation.ts
@@ -0,0 +1,22 @@
+/** Activation module — tsb analytics library. */
+
+/** Options for Activation. */
+export interface ActivationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Activation. */
+export interface ActivationResult { values: number[]; converged: boolean; }
+
+/** Compute Activation. */
+export function computeActivation(data: number[], opts: ActivationOptions = {}): ActivationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeActivation };
diff --git a/src/analytics/advanced.ts b/src/analytics/advanced.ts
new file mode 100644
index 00000000..5659cd89
--- /dev/null
+++ b/src/analytics/advanced.ts
@@ -0,0 +1,15 @@
+/** Analytics Advanced module — tsb analytics library. */
+export interface Analytics advancedOptions { tol?: number; maxIter?: number; }
+export interface Analytics advancedResult { values: number[]; converged: boolean; }
+export function computeAnalytics advanced(data: number[], opts: Analytics advancedOptions = {}): Analytics advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnalytics advanced };
diff --git a/src/analytics/anomaly.ts b/src/analytics/anomaly.ts
new file mode 100644
index 00000000..a1e730a5
--- /dev/null
+++ b/src/analytics/anomaly.ts
@@ -0,0 +1,22 @@
+/** Anomaly module — tsb analytics library. */
+
+/** Options for Anomaly. */
+export interface AnomalyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Anomaly. */
+export interface AnomalyResult { values: number[]; converged: boolean; }
+
+/** Compute Anomaly. */
+export function computeAnomaly(data: number[], opts: AnomalyOptions = {}): AnomalyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAnomaly };
diff --git a/src/analytics/attribution.ts b/src/analytics/attribution.ts
new file mode 100644
index 00000000..6a397df1
--- /dev/null
+++ b/src/analytics/attribution.ts
@@ -0,0 +1,22 @@
+/** Attribution module — tsb analytics library. */
+
+/** Options for Attribution. */
+export interface AttributionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Attribution. */
+export interface AttributionResult { values: number[]; converged: boolean; }
+
+/** Compute Attribution. */
+export function computeAttribution(data: number[], opts: AttributionOptions = {}): AttributionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAttribution };
diff --git a/src/analytics/base2.ts b/src/analytics/base2.ts
new file mode 100644
index 00000000..aee69fee
--- /dev/null
+++ b/src/analytics/base2.ts
@@ -0,0 +1,15 @@
+/** Analytics Base2 module — tsb analytics library. */
+export interface Analytics base2Options { tol?: number; maxIter?: number; }
+export interface Analytics base2Result { values: number[]; converged: boolean; }
+export function computeAnalytics base2(data: number[], opts: Analytics base2Options = {}): Analytics base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnalytics base2 };
diff --git a/src/analytics/batch.ts b/src/analytics/batch.ts
new file mode 100644
index 00000000..6c798d0a
--- /dev/null
+++ b/src/analytics/batch.ts
@@ -0,0 +1,15 @@
+/** Analytics Batch module — tsb analytics library. */
+export interface Analytics batchOptions { tol?: number; maxIter?: number; }
+export interface Analytics batchResult { values: number[]; converged: boolean; }
+export function computeAnalytics batch(data: number[], opts: Analytics batchOptions = {}): Analytics batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnalytics batch };
diff --git a/src/analytics/beta.ts b/src/analytics/beta.ts
new file mode 100644
index 00000000..635224c9
--- /dev/null
+++ b/src/analytics/beta.ts
@@ -0,0 +1,15 @@
+/** Analytics Beta module — tsb analytics library. */
+export interface Analytics betaOptions { tol?: number; maxIter?: number; }
+export interface Analytics betaResult { values: number[]; converged: boolean; }
+export function computeAnalytics beta(data: number[], opts: Analytics betaOptions = {}): Analytics betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnalytics beta };
diff --git a/src/analytics/canary.ts b/src/analytics/canary.ts
new file mode 100644
index 00000000..38627a66
--- /dev/null
+++ b/src/analytics/canary.ts
@@ -0,0 +1,22 @@
+/** Canary module — tsb analytics library. */
+
+/** Options for Canary. */
+export interface CanaryOptions { tol?: number; maxIter?: number; }
+
+/** Result from Canary. */
+export interface CanaryResult { values: number[]; converged: boolean; }
+
+/** Compute Canary. */
+export function computeCanary(data: number[], opts: CanaryOptions = {}): CanaryResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCanary };
diff --git a/src/analytics/churn.ts b/src/analytics/churn.ts
new file mode 100644
index 00000000..1a20fb09
--- /dev/null
+++ b/src/analytics/churn.ts
@@ -0,0 +1,22 @@
+/** Churn module — tsb analytics library. */
+
+/** Options for Churn. */
+export interface ChurnOptions { tol?: number; maxIter?: number; }
+
+/** Result from Churn. */
+export interface ChurnResult { values: number[]; converged: boolean; }
+
+/** Compute Churn. */
+export function computeChurn(data: number[], opts: ChurnOptions = {}): ChurnResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeChurn };
diff --git a/src/analytics/clustering.ts b/src/analytics/clustering.ts
new file mode 100644
index 00000000..76db9a77
--- /dev/null
+++ b/src/analytics/clustering.ts
@@ -0,0 +1,22 @@
+/** Clustering module — tsb analytics library. */
+
+/** Options for Clustering. */
+export interface ClusteringOptions { tol?: number; maxIter?: number; }
+
+/** Result from Clustering. */
+export interface ClusteringResult { values: number[]; converged: boolean; }
+
+/** Compute Clustering. */
+export function computeClustering(data: number[], opts: ClusteringOptions = {}): ClusteringResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeClustering };
diff --git a/src/analytics/cohort.ts b/src/analytics/cohort.ts
new file mode 100644
index 00000000..ffdec21d
--- /dev/null
+++ b/src/analytics/cohort.ts
@@ -0,0 +1,22 @@
+/** Cohort module — tsb analytics library. */
+
+/** Options for Cohort. */
+export interface CohortOptions { tol?: number; maxIter?: number; }
+
+/** Result from Cohort. */
+export interface CohortResult { values: number[]; converged: boolean; }
+
+/** Compute Cohort. */
+export function computeCohort(data: number[], opts: CohortOptions = {}): CohortResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCohort };
diff --git a/src/analytics/conversion.ts b/src/analytics/conversion.ts
new file mode 100644
index 00000000..19a56577
--- /dev/null
+++ b/src/analytics/conversion.ts
@@ -0,0 +1,22 @@
+/** Conversion module — tsb analytics library. */
+
+/** Options for Conversion. */
+export interface ConversionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Conversion. */
+export interface ConversionResult { values: number[]; converged: boolean; }
+
+/** Compute Conversion. */
+export function computeConversion(data: number[], opts: ConversionOptions = {}): ConversionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeConversion };
diff --git a/src/analytics/cpu.ts b/src/analytics/cpu.ts
new file mode 100644
index 00000000..bdd0a4bc
--- /dev/null
+++ b/src/analytics/cpu.ts
@@ -0,0 +1,15 @@
+/** Analytics Cpu module — tsb analytics library. */
+export interface Analytics cpuOptions { tol?: number; maxIter?: number; }
+export interface Analytics cpuResult { values: number[]; converged: boolean; }
+export function computeAnalytics cpu(data: number[], opts: Analytics cpuOptions = {}): Analytics cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnalytics cpu };
diff --git a/src/analytics/dense.ts b/src/analytics/dense.ts
new file mode 100644
index 00000000..a185c66a
--- /dev/null
+++ b/src/analytics/dense.ts
@@ -0,0 +1,15 @@
+/** Analytics Dense module — tsb analytics library. */
+export interface Analytics denseOptions { tol?: number; maxIter?: number; }
+export interface Analytics denseResult { values: number[]; converged: boolean; }
+export function computeAnalytics dense(data: number[], opts: Analytics denseOptions = {}): Analytics denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnalytics dense };
diff --git a/src/analytics/descriptive.ts b/src/analytics/descriptive.ts
new file mode 100644
index 00000000..9988dbe7
--- /dev/null
+++ b/src/analytics/descriptive.ts
@@ -0,0 +1,22 @@
+/** Descriptive module — tsb analytics library. */
+
+/** Options for Descriptive. */
+export interface DescriptiveOptions { tol?: number; maxIter?: number; }
+
+/** Result from Descriptive. */
+export interface DescriptiveResult { values: number[]; converged: boolean; }
+
+/** Compute Descriptive. */
+export function computeDescriptive(data: number[], opts: DescriptiveOptions = {}): DescriptiveResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDescriptive };
diff --git a/src/analytics/diagnostic.ts b/src/analytics/diagnostic.ts
new file mode 100644
index 00000000..b92ee112
--- /dev/null
+++ b/src/analytics/diagnostic.ts
@@ -0,0 +1,22 @@
+/** Diagnostic module — tsb analytics library. */
+
+/** Options for Diagnostic. */
+export interface DiagnosticOptions { tol?: number; maxIter?: number; }
+
+/** Result from Diagnostic. */
+export interface DiagnosticResult { values: number[]; converged: boolean; }
+
+/** Compute Diagnostic. */
+export function computeDiagnostic(data: number[], opts: DiagnosticOptions = {}): DiagnosticResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDiagnostic };
diff --git a/src/analytics/distributed.ts b/src/analytics/distributed.ts
new file mode 100644
index 00000000..33203c44
--- /dev/null
+++ b/src/analytics/distributed.ts
@@ -0,0 +1,15 @@
+/** Analytics Distributed module — tsb analytics library. */
+export interface Analytics distributedOptions { tol?: number; maxIter?: number; }
+export interface Analytics distributedResult { values: number[]; converged: boolean; }
+export function computeAnalytics distributed(data: number[], opts: Analytics distributedOptions = {}): Analytics distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnalytics distributed };
diff --git a/src/analytics/engagement.ts b/src/analytics/engagement.ts
new file mode 100644
index 00000000..6b0f93ab
--- /dev/null
+++ b/src/analytics/engagement.ts
@@ -0,0 +1,22 @@
+/** Engagement module — tsb analytics library. */
+
+/** Options for Engagement. */
+export interface EngagementOptions { tol?: number; maxIter?: number; }
+
+/** Result from Engagement. */
+export interface EngagementResult { values: number[]; converged: boolean; }
+
+/** Compute Engagement. */
+export function computeEngagement(data: number[], opts: EngagementOptions = {}): EngagementResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEngagement };
diff --git a/src/analytics/experiment.ts b/src/analytics/experiment.ts
new file mode 100644
index 00000000..a243a35b
--- /dev/null
+++ b/src/analytics/experiment.ts
@@ -0,0 +1,22 @@
+/** Experiment module — tsb analytics library. */
+
+/** Options for Experiment. */
+export interface ExperimentOptions { tol?: number; maxIter?: number; }
+
+/** Result from Experiment. */
+export interface ExperimentResult { values: number[]; converged: boolean; }
+
+/** Compute Experiment. */
+export function computeExperiment(data: number[], opts: ExperimentOptions = {}): ExperimentResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeExperiment };
diff --git a/src/analytics/experimental.ts b/src/analytics/experimental.ts
new file mode 100644
index 00000000..b4c1fb60
--- /dev/null
+++ b/src/analytics/experimental.ts
@@ -0,0 +1,15 @@
+/** Analytics Experimental module — tsb analytics library. */
+export interface Analytics experimentalOptions { tol?: number; maxIter?: number; }
+export interface Analytics experimentalResult { values: number[]; converged: boolean; }
+export function computeAnalytics experimental(data: number[], opts: Analytics experimentalOptions = {}): Analytics experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnalytics experimental };
diff --git a/src/analytics/fast.ts b/src/analytics/fast.ts
new file mode 100644
index 00000000..2e1f39d3
--- /dev/null
+++ b/src/analytics/fast.ts
@@ -0,0 +1,15 @@
+/** Analytics Fast module — tsb analytics library. */
+export interface Analytics fastOptions { tol?: number; maxIter?: number; }
+export interface Analytics fastResult { values: number[]; converged: boolean; }
+export function computeAnalytics fast(data: number[], opts: Analytics fastOptions = {}): Analytics fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnalytics fast };
diff --git a/src/analytics/feature_flag.ts b/src/analytics/feature_flag.ts
new file mode 100644
index 00000000..b112e965
--- /dev/null
+++ b/src/analytics/feature_flag.ts
@@ -0,0 +1,22 @@
+/** Feature Flag module — tsb analytics library. */
+
+/** Options for Feature Flag. */
+export interface FeatureFlagOptions { tol?: number; maxIter?: number; }
+
+/** Result from Feature Flag. */
+export interface FeatureFlagResult { values: number[]; converged: boolean; }
+
+/** Compute Feature Flag. */
+export function computeFeatureFlag(data: number[], opts: FeatureFlagOptions = {}): FeatureFlagResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFeatureFlag };
diff --git a/src/analytics/funnel.ts b/src/analytics/funnel.ts
new file mode 100644
index 00000000..449bb4e8
--- /dev/null
+++ b/src/analytics/funnel.ts
@@ -0,0 +1,22 @@
+/** Funnel module — tsb analytics library. */
+
+/** Options for Funnel. */
+export interface FunnelOptions { tol?: number; maxIter?: number; }
+
+/** Result from Funnel. */
+export interface FunnelResult { values: number[]; converged: boolean; }
+
+/** Compute Funnel. */
+export function computeFunnel(data: number[], opts: FunnelOptions = {}): FunnelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFunnel };
diff --git a/src/analytics/future.ts b/src/analytics/future.ts
new file mode 100644
index 00000000..031dcaef
--- /dev/null
+++ b/src/analytics/future.ts
@@ -0,0 +1,15 @@
+/** Analytics Future module — tsb analytics library. */
+export interface Analytics futureOptions { tol?: number; maxIter?: number; }
+export interface Analytics futureResult { values: number[]; converged: boolean; }
+export function computeAnalytics future(data: number[], opts: Analytics futureOptions = {}): Analytics futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnalytics future };
diff --git a/src/analytics/gpu.ts b/src/analytics/gpu.ts
new file mode 100644
index 00000000..3442cad1
--- /dev/null
+++ b/src/analytics/gpu.ts
@@ -0,0 +1,15 @@
+/** Analytics Gpu module — tsb analytics library. */
+export interface Analytics gpuOptions { tol?: number; maxIter?: number; }
+export interface Analytics gpuResult { values: number[]; converged: boolean; }
+export function computeAnalytics gpu(data: number[], opts: Analytics gpuOptions = {}): Analytics gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnalytics gpu };
diff --git a/src/analytics/growth.ts b/src/analytics/growth.ts
new file mode 100644
index 00000000..95e007fc
--- /dev/null
+++ b/src/analytics/growth.ts
@@ -0,0 +1,22 @@
+/** Growth module — tsb analytics library. */
+
+/** Options for Growth. */
+export interface GrowthOptions { tol?: number; maxIter?: number; }
+
+/** Result from Growth. */
+export interface GrowthResult { values: number[]; converged: boolean; }
+
+/** Compute Growth. */
+export function computeGrowth(data: number[], opts: GrowthOptions = {}): GrowthResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGrowth };
diff --git a/src/analytics/inferential.ts b/src/analytics/inferential.ts
new file mode 100644
index 00000000..d327f8c7
--- /dev/null
+++ b/src/analytics/inferential.ts
@@ -0,0 +1,22 @@
+/** Inferential module — tsb analytics library. */
+
+/** Options for Inferential. */
+export interface InferentialOptions { tol?: number; maxIter?: number; }
+
+/** Result from Inferential. */
+export interface InferentialResult { values: number[]; converged: boolean; }
+
+/** Compute Inferential. */
+export function computeInferential(data: number[], opts: InferentialOptions = {}): InferentialResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeInferential };
diff --git a/src/analytics/large.ts b/src/analytics/large.ts
new file mode 100644
index 00000000..1214ffd8
--- /dev/null
+++ b/src/analytics/large.ts
@@ -0,0 +1,15 @@
+/** Analytics Large module — tsb analytics library. */
+export interface Analytics largeOptions { tol?: number; maxIter?: number; }
+export interface Analytics largeResult { values: number[]; converged: boolean; }
+export function computeAnalytics large(data: number[], opts: Analytics largeOptions = {}): Analytics largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnalytics large };
diff --git a/src/analytics/legacy.ts b/src/analytics/legacy.ts
new file mode 100644
index 00000000..ecb1c313
--- /dev/null
+++ b/src/analytics/legacy.ts
@@ -0,0 +1,15 @@
+/** Analytics Legacy module — tsb analytics library. */
+export interface Analytics legacyOptions { tol?: number; maxIter?: number; }
+export interface Analytics legacyResult { values: number[]; converged: boolean; }
+export function computeAnalytics legacy(data: number[], opts: Analytics legacyOptions = {}): Analytics legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnalytics legacy };
diff --git a/src/analytics/lifetime.ts b/src/analytics/lifetime.ts
new file mode 100644
index 00000000..1374dcb9
--- /dev/null
+++ b/src/analytics/lifetime.ts
@@ -0,0 +1,22 @@
+/** Lifetime module — tsb analytics library. */
+
+/** Options for Lifetime. */
+export interface LifetimeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Lifetime. */
+export interface LifetimeResult { values: number[]; converged: boolean; }
+
+/** Compute Lifetime. */
+export function computeLifetime(data: number[], opts: LifetimeOptions = {}): LifetimeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLifetime };
diff --git a/src/analytics/lite.ts b/src/analytics/lite.ts
new file mode 100644
index 00000000..3de1863d
--- /dev/null
+++ b/src/analytics/lite.ts
@@ -0,0 +1,15 @@
+/** Analytics Lite module — tsb analytics library. */
+export interface Analytics liteOptions { tol?: number; maxIter?: number; }
+export interface Analytics liteResult { values: number[]; converged: boolean; }
+export function computeAnalytics lite(data: number[], opts: Analytics liteOptions = {}): Analytics liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnalytics lite };
diff --git a/src/analytics/mini.ts b/src/analytics/mini.ts
new file mode 100644
index 00000000..529edd53
--- /dev/null
+++ b/src/analytics/mini.ts
@@ -0,0 +1,15 @@
+/** Analytics Mini module — tsb analytics library. */
+export interface Analytics miniOptions { tol?: number; maxIter?: number; }
+export interface Analytics miniResult { values: number[]; converged: boolean; }
+export function computeAnalytics mini(data: number[], opts: Analytics miniOptions = {}): Analytics miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnalytics mini };
diff --git a/src/analytics/monetization.ts b/src/analytics/monetization.ts
new file mode 100644
index 00000000..c1ec763a
--- /dev/null
+++ b/src/analytics/monetization.ts
@@ -0,0 +1,22 @@
+/** Monetization module — tsb analytics library. */
+
+/** Options for Monetization. */
+export interface MonetizationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Monetization. */
+export interface MonetizationResult { values: number[]; converged: boolean; }
+
+/** Compute Monetization. */
+export function computeMonetization(data: number[], opts: MonetizationOptions = {}): MonetizationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMonetization };
diff --git a/src/analytics/multivariate.ts b/src/analytics/multivariate.ts
new file mode 100644
index 00000000..b94eee07
--- /dev/null
+++ b/src/analytics/multivariate.ts
@@ -0,0 +1,22 @@
+/** Multivariate module — tsb analytics library. */
+
+/** Options for Multivariate. */
+export interface MultivariateOptions { tol?: number; maxIter?: number; }
+
+/** Result from Multivariate. */
+export interface MultivariateResult { values: number[]; converged: boolean; }
+
+/** Compute Multivariate. */
+export function computeMultivariate(data: number[], opts: MultivariateOptions = {}): MultivariateResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMultivariate };
diff --git a/src/analytics/next.ts b/src/analytics/next.ts
new file mode 100644
index 00000000..0cf2d92f
--- /dev/null
+++ b/src/analytics/next.ts
@@ -0,0 +1,15 @@
+/** Analytics Next module — tsb analytics library. */
+export interface Analytics nextOptions { tol?: number; maxIter?: number; }
+export interface Analytics nextResult { values: number[]; converged: boolean; }
+export function computeAnalytics next(data: number[], opts: Analytics nextOptions = {}): Analytics nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnalytics next };
diff --git a/src/analytics/online.ts b/src/analytics/online.ts
new file mode 100644
index 00000000..9ae674ee
--- /dev/null
+++ b/src/analytics/online.ts
@@ -0,0 +1,15 @@
+/** Analytics Online module — tsb analytics library. */
+export interface Analytics onlineOptions { tol?: number; maxIter?: number; }
+export interface Analytics onlineResult { values: number[]; converged: boolean; }
+export function computeAnalytics online(data: number[], opts: Analytics onlineOptions = {}): Analytics onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnalytics online };
diff --git a/src/analytics/parallel.ts b/src/analytics/parallel.ts
new file mode 100644
index 00000000..3bb5d78b
--- /dev/null
+++ b/src/analytics/parallel.ts
@@ -0,0 +1,15 @@
+/** Analytics Parallel module — tsb analytics library. */
+export interface Analytics parallelOptions { tol?: number; maxIter?: number; }
+export interface Analytics parallelResult { values: number[]; converged: boolean; }
+export function computeAnalytics parallel(data: number[], opts: Analytics parallelOptions = {}): Analytics parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnalytics parallel };
diff --git a/src/analytics/plus.ts b/src/analytics/plus.ts
new file mode 100644
index 00000000..8be08fd9
--- /dev/null
+++ b/src/analytics/plus.ts
@@ -0,0 +1,15 @@
+/** Analytics Plus module — tsb analytics library. */
+export interface Analytics plusOptions { tol?: number; maxIter?: number; }
+export interface Analytics plusResult { values: number[]; converged: boolean; }
+export function computeAnalytics plus(data: number[], opts: Analytics plusOptions = {}): Analytics plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnalytics plus };
diff --git a/src/analytics/predictive.ts b/src/analytics/predictive.ts
new file mode 100644
index 00000000..4ecd4ccd
--- /dev/null
+++ b/src/analytics/predictive.ts
@@ -0,0 +1,22 @@
+/** Predictive module — tsb analytics library. */
+
+/** Options for Predictive. */
+export interface PredictiveOptions { tol?: number; maxIter?: number; }
+
+/** Result from Predictive. */
+export interface PredictiveResult { values: number[]; converged: boolean; }
+
+/** Compute Predictive. */
+export function computePredictive(data: number[], opts: PredictiveOptions = {}): PredictiveResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePredictive };
diff --git a/src/analytics/prescriptive.ts b/src/analytics/prescriptive.ts
new file mode 100644
index 00000000..f1f398dc
--- /dev/null
+++ b/src/analytics/prescriptive.ts
@@ -0,0 +1,22 @@
+/** Prescriptive module — tsb analytics library. */
+
+/** Options for Prescriptive. */
+export interface PrescriptiveOptions { tol?: number; maxIter?: number; }
+
+/** Result from Prescriptive. */
+export interface PrescriptiveResult { values: number[]; converged: boolean; }
+
+/** Compute Prescriptive. */
+export function computePrescriptive(data: number[], opts: PrescriptiveOptions = {}): PrescriptiveResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePrescriptive };
diff --git a/src/analytics/pro.ts b/src/analytics/pro.ts
new file mode 100644
index 00000000..c8221be5
--- /dev/null
+++ b/src/analytics/pro.ts
@@ -0,0 +1,15 @@
+/** Analytics Pro module — tsb analytics library. */
+export interface Analytics proOptions { tol?: number; maxIter?: number; }
+export interface Analytics proResult { values: number[]; converged: boolean; }
+export function computeAnalytics pro(data: number[], opts: Analytics proOptions = {}): Analytics proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnalytics pro };
diff --git a/src/analytics/referral.ts b/src/analytics/referral.ts
new file mode 100644
index 00000000..e9d8b622
--- /dev/null
+++ b/src/analytics/referral.ts
@@ -0,0 +1,22 @@
+/** Referral module — tsb analytics library. */
+
+/** Options for Referral. */
+export interface ReferralOptions { tol?: number; maxIter?: number; }
+
+/** Result from Referral. */
+export interface ReferralResult { values: number[]; converged: boolean; }
+
+/** Compute Referral. */
+export function computeReferral(data: number[], opts: ReferralOptions = {}): ReferralResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeReferral };
diff --git a/src/analytics/retention.ts b/src/analytics/retention.ts
new file mode 100644
index 00000000..f0956cb1
--- /dev/null
+++ b/src/analytics/retention.ts
@@ -0,0 +1,22 @@
+/** Retention module — tsb analytics library. */
+
+/** Options for Retention. */
+export interface RetentionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Retention. */
+export interface RetentionResult { values: number[]; converged: boolean; }
+
+/** Compute Retention. */
+export function computeRetention(data: number[], opts: RetentionOptions = {}): RetentionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRetention };
diff --git a/src/analytics/robust.ts b/src/analytics/robust.ts
new file mode 100644
index 00000000..feca18aa
--- /dev/null
+++ b/src/analytics/robust.ts
@@ -0,0 +1,15 @@
+/** Analytics Robust module — tsb analytics library. */
+export interface Analytics robustOptions { tol?: number; maxIter?: number; }
+export interface Analytics robustResult { values: number[]; converged: boolean; }
+export function computeAnalytics robust(data: number[], opts: Analytics robustOptions = {}): Analytics robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnalytics robust };
diff --git a/src/analytics/rollout.ts b/src/analytics/rollout.ts
new file mode 100644
index 00000000..c405f514
--- /dev/null
+++ b/src/analytics/rollout.ts
@@ -0,0 +1,22 @@
+/** Rollout module — tsb analytics library. */
+
+/** Options for Rollout. */
+export interface RolloutOptions { tol?: number; maxIter?: number; }
+
+/** Result from Rollout. */
+export interface RolloutResult { values: number[]; converged: boolean; }
+
+/** Compute Rollout. */
+export function computeRollout(data: number[], opts: RolloutOptions = {}): RolloutResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRollout };
diff --git a/src/analytics/seasonality.ts b/src/analytics/seasonality.ts
new file mode 100644
index 00000000..5eaefbde
--- /dev/null
+++ b/src/analytics/seasonality.ts
@@ -0,0 +1,22 @@
+/** Seasonality module — tsb analytics library. */
+
+/** Options for Seasonality. */
+export interface SeasonalityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Seasonality. */
+export interface SeasonalityResult { values: number[]; converged: boolean; }
+
+/** Compute Seasonality. */
+export function computeSeasonality(data: number[], opts: SeasonalityOptions = {}): SeasonalityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSeasonality };
diff --git a/src/analytics/segmentation.ts b/src/analytics/segmentation.ts
new file mode 100644
index 00000000..c74e19ce
--- /dev/null
+++ b/src/analytics/segmentation.ts
@@ -0,0 +1,22 @@
+/** Segmentation module — tsb analytics library. */
+
+/** Options for Segmentation. */
+export interface SegmentationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Segmentation. */
+export interface SegmentationResult { values: number[]; converged: boolean; }
+
+/** Compute Segmentation. */
+export function computeSegmentation(data: number[], opts: SegmentationOptions = {}): SegmentationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSegmentation };
diff --git a/src/analytics/small.ts b/src/analytics/small.ts
new file mode 100644
index 00000000..e22c64e7
--- /dev/null
+++ b/src/analytics/small.ts
@@ -0,0 +1,15 @@
+/** Analytics Small module — tsb analytics library. */
+export interface Analytics smallOptions { tol?: number; maxIter?: number; }
+export interface Analytics smallResult { values: number[]; converged: boolean; }
+export function computeAnalytics small(data: number[], opts: Analytics smallOptions = {}): Analytics smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnalytics small };
diff --git a/src/analytics/sparse.ts b/src/analytics/sparse.ts
new file mode 100644
index 00000000..e7ae4176
--- /dev/null
+++ b/src/analytics/sparse.ts
@@ -0,0 +1,15 @@
+/** Analytics Sparse module — tsb analytics library. */
+export interface Analytics sparseOptions { tol?: number; maxIter?: number; }
+export interface Analytics sparseResult { values: number[]; converged: boolean; }
+export function computeAnalytics sparse(data: number[], opts: Analytics sparseOptions = {}): Analytics sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnalytics sparse };
diff --git a/src/analytics/stable.ts b/src/analytics/stable.ts
new file mode 100644
index 00000000..53807cf9
--- /dev/null
+++ b/src/analytics/stable.ts
@@ -0,0 +1,15 @@
+/** Analytics Stable module — tsb analytics library. */
+export interface Analytics stableOptions { tol?: number; maxIter?: number; }
+export interface Analytics stableResult { values: number[]; converged: boolean; }
+export function computeAnalytics stable(data: number[], opts: Analytics stableOptions = {}): Analytics stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnalytics stable };
diff --git a/src/analytics/streaming.ts b/src/analytics/streaming.ts
new file mode 100644
index 00000000..c967778f
--- /dev/null
+++ b/src/analytics/streaming.ts
@@ -0,0 +1,15 @@
+/** Analytics Streaming module — tsb analytics library. */
+export interface Analytics streamingOptions { tol?: number; maxIter?: number; }
+export interface Analytics streamingResult { values: number[]; converged: boolean; }
+export function computeAnalytics streaming(data: number[], opts: Analytics streamingOptions = {}): Analytics streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnalytics streaming };
diff --git a/src/analytics/trend.ts b/src/analytics/trend.ts
new file mode 100644
index 00000000..801716a3
--- /dev/null
+++ b/src/analytics/trend.ts
@@ -0,0 +1,22 @@
+/** Trend module — tsb analytics library. */
+
+/** Options for Trend. */
+export interface TrendOptions { tol?: number; maxIter?: number; }
+
+/** Result from Trend. */
+export interface TrendResult { values: number[]; converged: boolean; }
+
+/** Compute Trend. */
+export function computeTrend(data: number[], opts: TrendOptions = {}): TrendResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTrend };
diff --git a/src/analytics/v2.ts b/src/analytics/v2.ts
new file mode 100644
index 00000000..f5ca5c0a
--- /dev/null
+++ b/src/analytics/v2.ts
@@ -0,0 +1,15 @@
+/** Analytics V2 module — tsb analytics library. */
+export interface Analytics v2Options { tol?: number; maxIter?: number; }
+export interface Analytics v2Result { values: number[]; converged: boolean; }
+export function computeAnalytics v2(data: number[], opts: Analytics v2Options = {}): Analytics v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnalytics v2 };
diff --git a/src/analytics/v3.ts b/src/analytics/v3.ts
new file mode 100644
index 00000000..daa16e32
--- /dev/null
+++ b/src/analytics/v3.ts
@@ -0,0 +1,15 @@
+/** Analytics V3 module — tsb analytics library. */
+export interface Analytics v3Options { tol?: number; maxIter?: number; }
+export interface Analytics v3Result { values: number[]; converged: boolean; }
+export function computeAnalytics v3(data: number[], opts: Analytics v3Options = {}): Analytics v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnalytics v3 };
diff --git a/src/analytics/virality.ts b/src/analytics/virality.ts
new file mode 100644
index 00000000..676622c1
--- /dev/null
+++ b/src/analytics/virality.ts
@@ -0,0 +1,22 @@
+/** Virality module — tsb analytics library. */
+
+/** Options for Virality. */
+export interface ViralityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Virality. */
+export interface ViralityResult { values: number[]; converged: boolean; }
+
+/** Compute Virality. */
+export function computeVirality(data: number[], opts: ViralityOptions = {}): ViralityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeVirality };
diff --git a/src/analytics/wasm.ts b/src/analytics/wasm.ts
new file mode 100644
index 00000000..bebfdc58
--- /dev/null
+++ b/src/analytics/wasm.ts
@@ -0,0 +1,15 @@
+/** Analytics Wasm module — tsb analytics library. */
+export interface Analytics wasmOptions { tol?: number; maxIter?: number; }
+export interface Analytics wasmResult { values: number[]; converged: boolean; }
+export function computeAnalytics wasm(data: number[], opts: Analytics wasmOptions = {}): Analytics wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnalytics wasm };
diff --git a/src/analytics/xlarge.ts b/src/analytics/xlarge.ts
new file mode 100644
index 00000000..dcd5bd52
--- /dev/null
+++ b/src/analytics/xlarge.ts
@@ -0,0 +1,15 @@
+/** Analytics Xlarge module — tsb analytics library. */
+export interface Analytics xlargeOptions { tol?: number; maxIter?: number; }
+export interface Analytics xlargeResult { values: number[]; converged: boolean; }
+export function computeAnalytics xlarge(data: number[], opts: Analytics xlargeOptions = {}): Analytics xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnalytics xlarge };
diff --git a/src/anthropology/advanced.ts b/src/anthropology/advanced.ts
new file mode 100644
index 00000000..3130d27e
--- /dev/null
+++ b/src/anthropology/advanced.ts
@@ -0,0 +1,15 @@
+/** Anthropology Advanced module — tsb analytics library. */
+export interface Anthropology advancedOptions { tol?: number; maxIter?: number; }
+export interface Anthropology advancedResult { values: number[]; converged: boolean; }
+export function computeAnthropology advanced(data: number[], opts: Anthropology advancedOptions = {}): Anthropology advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnthropology advanced };
diff --git a/src/anthropology/ancient_dna.ts b/src/anthropology/ancient_dna.ts
new file mode 100644
index 00000000..285bbdcb
--- /dev/null
+++ b/src/anthropology/ancient_dna.ts
@@ -0,0 +1,22 @@
+/** Ancient Dna module — tsb analytics library. */
+
+/** Options for Ancient Dna. */
+export interface AncientDnaOptions { tol?: number; maxIter?: number; }
+
+/** Result from Ancient Dna. */
+export interface AncientDnaResult { values: number[]; converged: boolean; }
+
+/** Compute Ancient Dna. */
+export function computeAncientDna(data: number[], opts: AncientDnaOptions = {}): AncientDnaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAncientDna };
diff --git a/src/anthropology/archaeobotany.ts b/src/anthropology/archaeobotany.ts
new file mode 100644
index 00000000..7ba20a61
--- /dev/null
+++ b/src/anthropology/archaeobotany.ts
@@ -0,0 +1,22 @@
+/** Archaeobotany module — tsb analytics library. */
+
+/** Options for Archaeobotany. */
+export interface ArchaeobotanyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Archaeobotany. */
+export interface ArchaeobotanyResult { values: number[]; converged: boolean; }
+
+/** Compute Archaeobotany. */
+export function computeArchaeobotany(data: number[], opts: ArchaeobotanyOptions = {}): ArchaeobotanyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeArchaeobotany };
diff --git a/src/anthropology/archaeological.ts b/src/anthropology/archaeological.ts
new file mode 100644
index 00000000..5a679f29
--- /dev/null
+++ b/src/anthropology/archaeological.ts
@@ -0,0 +1,22 @@
+/** Archaeological module — tsb analytics library. */
+
+/** Options for Archaeological. */
+export interface ArchaeologicalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Archaeological. */
+export interface ArchaeologicalResult { values: number[]; converged: boolean; }
+
+/** Compute Archaeological. */
+export function computeArchaeological(data: number[], opts: ArchaeologicalOptions = {}): ArchaeologicalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeArchaeological };
diff --git a/src/anthropology/argon_argon.ts b/src/anthropology/argon_argon.ts
new file mode 100644
index 00000000..0f839a06
--- /dev/null
+++ b/src/anthropology/argon_argon.ts
@@ -0,0 +1,22 @@
+/** Argon Argon module — tsb analytics library. */
+
+/** Options for Argon Argon. */
+export interface ArgonArgonOptions { tol?: number; maxIter?: number; }
+
+/** Result from Argon Argon. */
+export interface ArgonArgonResult { values: number[]; converged: boolean; }
+
+/** Compute Argon Argon. */
+export function computeArgonArgon(data: number[], opts: ArgonArgonOptions = {}): ArgonArgonResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeArgonArgon };
diff --git a/src/anthropology/base2.ts b/src/anthropology/base2.ts
new file mode 100644
index 00000000..3dabfcb9
--- /dev/null
+++ b/src/anthropology/base2.ts
@@ -0,0 +1,15 @@
+/** Anthropology Base2 module — tsb analytics library. */
+export interface Anthropology base2Options { tol?: number; maxIter?: number; }
+export interface Anthropology base2Result { values: number[]; converged: boolean; }
+export function computeAnthropology base2(data: number[], opts: Anthropology base2Options = {}): Anthropology base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnthropology base2 };
diff --git a/src/anthropology/batch.ts b/src/anthropology/batch.ts
new file mode 100644
index 00000000..5a705496
--- /dev/null
+++ b/src/anthropology/batch.ts
@@ -0,0 +1,15 @@
+/** Anthropology Batch module — tsb analytics library. */
+export interface Anthropology batchOptions { tol?: number; maxIter?: number; }
+export interface Anthropology batchResult { values: number[]; converged: boolean; }
+export function computeAnthropology batch(data: number[], opts: Anthropology batchOptions = {}): Anthropology batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnthropology batch };
diff --git a/src/anthropology/beta.ts b/src/anthropology/beta.ts
new file mode 100644
index 00000000..1565445f
--- /dev/null
+++ b/src/anthropology/beta.ts
@@ -0,0 +1,15 @@
+/** Anthropology Beta module — tsb analytics library. */
+export interface Anthropology betaOptions { tol?: number; maxIter?: number; }
+export interface Anthropology betaResult { values: number[]; converged: boolean; }
+export function computeAnthropology beta(data: number[], opts: Anthropology betaOptions = {}): Anthropology betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnthropology beta };
diff --git a/src/anthropology/biological.ts b/src/anthropology/biological.ts
new file mode 100644
index 00000000..d3df352a
--- /dev/null
+++ b/src/anthropology/biological.ts
@@ -0,0 +1,22 @@
+/** Biological module — tsb analytics library. */
+
+/** Options for Biological. */
+export interface BiologicalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Biological. */
+export interface BiologicalResult { values: number[]; converged: boolean; }
+
+/** Compute Biological. */
+export function computeBiological(data: number[], opts: BiologicalOptions = {}): BiologicalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBiological };
diff --git a/src/anthropology/cosmogenic_anth.ts b/src/anthropology/cosmogenic_anth.ts
new file mode 100644
index 00000000..077b119e
--- /dev/null
+++ b/src/anthropology/cosmogenic_anth.ts
@@ -0,0 +1,22 @@
+/** Cosmogenic Anth module — tsb analytics library. */
+
+/** Options for Cosmogenic Anth. */
+export interface CosmogenicAnthOptions { tol?: number; maxIter?: number; }
+
+/** Result from Cosmogenic Anth. */
+export interface CosmogenicAnthResult { values: number[]; converged: boolean; }
+
+/** Compute Cosmogenic Anth. */
+export function computeCosmogenicAnth(data: number[], opts: CosmogenicAnthOptions = {}): CosmogenicAnthResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCosmogenicAnth };
diff --git a/src/anthropology/cpu.ts b/src/anthropology/cpu.ts
new file mode 100644
index 00000000..3fa37827
--- /dev/null
+++ b/src/anthropology/cpu.ts
@@ -0,0 +1,15 @@
+/** Anthropology Cpu module — tsb analytics library. */
+export interface Anthropology cpuOptions { tol?: number; maxIter?: number; }
+export interface Anthropology cpuResult { values: number[]; converged: boolean; }
+export function computeAnthropology cpu(data: number[], opts: Anthropology cpuOptions = {}): Anthropology cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnthropology cpu };
diff --git a/src/anthropology/cultural.ts b/src/anthropology/cultural.ts
new file mode 100644
index 00000000..2e2c0c63
--- /dev/null
+++ b/src/anthropology/cultural.ts
@@ -0,0 +1,22 @@
+/** Cultural module — tsb analytics library. */
+
+/** Options for Cultural. */
+export interface CulturalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Cultural. */
+export interface CulturalResult { values: number[]; converged: boolean; }
+
+/** Compute Cultural. */
+export function computeCultural(data: number[], opts: CulturalOptions = {}): CulturalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCultural };
diff --git a/src/anthropology/dendrochronology.ts b/src/anthropology/dendrochronology.ts
new file mode 100644
index 00000000..2fc187f8
--- /dev/null
+++ b/src/anthropology/dendrochronology.ts
@@ -0,0 +1,22 @@
+/** Dendrochronology module — tsb analytics library. */
+
+/** Options for Dendrochronology. */
+export interface DendrochronologyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Dendrochronology. */
+export interface DendrochronologyResult { values: number[]; converged: boolean; }
+
+/** Compute Dendrochronology. */
+export function computeDendrochronology(data: number[], opts: DendrochronologyOptions = {}): DendrochronologyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDendrochronology };
diff --git a/src/anthropology/dense.ts b/src/anthropology/dense.ts
new file mode 100644
index 00000000..b62e52ae
--- /dev/null
+++ b/src/anthropology/dense.ts
@@ -0,0 +1,15 @@
+/** Anthropology Dense module — tsb analytics library. */
+export interface Anthropology denseOptions { tol?: number; maxIter?: number; }
+export interface Anthropology denseResult { values: number[]; converged: boolean; }
+export function computeAnthropology dense(data: number[], opts: Anthropology denseOptions = {}): Anthropology denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnthropology dense };
diff --git a/src/anthropology/distributed.ts b/src/anthropology/distributed.ts
new file mode 100644
index 00000000..a7323b6c
--- /dev/null
+++ b/src/anthropology/distributed.ts
@@ -0,0 +1,15 @@
+/** Anthropology Distributed module — tsb analytics library. */
+export interface Anthropology distributedOptions { tol?: number; maxIter?: number; }
+export interface Anthropology distributedResult { values: number[]; converged: boolean; }
+export function computeAnthropology distributed(data: number[], opts: Anthropology distributedOptions = {}): Anthropology distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnthropology distributed };
diff --git a/src/anthropology/electron_spin.ts b/src/anthropology/electron_spin.ts
new file mode 100644
index 00000000..0546ca27
--- /dev/null
+++ b/src/anthropology/electron_spin.ts
@@ -0,0 +1,22 @@
+/** Electron Spin module — tsb analytics library. */
+
+/** Options for Electron Spin. */
+export interface ElectronSpinOptions { tol?: number; maxIter?: number; }
+
+/** Result from Electron Spin. */
+export interface ElectronSpinResult { values: number[]; converged: boolean; }
+
+/** Compute Electron Spin. */
+export function computeElectronSpin(data: number[], opts: ElectronSpinOptions = {}): ElectronSpinResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeElectronSpin };
diff --git a/src/anthropology/experimental.ts b/src/anthropology/experimental.ts
new file mode 100644
index 00000000..03c3f259
--- /dev/null
+++ b/src/anthropology/experimental.ts
@@ -0,0 +1,15 @@
+/** Anthropology Experimental module — tsb analytics library. */
+export interface Anthropology experimentalOptions { tol?: number; maxIter?: number; }
+export interface Anthropology experimentalResult { values: number[]; converged: boolean; }
+export function computeAnthropology experimental(data: number[], opts: Anthropology experimentalOptions = {}): Anthropology experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnthropology experimental };
diff --git a/src/anthropology/fast.ts b/src/anthropology/fast.ts
new file mode 100644
index 00000000..dbf51f91
--- /dev/null
+++ b/src/anthropology/fast.ts
@@ -0,0 +1,15 @@
+/** Anthropology Fast module — tsb analytics library. */
+export interface Anthropology fastOptions { tol?: number; maxIter?: number; }
+export interface Anthropology fastResult { values: number[]; converged: boolean; }
+export function computeAnthropology fast(data: number[], opts: Anthropology fastOptions = {}): Anthropology fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnthropology fast };
diff --git a/src/anthropology/fission_track.ts b/src/anthropology/fission_track.ts
new file mode 100644
index 00000000..44959724
--- /dev/null
+++ b/src/anthropology/fission_track.ts
@@ -0,0 +1,22 @@
+/** Fission Track module — tsb analytics library. */
+
+/** Options for Fission Track. */
+export interface FissionTrackOptions { tol?: number; maxIter?: number; }
+
+/** Result from Fission Track. */
+export interface FissionTrackResult { values: number[]; converged: boolean; }
+
+/** Compute Fission Track. */
+export function computeFissionTrack(data: number[], opts: FissionTrackOptions = {}): FissionTrackResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFissionTrack };
diff --git a/src/anthropology/future.ts b/src/anthropology/future.ts
new file mode 100644
index 00000000..ceb74178
--- /dev/null
+++ b/src/anthropology/future.ts
@@ -0,0 +1,15 @@
+/** Anthropology Future module — tsb analytics library. */
+export interface Anthropology futureOptions { tol?: number; maxIter?: number; }
+export interface Anthropology futureResult { values: number[]; converged: boolean; }
+export function computeAnthropology future(data: number[], opts: Anthropology futureOptions = {}): Anthropology futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnthropology future };
diff --git a/src/anthropology/geoarchaeology.ts b/src/anthropology/geoarchaeology.ts
new file mode 100644
index 00000000..f2945d47
--- /dev/null
+++ b/src/anthropology/geoarchaeology.ts
@@ -0,0 +1,22 @@
+/** Geoarchaeology module — tsb analytics library. */
+
+/** Options for Geoarchaeology. */
+export interface GeoarchaeologyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Geoarchaeology. */
+export interface GeoarchaeologyResult { values: number[]; converged: boolean; }
+
+/** Compute Geoarchaeology. */
+export function computeGeoarchaeology(data: number[], opts: GeoarchaeologyOptions = {}): GeoarchaeologyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGeoarchaeology };
diff --git a/src/anthropology/gis_anth.ts b/src/anthropology/gis_anth.ts
new file mode 100644
index 00000000..6dd9ac72
--- /dev/null
+++ b/src/anthropology/gis_anth.ts
@@ -0,0 +1,22 @@
+/** Gis Anth module — tsb analytics library. */
+
+/** Options for Gis Anth. */
+export interface GisAnthOptions { tol?: number; maxIter?: number; }
+
+/** Result from Gis Anth. */
+export interface GisAnthResult { values: number[]; converged: boolean; }
+
+/** Compute Gis Anth. */
+export function computeGisAnth(data: number[], opts: GisAnthOptions = {}): GisAnthResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGisAnth };
diff --git a/src/anthropology/gpu.ts b/src/anthropology/gpu.ts
new file mode 100644
index 00000000..f141d11f
--- /dev/null
+++ b/src/anthropology/gpu.ts
@@ -0,0 +1,15 @@
+/** Anthropology Gpu module — tsb analytics library. */
+export interface Anthropology gpuOptions { tol?: number; maxIter?: number; }
+export interface Anthropology gpuResult { values: number[]; converged: boolean; }
+export function computeAnthropology gpu(data: number[], opts: Anthropology gpuOptions = {}): Anthropology gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnthropology gpu };
diff --git a/src/anthropology/human_evolution.ts b/src/anthropology/human_evolution.ts
new file mode 100644
index 00000000..531f74ad
--- /dev/null
+++ b/src/anthropology/human_evolution.ts
@@ -0,0 +1,22 @@
+/** Human Evolution module — tsb analytics library. */
+
+/** Options for Human Evolution. */
+export interface HumanEvolutionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Human Evolution. */
+export interface HumanEvolutionResult { values: number[]; converged: boolean; }
+
+/** Compute Human Evolution. */
+export function computeHumanEvolution(data: number[], opts: HumanEvolutionOptions = {}): HumanEvolutionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeHumanEvolution };
diff --git a/src/anthropology/landscape_anth.ts b/src/anthropology/landscape_anth.ts
new file mode 100644
index 00000000..33687d51
--- /dev/null
+++ b/src/anthropology/landscape_anth.ts
@@ -0,0 +1,22 @@
+/** Landscape Anth module — tsb analytics library. */
+
+/** Options for Landscape Anth. */
+export interface LandscapeAnthOptions { tol?: number; maxIter?: number; }
+
+/** Result from Landscape Anth. */
+export interface LandscapeAnthResult { values: number[]; converged: boolean; }
+
+/** Compute Landscape Anth. */
+export function computeLandscapeAnth(data: number[], opts: LandscapeAnthOptions = {}): LandscapeAnthResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLandscapeAnth };
diff --git a/src/anthropology/large.ts b/src/anthropology/large.ts
new file mode 100644
index 00000000..9e7b893e
--- /dev/null
+++ b/src/anthropology/large.ts
@@ -0,0 +1,15 @@
+/** Anthropology Large module — tsb analytics library. */
+export interface Anthropology largeOptions { tol?: number; maxIter?: number; }
+export interface Anthropology largeResult { values: number[]; converged: boolean; }
+export function computeAnthropology large(data: number[], opts: Anthropology largeOptions = {}): Anthropology largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnthropology large };
diff --git a/src/anthropology/legacy.ts b/src/anthropology/legacy.ts
new file mode 100644
index 00000000..53e406c2
--- /dev/null
+++ b/src/anthropology/legacy.ts
@@ -0,0 +1,15 @@
+/** Anthropology Legacy module — tsb analytics library. */
+export interface Anthropology legacyOptions { tol?: number; maxIter?: number; }
+export interface Anthropology legacyResult { values: number[]; converged: boolean; }
+export function computeAnthropology legacy(data: number[], opts: Anthropology legacyOptions = {}): Anthropology legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnthropology legacy };
diff --git a/src/anthropology/linguistic_anth.ts b/src/anthropology/linguistic_anth.ts
new file mode 100644
index 00000000..da26c691
--- /dev/null
+++ b/src/anthropology/linguistic_anth.ts
@@ -0,0 +1,22 @@
+/** Linguistic Anth module — tsb analytics library. */
+
+/** Options for Linguistic Anth. */
+export interface LinguisticAnthOptions { tol?: number; maxIter?: number; }
+
+/** Result from Linguistic Anth. */
+export interface LinguisticAnthResult { values: number[]; converged: boolean; }
+
+/** Compute Linguistic Anth. */
+export function computeLinguisticAnth(data: number[], opts: LinguisticAnthOptions = {}): LinguisticAnthResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLinguisticAnth };
diff --git a/src/anthropology/lite.ts b/src/anthropology/lite.ts
new file mode 100644
index 00000000..bb59a047
--- /dev/null
+++ b/src/anthropology/lite.ts
@@ -0,0 +1,15 @@
+/** Anthropology Lite module — tsb analytics library. */
+export interface Anthropology liteOptions { tol?: number; maxIter?: number; }
+export interface Anthropology liteResult { values: number[]; converged: boolean; }
+export function computeAnthropology lite(data: number[], opts: Anthropology liteOptions = {}): Anthropology liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnthropology lite };
diff --git a/src/anthropology/luminescence.ts b/src/anthropology/luminescence.ts
new file mode 100644
index 00000000..09585848
--- /dev/null
+++ b/src/anthropology/luminescence.ts
@@ -0,0 +1,22 @@
+/** Luminescence module — tsb analytics library. */
+
+/** Options for Luminescence. */
+export interface LuminescenceOptions { tol?: number; maxIter?: number; }
+
+/** Result from Luminescence. */
+export interface LuminescenceResult { values: number[]; converged: boolean; }
+
+/** Compute Luminescence. */
+export function computeLuminescence(data: number[], opts: LuminescenceOptions = {}): LuminescenceResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLuminescence };
diff --git a/src/anthropology/mini.ts b/src/anthropology/mini.ts
new file mode 100644
index 00000000..939776ab
--- /dev/null
+++ b/src/anthropology/mini.ts
@@ -0,0 +1,15 @@
+/** Anthropology Mini module — tsb analytics library. */
+export interface Anthropology miniOptions { tol?: number; maxIter?: number; }
+export interface Anthropology miniResult { values: number[]; converged: boolean; }
+export function computeAnthropology mini(data: number[], opts: Anthropology miniOptions = {}): Anthropology miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnthropology mini };
diff --git a/src/anthropology/modern_human_origins.ts b/src/anthropology/modern_human_origins.ts
new file mode 100644
index 00000000..ec590bac
--- /dev/null
+++ b/src/anthropology/modern_human_origins.ts
@@ -0,0 +1,22 @@
+/** Modern Human Origins module — tsb analytics library. */
+
+/** Options for Modern Human Origins. */
+export interface ModernHumanOriginsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Modern Human Origins. */
+export interface ModernHumanOriginsResult { values: number[]; converged: boolean; }
+
+/** Compute Modern Human Origins. */
+export function computeModernHumanOrigins(data: number[], opts: ModernHumanOriginsOptions = {}): ModernHumanOriginsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeModernHumanOrigins };
diff --git a/src/anthropology/molecular.ts b/src/anthropology/molecular.ts
new file mode 100644
index 00000000..114ab325
--- /dev/null
+++ b/src/anthropology/molecular.ts
@@ -0,0 +1,22 @@
+/** Molecular module — tsb analytics library. */
+
+/** Options for Molecular. */
+export interface MolecularOptions { tol?: number; maxIter?: number; }
+
+/** Result from Molecular. */
+export interface MolecularResult { values: number[]; converged: boolean; }
+
+/** Compute Molecular. */
+export function computeMolecular(data: number[], opts: MolecularOptions = {}): MolecularResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMolecular };
diff --git a/src/anthropology/morphometry.ts b/src/anthropology/morphometry.ts
new file mode 100644
index 00000000..860eb6fc
--- /dev/null
+++ b/src/anthropology/morphometry.ts
@@ -0,0 +1,22 @@
+/** Morphometry module — tsb analytics library. */
+
+/** Options for Morphometry. */
+export interface MorphometryOptions { tol?: number; maxIter?: number; }
+
+/** Result from Morphometry. */
+export interface MorphometryResult { values: number[]; converged: boolean; }
+
+/** Compute Morphometry. */
+export function computeMorphometry(data: number[], opts: MorphometryOptions = {}): MorphometryResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMorphometry };
diff --git a/src/anthropology/next.ts b/src/anthropology/next.ts
new file mode 100644
index 00000000..154ad334
--- /dev/null
+++ b/src/anthropology/next.ts
@@ -0,0 +1,15 @@
+/** Anthropology Next module — tsb analytics library. */
+export interface Anthropology nextOptions { tol?: number; maxIter?: number; }
+export interface Anthropology nextResult { values: number[]; converged: boolean; }
+export function computeAnthropology next(data: number[], opts: Anthropology nextOptions = {}): Anthropology nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnthropology next };
diff --git a/src/anthropology/online.ts b/src/anthropology/online.ts
new file mode 100644
index 00000000..8dcd7cc2
--- /dev/null
+++ b/src/anthropology/online.ts
@@ -0,0 +1,15 @@
+/** Anthropology Online module — tsb analytics library. */
+export interface Anthropology onlineOptions { tol?: number; maxIter?: number; }
+export interface Anthropology onlineResult { values: number[]; converged: boolean; }
+export function computeAnthropology online(data: number[], opts: Anthropology onlineOptions = {}): Anthropology onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnthropology online };
diff --git a/src/anthropology/paleoanthropology.ts b/src/anthropology/paleoanthropology.ts
new file mode 100644
index 00000000..3376d8e3
--- /dev/null
+++ b/src/anthropology/paleoanthropology.ts
@@ -0,0 +1,22 @@
+/** Paleoanthropology module — tsb analytics library. */
+
+/** Options for Paleoanthropology. */
+export interface PaleoanthropologyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Paleoanthropology. */
+export interface PaleoanthropologyResult { values: number[]; converged: boolean; }
+
+/** Compute Paleoanthropology. */
+export function computePaleoanthropology(data: number[], opts: PaleoanthropologyOptions = {}): PaleoanthropologyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePaleoanthropology };
diff --git a/src/anthropology/parallel.ts b/src/anthropology/parallel.ts
new file mode 100644
index 00000000..6c447a59
--- /dev/null
+++ b/src/anthropology/parallel.ts
@@ -0,0 +1,15 @@
+/** Anthropology Parallel module — tsb analytics library. */
+export interface Anthropology parallelOptions { tol?: number; maxIter?: number; }
+export interface Anthropology parallelResult { values: number[]; converged: boolean; }
+export function computeAnthropology parallel(data: number[], opts: Anthropology parallelOptions = {}): Anthropology parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnthropology parallel };
diff --git a/src/anthropology/physical.ts b/src/anthropology/physical.ts
new file mode 100644
index 00000000..21199935
--- /dev/null
+++ b/src/anthropology/physical.ts
@@ -0,0 +1,22 @@
+/** Physical module — tsb analytics library. */
+
+/** Options for Physical. */
+export interface PhysicalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Physical. */
+export interface PhysicalResult { values: number[]; converged: boolean; }
+
+/** Compute Physical. */
+export function computePhysical(data: number[], opts: PhysicalOptions = {}): PhysicalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePhysical };
diff --git a/src/anthropology/plus.ts b/src/anthropology/plus.ts
new file mode 100644
index 00000000..b285f415
--- /dev/null
+++ b/src/anthropology/plus.ts
@@ -0,0 +1,15 @@
+/** Anthropology Plus module — tsb analytics library. */
+export interface Anthropology plusOptions { tol?: number; maxIter?: number; }
+export interface Anthropology plusResult { values: number[]; converged: boolean; }
+export function computeAnthropology plus(data: number[], opts: Anthropology plusOptions = {}): Anthropology plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnthropology plus };
diff --git a/src/anthropology/population_genetics_anth.ts b/src/anthropology/population_genetics_anth.ts
new file mode 100644
index 00000000..28b2cf5f
--- /dev/null
+++ b/src/anthropology/population_genetics_anth.ts
@@ -0,0 +1,22 @@
+/** Population Genetics Anth module — tsb analytics library. */
+
+/** Options for Population Genetics Anth. */
+export interface PopulationGeneticsAnthOptions { tol?: number; maxIter?: number; }
+
+/** Result from Population Genetics Anth. */
+export interface PopulationGeneticsAnthResult { values: number[]; converged: boolean; }
+
+/** Compute Population Genetics Anth. */
+export function computePopulationGeneticsAnth(data: number[], opts: PopulationGeneticsAnthOptions = {}): PopulationGeneticsAnthResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePopulationGeneticsAnth };
diff --git a/src/anthropology/potassium_argon.ts b/src/anthropology/potassium_argon.ts
new file mode 100644
index 00000000..da13b0b7
--- /dev/null
+++ b/src/anthropology/potassium_argon.ts
@@ -0,0 +1,22 @@
+/** Potassium Argon module — tsb analytics library. */
+
+/** Options for Potassium Argon. */
+export interface PotassiumArgonOptions { tol?: number; maxIter?: number; }
+
+/** Result from Potassium Argon. */
+export interface PotassiumArgonResult { values: number[]; converged: boolean; }
+
+/** Compute Potassium Argon. */
+export function computePotassiumArgon(data: number[], opts: PotassiumArgonOptions = {}): PotassiumArgonResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePotassiumArgon };
diff --git a/src/anthropology/primatology.ts b/src/anthropology/primatology.ts
new file mode 100644
index 00000000..c29325fe
--- /dev/null
+++ b/src/anthropology/primatology.ts
@@ -0,0 +1,22 @@
+/** Primatology module — tsb analytics library. */
+
+/** Options for Primatology. */
+export interface PrimatologyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Primatology. */
+export interface PrimatologyResult { values: number[]; converged: boolean; }
+
+/** Compute Primatology. */
+export function computePrimatology(data: number[], opts: PrimatologyOptions = {}): PrimatologyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePrimatology };
diff --git a/src/anthropology/pro.ts b/src/anthropology/pro.ts
new file mode 100644
index 00000000..3f279bd1
--- /dev/null
+++ b/src/anthropology/pro.ts
@@ -0,0 +1,15 @@
+/** Anthropology Pro module — tsb analytics library. */
+export interface Anthropology proOptions { tol?: number; maxIter?: number; }
+export interface Anthropology proResult { values: number[]; converged: boolean; }
+export function computeAnthropology pro(data: number[], opts: Anthropology proOptions = {}): Anthropology proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnthropology pro };
diff --git a/src/anthropology/radiocarbon.ts b/src/anthropology/radiocarbon.ts
new file mode 100644
index 00000000..559dfb1c
--- /dev/null
+++ b/src/anthropology/radiocarbon.ts
@@ -0,0 +1,22 @@
+/** Radiocarbon module — tsb analytics library. */
+
+/** Options for Radiocarbon. */
+export interface RadiocarbonOptions { tol?: number; maxIter?: number; }
+
+/** Result from Radiocarbon. */
+export interface RadiocarbonResult { values: number[]; converged: boolean; }
+
+/** Compute Radiocarbon. */
+export function computeRadiocarbon(data: number[], opts: RadiocarbonOptions = {}): RadiocarbonResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRadiocarbon };
diff --git a/src/anthropology/remote_sensing_anth.ts b/src/anthropology/remote_sensing_anth.ts
new file mode 100644
index 00000000..3140b834
--- /dev/null
+++ b/src/anthropology/remote_sensing_anth.ts
@@ -0,0 +1,22 @@
+/** Remote Sensing Anth module — tsb analytics library. */
+
+/** Options for Remote Sensing Anth. */
+export interface RemoteSensingAnthOptions { tol?: number; maxIter?: number; }
+
+/** Result from Remote Sensing Anth. */
+export interface RemoteSensingAnthResult { values: number[]; converged: boolean; }
+
+/** Compute Remote Sensing Anth. */
+export function computeRemoteSensingAnth(data: number[], opts: RemoteSensingAnthOptions = {}): RemoteSensingAnthResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRemoteSensingAnth };
diff --git a/src/anthropology/robust.ts b/src/anthropology/robust.ts
new file mode 100644
index 00000000..37faec9f
--- /dev/null
+++ b/src/anthropology/robust.ts
@@ -0,0 +1,15 @@
+/** Anthropology Robust module — tsb analytics library. */
+export interface Anthropology robustOptions { tol?: number; maxIter?: number; }
+export interface Anthropology robustResult { values: number[]; converged: boolean; }
+export function computeAnthropology robust(data: number[], opts: Anthropology robustOptions = {}): Anthropology robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnthropology robust };
diff --git a/src/anthropology/small.ts b/src/anthropology/small.ts
new file mode 100644
index 00000000..75e092f7
--- /dev/null
+++ b/src/anthropology/small.ts
@@ -0,0 +1,15 @@
+/** Anthropology Small module — tsb analytics library. */
+export interface Anthropology smallOptions { tol?: number; maxIter?: number; }
+export interface Anthropology smallResult { values: number[]; converged: boolean; }
+export function computeAnthropology small(data: number[], opts: Anthropology smallOptions = {}): Anthropology smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnthropology small };
diff --git a/src/anthropology/sparse.ts b/src/anthropology/sparse.ts
new file mode 100644
index 00000000..8c4fbd6b
--- /dev/null
+++ b/src/anthropology/sparse.ts
@@ -0,0 +1,15 @@
+/** Anthropology Sparse module — tsb analytics library. */
+export interface Anthropology sparseOptions { tol?: number; maxIter?: number; }
+export interface Anthropology sparseResult { values: number[]; converged: boolean; }
+export function computeAnthropology sparse(data: number[], opts: Anthropology sparseOptions = {}): Anthropology sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnthropology sparse };
diff --git a/src/anthropology/stable.ts b/src/anthropology/stable.ts
new file mode 100644
index 00000000..513a4dd9
--- /dev/null
+++ b/src/anthropology/stable.ts
@@ -0,0 +1,15 @@
+/** Anthropology Stable module — tsb analytics library. */
+export interface Anthropology stableOptions { tol?: number; maxIter?: number; }
+export interface Anthropology stableResult { values: number[]; converged: boolean; }
+export function computeAnthropology stable(data: number[], opts: Anthropology stableOptions = {}): Anthropology stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnthropology stable };
diff --git a/src/anthropology/stable_isotope_anth.ts b/src/anthropology/stable_isotope_anth.ts
new file mode 100644
index 00000000..e0e6db7a
--- /dev/null
+++ b/src/anthropology/stable_isotope_anth.ts
@@ -0,0 +1,22 @@
+/** Stable Isotope Anth module — tsb analytics library. */
+
+/** Options for Stable Isotope Anth. */
+export interface StableIsotopeAnthOptions { tol?: number; maxIter?: number; }
+
+/** Result from Stable Isotope Anth. */
+export interface StableIsotopeAnthResult { values: number[]; converged: boolean; }
+
+/** Compute Stable Isotope Anth. */
+export function computeStableIsotopeAnth(data: number[], opts: StableIsotopeAnthOptions = {}): StableIsotopeAnthResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeStableIsotopeAnth };
diff --git a/src/anthropology/streaming.ts b/src/anthropology/streaming.ts
new file mode 100644
index 00000000..80229f72
--- /dev/null
+++ b/src/anthropology/streaming.ts
@@ -0,0 +1,15 @@
+/** Anthropology Streaming module — tsb analytics library. */
+export interface Anthropology streamingOptions { tol?: number; maxIter?: number; }
+export interface Anthropology streamingResult { values: number[]; converged: boolean; }
+export function computeAnthropology streaming(data: number[], opts: Anthropology streamingOptions = {}): Anthropology streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnthropology streaming };
diff --git a/src/anthropology/uranium_series.ts b/src/anthropology/uranium_series.ts
new file mode 100644
index 00000000..b7d6d794
--- /dev/null
+++ b/src/anthropology/uranium_series.ts
@@ -0,0 +1,22 @@
+/** Uranium Series module — tsb analytics library. */
+
+/** Options for Uranium Series. */
+export interface UraniumSeriesOptions { tol?: number; maxIter?: number; }
+
+/** Result from Uranium Series. */
+export interface UraniumSeriesResult { values: number[]; converged: boolean; }
+
+/** Compute Uranium Series. */
+export function computeUraniumSeries(data: number[], opts: UraniumSeriesOptions = {}): UraniumSeriesResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeUraniumSeries };
diff --git a/src/anthropology/v2.ts b/src/anthropology/v2.ts
new file mode 100644
index 00000000..b2e2d9ff
--- /dev/null
+++ b/src/anthropology/v2.ts
@@ -0,0 +1,15 @@
+/** Anthropology V2 module — tsb analytics library. */
+export interface Anthropology v2Options { tol?: number; maxIter?: number; }
+export interface Anthropology v2Result { values: number[]; converged: boolean; }
+export function computeAnthropology v2(data: number[], opts: Anthropology v2Options = {}): Anthropology v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnthropology v2 };
diff --git a/src/anthropology/v3.ts b/src/anthropology/v3.ts
new file mode 100644
index 00000000..48c26b8f
--- /dev/null
+++ b/src/anthropology/v3.ts
@@ -0,0 +1,15 @@
+/** Anthropology V3 module — tsb analytics library. */
+export interface Anthropology v3Options { tol?: number; maxIter?: number; }
+export interface Anthropology v3Result { values: number[]; converged: boolean; }
+export function computeAnthropology v3(data: number[], opts: Anthropology v3Options = {}): Anthropology v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnthropology v3 };
diff --git a/src/anthropology/wasm.ts b/src/anthropology/wasm.ts
new file mode 100644
index 00000000..edf0be05
--- /dev/null
+++ b/src/anthropology/wasm.ts
@@ -0,0 +1,15 @@
+/** Anthropology Wasm module — tsb analytics library. */
+export interface Anthropology wasmOptions { tol?: number; maxIter?: number; }
+export interface Anthropology wasmResult { values: number[]; converged: boolean; }
+export function computeAnthropology wasm(data: number[], opts: Anthropology wasmOptions = {}): Anthropology wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnthropology wasm };
diff --git a/src/anthropology/xlarge.ts b/src/anthropology/xlarge.ts
new file mode 100644
index 00000000..456da32f
--- /dev/null
+++ b/src/anthropology/xlarge.ts
@@ -0,0 +1,15 @@
+/** Anthropology Xlarge module — tsb analytics library. */
+export interface Anthropology xlargeOptions { tol?: number; maxIter?: number; }
+export interface Anthropology xlargeResult { values: number[]; converged: boolean; }
+export function computeAnthropology xlarge(data: number[], opts: Anthropology xlargeOptions = {}): Anthropology xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAnthropology xlarge };
diff --git a/src/anthropology/zooarchaeology.ts b/src/anthropology/zooarchaeology.ts
new file mode 100644
index 00000000..4dbcbf70
--- /dev/null
+++ b/src/anthropology/zooarchaeology.ts
@@ -0,0 +1,22 @@
+/** Zooarchaeology module — tsb analytics library. */
+
+/** Options for Zooarchaeology. */
+export interface ZooarchaeologyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Zooarchaeology. */
+export interface ZooarchaeologyResult { values: number[]; converged: boolean; }
+
+/** Compute Zooarchaeology. */
+export function computeZooarchaeology(data: number[], opts: ZooarchaeologyOptions = {}): ZooarchaeologyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeZooarchaeology };
diff --git a/src/astronomy/advanced.ts b/src/astronomy/advanced.ts
new file mode 100644
index 00000000..81574373
--- /dev/null
+++ b/src/astronomy/advanced.ts
@@ -0,0 +1,15 @@
+/** Astronomy Advanced module — tsb analytics library. */
+export interface Astronomy advancedOptions { tol?: number; maxIter?: number; }
+export interface Astronomy advancedResult { values: number[]; converged: boolean; }
+export function computeAstronomy advanced(data: number[], opts: Astronomy advancedOptions = {}): Astronomy advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAstronomy advanced };
diff --git a/src/astronomy/astrometry.ts b/src/astronomy/astrometry.ts
new file mode 100644
index 00000000..217fa973
--- /dev/null
+++ b/src/astronomy/astrometry.ts
@@ -0,0 +1,22 @@
+/** Astrometry module — tsb analytics library. */
+
+/** Options for Astrometry. */
+export interface AstrometryOptions { tol?: number; maxIter?: number; }
+
+/** Result from Astrometry. */
+export interface AstrometryResult { values: number[]; converged: boolean; }
+
+/** Compute Astrometry. */
+export function computeAstrometry(data: number[], opts: AstrometryOptions = {}): AstrometryResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAstrometry };
diff --git a/src/astronomy/base2.ts b/src/astronomy/base2.ts
new file mode 100644
index 00000000..7bd2b6af
--- /dev/null
+++ b/src/astronomy/base2.ts
@@ -0,0 +1,15 @@
+/** Astronomy Base2 module — tsb analytics library. */
+export interface Astronomy base2Options { tol?: number; maxIter?: number; }
+export interface Astronomy base2Result { values: number[]; converged: boolean; }
+export function computeAstronomy base2(data: number[], opts: Astronomy base2Options = {}): Astronomy base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAstronomy base2 };
diff --git a/src/astronomy/batch.ts b/src/astronomy/batch.ts
new file mode 100644
index 00000000..42c23a79
--- /dev/null
+++ b/src/astronomy/batch.ts
@@ -0,0 +1,15 @@
+/** Astronomy Batch module — tsb analytics library. */
+export interface Astronomy batchOptions { tol?: number; maxIter?: number; }
+export interface Astronomy batchResult { values: number[]; converged: boolean; }
+export function computeAstronomy batch(data: number[], opts: Astronomy batchOptions = {}): Astronomy batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAstronomy batch };
diff --git a/src/astronomy/beta.ts b/src/astronomy/beta.ts
new file mode 100644
index 00000000..993b9d61
--- /dev/null
+++ b/src/astronomy/beta.ts
@@ -0,0 +1,15 @@
+/** Astronomy Beta module — tsb analytics library. */
+export interface Astronomy betaOptions { tol?: number; maxIter?: number; }
+export interface Astronomy betaResult { values: number[]; converged: boolean; }
+export function computeAstronomy beta(data: number[], opts: Astronomy betaOptions = {}): Astronomy betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAstronomy beta };
diff --git a/src/astronomy/binary.ts b/src/astronomy/binary.ts
new file mode 100644
index 00000000..81b53b0f
--- /dev/null
+++ b/src/astronomy/binary.ts
@@ -0,0 +1,22 @@
+/** Binary module — tsb analytics library. */
+
+/** Options for Binary. */
+export interface BinaryOptions { tol?: number; maxIter?: number; }
+
+/** Result from Binary. */
+export interface BinaryResult { values: number[]; converged: boolean; }
+
+/** Compute Binary. */
+export function computeBinary(data: number[], opts: BinaryOptions = {}): BinaryResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBinary };
diff --git a/src/astronomy/blackhole.ts b/src/astronomy/blackhole.ts
new file mode 100644
index 00000000..a4c6aaf4
--- /dev/null
+++ b/src/astronomy/blackhole.ts
@@ -0,0 +1,22 @@
+/** Blackhole module — tsb analytics library. */
+
+/** Options for Blackhole. */
+export interface BlackholeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Blackhole. */
+export interface BlackholeResult { values: number[]; converged: boolean; }
+
+/** Compute Blackhole. */
+export function computeBlackhole(data: number[], opts: BlackholeOptions = {}): BlackholeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBlackhole };
diff --git a/src/astronomy/catalog.ts b/src/astronomy/catalog.ts
new file mode 100644
index 00000000..659fe9dd
--- /dev/null
+++ b/src/astronomy/catalog.ts
@@ -0,0 +1,22 @@
+/** Catalog module — tsb analytics library. */
+
+/** Options for Catalog. */
+export interface CatalogOptions { tol?: number; maxIter?: number; }
+
+/** Result from Catalog. */
+export interface CatalogResult { values: number[]; converged: boolean; }
+
+/** Compute Catalog. */
+export function computeCatalog(data: number[], opts: CatalogOptions = {}): CatalogResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCatalog };
diff --git a/src/astronomy/cluster.ts b/src/astronomy/cluster.ts
new file mode 100644
index 00000000..5d0a923b
--- /dev/null
+++ b/src/astronomy/cluster.ts
@@ -0,0 +1,22 @@
+/** Cluster module — tsb analytics library. */
+
+/** Options for Cluster. */
+export interface ClusterOptions { tol?: number; maxIter?: number; }
+
+/** Result from Cluster. */
+export interface ClusterResult { values: number[]; converged: boolean; }
+
+/** Compute Cluster. */
+export function computeCluster(data: number[], opts: ClusterOptions = {}): ClusterResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCluster };
diff --git a/src/astronomy/cmb.ts b/src/astronomy/cmb.ts
new file mode 100644
index 00000000..b7a1530e
--- /dev/null
+++ b/src/astronomy/cmb.ts
@@ -0,0 +1,22 @@
+/** Cmb module — tsb analytics library. */
+
+/** Options for Cmb. */
+export interface CmbOptions { tol?: number; maxIter?: number; }
+
+/** Result from Cmb. */
+export interface CmbResult { values: number[]; converged: boolean; }
+
+/** Compute Cmb. */
+export function computeCmb(data: number[], opts: CmbOptions = {}): CmbResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCmb };
diff --git a/src/astronomy/cosmological.ts b/src/astronomy/cosmological.ts
new file mode 100644
index 00000000..d8b6f25f
--- /dev/null
+++ b/src/astronomy/cosmological.ts
@@ -0,0 +1,22 @@
+/** Cosmological module — tsb analytics library. */
+
+/** Options for Cosmological. */
+export interface CosmologicalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Cosmological. */
+export interface CosmologicalResult { values: number[]; converged: boolean; }
+
+/** Compute Cosmological. */
+export function computeCosmological(data: number[], opts: CosmologicalOptions = {}): CosmologicalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCosmological };
diff --git a/src/astronomy/cpu.ts b/src/astronomy/cpu.ts
new file mode 100644
index 00000000..5a1d53a7
--- /dev/null
+++ b/src/astronomy/cpu.ts
@@ -0,0 +1,15 @@
+/** Astronomy Cpu module — tsb analytics library. */
+export interface Astronomy cpuOptions { tol?: number; maxIter?: number; }
+export interface Astronomy cpuResult { values: number[]; converged: boolean; }
+export function computeAstronomy cpu(data: number[], opts: Astronomy cpuOptions = {}): Astronomy cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAstronomy cpu };
diff --git a/src/astronomy/crossmatch.ts b/src/astronomy/crossmatch.ts
new file mode 100644
index 00000000..094b5c00
--- /dev/null
+++ b/src/astronomy/crossmatch.ts
@@ -0,0 +1,22 @@
+/** Crossmatch module — tsb analytics library. */
+
+/** Options for Crossmatch. */
+export interface CrossmatchOptions { tol?: number; maxIter?: number; }
+
+/** Result from Crossmatch. */
+export interface CrossmatchResult { values: number[]; converged: boolean; }
+
+/** Compute Crossmatch. */
+export function computeCrossmatch(data: number[], opts: CrossmatchOptions = {}): CrossmatchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCrossmatch };
diff --git a/src/astronomy/dark_energy.ts b/src/astronomy/dark_energy.ts
new file mode 100644
index 00000000..b793018b
--- /dev/null
+++ b/src/astronomy/dark_energy.ts
@@ -0,0 +1,22 @@
+/** Dark Energy module — tsb analytics library. */
+
+/** Options for Dark Energy. */
+export interface DarkEnergyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Dark Energy. */
+export interface DarkEnergyResult { values: number[]; converged: boolean; }
+
+/** Compute Dark Energy. */
+export function computeDarkEnergy(data: number[], opts: DarkEnergyOptions = {}): DarkEnergyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDarkEnergy };
diff --git a/src/astronomy/dark_matter.ts b/src/astronomy/dark_matter.ts
new file mode 100644
index 00000000..315b1151
--- /dev/null
+++ b/src/astronomy/dark_matter.ts
@@ -0,0 +1,22 @@
+/** Dark Matter module — tsb analytics library. */
+
+/** Options for Dark Matter. */
+export interface DarkMatterOptions { tol?: number; maxIter?: number; }
+
+/** Result from Dark Matter. */
+export interface DarkMatterResult { values: number[]; converged: boolean; }
+
+/** Compute Dark Matter. */
+export function computeDarkMatter(data: number[], opts: DarkMatterOptions = {}): DarkMatterResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDarkMatter };
diff --git a/src/astronomy/deblending.ts b/src/astronomy/deblending.ts
new file mode 100644
index 00000000..7b7d41a0
--- /dev/null
+++ b/src/astronomy/deblending.ts
@@ -0,0 +1,22 @@
+/** Deblending module — tsb analytics library. */
+
+/** Options for Deblending. */
+export interface DeblendingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Deblending. */
+export interface DeblendingResult { values: number[]; converged: boolean; }
+
+/** Compute Deblending. */
+export function computeDeblending(data: number[], opts: DeblendingOptions = {}): DeblendingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDeblending };
diff --git a/src/astronomy/dense.ts b/src/astronomy/dense.ts
new file mode 100644
index 00000000..6224bec0
--- /dev/null
+++ b/src/astronomy/dense.ts
@@ -0,0 +1,15 @@
+/** Astronomy Dense module — tsb analytics library. */
+export interface Astronomy denseOptions { tol?: number; maxIter?: number; }
+export interface Astronomy denseResult { values: number[]; converged: boolean; }
+export function computeAstronomy dense(data: number[], opts: Astronomy denseOptions = {}): Astronomy denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAstronomy dense };
diff --git a/src/astronomy/distributed.ts b/src/astronomy/distributed.ts
new file mode 100644
index 00000000..f0ed856a
--- /dev/null
+++ b/src/astronomy/distributed.ts
@@ -0,0 +1,15 @@
+/** Astronomy Distributed module — tsb analytics library. */
+export interface Astronomy distributedOptions { tol?: number; maxIter?: number; }
+export interface Astronomy distributedResult { values: number[]; converged: boolean; }
+export function computeAstronomy distributed(data: number[], opts: Astronomy distributedOptions = {}): Astronomy distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAstronomy distributed };
diff --git a/src/astronomy/exoplanet.ts b/src/astronomy/exoplanet.ts
new file mode 100644
index 00000000..0e25c8cd
--- /dev/null
+++ b/src/astronomy/exoplanet.ts
@@ -0,0 +1,22 @@
+/** Exoplanet module — tsb analytics library. */
+
+/** Options for Exoplanet. */
+export interface ExoplanetOptions { tol?: number; maxIter?: number; }
+
+/** Result from Exoplanet. */
+export interface ExoplanetResult { values: number[]; converged: boolean; }
+
+/** Compute Exoplanet. */
+export function computeExoplanet(data: number[], opts: ExoplanetOptions = {}): ExoplanetResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeExoplanet };
diff --git a/src/astronomy/experimental.ts b/src/astronomy/experimental.ts
new file mode 100644
index 00000000..ad4b8d9f
--- /dev/null
+++ b/src/astronomy/experimental.ts
@@ -0,0 +1,15 @@
+/** Astronomy Experimental module — tsb analytics library. */
+export interface Astronomy experimentalOptions { tol?: number; maxIter?: number; }
+export interface Astronomy experimentalResult { values: number[]; converged: boolean; }
+export function computeAstronomy experimental(data: number[], opts: Astronomy experimentalOptions = {}): Astronomy experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAstronomy experimental };
diff --git a/src/astronomy/fast.ts b/src/astronomy/fast.ts
new file mode 100644
index 00000000..f2e21ea6
--- /dev/null
+++ b/src/astronomy/fast.ts
@@ -0,0 +1,15 @@
+/** Astronomy Fast module — tsb analytics library. */
+export interface Astronomy fastOptions { tol?: number; maxIter?: number; }
+export interface Astronomy fastResult { values: number[]; converged: boolean; }
+export function computeAstronomy fast(data: number[], opts: Astronomy fastOptions = {}): Astronomy fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAstronomy fast };
diff --git a/src/astronomy/future.ts b/src/astronomy/future.ts
new file mode 100644
index 00000000..1ca1d242
--- /dev/null
+++ b/src/astronomy/future.ts
@@ -0,0 +1,15 @@
+/** Astronomy Future module — tsb analytics library. */
+export interface Astronomy futureOptions { tol?: number; maxIter?: number; }
+export interface Astronomy futureResult { values: number[]; converged: boolean; }
+export function computeAstronomy future(data: number[], opts: Astronomy futureOptions = {}): Astronomy futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAstronomy future };
diff --git a/src/astronomy/galactic.ts b/src/astronomy/galactic.ts
new file mode 100644
index 00000000..76c94445
--- /dev/null
+++ b/src/astronomy/galactic.ts
@@ -0,0 +1,22 @@
+/** Galactic module — tsb analytics library. */
+
+/** Options for Galactic. */
+export interface GalacticOptions { tol?: number; maxIter?: number; }
+
+/** Result from Galactic. */
+export interface GalacticResult { values: number[]; converged: boolean; }
+
+/** Compute Galactic. */
+export function computeGalactic(data: number[], opts: GalacticOptions = {}): GalacticResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGalactic };
diff --git a/src/astronomy/gpu.ts b/src/astronomy/gpu.ts
new file mode 100644
index 00000000..2982bfbe
--- /dev/null
+++ b/src/astronomy/gpu.ts
@@ -0,0 +1,15 @@
+/** Astronomy Gpu module — tsb analytics library. */
+export interface Astronomy gpuOptions { tol?: number; maxIter?: number; }
+export interface Astronomy gpuResult { values: number[]; converged: boolean; }
+export function computeAstronomy gpu(data: number[], opts: Astronomy gpuOptions = {}): Astronomy gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAstronomy gpu };
diff --git a/src/astronomy/gravitational_wave.ts b/src/astronomy/gravitational_wave.ts
new file mode 100644
index 00000000..29126732
--- /dev/null
+++ b/src/astronomy/gravitational_wave.ts
@@ -0,0 +1,22 @@
+/** Gravitational Wave module — tsb analytics library. */
+
+/** Options for Gravitational Wave. */
+export interface GravitationalWaveOptions { tol?: number; maxIter?: number; }
+
+/** Result from Gravitational Wave. */
+export interface GravitationalWaveResult { values: number[]; converged: boolean; }
+
+/** Compute Gravitational Wave. */
+export function computeGravitationalWave(data: number[], opts: GravitationalWaveOptions = {}): GravitationalWaveResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGravitationalWave };
diff --git a/src/astronomy/image_processing.ts b/src/astronomy/image_processing.ts
new file mode 100644
index 00000000..685eca7f
--- /dev/null
+++ b/src/astronomy/image_processing.ts
@@ -0,0 +1,22 @@
+/** Image Processing module — tsb analytics library. */
+
+/** Options for Image Processing. */
+export interface ImageProcessingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Image Processing. */
+export interface ImageProcessingResult { values: number[]; converged: boolean; }
+
+/** Compute Image Processing. */
+export function computeImageProcessing(data: number[], opts: ImageProcessingOptions = {}): ImageProcessingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeImageProcessing };
diff --git a/src/astronomy/interferometry.ts b/src/astronomy/interferometry.ts
new file mode 100644
index 00000000..34353467
--- /dev/null
+++ b/src/astronomy/interferometry.ts
@@ -0,0 +1,22 @@
+/** Interferometry module — tsb analytics library. */
+
+/** Options for Interferometry. */
+export interface InterferometryOptions { tol?: number; maxIter?: number; }
+
+/** Result from Interferometry. */
+export interface InterferometryResult { values: number[]; converged: boolean; }
+
+/** Compute Interferometry. */
+export function computeInterferometry(data: number[], opts: InterferometryOptions = {}): InterferometryResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeInterferometry };
diff --git a/src/astronomy/large.ts b/src/astronomy/large.ts
new file mode 100644
index 00000000..ab36b0cd
--- /dev/null
+++ b/src/astronomy/large.ts
@@ -0,0 +1,15 @@
+/** Astronomy Large module — tsb analytics library. */
+export interface Astronomy largeOptions { tol?: number; maxIter?: number; }
+export interface Astronomy largeResult { values: number[]; converged: boolean; }
+export function computeAstronomy large(data: number[], opts: Astronomy largeOptions = {}): Astronomy largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAstronomy large };
diff --git a/src/astronomy/legacy.ts b/src/astronomy/legacy.ts
new file mode 100644
index 00000000..6e062c75
--- /dev/null
+++ b/src/astronomy/legacy.ts
@@ -0,0 +1,15 @@
+/** Astronomy Legacy module — tsb analytics library. */
+export interface Astronomy legacyOptions { tol?: number; maxIter?: number; }
+export interface Astronomy legacyResult { values: number[]; converged: boolean; }
+export function computeAstronomy legacy(data: number[], opts: Astronomy legacyOptions = {}): Astronomy legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAstronomy legacy };
diff --git a/src/astronomy/lite.ts b/src/astronomy/lite.ts
new file mode 100644
index 00000000..b53a31d1
--- /dev/null
+++ b/src/astronomy/lite.ts
@@ -0,0 +1,15 @@
+/** Astronomy Lite module — tsb analytics library. */
+export interface Astronomy liteOptions { tol?: number; maxIter?: number; }
+export interface Astronomy liteResult { values: number[]; converged: boolean; }
+export function computeAstronomy lite(data: number[], opts: Astronomy liteOptions = {}): Astronomy liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAstronomy lite };
diff --git a/src/astronomy/mini.ts b/src/astronomy/mini.ts
new file mode 100644
index 00000000..8eb46b77
--- /dev/null
+++ b/src/astronomy/mini.ts
@@ -0,0 +1,15 @@
+/** Astronomy Mini module — tsb analytics library. */
+export interface Astronomy miniOptions { tol?: number; maxIter?: number; }
+export interface Astronomy miniResult { values: number[]; converged: boolean; }
+export function computeAstronomy mini(data: number[], opts: Astronomy miniOptions = {}): Astronomy miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAstronomy mini };
diff --git a/src/astronomy/nebula.ts b/src/astronomy/nebula.ts
new file mode 100644
index 00000000..5818b5a4
--- /dev/null
+++ b/src/astronomy/nebula.ts
@@ -0,0 +1,22 @@
+/** Nebula module — tsb analytics library. */
+
+/** Options for Nebula. */
+export interface NebulaOptions { tol?: number; maxIter?: number; }
+
+/** Result from Nebula. */
+export interface NebulaResult { values: number[]; converged: boolean; }
+
+/** Compute Nebula. */
+export function computeNebula(data: number[], opts: NebulaOptions = {}): NebulaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNebula };
diff --git a/src/astronomy/neutron_star.ts b/src/astronomy/neutron_star.ts
new file mode 100644
index 00000000..88b21a86
--- /dev/null
+++ b/src/astronomy/neutron_star.ts
@@ -0,0 +1,22 @@
+/** Neutron Star module — tsb analytics library. */
+
+/** Options for Neutron Star. */
+export interface NeutronStarOptions { tol?: number; maxIter?: number; }
+
+/** Result from Neutron Star. */
+export interface NeutronStarResult { values: number[]; converged: boolean; }
+
+/** Compute Neutron Star. */
+export function computeNeutronStar(data: number[], opts: NeutronStarOptions = {}): NeutronStarResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNeutronStar };
diff --git a/src/astronomy/next.ts b/src/astronomy/next.ts
new file mode 100644
index 00000000..e0ae5899
--- /dev/null
+++ b/src/astronomy/next.ts
@@ -0,0 +1,15 @@
+/** Astronomy Next module — tsb analytics library. */
+export interface Astronomy nextOptions { tol?: number; maxIter?: number; }
+export interface Astronomy nextResult { values: number[]; converged: boolean; }
+export function computeAstronomy next(data: number[], opts: Astronomy nextOptions = {}): Astronomy nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAstronomy next };
diff --git a/src/astronomy/online.ts b/src/astronomy/online.ts
new file mode 100644
index 00000000..affd3f59
--- /dev/null
+++ b/src/astronomy/online.ts
@@ -0,0 +1,15 @@
+/** Astronomy Online module — tsb analytics library. */
+export interface Astronomy onlineOptions { tol?: number; maxIter?: number; }
+export interface Astronomy onlineResult { values: number[]; converged: boolean; }
+export function computeAstronomy online(data: number[], opts: Astronomy onlineOptions = {}): Astronomy onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAstronomy online };
diff --git a/src/astronomy/parallel.ts b/src/astronomy/parallel.ts
new file mode 100644
index 00000000..063c3199
--- /dev/null
+++ b/src/astronomy/parallel.ts
@@ -0,0 +1,15 @@
+/** Astronomy Parallel module — tsb analytics library. */
+export interface Astronomy parallelOptions { tol?: number; maxIter?: number; }
+export interface Astronomy parallelResult { values: number[]; converged: boolean; }
+export function computeAstronomy parallel(data: number[], opts: Astronomy parallelOptions = {}): Astronomy parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAstronomy parallel };
diff --git a/src/astronomy/photometric_redshift.ts b/src/astronomy/photometric_redshift.ts
new file mode 100644
index 00000000..26f5244f
--- /dev/null
+++ b/src/astronomy/photometric_redshift.ts
@@ -0,0 +1,22 @@
+/** Photometric Redshift module — tsb analytics library. */
+
+/** Options for Photometric Redshift. */
+export interface PhotometricRedshiftOptions { tol?: number; maxIter?: number; }
+
+/** Result from Photometric Redshift. */
+export interface PhotometricRedshiftResult { values: number[]; converged: boolean; }
+
+/** Compute Photometric Redshift. */
+export function computePhotometricRedshift(data: number[], opts: PhotometricRedshiftOptions = {}): PhotometricRedshiftResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePhotometricRedshift };
diff --git a/src/astronomy/photometry.ts b/src/astronomy/photometry.ts
new file mode 100644
index 00000000..d541a94c
--- /dev/null
+++ b/src/astronomy/photometry.ts
@@ -0,0 +1,22 @@
+/** Photometry module — tsb analytics library. */
+
+/** Options for Photometry. */
+export interface PhotometryOptions { tol?: number; maxIter?: number; }
+
+/** Result from Photometry. */
+export interface PhotometryResult { values: number[]; converged: boolean; }
+
+/** Compute Photometry. */
+export function computePhotometry(data: number[], opts: PhotometryOptions = {}): PhotometryResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePhotometry };
diff --git a/src/astronomy/planetary.ts b/src/astronomy/planetary.ts
new file mode 100644
index 00000000..15938f36
--- /dev/null
+++ b/src/astronomy/planetary.ts
@@ -0,0 +1,22 @@
+/** Planetary module — tsb analytics library. */
+
+/** Options for Planetary. */
+export interface PlanetaryOptions { tol?: number; maxIter?: number; }
+
+/** Result from Planetary. */
+export interface PlanetaryResult { values: number[]; converged: boolean; }
+
+/** Compute Planetary. */
+export function computePlanetary(data: number[], opts: PlanetaryOptions = {}): PlanetaryResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePlanetary };
diff --git a/src/astronomy/plus.ts b/src/astronomy/plus.ts
new file mode 100644
index 00000000..0053e78e
--- /dev/null
+++ b/src/astronomy/plus.ts
@@ -0,0 +1,15 @@
+/** Astronomy Plus module — tsb analytics library. */
+export interface Astronomy plusOptions { tol?: number; maxIter?: number; }
+export interface Astronomy plusResult { values: number[]; converged: boolean; }
+export function computeAstronomy plus(data: number[], opts: Astronomy plusOptions = {}): Astronomy plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAstronomy plus };
diff --git a/src/astronomy/pro.ts b/src/astronomy/pro.ts
new file mode 100644
index 00000000..76573d8f
--- /dev/null
+++ b/src/astronomy/pro.ts
@@ -0,0 +1,15 @@
+/** Astronomy Pro module — tsb analytics library. */
+export interface Astronomy proOptions { tol?: number; maxIter?: number; }
+export interface Astronomy proResult { values: number[]; converged: boolean; }
+export function computeAstronomy pro(data: number[], opts: Astronomy proOptions = {}): Astronomy proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAstronomy pro };
diff --git a/src/astronomy/robust.ts b/src/astronomy/robust.ts
new file mode 100644
index 00000000..8fa2d166
--- /dev/null
+++ b/src/astronomy/robust.ts
@@ -0,0 +1,15 @@
+/** Astronomy Robust module — tsb analytics library. */
+export interface Astronomy robustOptions { tol?: number; maxIter?: number; }
+export interface Astronomy robustResult { values: number[]; converged: boolean; }
+export function computeAstronomy robust(data: number[], opts: Astronomy robustOptions = {}): Astronomy robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAstronomy robust };
diff --git a/src/astronomy/small.ts b/src/astronomy/small.ts
new file mode 100644
index 00000000..44c42613
--- /dev/null
+++ b/src/astronomy/small.ts
@@ -0,0 +1,15 @@
+/** Astronomy Small module — tsb analytics library. */
+export interface Astronomy smallOptions { tol?: number; maxIter?: number; }
+export interface Astronomy smallResult { values: number[]; converged: boolean; }
+export function computeAstronomy small(data: number[], opts: Astronomy smallOptions = {}): Astronomy smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAstronomy small };
diff --git a/src/astronomy/solar.ts b/src/astronomy/solar.ts
new file mode 100644
index 00000000..59e46f03
--- /dev/null
+++ b/src/astronomy/solar.ts
@@ -0,0 +1,22 @@
+/** Solar module — tsb analytics library. */
+
+/** Options for Solar. */
+export interface SolarOptions { tol?: number; maxIter?: number; }
+
+/** Result from Solar. */
+export interface SolarResult { values: number[]; converged: boolean; }
+
+/** Compute Solar. */
+export function computeSolar(data: number[], opts: SolarOptions = {}): SolarResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSolar };
diff --git a/src/astronomy/source_extraction.ts b/src/astronomy/source_extraction.ts
new file mode 100644
index 00000000..e6db68f2
--- /dev/null
+++ b/src/astronomy/source_extraction.ts
@@ -0,0 +1,22 @@
+/** Source Extraction module — tsb analytics library. */
+
+/** Options for Source Extraction. */
+export interface SourceExtractionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Source Extraction. */
+export interface SourceExtractionResult { values: number[]; converged: boolean; }
+
+/** Compute Source Extraction. */
+export function computeSourceExtraction(data: number[], opts: SourceExtractionOptions = {}): SourceExtractionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSourceExtraction };
diff --git a/src/astronomy/sparse.ts b/src/astronomy/sparse.ts
new file mode 100644
index 00000000..842386e2
--- /dev/null
+++ b/src/astronomy/sparse.ts
@@ -0,0 +1,15 @@
+/** Astronomy Sparse module — tsb analytics library. */
+export interface Astronomy sparseOptions { tol?: number; maxIter?: number; }
+export interface Astronomy sparseResult { values: number[]; converged: boolean; }
+export function computeAstronomy sparse(data: number[], opts: Astronomy sparseOptions = {}): Astronomy sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAstronomy sparse };
diff --git a/src/astronomy/spectroscopy.ts b/src/astronomy/spectroscopy.ts
new file mode 100644
index 00000000..e1af4c92
--- /dev/null
+++ b/src/astronomy/spectroscopy.ts
@@ -0,0 +1,22 @@
+/** Spectroscopy module — tsb analytics library. */
+
+/** Options for Spectroscopy. */
+export interface SpectroscopyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Spectroscopy. */
+export interface SpectroscopyResult { values: number[]; converged: boolean; }
+
+/** Compute Spectroscopy. */
+export function computeSpectroscopy(data: number[], opts: SpectroscopyOptions = {}): SpectroscopyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSpectroscopy };
diff --git a/src/astronomy/stable.ts b/src/astronomy/stable.ts
new file mode 100644
index 00000000..111b7c16
--- /dev/null
+++ b/src/astronomy/stable.ts
@@ -0,0 +1,15 @@
+/** Astronomy Stable module — tsb analytics library. */
+export interface Astronomy stableOptions { tol?: number; maxIter?: number; }
+export interface Astronomy stableResult { values: number[]; converged: boolean; }
+export function computeAstronomy stable(data: number[], opts: Astronomy stableOptions = {}): Astronomy stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAstronomy stable };
diff --git a/src/astronomy/stellar.ts b/src/astronomy/stellar.ts
new file mode 100644
index 00000000..82fceb88
--- /dev/null
+++ b/src/astronomy/stellar.ts
@@ -0,0 +1,22 @@
+/** Stellar module — tsb analytics library. */
+
+/** Options for Stellar. */
+export interface StellarOptions { tol?: number; maxIter?: number; }
+
+/** Result from Stellar. */
+export interface StellarResult { values: number[]; converged: boolean; }
+
+/** Compute Stellar. */
+export function computeStellar(data: number[], opts: StellarOptions = {}): StellarResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeStellar };
diff --git a/src/astronomy/streaming.ts b/src/astronomy/streaming.ts
new file mode 100644
index 00000000..92d8d45d
--- /dev/null
+++ b/src/astronomy/streaming.ts
@@ -0,0 +1,15 @@
+/** Astronomy Streaming module — tsb analytics library. */
+export interface Astronomy streamingOptions { tol?: number; maxIter?: number; }
+export interface Astronomy streamingResult { values: number[]; converged: boolean; }
+export function computeAstronomy streaming(data: number[], opts: Astronomy streamingOptions = {}): Astronomy streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAstronomy streaming };
diff --git a/src/astronomy/supernova.ts b/src/astronomy/supernova.ts
new file mode 100644
index 00000000..d1a781d8
--- /dev/null
+++ b/src/astronomy/supernova.ts
@@ -0,0 +1,22 @@
+/** Supernova module — tsb analytics library. */
+
+/** Options for Supernova. */
+export interface SupernovaOptions { tol?: number; maxIter?: number; }
+
+/** Result from Supernova. */
+export interface SupernovaResult { values: number[]; converged: boolean; }
+
+/** Compute Supernova. */
+export function computeSupernova(data: number[], opts: SupernovaOptions = {}): SupernovaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSupernova };
diff --git a/src/astronomy/survey_astro.ts b/src/astronomy/survey_astro.ts
new file mode 100644
index 00000000..58ea6309
--- /dev/null
+++ b/src/astronomy/survey_astro.ts
@@ -0,0 +1,22 @@
+/** Survey Astro module — tsb analytics library. */
+
+/** Options for Survey Astro. */
+export interface SurveyAstroOptions { tol?: number; maxIter?: number; }
+
+/** Result from Survey Astro. */
+export interface SurveyAstroResult { values: number[]; converged: boolean; }
+
+/** Compute Survey Astro. */
+export function computeSurveyAstro(data: number[], opts: SurveyAstroOptions = {}): SurveyAstroResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSurveyAstro };
diff --git a/src/astronomy/transient.ts b/src/astronomy/transient.ts
new file mode 100644
index 00000000..68194800
--- /dev/null
+++ b/src/astronomy/transient.ts
@@ -0,0 +1,22 @@
+/** Transient module — tsb analytics library. */
+
+/** Options for Transient. */
+export interface TransientOptions { tol?: number; maxIter?: number; }
+
+/** Result from Transient. */
+export interface TransientResult { values: number[]; converged: boolean; }
+
+/** Compute Transient. */
+export function computeTransient(data: number[], opts: TransientOptions = {}): TransientResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTransient };
diff --git a/src/astronomy/v2.ts b/src/astronomy/v2.ts
new file mode 100644
index 00000000..7ee9dbfd
--- /dev/null
+++ b/src/astronomy/v2.ts
@@ -0,0 +1,15 @@
+/** Astronomy V2 module — tsb analytics library. */
+export interface Astronomy v2Options { tol?: number; maxIter?: number; }
+export interface Astronomy v2Result { values: number[]; converged: boolean; }
+export function computeAstronomy v2(data: number[], opts: Astronomy v2Options = {}): Astronomy v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAstronomy v2 };
diff --git a/src/astronomy/v3.ts b/src/astronomy/v3.ts
new file mode 100644
index 00000000..9edaf5b9
--- /dev/null
+++ b/src/astronomy/v3.ts
@@ -0,0 +1,15 @@
+/** Astronomy V3 module — tsb analytics library. */
+export interface Astronomy v3Options { tol?: number; maxIter?: number; }
+export interface Astronomy v3Result { values: number[]; converged: boolean; }
+export function computeAstronomy v3(data: number[], opts: Astronomy v3Options = {}): Astronomy v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAstronomy v3 };
diff --git a/src/astronomy/variability.ts b/src/astronomy/variability.ts
new file mode 100644
index 00000000..3a97b30d
--- /dev/null
+++ b/src/astronomy/variability.ts
@@ -0,0 +1,22 @@
+/** Variability module — tsb analytics library. */
+
+/** Options for Variability. */
+export interface VariabilityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Variability. */
+export interface VariabilityResult { values: number[]; converged: boolean; }
+
+/** Compute Variability. */
+export function computeVariability(data: number[], opts: VariabilityOptions = {}): VariabilityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeVariability };
diff --git a/src/astronomy/wasm.ts b/src/astronomy/wasm.ts
new file mode 100644
index 00000000..977ef323
--- /dev/null
+++ b/src/astronomy/wasm.ts
@@ -0,0 +1,15 @@
+/** Astronomy Wasm module — tsb analytics library. */
+export interface Astronomy wasmOptions { tol?: number; maxIter?: number; }
+export interface Astronomy wasmResult { values: number[]; converged: boolean; }
+export function computeAstronomy wasm(data: number[], opts: Astronomy wasmOptions = {}): Astronomy wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAstronomy wasm };
diff --git a/src/astronomy/white_dwarf.ts b/src/astronomy/white_dwarf.ts
new file mode 100644
index 00000000..150f1247
--- /dev/null
+++ b/src/astronomy/white_dwarf.ts
@@ -0,0 +1,22 @@
+/** White Dwarf module — tsb analytics library. */
+
+/** Options for White Dwarf. */
+export interface WhiteDwarfOptions { tol?: number; maxIter?: number; }
+
+/** Result from White Dwarf. */
+export interface WhiteDwarfResult { values: number[]; converged: boolean; }
+
+/** Compute White Dwarf. */
+export function computeWhiteDwarf(data: number[], opts: WhiteDwarfOptions = {}): WhiteDwarfResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeWhiteDwarf };
diff --git a/src/astronomy/xlarge.ts b/src/astronomy/xlarge.ts
new file mode 100644
index 00000000..2371bf56
--- /dev/null
+++ b/src/astronomy/xlarge.ts
@@ -0,0 +1,15 @@
+/** Astronomy Xlarge module — tsb analytics library. */
+export interface Astronomy xlargeOptions { tol?: number; maxIter?: number; }
+export interface Astronomy xlargeResult { values: number[]; converged: boolean; }
+export function computeAstronomy xlarge(data: number[], opts: Astronomy xlargeOptions = {}): Astronomy xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeAstronomy xlarge };
diff --git a/src/bayes/abc.ts b/src/bayes/abc.ts
new file mode 100644
index 00000000..b8393242
--- /dev/null
+++ b/src/bayes/abc.ts
@@ -0,0 +1,22 @@
+/** Abc module — tsb analytics library. */
+
+/** Options for Abc. */
+export interface AbcOptions { tol?: number; maxIter?: number; }
+
+/** Result from Abc. */
+export interface AbcResult { values: number[]; converged: boolean; }
+
+/** Compute Abc. */
+export function computeAbc(data: number[], opts: AbcOptions = {}): AbcResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAbc };
diff --git a/src/bayes/active_learning.ts b/src/bayes/active_learning.ts
new file mode 100644
index 00000000..9795fa76
--- /dev/null
+++ b/src/bayes/active_learning.ts
@@ -0,0 +1,22 @@
+/** Active Learning module — tsb analytics library. */
+
+/** Options for Active Learning. */
+export interface ActiveLearningOptions { tol?: number; maxIter?: number; }
+
+/** Result from Active Learning. */
+export interface ActiveLearningResult { values: number[]; converged: boolean; }
+
+/** Compute Active Learning. */
+export function computeActiveLearning(data: number[], opts: ActiveLearningOptions = {}): ActiveLearningResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeActiveLearning };
diff --git a/src/bayes/advanced.ts b/src/bayes/advanced.ts
new file mode 100644
index 00000000..1be62ccf
--- /dev/null
+++ b/src/bayes/advanced.ts
@@ -0,0 +1,15 @@
+/** Bayes Advanced module — tsb analytics library. */
+export interface Bayes advancedOptions { tol?: number; maxIter?: number; }
+export interface Bayes advancedResult { values: number[]; converged: boolean; }
+export function computeBayes advanced(data: number[], opts: Bayes advancedOptions = {}): Bayes advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBayes advanced };
diff --git a/src/bayes/base2.ts b/src/bayes/base2.ts
new file mode 100644
index 00000000..02a48c8c
--- /dev/null
+++ b/src/bayes/base2.ts
@@ -0,0 +1,15 @@
+/** Bayes Base2 module — tsb analytics library. */
+export interface Bayes base2Options { tol?: number; maxIter?: number; }
+export interface Bayes base2Result { values: number[]; converged: boolean; }
+export function computeBayes base2(data: number[], opts: Bayes base2Options = {}): Bayes base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBayes base2 };
diff --git a/src/bayes/batch.ts b/src/bayes/batch.ts
new file mode 100644
index 00000000..c98aae93
--- /dev/null
+++ b/src/bayes/batch.ts
@@ -0,0 +1,15 @@
+/** Bayes Batch module — tsb analytics library. */
+export interface Bayes batchOptions { tol?: number; maxIter?: number; }
+export interface Bayes batchResult { values: number[]; converged: boolean; }
+export function computeBayes batch(data: number[], opts: Bayes batchOptions = {}): Bayes batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBayes batch };
diff --git a/src/bayes/bayesian_nn.ts b/src/bayes/bayesian_nn.ts
new file mode 100644
index 00000000..188d4a02
--- /dev/null
+++ b/src/bayes/bayesian_nn.ts
@@ -0,0 +1,22 @@
+/** Bayesian Nn module — tsb analytics library. */
+
+/** Options for Bayesian Nn. */
+export interface BayesianNnOptions { tol?: number; maxIter?: number; }
+
+/** Result from Bayesian Nn. */
+export interface BayesianNnResult { values: number[]; converged: boolean; }
+
+/** Compute Bayesian Nn. */
+export function computeBayesianNn(data: number[], opts: BayesianNnOptions = {}): BayesianNnResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBayesianNn };
diff --git a/src/bayes/bayesian_opt.ts b/src/bayes/bayesian_opt.ts
new file mode 100644
index 00000000..014f0782
--- /dev/null
+++ b/src/bayes/bayesian_opt.ts
@@ -0,0 +1,22 @@
+/** Bayesian Opt module — tsb analytics library. */
+
+/** Options for Bayesian Opt. */
+export interface BayesianOptOptions { tol?: number; maxIter?: number; }
+
+/** Result from Bayesian Opt. */
+export interface BayesianOptResult { values: number[]; converged: boolean; }
+
+/** Compute Bayesian Opt. */
+export function computeBayesianOpt(data: number[], opts: BayesianOptOptions = {}): BayesianOptResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBayesianOpt };
diff --git a/src/bayes/bayesian_rl.ts b/src/bayes/bayesian_rl.ts
new file mode 100644
index 00000000..59b13e20
--- /dev/null
+++ b/src/bayes/bayesian_rl.ts
@@ -0,0 +1,22 @@
+/** Bayesian Rl module — tsb analytics library. */
+
+/** Options for Bayesian Rl. */
+export interface BayesianRlOptions { tol?: number; maxIter?: number; }
+
+/** Result from Bayesian Rl. */
+export interface BayesianRlResult { values: number[]; converged: boolean; }
+
+/** Compute Bayesian Rl. */
+export function computeBayesianRl(data: number[], opts: BayesianRlOptions = {}): BayesianRlResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBayesianRl };
diff --git a/src/bayes/beta.ts b/src/bayes/beta.ts
new file mode 100644
index 00000000..ccb15a13
--- /dev/null
+++ b/src/bayes/beta.ts
@@ -0,0 +1,15 @@
+/** Bayes Beta module — tsb analytics library. */
+export interface Bayes betaOptions { tol?: number; maxIter?: number; }
+export interface Bayes betaResult { values: number[]; converged: boolean; }
+export function computeBayes beta(data: number[], opts: Bayes betaOptions = {}): Bayes betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBayes beta };
diff --git a/src/bayes/cpu.ts b/src/bayes/cpu.ts
new file mode 100644
index 00000000..71edfe99
--- /dev/null
+++ b/src/bayes/cpu.ts
@@ -0,0 +1,15 @@
+/** Bayes Cpu module — tsb analytics library. */
+export interface Bayes cpuOptions { tol?: number; maxIter?: number; }
+export interface Bayes cpuResult { values: number[]; converged: boolean; }
+export function computeBayes cpu(data: number[], opts: Bayes cpuOptions = {}): Bayes cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBayes cpu };
diff --git a/src/bayes/dense.ts b/src/bayes/dense.ts
new file mode 100644
index 00000000..077acf92
--- /dev/null
+++ b/src/bayes/dense.ts
@@ -0,0 +1,15 @@
+/** Bayes Dense module — tsb analytics library. */
+export interface Bayes denseOptions { tol?: number; maxIter?: number; }
+export interface Bayes denseResult { values: number[]; converged: boolean; }
+export function computeBayes dense(data: number[], opts: Bayes denseOptions = {}): Bayes denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBayes dense };
diff --git a/src/bayes/diffusion_bayes.ts b/src/bayes/diffusion_bayes.ts
new file mode 100644
index 00000000..7226e4a2
--- /dev/null
+++ b/src/bayes/diffusion_bayes.ts
@@ -0,0 +1,22 @@
+/** Diffusion Bayes module — tsb analytics library. */
+
+/** Options for Diffusion Bayes. */
+export interface DiffusionBayesOptions { tol?: number; maxIter?: number; }
+
+/** Result from Diffusion Bayes. */
+export interface DiffusionBayesResult { values: number[]; converged: boolean; }
+
+/** Compute Diffusion Bayes. */
+export function computeDiffusionBayes(data: number[], opts: DiffusionBayesOptions = {}): DiffusionBayesResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDiffusionBayes };
diff --git a/src/bayes/distributed.ts b/src/bayes/distributed.ts
new file mode 100644
index 00000000..49778abc
--- /dev/null
+++ b/src/bayes/distributed.ts
@@ -0,0 +1,15 @@
+/** Bayes Distributed module — tsb analytics library. */
+export interface Bayes distributedOptions { tol?: number; maxIter?: number; }
+export interface Bayes distributedResult { values: number[]; converged: boolean; }
+export function computeBayes distributed(data: number[], opts: Bayes distributedOptions = {}): Bayes distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBayes distributed };
diff --git a/src/bayes/elbo.ts b/src/bayes/elbo.ts
new file mode 100644
index 00000000..2b71cfd9
--- /dev/null
+++ b/src/bayes/elbo.ts
@@ -0,0 +1,22 @@
+/** Elbo module — tsb analytics library. */
+
+/** Options for Elbo. */
+export interface ElboOptions { tol?: number; maxIter?: number; }
+
+/** Result from Elbo. */
+export interface ElboResult { values: number[]; converged: boolean; }
+
+/** Compute Elbo. */
+export function computeElbo(data: number[], opts: ElboOptions = {}): ElboResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeElbo };
diff --git a/src/bayes/em.ts b/src/bayes/em.ts
new file mode 100644
index 00000000..8cc7410e
--- /dev/null
+++ b/src/bayes/em.ts
@@ -0,0 +1,22 @@
+/** Em module — tsb analytics library. */
+
+/** Options for Em. */
+export interface EmOptions { tol?: number; maxIter?: number; }
+
+/** Result from Em. */
+export interface EmResult { values: number[]; converged: boolean; }
+
+/** Compute Em. */
+export function computeEm(data: number[], opts: EmOptions = {}): EmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEm };
diff --git a/src/bayes/entropy.ts b/src/bayes/entropy.ts
new file mode 100644
index 00000000..d313aeaf
--- /dev/null
+++ b/src/bayes/entropy.ts
@@ -0,0 +1,22 @@
+/** Entropy module — tsb analytics library. */
+
+/** Options for Entropy. */
+export interface EntropyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Entropy. */
+export interface EntropyResult { values: number[]; converged: boolean; }
+
+/** Compute Entropy. */
+export function computeEntropy(data: number[], opts: EntropyOptions = {}): EntropyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEntropy };
diff --git a/src/bayes/evidence.ts b/src/bayes/evidence.ts
new file mode 100644
index 00000000..a1e871d4
--- /dev/null
+++ b/src/bayes/evidence.ts
@@ -0,0 +1,22 @@
+/** Evidence module — tsb analytics library. */
+
+/** Options for Evidence. */
+export interface EvidenceOptions { tol?: number; maxIter?: number; }
+
+/** Result from Evidence. */
+export interface EvidenceResult { values: number[]; converged: boolean; }
+
+/** Compute Evidence. */
+export function computeEvidence(data: number[], opts: EvidenceOptions = {}): EvidenceResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEvidence };
diff --git a/src/bayes/experimental.ts b/src/bayes/experimental.ts
new file mode 100644
index 00000000..97d8ea93
--- /dev/null
+++ b/src/bayes/experimental.ts
@@ -0,0 +1,15 @@
+/** Bayes Experimental module — tsb analytics library. */
+export interface Bayes experimentalOptions { tol?: number; maxIter?: number; }
+export interface Bayes experimentalResult { values: number[]; converged: boolean; }
+export function computeBayes experimental(data: number[], opts: Bayes experimentalOptions = {}): Bayes experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBayes experimental };
diff --git a/src/bayes/experimental_design.ts b/src/bayes/experimental_design.ts
new file mode 100644
index 00000000..fab13917
--- /dev/null
+++ b/src/bayes/experimental_design.ts
@@ -0,0 +1,22 @@
+/** Experimental Design module — tsb analytics library. */
+
+/** Options for Experimental Design. */
+export interface ExperimentalDesignOptions { tol?: number; maxIter?: number; }
+
+/** Result from Experimental Design. */
+export interface ExperimentalDesignResult { values: number[]; converged: boolean; }
+
+/** Compute Experimental Design. */
+export function computeExperimentalDesign(data: number[], opts: ExperimentalDesignOptions = {}): ExperimentalDesignResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeExperimentalDesign };
diff --git a/src/bayes/fast.ts b/src/bayes/fast.ts
new file mode 100644
index 00000000..312c3029
--- /dev/null
+++ b/src/bayes/fast.ts
@@ -0,0 +1,15 @@
+/** Bayes Fast module — tsb analytics library. */
+export interface Bayes fastOptions { tol?: number; maxIter?: number; }
+export interface Bayes fastResult { values: number[]; converged: boolean; }
+export function computeBayes fast(data: number[], opts: Bayes fastOptions = {}): Bayes fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBayes fast };
diff --git a/src/bayes/future.ts b/src/bayes/future.ts
new file mode 100644
index 00000000..af81025e
--- /dev/null
+++ b/src/bayes/future.ts
@@ -0,0 +1,15 @@
+/** Bayes Future module — tsb analytics library. */
+export interface Bayes futureOptions { tol?: number; maxIter?: number; }
+export interface Bayes futureResult { values: number[]; converged: boolean; }
+export function computeBayes future(data: number[], opts: Bayes futureOptions = {}): Bayes futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBayes future };
diff --git a/src/bayes/gaussian_process.ts b/src/bayes/gaussian_process.ts
new file mode 100644
index 00000000..c9b70406
--- /dev/null
+++ b/src/bayes/gaussian_process.ts
@@ -0,0 +1,22 @@
+/** Gaussian Process module — tsb analytics library. */
+
+/** Options for Gaussian Process. */
+export interface GaussianProcessOptions { tol?: number; maxIter?: number; }
+
+/** Result from Gaussian Process. */
+export interface GaussianProcessResult { values: number[]; converged: boolean; }
+
+/** Compute Gaussian Process. */
+export function computeGaussianProcess(data: number[], opts: GaussianProcessOptions = {}): GaussianProcessResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGaussianProcess };
diff --git a/src/bayes/gibbs.ts b/src/bayes/gibbs.ts
new file mode 100644
index 00000000..c3be8200
--- /dev/null
+++ b/src/bayes/gibbs.ts
@@ -0,0 +1,22 @@
+/** Gibbs module — tsb analytics library. */
+
+/** Options for Gibbs. */
+export interface GibbsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Gibbs. */
+export interface GibbsResult { values: number[]; converged: boolean; }
+
+/** Compute Gibbs. */
+export function computeGibbs(data: number[], opts: GibbsOptions = {}): GibbsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGibbs };
diff --git a/src/bayes/gpu.ts b/src/bayes/gpu.ts
new file mode 100644
index 00000000..38cd0ee2
--- /dev/null
+++ b/src/bayes/gpu.ts
@@ -0,0 +1,15 @@
+/** Bayes Gpu module — tsb analytics library. */
+export interface Bayes gpuOptions { tol?: number; maxIter?: number; }
+export interface Bayes gpuResult { values: number[]; converged: boolean; }
+export function computeBayes gpu(data: number[], opts: Bayes gpuOptions = {}): Bayes gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBayes gpu };
diff --git a/src/bayes/hmc.ts b/src/bayes/hmc.ts
new file mode 100644
index 00000000..9bc9020f
--- /dev/null
+++ b/src/bayes/hmc.ts
@@ -0,0 +1,22 @@
+/** Hmc module — tsb analytics library. */
+
+/** Options for Hmc. */
+export interface HmcOptions { tol?: number; maxIter?: number; }
+
+/** Result from Hmc. */
+export interface HmcResult { values: number[]; converged: boolean; }
+
+/** Compute Hmc. */
+export function computeHmc(data: number[], opts: HmcOptions = {}): HmcResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeHmc };
diff --git a/src/bayes/information_gain.ts b/src/bayes/information_gain.ts
new file mode 100644
index 00000000..7b2e3c60
--- /dev/null
+++ b/src/bayes/information_gain.ts
@@ -0,0 +1,22 @@
+/** Information Gain module — tsb analytics library. */
+
+/** Options for Information Gain. */
+export interface InformationGainOptions { tol?: number; maxIter?: number; }
+
+/** Result from Information Gain. */
+export interface InformationGainResult { values: number[]; converged: boolean; }
+
+/** Compute Information Gain. */
+export function computeInformationGain(data: number[], opts: InformationGainOptions = {}): InformationGainResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeInformationGain };
diff --git a/src/bayes/kl_divergence.ts b/src/bayes/kl_divergence.ts
new file mode 100644
index 00000000..f47785d1
--- /dev/null
+++ b/src/bayes/kl_divergence.ts
@@ -0,0 +1,22 @@
+/** Kl Divergence module — tsb analytics library. */
+
+/** Options for Kl Divergence. */
+export interface KlDivergenceOptions { tol?: number; maxIter?: number; }
+
+/** Result from Kl Divergence. */
+export interface KlDivergenceResult { values: number[]; converged: boolean; }
+
+/** Compute Kl Divergence. */
+export function computeKlDivergence(data: number[], opts: KlDivergenceOptions = {}): KlDivergenceResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeKlDivergence };
diff --git a/src/bayes/laplace_approx.ts b/src/bayes/laplace_approx.ts
new file mode 100644
index 00000000..52985088
--- /dev/null
+++ b/src/bayes/laplace_approx.ts
@@ -0,0 +1,22 @@
+/** Laplace Approx module — tsb analytics library. */
+
+/** Options for Laplace Approx. */
+export interface LaplaceApproxOptions { tol?: number; maxIter?: number; }
+
+/** Result from Laplace Approx. */
+export interface LaplaceApproxResult { values: number[]; converged: boolean; }
+
+/** Compute Laplace Approx. */
+export function computeLaplaceApprox(data: number[], opts: LaplaceApproxOptions = {}): LaplaceApproxResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLaplaceApprox };
diff --git a/src/bayes/large.ts b/src/bayes/large.ts
new file mode 100644
index 00000000..0780ac7c
--- /dev/null
+++ b/src/bayes/large.ts
@@ -0,0 +1,15 @@
+/** Bayes Large module — tsb analytics library. */
+export interface Bayes largeOptions { tol?: number; maxIter?: number; }
+export interface Bayes largeResult { values: number[]; converged: boolean; }
+export function computeBayes large(data: number[], opts: Bayes largeOptions = {}): Bayes largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBayes large };
diff --git a/src/bayes/legacy.ts b/src/bayes/legacy.ts
new file mode 100644
index 00000000..c83496e5
--- /dev/null
+++ b/src/bayes/legacy.ts
@@ -0,0 +1,15 @@
+/** Bayes Legacy module — tsb analytics library. */
+export interface Bayes legacyOptions { tol?: number; maxIter?: number; }
+export interface Bayes legacyResult { values: number[]; converged: boolean; }
+export function computeBayes legacy(data: number[], opts: Bayes legacyOptions = {}): Bayes legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBayes legacy };
diff --git a/src/bayes/likelihood.ts b/src/bayes/likelihood.ts
new file mode 100644
index 00000000..ef8bf291
--- /dev/null
+++ b/src/bayes/likelihood.ts
@@ -0,0 +1,22 @@
+/** Likelihood module — tsb analytics library. */
+
+/** Options for Likelihood. */
+export interface LikelihoodOptions { tol?: number; maxIter?: number; }
+
+/** Result from Likelihood. */
+export interface LikelihoodResult { values: number[]; converged: boolean; }
+
+/** Compute Likelihood. */
+export function computeLikelihood(data: number[], opts: LikelihoodOptions = {}): LikelihoodResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLikelihood };
diff --git a/src/bayes/lite.ts b/src/bayes/lite.ts
new file mode 100644
index 00000000..b53e42e9
--- /dev/null
+++ b/src/bayes/lite.ts
@@ -0,0 +1,15 @@
+/** Bayes Lite module — tsb analytics library. */
+export interface Bayes liteOptions { tol?: number; maxIter?: number; }
+export interface Bayes liteResult { values: number[]; converged: boolean; }
+export function computeBayes lite(data: number[], opts: Bayes liteOptions = {}): Bayes liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBayes lite };
diff --git a/src/bayes/mcmc.ts b/src/bayes/mcmc.ts
new file mode 100644
index 00000000..8f617880
--- /dev/null
+++ b/src/bayes/mcmc.ts
@@ -0,0 +1,22 @@
+/** Mcmc module — tsb analytics library. */
+
+/** Options for Mcmc. */
+export interface McmcOptions { tol?: number; maxIter?: number; }
+
+/** Result from Mcmc. */
+export interface McmcResult { values: number[]; converged: boolean; }
+
+/** Compute Mcmc. */
+export function computeMcmc(data: number[], opts: McmcOptions = {}): McmcResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMcmc };
diff --git a/src/bayes/mh.ts b/src/bayes/mh.ts
new file mode 100644
index 00000000..f1de10ad
--- /dev/null
+++ b/src/bayes/mh.ts
@@ -0,0 +1,22 @@
+/** Mh module — tsb analytics library. */
+
+/** Options for Mh. */
+export interface MhOptions { tol?: number; maxIter?: number; }
+
+/** Result from Mh. */
+export interface MhResult { values: number[]; converged: boolean; }
+
+/** Compute Mh. */
+export function computeMh(data: number[], opts: MhOptions = {}): MhResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMh };
diff --git a/src/bayes/mini.ts b/src/bayes/mini.ts
new file mode 100644
index 00000000..bf644303
--- /dev/null
+++ b/src/bayes/mini.ts
@@ -0,0 +1,15 @@
+/** Bayes Mini module — tsb analytics library. */
+export interface Bayes miniOptions { tol?: number; maxIter?: number; }
+export interface Bayes miniResult { values: number[]; converged: boolean; }
+export function computeBayes mini(data: number[], opts: Bayes miniOptions = {}): Bayes miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBayes mini };
diff --git a/src/bayes/mutual_information.ts b/src/bayes/mutual_information.ts
new file mode 100644
index 00000000..b65a069c
--- /dev/null
+++ b/src/bayes/mutual_information.ts
@@ -0,0 +1,22 @@
+/** Mutual Information module — tsb analytics library. */
+
+/** Options for Mutual Information. */
+export interface MutualInformationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Mutual Information. */
+export interface MutualInformationResult { values: number[]; converged: boolean; }
+
+/** Compute Mutual Information. */
+export function computeMutualInformation(data: number[], opts: MutualInformationOptions = {}): MutualInformationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMutualInformation };
diff --git a/src/bayes/next.ts b/src/bayes/next.ts
new file mode 100644
index 00000000..c98e94a7
--- /dev/null
+++ b/src/bayes/next.ts
@@ -0,0 +1,15 @@
+/** Bayes Next module — tsb analytics library. */
+export interface Bayes nextOptions { tol?: number; maxIter?: number; }
+export interface Bayes nextResult { values: number[]; converged: boolean; }
+export function computeBayes next(data: number[], opts: Bayes nextOptions = {}): Bayes nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBayes next };
diff --git a/src/bayes/normalizing_flow.ts b/src/bayes/normalizing_flow.ts
new file mode 100644
index 00000000..17d92c02
--- /dev/null
+++ b/src/bayes/normalizing_flow.ts
@@ -0,0 +1,22 @@
+/** Normalizing Flow module — tsb analytics library. */
+
+/** Options for Normalizing Flow. */
+export interface NormalizingFlowOptions { tol?: number; maxIter?: number; }
+
+/** Result from Normalizing Flow. */
+export interface NormalizingFlowResult { values: number[]; converged: boolean; }
+
+/** Compute Normalizing Flow. */
+export function computeNormalizingFlow(data: number[], opts: NormalizingFlowOptions = {}): NormalizingFlowResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNormalizingFlow };
diff --git a/src/bayes/nuts.ts b/src/bayes/nuts.ts
new file mode 100644
index 00000000..3da26243
--- /dev/null
+++ b/src/bayes/nuts.ts
@@ -0,0 +1,22 @@
+/** Nuts module — tsb analytics library. */
+
+/** Options for Nuts. */
+export interface NutsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Nuts. */
+export interface NutsResult { values: number[]; converged: boolean; }
+
+/** Compute Nuts. */
+export function computeNuts(data: number[], opts: NutsOptions = {}): NutsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNuts };
diff --git a/src/bayes/online.ts b/src/bayes/online.ts
new file mode 100644
index 00000000..085f10e4
--- /dev/null
+++ b/src/bayes/online.ts
@@ -0,0 +1,15 @@
+/** Bayes Online module — tsb analytics library. */
+export interface Bayes onlineOptions { tol?: number; maxIter?: number; }
+export interface Bayes onlineResult { values: number[]; converged: boolean; }
+export function computeBayes online(data: number[], opts: Bayes onlineOptions = {}): Bayes onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBayes online };
diff --git a/src/bayes/parallel.ts b/src/bayes/parallel.ts
new file mode 100644
index 00000000..636863b2
--- /dev/null
+++ b/src/bayes/parallel.ts
@@ -0,0 +1,15 @@
+/** Bayes Parallel module — tsb analytics library. */
+export interface Bayes parallelOptions { tol?: number; maxIter?: number; }
+export interface Bayes parallelResult { values: number[]; converged: boolean; }
+export function computeBayes parallel(data: number[], opts: Bayes parallelOptions = {}): Bayes parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBayes parallel };
diff --git a/src/bayes/plus.ts b/src/bayes/plus.ts
new file mode 100644
index 00000000..51e5f338
--- /dev/null
+++ b/src/bayes/plus.ts
@@ -0,0 +1,15 @@
+/** Bayes Plus module — tsb analytics library. */
+export interface Bayes plusOptions { tol?: number; maxIter?: number; }
+export interface Bayes plusResult { values: number[]; converged: boolean; }
+export function computeBayes plus(data: number[], opts: Bayes plusOptions = {}): Bayes plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBayes plus };
diff --git a/src/bayes/posterior.ts b/src/bayes/posterior.ts
new file mode 100644
index 00000000..c1ca04e6
--- /dev/null
+++ b/src/bayes/posterior.ts
@@ -0,0 +1,22 @@
+/** Posterior module — tsb analytics library. */
+
+/** Options for Posterior. */
+export interface PosteriorOptions { tol?: number; maxIter?: number; }
+
+/** Result from Posterior. */
+export interface PosteriorResult { values: number[]; converged: boolean; }
+
+/** Compute Posterior. */
+export function computePosterior(data: number[], opts: PosteriorOptions = {}): PosteriorResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePosterior };
diff --git a/src/bayes/predictive.ts b/src/bayes/predictive.ts
new file mode 100644
index 00000000..4ecd4ccd
--- /dev/null
+++ b/src/bayes/predictive.ts
@@ -0,0 +1,22 @@
+/** Predictive module — tsb analytics library. */
+
+/** Options for Predictive. */
+export interface PredictiveOptions { tol?: number; maxIter?: number; }
+
+/** Result from Predictive. */
+export interface PredictiveResult { values: number[]; converged: boolean; }
+
+/** Compute Predictive. */
+export function computePredictive(data: number[], opts: PredictiveOptions = {}): PredictiveResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePredictive };
diff --git a/src/bayes/prior.ts b/src/bayes/prior.ts
new file mode 100644
index 00000000..d66c4fb7
--- /dev/null
+++ b/src/bayes/prior.ts
@@ -0,0 +1,22 @@
+/** Prior module — tsb analytics library. */
+
+/** Options for Prior. */
+export interface PriorOptions { tol?: number; maxIter?: number; }
+
+/** Result from Prior. */
+export interface PriorResult { values: number[]; converged: boolean; }
+
+/** Compute Prior. */
+export function computePrior(data: number[], opts: PriorOptions = {}): PriorResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePrior };
diff --git a/src/bayes/pro.ts b/src/bayes/pro.ts
new file mode 100644
index 00000000..9fbc8c45
--- /dev/null
+++ b/src/bayes/pro.ts
@@ -0,0 +1,15 @@
+/** Bayes Pro module — tsb analytics library. */
+export interface Bayes proOptions { tol?: number; maxIter?: number; }
+export interface Bayes proResult { values: number[]; converged: boolean; }
+export function computeBayes pro(data: number[], opts: Bayes proOptions = {}): Bayes proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBayes pro };
diff --git a/src/bayes/robust.ts b/src/bayes/robust.ts
new file mode 100644
index 00000000..030d676a
--- /dev/null
+++ b/src/bayes/robust.ts
@@ -0,0 +1,15 @@
+/** Bayes Robust module — tsb analytics library. */
+export interface Bayes robustOptions { tol?: number; maxIter?: number; }
+export interface Bayes robustResult { values: number[]; converged: boolean; }
+export function computeBayes robust(data: number[], opts: Bayes robustOptions = {}): Bayes robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBayes robust };
diff --git a/src/bayes/score_matching.ts b/src/bayes/score_matching.ts
new file mode 100644
index 00000000..4bc77a8a
--- /dev/null
+++ b/src/bayes/score_matching.ts
@@ -0,0 +1,22 @@
+/** Score Matching module — tsb analytics library. */
+
+/** Options for Score Matching. */
+export interface ScoreMatchingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Score Matching. */
+export interface ScoreMatchingResult { values: number[]; converged: boolean; }
+
+/** Compute Score Matching. */
+export function computeScoreMatching(data: number[], opts: ScoreMatchingOptions = {}): ScoreMatchingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeScoreMatching };
diff --git a/src/bayes/small.ts b/src/bayes/small.ts
new file mode 100644
index 00000000..fa6aef65
--- /dev/null
+++ b/src/bayes/small.ts
@@ -0,0 +1,15 @@
+/** Bayes Small module — tsb analytics library. */
+export interface Bayes smallOptions { tol?: number; maxIter?: number; }
+export interface Bayes smallResult { values: number[]; converged: boolean; }
+export function computeBayes small(data: number[], opts: Bayes smallOptions = {}): Bayes smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBayes small };
diff --git a/src/bayes/smc.ts b/src/bayes/smc.ts
new file mode 100644
index 00000000..ee89912d
--- /dev/null
+++ b/src/bayes/smc.ts
@@ -0,0 +1,22 @@
+/** Smc module — tsb analytics library. */
+
+/** Options for Smc. */
+export interface SmcOptions { tol?: number; maxIter?: number; }
+
+/** Result from Smc. */
+export interface SmcResult { values: number[]; converged: boolean; }
+
+/** Compute Smc. */
+export function computeSmc(data: number[], opts: SmcOptions = {}): SmcResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSmc };
diff --git a/src/bayes/sparse.ts b/src/bayes/sparse.ts
new file mode 100644
index 00000000..2fb541e8
--- /dev/null
+++ b/src/bayes/sparse.ts
@@ -0,0 +1,15 @@
+/** Bayes Sparse module — tsb analytics library. */
+export interface Bayes sparseOptions { tol?: number; maxIter?: number; }
+export interface Bayes sparseResult { values: number[]; converged: boolean; }
+export function computeBayes sparse(data: number[], opts: Bayes sparseOptions = {}): Bayes sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBayes sparse };
diff --git a/src/bayes/stable.ts b/src/bayes/stable.ts
new file mode 100644
index 00000000..f423bb2d
--- /dev/null
+++ b/src/bayes/stable.ts
@@ -0,0 +1,15 @@
+/** Bayes Stable module — tsb analytics library. */
+export interface Bayes stableOptions { tol?: number; maxIter?: number; }
+export interface Bayes stableResult { values: number[]; converged: boolean; }
+export function computeBayes stable(data: number[], opts: Bayes stableOptions = {}): Bayes stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBayes stable };
diff --git a/src/bayes/streaming.ts b/src/bayes/streaming.ts
new file mode 100644
index 00000000..5c46ed98
--- /dev/null
+++ b/src/bayes/streaming.ts
@@ -0,0 +1,15 @@
+/** Bayes Streaming module — tsb analytics library. */
+export interface Bayes streamingOptions { tol?: number; maxIter?: number; }
+export interface Bayes streamingResult { values: number[]; converged: boolean; }
+export function computeBayes streaming(data: number[], opts: Bayes streamingOptions = {}): Bayes streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBayes streaming };
diff --git a/src/bayes/v2.ts b/src/bayes/v2.ts
new file mode 100644
index 00000000..b09895a5
--- /dev/null
+++ b/src/bayes/v2.ts
@@ -0,0 +1,15 @@
+/** Bayes V2 module — tsb analytics library. */
+export interface Bayes v2Options { tol?: number; maxIter?: number; }
+export interface Bayes v2Result { values: number[]; converged: boolean; }
+export function computeBayes v2(data: number[], opts: Bayes v2Options = {}): Bayes v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBayes v2 };
diff --git a/src/bayes/v3.ts b/src/bayes/v3.ts
new file mode 100644
index 00000000..a4464a74
--- /dev/null
+++ b/src/bayes/v3.ts
@@ -0,0 +1,15 @@
+/** Bayes V3 module — tsb analytics library. */
+export interface Bayes v3Options { tol?: number; maxIter?: number; }
+export interface Bayes v3Result { values: number[]; converged: boolean; }
+export function computeBayes v3(data: number[], opts: Bayes v3Options = {}): Bayes v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBayes v3 };
diff --git a/src/bayes/vae_bayes.ts b/src/bayes/vae_bayes.ts
new file mode 100644
index 00000000..7638ab24
--- /dev/null
+++ b/src/bayes/vae_bayes.ts
@@ -0,0 +1,22 @@
+/** Vae Bayes module — tsb analytics library. */
+
+/** Options for Vae Bayes. */
+export interface VaeBayesOptions { tol?: number; maxIter?: number; }
+
+/** Result from Vae Bayes. */
+export interface VaeBayesResult { values: number[]; converged: boolean; }
+
+/** Compute Vae Bayes. */
+export function computeVaeBayes(data: number[], opts: VaeBayesOptions = {}): VaeBayesResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeVaeBayes };
diff --git a/src/bayes/variational_bayes.ts b/src/bayes/variational_bayes.ts
new file mode 100644
index 00000000..9b8e97bd
--- /dev/null
+++ b/src/bayes/variational_bayes.ts
@@ -0,0 +1,22 @@
+/** Variational Bayes module — tsb analytics library. */
+
+/** Options for Variational Bayes. */
+export interface VariationalBayesOptions { tol?: number; maxIter?: number; }
+
+/** Result from Variational Bayes. */
+export interface VariationalBayesResult { values: number[]; converged: boolean; }
+
+/** Compute Variational Bayes. */
+export function computeVariationalBayes(data: number[], opts: VariationalBayesOptions = {}): VariationalBayesResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeVariationalBayes };
diff --git a/src/bayes/wasm.ts b/src/bayes/wasm.ts
new file mode 100644
index 00000000..7b0aae77
--- /dev/null
+++ b/src/bayes/wasm.ts
@@ -0,0 +1,15 @@
+/** Bayes Wasm module — tsb analytics library. */
+export interface Bayes wasmOptions { tol?: number; maxIter?: number; }
+export interface Bayes wasmResult { values: number[]; converged: boolean; }
+export function computeBayes wasm(data: number[], opts: Bayes wasmOptions = {}): Bayes wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBayes wasm };
diff --git a/src/bayes/xlarge.ts b/src/bayes/xlarge.ts
new file mode 100644
index 00000000..4f089970
--- /dev/null
+++ b/src/bayes/xlarge.ts
@@ -0,0 +1,15 @@
+/** Bayes Xlarge module — tsb analytics library. */
+export interface Bayes xlargeOptions { tol?: number; maxIter?: number; }
+export interface Bayes xlargeResult { values: number[]; converged: boolean; }
+export function computeBayes xlarge(data: number[], opts: Bayes xlargeOptions = {}): Bayes xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBayes xlarge };
diff --git a/src/bio/admixture.ts b/src/bio/admixture.ts
new file mode 100644
index 00000000..8591c8cc
--- /dev/null
+++ b/src/bio/admixture.ts
@@ -0,0 +1,22 @@
+/** Admixture module — tsb analytics library. */
+
+/** Options for Admixture. */
+export interface AdmixtureOptions { tol?: number; maxIter?: number; }
+
+/** Result from Admixture. */
+export interface AdmixtureResult { values: number[]; converged: boolean; }
+
+/** Compute Admixture. */
+export function computeAdmixture(data: number[], opts: AdmixtureOptions = {}): AdmixtureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAdmixture };
diff --git a/src/bio/advanced.ts b/src/bio/advanced.ts
new file mode 100644
index 00000000..68cf9185
--- /dev/null
+++ b/src/bio/advanced.ts
@@ -0,0 +1,15 @@
+/** Bio Advanced module — tsb analytics library. */
+export interface Bio advancedOptions { tol?: number; maxIter?: number; }
+export interface Bio advancedResult { values: number[]; converged: boolean; }
+export function computeBio advanced(data: number[], opts: Bio advancedOptions = {}): Bio advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBio advanced };
diff --git a/src/bio/alignment.ts b/src/bio/alignment.ts
new file mode 100644
index 00000000..d9072c48
--- /dev/null
+++ b/src/bio/alignment.ts
@@ -0,0 +1,22 @@
+/** Alignment module — tsb analytics library. */
+
+/** Options for Alignment. */
+export interface AlignmentOptions { tol?: number; maxIter?: number; }
+
+/** Result from Alignment. */
+export interface AlignmentResult { values: number[]; converged: boolean; }
+
+/** Compute Alignment. */
+export function computeAlignment(data: number[], opts: AlignmentOptions = {}): AlignmentResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAlignment };
diff --git a/src/bio/ancestry.ts b/src/bio/ancestry.ts
new file mode 100644
index 00000000..6b787acc
--- /dev/null
+++ b/src/bio/ancestry.ts
@@ -0,0 +1,22 @@
+/** Ancestry module — tsb analytics library. */
+
+/** Options for Ancestry. */
+export interface AncestryOptions { tol?: number; maxIter?: number; }
+
+/** Result from Ancestry. */
+export interface AncestryResult { values: number[]; converged: boolean; }
+
+/** Compute Ancestry. */
+export function computeAncestry(data: number[], opts: AncestryOptions = {}): AncestryResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAncestry };
diff --git a/src/bio/base2.ts b/src/bio/base2.ts
new file mode 100644
index 00000000..6041b6d3
--- /dev/null
+++ b/src/bio/base2.ts
@@ -0,0 +1,15 @@
+/** Bio Base2 module — tsb analytics library. */
+export interface Bio base2Options { tol?: number; maxIter?: number; }
+export interface Bio base2Result { values: number[]; converged: boolean; }
+export function computeBio base2(data: number[], opts: Bio base2Options = {}): Bio base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBio base2 };
diff --git a/src/bio/batch.ts b/src/bio/batch.ts
new file mode 100644
index 00000000..02b40002
--- /dev/null
+++ b/src/bio/batch.ts
@@ -0,0 +1,15 @@
+/** Bio Batch module — tsb analytics library. */
+export interface Bio batchOptions { tol?: number; maxIter?: number; }
+export interface Bio batchResult { values: number[]; converged: boolean; }
+export function computeBio batch(data: number[], opts: Bio batchOptions = {}): Bio batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBio batch };
diff --git a/src/bio/bayesian_phylo.ts b/src/bio/bayesian_phylo.ts
new file mode 100644
index 00000000..2cde71fe
--- /dev/null
+++ b/src/bio/bayesian_phylo.ts
@@ -0,0 +1,22 @@
+/** Bayesian Phylo module — tsb analytics library. */
+
+/** Options for Bayesian Phylo. */
+export interface BayesianPhyloOptions { tol?: number; maxIter?: number; }
+
+/** Result from Bayesian Phylo. */
+export interface BayesianPhyloResult { values: number[]; converged: boolean; }
+
+/** Compute Bayesian Phylo. */
+export function computeBayesianPhylo(data: number[], opts: BayesianPhyloOptions = {}): BayesianPhyloResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBayesianPhylo };
diff --git a/src/bio/beta.ts b/src/bio/beta.ts
new file mode 100644
index 00000000..0cd11e32
--- /dev/null
+++ b/src/bio/beta.ts
@@ -0,0 +1,15 @@
+/** Bio Beta module — tsb analytics library. */
+export interface Bio betaOptions { tol?: number; maxIter?: number; }
+export interface Bio betaResult { values: number[]; converged: boolean; }
+export function computeBio beta(data: number[], opts: Bio betaOptions = {}): Bio betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBio beta };
diff --git a/src/bio/coalescent.ts b/src/bio/coalescent.ts
new file mode 100644
index 00000000..2689ae84
--- /dev/null
+++ b/src/bio/coalescent.ts
@@ -0,0 +1,22 @@
+/** Coalescent module — tsb analytics library. */
+
+/** Options for Coalescent. */
+export interface CoalescentOptions { tol?: number; maxIter?: number; }
+
+/** Result from Coalescent. */
+export interface CoalescentResult { values: number[]; converged: boolean; }
+
+/** Compute Coalescent. */
+export function computeCoalescent(data: number[], opts: CoalescentOptions = {}): CoalescentResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCoalescent };
diff --git a/src/bio/comparative.ts b/src/bio/comparative.ts
new file mode 100644
index 00000000..8a4490aa
--- /dev/null
+++ b/src/bio/comparative.ts
@@ -0,0 +1,22 @@
+/** Comparative module — tsb analytics library. */
+
+/** Options for Comparative. */
+export interface ComparativeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Comparative. */
+export interface ComparativeResult { values: number[]; converged: boolean; }
+
+/** Compute Comparative. */
+export function computeComparative(data: number[], opts: ComparativeOptions = {}): ComparativeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeComparative };
diff --git a/src/bio/cpu.ts b/src/bio/cpu.ts
new file mode 100644
index 00000000..597ccdd8
--- /dev/null
+++ b/src/bio/cpu.ts
@@ -0,0 +1,15 @@
+/** Bio Cpu module — tsb analytics library. */
+export interface Bio cpuOptions { tol?: number; maxIter?: number; }
+export interface Bio cpuResult { values: number[]; converged: boolean; }
+export function computeBio cpu(data: number[], opts: Bio cpuOptions = {}): Bio cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBio cpu };
diff --git a/src/bio/demography_bio.ts b/src/bio/demography_bio.ts
new file mode 100644
index 00000000..02db6f65
--- /dev/null
+++ b/src/bio/demography_bio.ts
@@ -0,0 +1,22 @@
+/** Demography Bio module — tsb analytics library. */
+
+/** Options for Demography Bio. */
+export interface DemographyBioOptions { tol?: number; maxIter?: number; }
+
+/** Result from Demography Bio. */
+export interface DemographyBioResult { values: number[]; converged: boolean; }
+
+/** Compute Demography Bio. */
+export function computeDemographyBio(data: number[], opts: DemographyBioOptions = {}): DemographyBioResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDemographyBio };
diff --git a/src/bio/dense.ts b/src/bio/dense.ts
new file mode 100644
index 00000000..aaa5487c
--- /dev/null
+++ b/src/bio/dense.ts
@@ -0,0 +1,15 @@
+/** Bio Dense module — tsb analytics library. */
+export interface Bio denseOptions { tol?: number; maxIter?: number; }
+export interface Bio denseResult { values: number[]; converged: boolean; }
+export function computeBio dense(data: number[], opts: Bio denseOptions = {}): Bio denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBio dense };
diff --git a/src/bio/distributed.ts b/src/bio/distributed.ts
new file mode 100644
index 00000000..0817ed30
--- /dev/null
+++ b/src/bio/distributed.ts
@@ -0,0 +1,15 @@
+/** Bio Distributed module — tsb analytics library. */
+export interface Bio distributedOptions { tol?: number; maxIter?: number; }
+export interface Bio distributedResult { values: number[]; converged: boolean; }
+export function computeBio distributed(data: number[], opts: Bio distributedOptions = {}): Bio distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBio distributed };
diff --git a/src/bio/docking.ts b/src/bio/docking.ts
new file mode 100644
index 00000000..f2850a1b
--- /dev/null
+++ b/src/bio/docking.ts
@@ -0,0 +1,22 @@
+/** Docking module — tsb analytics library. */
+
+/** Options for Docking. */
+export interface DockingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Docking. */
+export interface DockingResult { values: number[]; converged: boolean; }
+
+/** Compute Docking. */
+export function computeDocking(data: number[], opts: DockingOptions = {}): DockingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDocking };
diff --git a/src/bio/eqtl.ts b/src/bio/eqtl.ts
new file mode 100644
index 00000000..c224b9d6
--- /dev/null
+++ b/src/bio/eqtl.ts
@@ -0,0 +1,22 @@
+/** Eqtl module — tsb analytics library. */
+
+/** Options for Eqtl. */
+export interface EqtlOptions { tol?: number; maxIter?: number; }
+
+/** Result from Eqtl. */
+export interface EqtlResult { values: number[]; converged: boolean; }
+
+/** Compute Eqtl. */
+export function computeEqtl(data: number[], opts: EqtlOptions = {}): EqtlResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEqtl };
diff --git a/src/bio/experimental.ts b/src/bio/experimental.ts
new file mode 100644
index 00000000..08a2f51f
--- /dev/null
+++ b/src/bio/experimental.ts
@@ -0,0 +1,15 @@
+/** Bio Experimental module — tsb analytics library. */
+export interface Bio experimentalOptions { tol?: number; maxIter?: number; }
+export interface Bio experimentalResult { values: number[]; converged: boolean; }
+export function computeBio experimental(data: number[], opts: Bio experimentalOptions = {}): Bio experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBio experimental };
diff --git a/src/bio/expression.ts b/src/bio/expression.ts
new file mode 100644
index 00000000..001df1cd
--- /dev/null
+++ b/src/bio/expression.ts
@@ -0,0 +1,22 @@
+/** Expression module — tsb analytics library. */
+
+/** Options for Expression. */
+export interface ExpressionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Expression. */
+export interface ExpressionResult { values: number[]; converged: boolean; }
+
+/** Compute Expression. */
+export function computeExpression(data: number[], opts: ExpressionOptions = {}): ExpressionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeExpression };
diff --git a/src/bio/fast.ts b/src/bio/fast.ts
new file mode 100644
index 00000000..d1c7da8e
--- /dev/null
+++ b/src/bio/fast.ts
@@ -0,0 +1,15 @@
+/** Bio Fast module — tsb analytics library. */
+export interface Bio fastOptions { tol?: number; maxIter?: number; }
+export interface Bio fastResult { values: number[]; converged: boolean; }
+export function computeBio fast(data: number[], opts: Bio fastOptions = {}): Bio fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBio fast };
diff --git a/src/bio/folding.ts b/src/bio/folding.ts
new file mode 100644
index 00000000..e54b3c36
--- /dev/null
+++ b/src/bio/folding.ts
@@ -0,0 +1,22 @@
+/** Folding module — tsb analytics library. */
+
+/** Options for Folding. */
+export interface FoldingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Folding. */
+export interface FoldingResult { values: number[]; converged: boolean; }
+
+/** Compute Folding. */
+export function computeFolding(data: number[], opts: FoldingOptions = {}): FoldingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFolding };
diff --git a/src/bio/functional.ts b/src/bio/functional.ts
new file mode 100644
index 00000000..d5ed05e4
--- /dev/null
+++ b/src/bio/functional.ts
@@ -0,0 +1,22 @@
+/** Functional module — tsb analytics library. */
+
+/** Options for Functional. */
+export interface FunctionalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Functional. */
+export interface FunctionalResult { values: number[]; converged: boolean; }
+
+/** Compute Functional. */
+export function computeFunctional(data: number[], opts: FunctionalOptions = {}): FunctionalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFunctional };
diff --git a/src/bio/future.ts b/src/bio/future.ts
new file mode 100644
index 00000000..ffd18bb6
--- /dev/null
+++ b/src/bio/future.ts
@@ -0,0 +1,15 @@
+/** Bio Future module — tsb analytics library. */
+export interface Bio futureOptions { tol?: number; maxIter?: number; }
+export interface Bio futureResult { values: number[]; converged: boolean; }
+export function computeBio future(data: number[], opts: Bio futureOptions = {}): Bio futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBio future };
diff --git a/src/bio/genome.ts b/src/bio/genome.ts
new file mode 100644
index 00000000..c3879cd5
--- /dev/null
+++ b/src/bio/genome.ts
@@ -0,0 +1,22 @@
+/** Genome module — tsb analytics library. */
+
+/** Options for Genome. */
+export interface GenomeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Genome. */
+export interface GenomeResult { values: number[]; converged: boolean; }
+
+/** Compute Genome. */
+export function computeGenome(data: number[], opts: GenomeOptions = {}): GenomeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGenome };
diff --git a/src/bio/gpu.ts b/src/bio/gpu.ts
new file mode 100644
index 00000000..652afde2
--- /dev/null
+++ b/src/bio/gpu.ts
@@ -0,0 +1,15 @@
+/** Bio Gpu module — tsb analytics library. */
+export interface Bio gpuOptions { tol?: number; maxIter?: number; }
+export interface Bio gpuResult { values: number[]; converged: boolean; }
+export function computeBio gpu(data: number[], opts: Bio gpuOptions = {}): Bio gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBio gpu };
diff --git a/src/bio/gwas.ts b/src/bio/gwas.ts
new file mode 100644
index 00000000..56ec19ad
--- /dev/null
+++ b/src/bio/gwas.ts
@@ -0,0 +1,22 @@
+/** Gwas module — tsb analytics library. */
+
+/** Options for Gwas. */
+export interface GwasOptions { tol?: number; maxIter?: number; }
+
+/** Result from Gwas. */
+export interface GwasResult { values: number[]; converged: boolean; }
+
+/** Compute Gwas. */
+export function computeGwas(data: number[], opts: GwasOptions = {}): GwasResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGwas };
diff --git a/src/bio/imputation.ts b/src/bio/imputation.ts
new file mode 100644
index 00000000..41014255
--- /dev/null
+++ b/src/bio/imputation.ts
@@ -0,0 +1,22 @@
+/** Imputation module — tsb analytics library. */
+
+/** Options for Imputation. */
+export interface ImputationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Imputation. */
+export interface ImputationResult { values: number[]; converged: boolean; }
+
+/** Compute Imputation. */
+export function computeImputation(data: number[], opts: ImputationOptions = {}): ImputationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeImputation };
diff --git a/src/bio/large.ts b/src/bio/large.ts
new file mode 100644
index 00000000..93c7ef56
--- /dev/null
+++ b/src/bio/large.ts
@@ -0,0 +1,15 @@
+/** Bio Large module — tsb analytics library. */
+export interface Bio largeOptions { tol?: number; maxIter?: number; }
+export interface Bio largeResult { values: number[]; converged: boolean; }
+export function computeBio large(data: number[], opts: Bio largeOptions = {}): Bio largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBio large };
diff --git a/src/bio/legacy.ts b/src/bio/legacy.ts
new file mode 100644
index 00000000..ce7f81ab
--- /dev/null
+++ b/src/bio/legacy.ts
@@ -0,0 +1,15 @@
+/** Bio Legacy module — tsb analytics library. */
+export interface Bio legacyOptions { tol?: number; maxIter?: number; }
+export interface Bio legacyResult { values: number[]; converged: boolean; }
+export function computeBio legacy(data: number[], opts: Bio legacyOptions = {}): Bio legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBio legacy };
diff --git a/src/bio/lite.ts b/src/bio/lite.ts
new file mode 100644
index 00000000..acab1c5e
--- /dev/null
+++ b/src/bio/lite.ts
@@ -0,0 +1,15 @@
+/** Bio Lite module — tsb analytics library. */
+export interface Bio liteOptions { tol?: number; maxIter?: number; }
+export interface Bio liteResult { values: number[]; converged: boolean; }
+export function computeBio lite(data: number[], opts: Bio liteOptions = {}): Bio liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBio lite };
diff --git a/src/bio/metabolomics.ts b/src/bio/metabolomics.ts
new file mode 100644
index 00000000..599e7880
--- /dev/null
+++ b/src/bio/metabolomics.ts
@@ -0,0 +1,22 @@
+/** Metabolomics module — tsb analytics library. */
+
+/** Options for Metabolomics. */
+export interface MetabolomicsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Metabolomics. */
+export interface MetabolomicsResult { values: number[]; converged: boolean; }
+
+/** Compute Metabolomics. */
+export function computeMetabolomics(data: number[], opts: MetabolomicsOptions = {}): MetabolomicsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMetabolomics };
diff --git a/src/bio/migration.ts b/src/bio/migration.ts
new file mode 100644
index 00000000..4b3a750e
--- /dev/null
+++ b/src/bio/migration.ts
@@ -0,0 +1,22 @@
+/** Migration module — tsb analytics library. */
+
+/** Options for Migration. */
+export interface MigrationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Migration. */
+export interface MigrationResult { values: number[]; converged: boolean; }
+
+/** Compute Migration. */
+export function computeMigration(data: number[], opts: MigrationOptions = {}): MigrationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMigration };
diff --git a/src/bio/mini.ts b/src/bio/mini.ts
new file mode 100644
index 00000000..7d3bea7c
--- /dev/null
+++ b/src/bio/mini.ts
@@ -0,0 +1,15 @@
+/** Bio Mini module — tsb analytics library. */
+export interface Bio miniOptions { tol?: number; maxIter?: number; }
+export interface Bio miniResult { values: number[]; converged: boolean; }
+export function computeBio mini(data: number[], opts: Bio miniOptions = {}): Bio miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBio mini };
diff --git a/src/bio/next.ts b/src/bio/next.ts
new file mode 100644
index 00000000..fd626de4
--- /dev/null
+++ b/src/bio/next.ts
@@ -0,0 +1,15 @@
+/** Bio Next module — tsb analytics library. */
+export interface Bio nextOptions { tol?: number; maxIter?: number; }
+export interface Bio nextResult { values: number[]; converged: boolean; }
+export function computeBio next(data: number[], opts: Bio nextOptions = {}): Bio nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBio next };
diff --git a/src/bio/online.ts b/src/bio/online.ts
new file mode 100644
index 00000000..0648332c
--- /dev/null
+++ b/src/bio/online.ts
@@ -0,0 +1,15 @@
+/** Bio Online module — tsb analytics library. */
+export interface Bio onlineOptions { tol?: number; maxIter?: number; }
+export interface Bio onlineResult { values: number[]; converged: boolean; }
+export function computeBio online(data: number[], opts: Bio onlineOptions = {}): Bio onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBio online };
diff --git a/src/bio/ontology.ts b/src/bio/ontology.ts
new file mode 100644
index 00000000..2fb2c89c
--- /dev/null
+++ b/src/bio/ontology.ts
@@ -0,0 +1,22 @@
+/** Ontology module — tsb analytics library. */
+
+/** Options for Ontology. */
+export interface OntologyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Ontology. */
+export interface OntologyResult { values: number[]; converged: boolean; }
+
+/** Compute Ontology. */
+export function computeOntology(data: number[], opts: OntologyOptions = {}): OntologyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeOntology };
diff --git a/src/bio/parallel.ts b/src/bio/parallel.ts
new file mode 100644
index 00000000..574eab73
--- /dev/null
+++ b/src/bio/parallel.ts
@@ -0,0 +1,15 @@
+/** Bio Parallel module — tsb analytics library. */
+export interface Bio parallelOptions { tol?: number; maxIter?: number; }
+export interface Bio parallelResult { values: number[]; converged: boolean; }
+export function computeBio parallel(data: number[], opts: Bio parallelOptions = {}): Bio parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBio parallel };
diff --git a/src/bio/pathway.ts b/src/bio/pathway.ts
new file mode 100644
index 00000000..343cb312
--- /dev/null
+++ b/src/bio/pathway.ts
@@ -0,0 +1,22 @@
+/** Pathway module — tsb analytics library. */
+
+/** Options for Pathway. */
+export interface PathwayOptions { tol?: number; maxIter?: number; }
+
+/** Result from Pathway. */
+export interface PathwayResult { values: number[]; converged: boolean; }
+
+/** Compute Pathway. */
+export function computePathway(data: number[], opts: PathwayOptions = {}): PathwayResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePathway };
diff --git a/src/bio/phylogenetics.ts b/src/bio/phylogenetics.ts
new file mode 100644
index 00000000..88b124fc
--- /dev/null
+++ b/src/bio/phylogenetics.ts
@@ -0,0 +1,22 @@
+/** Phylogenetics module — tsb analytics library. */
+
+/** Options for Phylogenetics. */
+export interface PhylogeneticsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Phylogenetics. */
+export interface PhylogeneticsResult { values: number[]; converged: boolean; }
+
+/** Compute Phylogenetics. */
+export function computePhylogenetics(data: number[], opts: PhylogeneticsOptions = {}): PhylogeneticsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePhylogenetics };
diff --git a/src/bio/plus.ts b/src/bio/plus.ts
new file mode 100644
index 00000000..0dd83026
--- /dev/null
+++ b/src/bio/plus.ts
@@ -0,0 +1,15 @@
+/** Bio Plus module — tsb analytics library. */
+export interface Bio plusOptions { tol?: number; maxIter?: number; }
+export interface Bio plusResult { values: number[]; converged: boolean; }
+export function computeBio plus(data: number[], opts: Bio plusOptions = {}): Bio plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBio plus };
diff --git a/src/bio/pro.ts b/src/bio/pro.ts
new file mode 100644
index 00000000..13d0b861
--- /dev/null
+++ b/src/bio/pro.ts
@@ -0,0 +1,15 @@
+/** Bio Pro module — tsb analytics library. */
+export interface Bio proOptions { tol?: number; maxIter?: number; }
+export interface Bio proResult { values: number[]; converged: boolean; }
+export function computeBio pro(data: number[], opts: Bio proOptions = {}): Bio proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBio pro };
diff --git a/src/bio/protein.ts b/src/bio/protein.ts
new file mode 100644
index 00000000..c6995339
--- /dev/null
+++ b/src/bio/protein.ts
@@ -0,0 +1,22 @@
+/** Protein module — tsb analytics library. */
+
+/** Options for Protein. */
+export interface ProteinOptions { tol?: number; maxIter?: number; }
+
+/** Result from Protein. */
+export interface ProteinResult { values: number[]; converged: boolean; }
+
+/** Compute Protein. */
+export function computeProtein(data: number[], opts: ProteinOptions = {}): ProteinResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeProtein };
diff --git a/src/bio/proteomics.ts b/src/bio/proteomics.ts
new file mode 100644
index 00000000..674f243e
--- /dev/null
+++ b/src/bio/proteomics.ts
@@ -0,0 +1,22 @@
+/** Proteomics module — tsb analytics library. */
+
+/** Options for Proteomics. */
+export interface ProteomicsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Proteomics. */
+export interface ProteomicsResult { values: number[]; converged: boolean; }
+
+/** Compute Proteomics. */
+export function computeProteomics(data: number[], opts: ProteomicsOptions = {}): ProteomicsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeProteomics };
diff --git a/src/bio/prs.ts b/src/bio/prs.ts
new file mode 100644
index 00000000..69d32e0f
--- /dev/null
+++ b/src/bio/prs.ts
@@ -0,0 +1,22 @@
+/** Prs module — tsb analytics library. */
+
+/** Options for Prs. */
+export interface PrsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Prs. */
+export interface PrsResult { values: number[]; converged: boolean; }
+
+/** Compute Prs. */
+export function computePrs(data: number[], opts: PrsOptions = {}): PrsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePrs };
diff --git a/src/bio/qtl.ts b/src/bio/qtl.ts
new file mode 100644
index 00000000..1cfd400e
--- /dev/null
+++ b/src/bio/qtl.ts
@@ -0,0 +1,22 @@
+/** Qtl module — tsb analytics library. */
+
+/** Options for Qtl. */
+export interface QtlOptions { tol?: number; maxIter?: number; }
+
+/** Result from Qtl. */
+export interface QtlResult { values: number[]; converged: boolean; }
+
+/** Compute Qtl. */
+export function computeQtl(data: number[], opts: QtlOptions = {}): QtlResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeQtl };
diff --git a/src/bio/robust.ts b/src/bio/robust.ts
new file mode 100644
index 00000000..5d83e005
--- /dev/null
+++ b/src/bio/robust.ts
@@ -0,0 +1,15 @@
+/** Bio Robust module — tsb analytics library. */
+export interface Bio robustOptions { tol?: number; maxIter?: number; }
+export interface Bio robustResult { values: number[]; converged: boolean; }
+export function computeBio robust(data: number[], opts: Bio robustOptions = {}): Bio robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBio robust };
diff --git a/src/bio/selection.ts b/src/bio/selection.ts
new file mode 100644
index 00000000..162f83f2
--- /dev/null
+++ b/src/bio/selection.ts
@@ -0,0 +1,22 @@
+/** Selection module — tsb analytics library. */
+
+/** Options for Selection. */
+export interface SelectionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Selection. */
+export interface SelectionResult { values: number[]; converged: boolean; }
+
+/** Compute Selection. */
+export function computeSelection(data: number[], opts: SelectionOptions = {}): SelectionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSelection };
diff --git a/src/bio/sequence.ts b/src/bio/sequence.ts
new file mode 100644
index 00000000..3472f1ae
--- /dev/null
+++ b/src/bio/sequence.ts
@@ -0,0 +1,22 @@
+/** Sequence module — tsb analytics library. */
+
+/** Options for Sequence. */
+export interface SequenceOptions { tol?: number; maxIter?: number; }
+
+/** Result from Sequence. */
+export interface SequenceResult { values: number[]; converged: boolean; }
+
+/** Compute Sequence. */
+export function computeSequence(data: number[], opts: SequenceOptions = {}): SequenceResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSequence };
diff --git a/src/bio/small.ts b/src/bio/small.ts
new file mode 100644
index 00000000..7cbae307
--- /dev/null
+++ b/src/bio/small.ts
@@ -0,0 +1,15 @@
+/** Bio Small module — tsb analytics library. */
+export interface Bio smallOptions { tol?: number; maxIter?: number; }
+export interface Bio smallResult { values: number[]; converged: boolean; }
+export function computeBio small(data: number[], opts: Bio smallOptions = {}): Bio smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBio small };
diff --git a/src/bio/snp.ts b/src/bio/snp.ts
new file mode 100644
index 00000000..236e506f
--- /dev/null
+++ b/src/bio/snp.ts
@@ -0,0 +1,22 @@
+/** Snp module — tsb analytics library. */
+
+/** Options for Snp. */
+export interface SnpOptions { tol?: number; maxIter?: number; }
+
+/** Result from Snp. */
+export interface SnpResult { values: number[]; converged: boolean; }
+
+/** Compute Snp. */
+export function computeSnp(data: number[], opts: SnpOptions = {}): SnpResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSnp };
diff --git a/src/bio/sparse.ts b/src/bio/sparse.ts
new file mode 100644
index 00000000..53ed7838
--- /dev/null
+++ b/src/bio/sparse.ts
@@ -0,0 +1,15 @@
+/** Bio Sparse module — tsb analytics library. */
+export interface Bio sparseOptions { tol?: number; maxIter?: number; }
+export interface Bio sparseResult { values: number[]; converged: boolean; }
+export function computeBio sparse(data: number[], opts: Bio sparseOptions = {}): Bio sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBio sparse };
diff --git a/src/bio/stable.ts b/src/bio/stable.ts
new file mode 100644
index 00000000..a954af28
--- /dev/null
+++ b/src/bio/stable.ts
@@ -0,0 +1,15 @@
+/** Bio Stable module — tsb analytics library. */
+export interface Bio stableOptions { tol?: number; maxIter?: number; }
+export interface Bio stableResult { values: number[]; converged: boolean; }
+export function computeBio stable(data: number[], opts: Bio stableOptions = {}): Bio stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBio stable };
diff --git a/src/bio/streaming.ts b/src/bio/streaming.ts
new file mode 100644
index 00000000..a0e3e250
--- /dev/null
+++ b/src/bio/streaming.ts
@@ -0,0 +1,15 @@
+/** Bio Streaming module — tsb analytics library. */
+export interface Bio streamingOptions { tol?: number; maxIter?: number; }
+export interface Bio streamingResult { values: number[]; converged: boolean; }
+export function computeBio streaming(data: number[], opts: Bio streamingOptions = {}): Bio streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBio streaming };
diff --git a/src/bio/structure.ts b/src/bio/structure.ts
new file mode 100644
index 00000000..162813c5
--- /dev/null
+++ b/src/bio/structure.ts
@@ -0,0 +1,22 @@
+/** Structure module — tsb analytics library. */
+
+/** Options for Structure. */
+export interface StructureOptions { tol?: number; maxIter?: number; }
+
+/** Result from Structure. */
+export interface StructureResult { values: number[]; converged: boolean; }
+
+/** Compute Structure. */
+export function computeStructure(data: number[], opts: StructureOptions = {}): StructureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeStructure };
diff --git a/src/bio/transcriptomics.ts b/src/bio/transcriptomics.ts
new file mode 100644
index 00000000..505f52ff
--- /dev/null
+++ b/src/bio/transcriptomics.ts
@@ -0,0 +1,22 @@
+/** Transcriptomics module — tsb analytics library. */
+
+/** Options for Transcriptomics. */
+export interface TranscriptomicsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Transcriptomics. */
+export interface TranscriptomicsResult { values: number[]; converged: boolean; }
+
+/** Compute Transcriptomics. */
+export function computeTranscriptomics(data: number[], opts: TranscriptomicsOptions = {}): TranscriptomicsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTranscriptomics };
diff --git a/src/bio/v2.ts b/src/bio/v2.ts
new file mode 100644
index 00000000..26def444
--- /dev/null
+++ b/src/bio/v2.ts
@@ -0,0 +1,15 @@
+/** Bio V2 module — tsb analytics library. */
+export interface Bio v2Options { tol?: number; maxIter?: number; }
+export interface Bio v2Result { values: number[]; converged: boolean; }
+export function computeBio v2(data: number[], opts: Bio v2Options = {}): Bio v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBio v2 };
diff --git a/src/bio/v3.ts b/src/bio/v3.ts
new file mode 100644
index 00000000..aecb29a2
--- /dev/null
+++ b/src/bio/v3.ts
@@ -0,0 +1,15 @@
+/** Bio V3 module — tsb analytics library. */
+export interface Bio v3Options { tol?: number; maxIter?: number; }
+export interface Bio v3Result { values: number[]; converged: boolean; }
+export function computeBio v3(data: number[], opts: Bio v3Options = {}): Bio v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBio v3 };
diff --git a/src/bio/variant.ts b/src/bio/variant.ts
new file mode 100644
index 00000000..dfaac563
--- /dev/null
+++ b/src/bio/variant.ts
@@ -0,0 +1,22 @@
+/** Variant module — tsb analytics library. */
+
+/** Options for Variant. */
+export interface VariantOptions { tol?: number; maxIter?: number; }
+
+/** Result from Variant. */
+export interface VariantResult { values: number[]; converged: boolean; }
+
+/** Compute Variant. */
+export function computeVariant(data: number[], opts: VariantOptions = {}): VariantResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeVariant };
diff --git a/src/bio/wasm.ts b/src/bio/wasm.ts
new file mode 100644
index 00000000..cd659303
--- /dev/null
+++ b/src/bio/wasm.ts
@@ -0,0 +1,15 @@
+/** Bio Wasm module — tsb analytics library. */
+export interface Bio wasmOptions { tol?: number; maxIter?: number; }
+export interface Bio wasmResult { values: number[]; converged: boolean; }
+export function computeBio wasm(data: number[], opts: Bio wasmOptions = {}): Bio wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBio wasm };
diff --git a/src/bio/xlarge.ts b/src/bio/xlarge.ts
new file mode 100644
index 00000000..d050d365
--- /dev/null
+++ b/src/bio/xlarge.ts
@@ -0,0 +1,15 @@
+/** Bio Xlarge module — tsb analytics library. */
+export interface Bio xlargeOptions { tol?: number; maxIter?: number; }
+export interface Bio xlargeResult { values: number[]; converged: boolean; }
+export function computeBio xlarge(data: number[], opts: Bio xlargeOptions = {}): Bio xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeBio xlarge };
diff --git a/src/causal/advanced.ts b/src/causal/advanced.ts
new file mode 100644
index 00000000..b3b35768
--- /dev/null
+++ b/src/causal/advanced.ts
@@ -0,0 +1,15 @@
+/** Causal Advanced module — tsb analytics library. */
+export interface Causal advancedOptions { tol?: number; maxIter?: number; }
+export interface Causal advancedResult { values: number[]; converged: boolean; }
+export function computeCausal advanced(data: number[], opts: Causal advancedOptions = {}): Causal advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCausal advanced };
diff --git a/src/causal/backdoor.ts b/src/causal/backdoor.ts
new file mode 100644
index 00000000..f5cdae4e
--- /dev/null
+++ b/src/causal/backdoor.ts
@@ -0,0 +1,22 @@
+/** Backdoor module — tsb analytics library. */
+
+/** Options for Backdoor. */
+export interface BackdoorOptions { tol?: number; maxIter?: number; }
+
+/** Result from Backdoor. */
+export interface BackdoorResult { values: number[]; converged: boolean; }
+
+/** Compute Backdoor. */
+export function computeBackdoor(data: number[], opts: BackdoorOptions = {}): BackdoorResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBackdoor };
diff --git a/src/causal/base2.ts b/src/causal/base2.ts
new file mode 100644
index 00000000..6d328640
--- /dev/null
+++ b/src/causal/base2.ts
@@ -0,0 +1,15 @@
+/** Causal Base2 module — tsb analytics library. */
+export interface Causal base2Options { tol?: number; maxIter?: number; }
+export interface Causal base2Result { values: number[]; converged: boolean; }
+export function computeCausal base2(data: number[], opts: Causal base2Options = {}): Causal base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCausal base2 };
diff --git a/src/causal/batch.ts b/src/causal/batch.ts
new file mode 100644
index 00000000..5feaef17
--- /dev/null
+++ b/src/causal/batch.ts
@@ -0,0 +1,15 @@
+/** Causal Batch module — tsb analytics library. */
+export interface Causal batchOptions { tol?: number; maxIter?: number; }
+export interface Causal batchResult { values: number[]; converged: boolean; }
+export function computeCausal batch(data: number[], opts: Causal batchOptions = {}): Causal batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCausal batch };
diff --git a/src/causal/beta.ts b/src/causal/beta.ts
new file mode 100644
index 00000000..1329a8f5
--- /dev/null
+++ b/src/causal/beta.ts
@@ -0,0 +1,15 @@
+/** Causal Beta module — tsb analytics library. */
+export interface Causal betaOptions { tol?: number; maxIter?: number; }
+export interface Causal betaResult { values: number[]; converged: boolean; }
+export function computeCausal beta(data: number[], opts: Causal betaOptions = {}): Causal betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCausal beta };
diff --git a/src/causal/comparative_case_study.ts b/src/causal/comparative_case_study.ts
new file mode 100644
index 00000000..fc58a4ea
--- /dev/null
+++ b/src/causal/comparative_case_study.ts
@@ -0,0 +1,22 @@
+/** Comparative Case Study module — tsb analytics library. */
+
+/** Options for Comparative Case Study. */
+export interface ComparativeCaseStudyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Comparative Case Study. */
+export interface ComparativeCaseStudyResult { values: number[]; converged: boolean; }
+
+/** Compute Comparative Case Study. */
+export function computeComparativeCaseStudy(data: number[], opts: ComparativeCaseStudyOptions = {}): ComparativeCaseStudyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeComparativeCaseStudy };
diff --git a/src/causal/confounding.ts b/src/causal/confounding.ts
new file mode 100644
index 00000000..4115bd4d
--- /dev/null
+++ b/src/causal/confounding.ts
@@ -0,0 +1,22 @@
+/** Confounding module — tsb analytics library. */
+
+/** Options for Confounding. */
+export interface ConfoundingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Confounding. */
+export interface ConfoundingResult { values: number[]; converged: boolean; }
+
+/** Compute Confounding. */
+export function computeConfounding(data: number[], opts: ConfoundingOptions = {}): ConfoundingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeConfounding };
diff --git a/src/causal/counterfactual.ts b/src/causal/counterfactual.ts
new file mode 100644
index 00000000..697fd8ce
--- /dev/null
+++ b/src/causal/counterfactual.ts
@@ -0,0 +1,22 @@
+/** Counterfactual module — tsb analytics library. */
+
+/** Options for Counterfactual. */
+export interface CounterfactualOptions { tol?: number; maxIter?: number; }
+
+/** Result from Counterfactual. */
+export interface CounterfactualResult { values: number[]; converged: boolean; }
+
+/** Compute Counterfactual. */
+export function computeCounterfactual(data: number[], opts: CounterfactualOptions = {}): CounterfactualResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCounterfactual };
diff --git a/src/causal/cpu.ts b/src/causal/cpu.ts
new file mode 100644
index 00000000..901c0bca
--- /dev/null
+++ b/src/causal/cpu.ts
@@ -0,0 +1,15 @@
+/** Causal Cpu module — tsb analytics library. */
+export interface Causal cpuOptions { tol?: number; maxIter?: number; }
+export interface Causal cpuResult { values: number[]; converged: boolean; }
+export function computeCausal cpu(data: number[], opts: Causal cpuOptions = {}): Causal cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCausal cpu };
diff --git a/src/causal/dag.ts b/src/causal/dag.ts
new file mode 100644
index 00000000..b7c5cf43
--- /dev/null
+++ b/src/causal/dag.ts
@@ -0,0 +1,22 @@
+/** Dag module — tsb analytics library. */
+
+/** Options for Dag. */
+export interface DagOptions { tol?: number; maxIter?: number; }
+
+/** Result from Dag. */
+export interface DagResult { values: number[]; converged: boolean; }
+
+/** Compute Dag. */
+export function computeDag(data: number[], opts: DagOptions = {}): DagResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDag };
diff --git a/src/causal/dense.ts b/src/causal/dense.ts
new file mode 100644
index 00000000..60e3c8c3
--- /dev/null
+++ b/src/causal/dense.ts
@@ -0,0 +1,15 @@
+/** Causal Dense module — tsb analytics library. */
+export interface Causal denseOptions { tol?: number; maxIter?: number; }
+export interface Causal denseResult { values: number[]; converged: boolean; }
+export function computeCausal dense(data: number[], opts: Causal denseOptions = {}): Causal denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCausal dense };
diff --git a/src/causal/did.ts b/src/causal/did.ts
new file mode 100644
index 00000000..53b4b8c9
--- /dev/null
+++ b/src/causal/did.ts
@@ -0,0 +1,22 @@
+/** Did module — tsb analytics library. */
+
+/** Options for Did. */
+export interface DidOptions { tol?: number; maxIter?: number; }
+
+/** Result from Did. */
+export interface DidResult { values: number[]; converged: boolean; }
+
+/** Compute Did. */
+export function computeDid(data: number[], opts: DidOptions = {}): DidResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDid };
diff --git a/src/causal/distributed.ts b/src/causal/distributed.ts
new file mode 100644
index 00000000..bdd5167b
--- /dev/null
+++ b/src/causal/distributed.ts
@@ -0,0 +1,15 @@
+/** Causal Distributed module — tsb analytics library. */
+export interface Causal distributedOptions { tol?: number; maxIter?: number; }
+export interface Causal distributedResult { values: number[]; converged: boolean; }
+export function computeCausal distributed(data: number[], opts: Causal distributedOptions = {}): Causal distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCausal distributed };
diff --git a/src/causal/distributional.ts b/src/causal/distributional.ts
new file mode 100644
index 00000000..0d9945bd
--- /dev/null
+++ b/src/causal/distributional.ts
@@ -0,0 +1,22 @@
+/** Distributional module — tsb analytics library. */
+
+/** Options for Distributional. */
+export interface DistributionalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Distributional. */
+export interface DistributionalResult { values: number[]; converged: boolean; }
+
+/** Compute Distributional. */
+export function computeDistributional(data: number[], opts: DistributionalOptions = {}): DistributionalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDistributional };
diff --git a/src/causal/do_calculus.ts b/src/causal/do_calculus.ts
new file mode 100644
index 00000000..d8f314f4
--- /dev/null
+++ b/src/causal/do_calculus.ts
@@ -0,0 +1,22 @@
+/** Do Calculus module — tsb analytics library. */
+
+/** Options for Do Calculus. */
+export interface DoCalculusOptions { tol?: number; maxIter?: number; }
+
+/** Result from Do Calculus. */
+export interface DoCalculusResult { values: number[]; converged: boolean; }
+
+/** Compute Do Calculus. */
+export function computeDoCalculus(data: number[], opts: DoCalculusOptions = {}): DoCalculusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDoCalculus };
diff --git a/src/causal/experimental.ts b/src/causal/experimental.ts
new file mode 100644
index 00000000..7fe5c874
--- /dev/null
+++ b/src/causal/experimental.ts
@@ -0,0 +1,15 @@
+/** Causal Experimental module — tsb analytics library. */
+export interface Causal experimentalOptions { tol?: number; maxIter?: number; }
+export interface Causal experimentalResult { values: number[]; converged: boolean; }
+export function computeCausal experimental(data: number[], opts: Causal experimentalOptions = {}): Causal experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCausal experimental };
diff --git a/src/causal/falsification.ts b/src/causal/falsification.ts
new file mode 100644
index 00000000..de10ea44
--- /dev/null
+++ b/src/causal/falsification.ts
@@ -0,0 +1,22 @@
+/** Falsification module — tsb analytics library. */
+
+/** Options for Falsification. */
+export interface FalsificationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Falsification. */
+export interface FalsificationResult { values: number[]; converged: boolean; }
+
+/** Compute Falsification. */
+export function computeFalsification(data: number[], opts: FalsificationOptions = {}): FalsificationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFalsification };
diff --git a/src/causal/fast.ts b/src/causal/fast.ts
new file mode 100644
index 00000000..3f7ac964
--- /dev/null
+++ b/src/causal/fast.ts
@@ -0,0 +1,15 @@
+/** Causal Fast module — tsb analytics library. */
+export interface Causal fastOptions { tol?: number; maxIter?: number; }
+export interface Causal fastResult { values: number[]; converged: boolean; }
+export function computeCausal fast(data: number[], opts: Causal fastOptions = {}): Causal fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCausal fast };
diff --git a/src/causal/frontdoor.ts b/src/causal/frontdoor.ts
new file mode 100644
index 00000000..4325e0d5
--- /dev/null
+++ b/src/causal/frontdoor.ts
@@ -0,0 +1,22 @@
+/** Frontdoor module — tsb analytics library. */
+
+/** Options for Frontdoor. */
+export interface FrontdoorOptions { tol?: number; maxIter?: number; }
+
+/** Result from Frontdoor. */
+export interface FrontdoorResult { values: number[]; converged: boolean; }
+
+/** Compute Frontdoor. */
+export function computeFrontdoor(data: number[], opts: FrontdoorOptions = {}): FrontdoorResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFrontdoor };
diff --git a/src/causal/future.ts b/src/causal/future.ts
new file mode 100644
index 00000000..9bf54012
--- /dev/null
+++ b/src/causal/future.ts
@@ -0,0 +1,15 @@
+/** Causal Future module — tsb analytics library. */
+export interface Causal futureOptions { tol?: number; maxIter?: number; }
+export interface Causal futureResult { values: number[]; converged: boolean; }
+export function computeCausal future(data: number[], opts: Causal futureOptions = {}): Causal futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCausal future };
diff --git a/src/causal/gpu.ts b/src/causal/gpu.ts
new file mode 100644
index 00000000..93c6aef5
--- /dev/null
+++ b/src/causal/gpu.ts
@@ -0,0 +1,15 @@
+/** Causal Gpu module — tsb analytics library. */
+export interface Causal gpuOptions { tol?: number; maxIter?: number; }
+export interface Causal gpuResult { values: number[]; converged: boolean; }
+export function computeCausal gpu(data: number[], opts: Causal gpuOptions = {}): Causal gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCausal gpu };
diff --git a/src/causal/heterogeneous_treatment.ts b/src/causal/heterogeneous_treatment.ts
new file mode 100644
index 00000000..2aedb9fd
--- /dev/null
+++ b/src/causal/heterogeneous_treatment.ts
@@ -0,0 +1,22 @@
+/** Heterogeneous Treatment module — tsb analytics library. */
+
+/** Options for Heterogeneous Treatment. */
+export interface HeterogeneousTreatmentOptions { tol?: number; maxIter?: number; }
+
+/** Result from Heterogeneous Treatment. */
+export interface HeterogeneousTreatmentResult { values: number[]; converged: boolean; }
+
+/** Compute Heterogeneous Treatment. */
+export function computeHeterogeneousTreatment(data: number[], opts: HeterogeneousTreatmentOptions = {}): HeterogeneousTreatmentResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeHeterogeneousTreatment };
diff --git a/src/causal/identification.ts b/src/causal/identification.ts
new file mode 100644
index 00000000..e3e0be60
--- /dev/null
+++ b/src/causal/identification.ts
@@ -0,0 +1,22 @@
+/** Identification module — tsb analytics library. */
+
+/** Options for Identification. */
+export interface IdentificationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Identification. */
+export interface IdentificationResult { values: number[]; converged: boolean; }
+
+/** Compute Identification. */
+export function computeIdentification(data: number[], opts: IdentificationOptions = {}): IdentificationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeIdentification };
diff --git a/src/causal/interrupted_time_series.ts b/src/causal/interrupted_time_series.ts
new file mode 100644
index 00000000..678349ee
--- /dev/null
+++ b/src/causal/interrupted_time_series.ts
@@ -0,0 +1,22 @@
+/** Interrupted Time Series module — tsb analytics library. */
+
+/** Options for Interrupted Time Series. */
+export interface InterruptedTimeSeriesOptions { tol?: number; maxIter?: number; }
+
+/** Result from Interrupted Time Series. */
+export interface InterruptedTimeSeriesResult { values: number[]; converged: boolean; }
+
+/** Compute Interrupted Time Series. */
+export function computeInterruptedTimeSeries(data: number[], opts: InterruptedTimeSeriesOptions = {}): InterruptedTimeSeriesResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeInterruptedTimeSeries };
diff --git a/src/causal/iv.ts b/src/causal/iv.ts
new file mode 100644
index 00000000..1b756ecd
--- /dev/null
+++ b/src/causal/iv.ts
@@ -0,0 +1,22 @@
+/** Iv module — tsb analytics library. */
+
+/** Options for Iv. */
+export interface IvOptions { tol?: number; maxIter?: number; }
+
+/** Result from Iv. */
+export interface IvResult { values: number[]; converged: boolean; }
+
+/** Compute Iv. */
+export function computeIv(data: number[], opts: IvOptions = {}): IvResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeIv };
diff --git a/src/causal/large.ts b/src/causal/large.ts
new file mode 100644
index 00000000..dd4f8d12
--- /dev/null
+++ b/src/causal/large.ts
@@ -0,0 +1,15 @@
+/** Causal Large module — tsb analytics library. */
+export interface Causal largeOptions { tol?: number; maxIter?: number; }
+export interface Causal largeResult { values: number[]; converged: boolean; }
+export function computeCausal large(data: number[], opts: Causal largeOptions = {}): Causal largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCausal large };
diff --git a/src/causal/legacy.ts b/src/causal/legacy.ts
new file mode 100644
index 00000000..57a21bb3
--- /dev/null
+++ b/src/causal/legacy.ts
@@ -0,0 +1,15 @@
+/** Causal Legacy module — tsb analytics library. */
+export interface Causal legacyOptions { tol?: number; maxIter?: number; }
+export interface Causal legacyResult { values: number[]; converged: boolean; }
+export function computeCausal legacy(data: number[], opts: Causal legacyOptions = {}): Causal legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCausal legacy };
diff --git a/src/causal/lite.ts b/src/causal/lite.ts
new file mode 100644
index 00000000..a6307bc1
--- /dev/null
+++ b/src/causal/lite.ts
@@ -0,0 +1,15 @@
+/** Causal Lite module — tsb analytics library. */
+export interface Causal liteOptions { tol?: number; maxIter?: number; }
+export interface Causal liteResult { values: number[]; converged: boolean; }
+export function computeCausal lite(data: number[], opts: Causal liteOptions = {}): Causal liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCausal lite };
diff --git a/src/causal/local_average_treatment.ts b/src/causal/local_average_treatment.ts
new file mode 100644
index 00000000..08bf5644
--- /dev/null
+++ b/src/causal/local_average_treatment.ts
@@ -0,0 +1,22 @@
+/** Local Average Treatment module — tsb analytics library. */
+
+/** Options for Local Average Treatment. */
+export interface LocalAverageTreatmentOptions { tol?: number; maxIter?: number; }
+
+/** Result from Local Average Treatment. */
+export interface LocalAverageTreatmentResult { values: number[]; converged: boolean; }
+
+/** Compute Local Average Treatment. */
+export function computeLocalAverageTreatment(data: number[], opts: LocalAverageTreatmentOptions = {}): LocalAverageTreatmentResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLocalAverageTreatment };
diff --git a/src/causal/marginal_treatment.ts b/src/causal/marginal_treatment.ts
new file mode 100644
index 00000000..023fd5c2
--- /dev/null
+++ b/src/causal/marginal_treatment.ts
@@ -0,0 +1,22 @@
+/** Marginal Treatment module — tsb analytics library. */
+
+/** Options for Marginal Treatment. */
+export interface MarginalTreatmentOptions { tol?: number; maxIter?: number; }
+
+/** Result from Marginal Treatment. */
+export interface MarginalTreatmentResult { values: number[]; converged: boolean; }
+
+/** Compute Marginal Treatment. */
+export function computeMarginalTreatment(data: number[], opts: MarginalTreatmentOptions = {}): MarginalTreatmentResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMarginalTreatment };
diff --git a/src/causal/matching.ts b/src/causal/matching.ts
new file mode 100644
index 00000000..c60f8d3e
--- /dev/null
+++ b/src/causal/matching.ts
@@ -0,0 +1,22 @@
+/** Matching module — tsb analytics library. */
+
+/** Options for Matching. */
+export interface MatchingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Matching. */
+export interface MatchingResult { values: number[]; converged: boolean; }
+
+/** Compute Matching. */
+export function computeMatching(data: number[], opts: MatchingOptions = {}): MatchingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMatching };
diff --git a/src/causal/mechanism.ts b/src/causal/mechanism.ts
new file mode 100644
index 00000000..7e52ea2f
--- /dev/null
+++ b/src/causal/mechanism.ts
@@ -0,0 +1,22 @@
+/** Mechanism module — tsb analytics library. */
+
+/** Options for Mechanism. */
+export interface MechanismOptions { tol?: number; maxIter?: number; }
+
+/** Result from Mechanism. */
+export interface MechanismResult { values: number[]; converged: boolean; }
+
+/** Compute Mechanism. */
+export function computeMechanism(data: number[], opts: MechanismOptions = {}): MechanismResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMechanism };
diff --git a/src/causal/mediation.ts b/src/causal/mediation.ts
new file mode 100644
index 00000000..b2954938
--- /dev/null
+++ b/src/causal/mediation.ts
@@ -0,0 +1,22 @@
+/** Mediation module — tsb analytics library. */
+
+/** Options for Mediation. */
+export interface MediationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Mediation. */
+export interface MediationResult { values: number[]; converged: boolean; }
+
+/** Compute Mediation. */
+export function computeMediation(data: number[], opts: MediationOptions = {}): MediationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMediation };
diff --git a/src/causal/mini.ts b/src/causal/mini.ts
new file mode 100644
index 00000000..d4facbc3
--- /dev/null
+++ b/src/causal/mini.ts
@@ -0,0 +1,15 @@
+/** Causal Mini module — tsb analytics library. */
+export interface Causal miniOptions { tol?: number; maxIter?: number; }
+export interface Causal miniResult { values: number[]; converged: boolean; }
+export function computeCausal mini(data: number[], opts: Causal miniOptions = {}): Causal miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCausal mini };
diff --git a/src/causal/natural_experiment.ts b/src/causal/natural_experiment.ts
new file mode 100644
index 00000000..5049d8a7
--- /dev/null
+++ b/src/causal/natural_experiment.ts
@@ -0,0 +1,22 @@
+/** Natural Experiment module — tsb analytics library. */
+
+/** Options for Natural Experiment. */
+export interface NaturalExperimentOptions { tol?: number; maxIter?: number; }
+
+/** Result from Natural Experiment. */
+export interface NaturalExperimentResult { values: number[]; converged: boolean; }
+
+/** Compute Natural Experiment. */
+export function computeNaturalExperiment(data: number[], opts: NaturalExperimentOptions = {}): NaturalExperimentResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNaturalExperiment };
diff --git a/src/causal/next.ts b/src/causal/next.ts
new file mode 100644
index 00000000..73b08370
--- /dev/null
+++ b/src/causal/next.ts
@@ -0,0 +1,15 @@
+/** Causal Next module — tsb analytics library. */
+export interface Causal nextOptions { tol?: number; maxIter?: number; }
+export interface Causal nextResult { values: number[]; converged: boolean; }
+export function computeCausal next(data: number[], opts: Causal nextOptions = {}): Causal nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCausal next };
diff --git a/src/causal/online.ts b/src/causal/online.ts
new file mode 100644
index 00000000..0fb73c78
--- /dev/null
+++ b/src/causal/online.ts
@@ -0,0 +1,15 @@
+/** Causal Online module — tsb analytics library. */
+export interface Causal onlineOptions { tol?: number; maxIter?: number; }
+export interface Causal onlineResult { values: number[]; converged: boolean; }
+export function computeCausal online(data: number[], opts: Causal onlineOptions = {}): Causal onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCausal online };
diff --git a/src/causal/parallel.ts b/src/causal/parallel.ts
new file mode 100644
index 00000000..2994564f
--- /dev/null
+++ b/src/causal/parallel.ts
@@ -0,0 +1,15 @@
+/** Causal Parallel module — tsb analytics library. */
+export interface Causal parallelOptions { tol?: number; maxIter?: number; }
+export interface Causal parallelResult { values: number[]; converged: boolean; }
+export function computeCausal parallel(data: number[], opts: Causal parallelOptions = {}): Causal parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCausal parallel };
diff --git a/src/causal/placebo.ts b/src/causal/placebo.ts
new file mode 100644
index 00000000..3a3da5b7
--- /dev/null
+++ b/src/causal/placebo.ts
@@ -0,0 +1,22 @@
+/** Placebo module — tsb analytics library. */
+
+/** Options for Placebo. */
+export interface PlaceboOptions { tol?: number; maxIter?: number; }
+
+/** Result from Placebo. */
+export interface PlaceboResult { values: number[]; converged: boolean; }
+
+/** Compute Placebo. */
+export function computePlacebo(data: number[], opts: PlaceboOptions = {}): PlaceboResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePlacebo };
diff --git a/src/causal/plus.ts b/src/causal/plus.ts
new file mode 100644
index 00000000..d2f7cb26
--- /dev/null
+++ b/src/causal/plus.ts
@@ -0,0 +1,15 @@
+/** Causal Plus module — tsb analytics library. */
+export interface Causal plusOptions { tol?: number; maxIter?: number; }
+export interface Causal plusResult { values: number[]; converged: boolean; }
+export function computeCausal plus(data: number[], opts: Causal plusOptions = {}): Causal plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCausal plus };
diff --git a/src/causal/policy.ts b/src/causal/policy.ts
new file mode 100644
index 00000000..cf41f468
--- /dev/null
+++ b/src/causal/policy.ts
@@ -0,0 +1,22 @@
+/** Policy module — tsb analytics library. */
+
+/** Options for Policy. */
+export interface PolicyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Policy. */
+export interface PolicyResult { values: number[]; converged: boolean; }
+
+/** Compute Policy. */
+export function computePolicy(data: number[], opts: PolicyOptions = {}): PolicyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePolicy };
diff --git a/src/causal/principal_stratification.ts b/src/causal/principal_stratification.ts
new file mode 100644
index 00000000..4e7b04fa
--- /dev/null
+++ b/src/causal/principal_stratification.ts
@@ -0,0 +1,22 @@
+/** Principal Stratification module — tsb analytics library. */
+
+/** Options for Principal Stratification. */
+export interface PrincipalStratificationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Principal Stratification. */
+export interface PrincipalStratificationResult { values: number[]; converged: boolean; }
+
+/** Compute Principal Stratification. */
+export function computePrincipalStratification(data: number[], opts: PrincipalStratificationOptions = {}): PrincipalStratificationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePrincipalStratification };
diff --git a/src/causal/pro.ts b/src/causal/pro.ts
new file mode 100644
index 00000000..74acd9aa
--- /dev/null
+++ b/src/causal/pro.ts
@@ -0,0 +1,15 @@
+/** Causal Pro module — tsb analytics library. */
+export interface Causal proOptions { tol?: number; maxIter?: number; }
+export interface Causal proResult { values: number[]; converged: boolean; }
+export function computeCausal pro(data: number[], opts: Causal proOptions = {}): Causal proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCausal pro };
diff --git a/src/causal/propensity.ts b/src/causal/propensity.ts
new file mode 100644
index 00000000..6fe7a19e
--- /dev/null
+++ b/src/causal/propensity.ts
@@ -0,0 +1,22 @@
+/** Propensity module — tsb analytics library. */
+
+/** Options for Propensity. */
+export interface PropensityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Propensity. */
+export interface PropensityResult { values: number[]; converged: boolean; }
+
+/** Compute Propensity. */
+export function computePropensity(data: number[], opts: PropensityOptions = {}): PropensityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePropensity };
diff --git a/src/causal/quantile_treatment.ts b/src/causal/quantile_treatment.ts
new file mode 100644
index 00000000..804212c9
--- /dev/null
+++ b/src/causal/quantile_treatment.ts
@@ -0,0 +1,22 @@
+/** Quantile Treatment module — tsb analytics library. */
+
+/** Options for Quantile Treatment. */
+export interface QuantileTreatmentOptions { tol?: number; maxIter?: number; }
+
+/** Result from Quantile Treatment. */
+export interface QuantileTreatmentResult { values: number[]; converged: boolean; }
+
+/** Compute Quantile Treatment. */
+export function computeQuantileTreatment(data: number[], opts: QuantileTreatmentOptions = {}): QuantileTreatmentResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeQuantileTreatment };
diff --git a/src/causal/rdd.ts b/src/causal/rdd.ts
new file mode 100644
index 00000000..c7161ac0
--- /dev/null
+++ b/src/causal/rdd.ts
@@ -0,0 +1,22 @@
+/** Rdd module — tsb analytics library. */
+
+/** Options for Rdd. */
+export interface RddOptions { tol?: number; maxIter?: number; }
+
+/** Result from Rdd. */
+export interface RddResult { values: number[]; converged: boolean; }
+
+/** Compute Rdd. */
+export function computeRdd(data: number[], opts: RddOptions = {}): RddResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRdd };
diff --git a/src/causal/regression_discontinuity.ts b/src/causal/regression_discontinuity.ts
new file mode 100644
index 00000000..dc526aa9
--- /dev/null
+++ b/src/causal/regression_discontinuity.ts
@@ -0,0 +1,22 @@
+/** Regression Discontinuity module — tsb analytics library. */
+
+/** Options for Regression Discontinuity. */
+export interface RegressionDiscontinuityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Regression Discontinuity. */
+export interface RegressionDiscontinuityResult { values: number[]; converged: boolean; }
+
+/** Compute Regression Discontinuity. */
+export function computeRegressionDiscontinuity(data: number[], opts: RegressionDiscontinuityOptions = {}): RegressionDiscontinuityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRegressionDiscontinuity };
diff --git a/src/causal/robust.ts b/src/causal/robust.ts
new file mode 100644
index 00000000..48ce0984
--- /dev/null
+++ b/src/causal/robust.ts
@@ -0,0 +1,15 @@
+/** Causal Robust module — tsb analytics library. */
+export interface Causal robustOptions { tol?: number; maxIter?: number; }
+export interface Causal robustResult { values: number[]; converged: boolean; }
+export function computeCausal robust(data: number[], opts: Causal robustOptions = {}): Causal robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCausal robust };
diff --git a/src/causal/sensitivity.ts b/src/causal/sensitivity.ts
new file mode 100644
index 00000000..2c021c56
--- /dev/null
+++ b/src/causal/sensitivity.ts
@@ -0,0 +1,22 @@
+/** Sensitivity module — tsb analytics library. */
+
+/** Options for Sensitivity. */
+export interface SensitivityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Sensitivity. */
+export interface SensitivityResult { values: number[]; converged: boolean; }
+
+/** Compute Sensitivity. */
+export function computeSensitivity(data: number[], opts: SensitivityOptions = {}): SensitivityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSensitivity };
diff --git a/src/causal/small.ts b/src/causal/small.ts
new file mode 100644
index 00000000..0c5d6589
--- /dev/null
+++ b/src/causal/small.ts
@@ -0,0 +1,15 @@
+/** Causal Small module — tsb analytics library. */
+export interface Causal smallOptions { tol?: number; maxIter?: number; }
+export interface Causal smallResult { values: number[]; converged: boolean; }
+export function computeCausal small(data: number[], opts: Causal smallOptions = {}): Causal smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCausal small };
diff --git a/src/causal/sparse.ts b/src/causal/sparse.ts
new file mode 100644
index 00000000..b433ee22
--- /dev/null
+++ b/src/causal/sparse.ts
@@ -0,0 +1,15 @@
+/** Causal Sparse module — tsb analytics library. */
+export interface Causal sparseOptions { tol?: number; maxIter?: number; }
+export interface Causal sparseResult { values: number[]; converged: boolean; }
+export function computeCausal sparse(data: number[], opts: Causal sparseOptions = {}): Causal sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCausal sparse };
diff --git a/src/causal/stable.ts b/src/causal/stable.ts
new file mode 100644
index 00000000..71d1e254
--- /dev/null
+++ b/src/causal/stable.ts
@@ -0,0 +1,15 @@
+/** Causal Stable module — tsb analytics library. */
+export interface Causal stableOptions { tol?: number; maxIter?: number; }
+export interface Causal stableResult { values: number[]; converged: boolean; }
+export function computeCausal stable(data: number[], opts: Causal stableOptions = {}): Causal stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCausal stable };
diff --git a/src/causal/streaming.ts b/src/causal/streaming.ts
new file mode 100644
index 00000000..0d6d25e1
--- /dev/null
+++ b/src/causal/streaming.ts
@@ -0,0 +1,15 @@
+/** Causal Streaming module — tsb analytics library. */
+export interface Causal streamingOptions { tol?: number; maxIter?: number; }
+export interface Causal streamingResult { values: number[]; converged: boolean; }
+export function computeCausal streaming(data: number[], opts: Causal streamingOptions = {}): Causal streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCausal streaming };
diff --git a/src/causal/synthetic_control.ts b/src/causal/synthetic_control.ts
new file mode 100644
index 00000000..bc750420
--- /dev/null
+++ b/src/causal/synthetic_control.ts
@@ -0,0 +1,22 @@
+/** Synthetic Control module — tsb analytics library. */
+
+/** Options for Synthetic Control. */
+export interface SyntheticControlOptions { tol?: number; maxIter?: number; }
+
+/** Result from Synthetic Control. */
+export interface SyntheticControlResult { values: number[]; converged: boolean; }
+
+/** Compute Synthetic Control. */
+export function computeSyntheticControl(data: number[], opts: SyntheticControlOptions = {}): SyntheticControlResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSyntheticControl };
diff --git a/src/causal/synthetic_diff.ts b/src/causal/synthetic_diff.ts
new file mode 100644
index 00000000..850ef253
--- /dev/null
+++ b/src/causal/synthetic_diff.ts
@@ -0,0 +1,22 @@
+/** Synthetic Diff module — tsb analytics library. */
+
+/** Options for Synthetic Diff. */
+export interface SyntheticDiffOptions { tol?: number; maxIter?: number; }
+
+/** Result from Synthetic Diff. */
+export interface SyntheticDiffResult { values: number[]; converged: boolean; }
+
+/** Compute Synthetic Diff. */
+export function computeSyntheticDiff(data: number[], opts: SyntheticDiffOptions = {}): SyntheticDiffResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSyntheticDiff };
diff --git a/src/causal/v2.ts b/src/causal/v2.ts
new file mode 100644
index 00000000..4b15428f
--- /dev/null
+++ b/src/causal/v2.ts
@@ -0,0 +1,15 @@
+/** Causal V2 module — tsb analytics library. */
+export interface Causal v2Options { tol?: number; maxIter?: number; }
+export interface Causal v2Result { values: number[]; converged: boolean; }
+export function computeCausal v2(data: number[], opts: Causal v2Options = {}): Causal v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCausal v2 };
diff --git a/src/causal/v3.ts b/src/causal/v3.ts
new file mode 100644
index 00000000..33a5793a
--- /dev/null
+++ b/src/causal/v3.ts
@@ -0,0 +1,15 @@
+/** Causal V3 module — tsb analytics library. */
+export interface Causal v3Options { tol?: number; maxIter?: number; }
+export interface Causal v3Result { values: number[]; converged: boolean; }
+export function computeCausal v3(data: number[], opts: Causal v3Options = {}): Causal v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCausal v3 };
diff --git a/src/causal/wasm.ts b/src/causal/wasm.ts
new file mode 100644
index 00000000..ebf49ec2
--- /dev/null
+++ b/src/causal/wasm.ts
@@ -0,0 +1,15 @@
+/** Causal Wasm module — tsb analytics library. */
+export interface Causal wasmOptions { tol?: number; maxIter?: number; }
+export interface Causal wasmResult { values: number[]; converged: boolean; }
+export function computeCausal wasm(data: number[], opts: Causal wasmOptions = {}): Causal wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCausal wasm };
diff --git a/src/causal/xlarge.ts b/src/causal/xlarge.ts
new file mode 100644
index 00000000..2bcae57a
--- /dev/null
+++ b/src/causal/xlarge.ts
@@ -0,0 +1,15 @@
+/** Causal Xlarge module — tsb analytics library. */
+export interface Causal xlargeOptions { tol?: number; maxIter?: number; }
+export interface Causal xlargeResult { values: number[]; converged: boolean; }
+export function computeCausal xlarge(data: number[], opts: Causal xlargeOptions = {}): Causal xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCausal xlarge };
diff --git a/src/climate/advanced.ts b/src/climate/advanced.ts
new file mode 100644
index 00000000..6425cc9a
--- /dev/null
+++ b/src/climate/advanced.ts
@@ -0,0 +1,15 @@
+/** Climate Advanced module — tsb analytics library. */
+export interface Climate advancedOptions { tol?: number; maxIter?: number; }
+export interface Climate advancedResult { values: number[]; converged: boolean; }
+export function computeClimate advanced(data: number[], opts: Climate advancedOptions = {}): Climate advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeClimate advanced };
diff --git a/src/climate/aerosol.ts b/src/climate/aerosol.ts
new file mode 100644
index 00000000..264820ae
--- /dev/null
+++ b/src/climate/aerosol.ts
@@ -0,0 +1,22 @@
+/** Aerosol module — tsb analytics library. */
+
+/** Options for Aerosol. */
+export interface AerosolOptions { tol?: number; maxIter?: number; }
+
+/** Result from Aerosol. */
+export interface AerosolResult { values: number[]; converged: boolean; }
+
+/** Compute Aerosol. */
+export function computeAerosol(data: number[], opts: AerosolOptions = {}): AerosolResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAerosol };
diff --git a/src/climate/albedo.ts b/src/climate/albedo.ts
new file mode 100644
index 00000000..a86c9dbd
--- /dev/null
+++ b/src/climate/albedo.ts
@@ -0,0 +1,22 @@
+/** Albedo module — tsb analytics library. */
+
+/** Options for Albedo. */
+export interface AlbedoOptions { tol?: number; maxIter?: number; }
+
+/** Result from Albedo. */
+export interface AlbedoResult { values: number[]; converged: boolean; }
+
+/** Compute Albedo. */
+export function computeAlbedo(data: number[], opts: AlbedoOptions = {}): AlbedoResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAlbedo };
diff --git a/src/climate/amo.ts b/src/climate/amo.ts
new file mode 100644
index 00000000..697a7848
--- /dev/null
+++ b/src/climate/amo.ts
@@ -0,0 +1,22 @@
+/** Amo module — tsb analytics library. */
+
+/** Options for Amo. */
+export interface AmoOptions { tol?: number; maxIter?: number; }
+
+/** Result from Amo. */
+export interface AmoResult { values: number[]; converged: boolean; }
+
+/** Compute Amo. */
+export function computeAmo(data: number[], opts: AmoOptions = {}): AmoResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAmo };
diff --git a/src/climate/attribution_climate.ts b/src/climate/attribution_climate.ts
new file mode 100644
index 00000000..20a70879
--- /dev/null
+++ b/src/climate/attribution_climate.ts
@@ -0,0 +1,22 @@
+/** Attribution Climate module — tsb analytics library. */
+
+/** Options for Attribution Climate. */
+export interface AttributionClimateOptions { tol?: number; maxIter?: number; }
+
+/** Result from Attribution Climate. */
+export interface AttributionClimateResult { values: number[]; converged: boolean; }
+
+/** Compute Attribution Climate. */
+export function computeAttributionClimate(data: number[], opts: AttributionClimateOptions = {}): AttributionClimateResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAttributionClimate };
diff --git a/src/climate/base2.ts b/src/climate/base2.ts
new file mode 100644
index 00000000..505ae5e7
--- /dev/null
+++ b/src/climate/base2.ts
@@ -0,0 +1,15 @@
+/** Climate Base2 module — tsb analytics library. */
+export interface Climate base2Options { tol?: number; maxIter?: number; }
+export interface Climate base2Result { values: number[]; converged: boolean; }
+export function computeClimate base2(data: number[], opts: Climate base2Options = {}): Climate base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeClimate base2 };
diff --git a/src/climate/batch.ts b/src/climate/batch.ts
new file mode 100644
index 00000000..2b298ca9
--- /dev/null
+++ b/src/climate/batch.ts
@@ -0,0 +1,15 @@
+/** Climate Batch module — tsb analytics library. */
+export interface Climate batchOptions { tol?: number; maxIter?: number; }
+export interface Climate batchResult { values: number[]; converged: boolean; }
+export function computeClimate batch(data: number[], opts: Climate batchOptions = {}): Climate batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeClimate batch };
diff --git a/src/climate/beta.ts b/src/climate/beta.ts
new file mode 100644
index 00000000..8ba0e423
--- /dev/null
+++ b/src/climate/beta.ts
@@ -0,0 +1,15 @@
+/** Climate Beta module — tsb analytics library. */
+export interface Climate betaOptions { tol?: number; maxIter?: number; }
+export interface Climate betaResult { values: number[]; converged: boolean; }
+export function computeClimate beta(data: number[], opts: Climate betaOptions = {}): Climate betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeClimate beta };
diff --git a/src/climate/carbon.ts b/src/climate/carbon.ts
new file mode 100644
index 00000000..038519ba
--- /dev/null
+++ b/src/climate/carbon.ts
@@ -0,0 +1,22 @@
+/** Carbon module — tsb analytics library. */
+
+/** Options for Carbon. */
+export interface CarbonOptions { tol?: number; maxIter?: number; }
+
+/** Result from Carbon. */
+export interface CarbonResult { values: number[]; converged: boolean; }
+
+/** Compute Carbon. */
+export function computeCarbon(data: number[], opts: CarbonOptions = {}): CarbonResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCarbon };
diff --git a/src/climate/circulation.ts b/src/climate/circulation.ts
new file mode 100644
index 00000000..0b253cee
--- /dev/null
+++ b/src/climate/circulation.ts
@@ -0,0 +1,22 @@
+/** Circulation module — tsb analytics library. */
+
+/** Options for Circulation. */
+export interface CirculationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Circulation. */
+export interface CirculationResult { values: number[]; converged: boolean; }
+
+/** Compute Circulation. */
+export function computeCirculation(data: number[], opts: CirculationOptions = {}): CirculationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCirculation };
diff --git a/src/climate/cpu.ts b/src/climate/cpu.ts
new file mode 100644
index 00000000..f8ed3a58
--- /dev/null
+++ b/src/climate/cpu.ts
@@ -0,0 +1,15 @@
+/** Climate Cpu module — tsb analytics library. */
+export interface Climate cpuOptions { tol?: number; maxIter?: number; }
+export interface Climate cpuResult { values: number[]; converged: boolean; }
+export function computeClimate cpu(data: number[], opts: Climate cpuOptions = {}): Climate cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeClimate cpu };
diff --git a/src/climate/dense.ts b/src/climate/dense.ts
new file mode 100644
index 00000000..7af9c1bd
--- /dev/null
+++ b/src/climate/dense.ts
@@ -0,0 +1,15 @@
+/** Climate Dense module — tsb analytics library. */
+export interface Climate denseOptions { tol?: number; maxIter?: number; }
+export interface Climate denseResult { values: number[]; converged: boolean; }
+export function computeClimate dense(data: number[], opts: Climate denseOptions = {}): Climate denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeClimate dense };
diff --git a/src/climate/distributed.ts b/src/climate/distributed.ts
new file mode 100644
index 00000000..e1ff3172
--- /dev/null
+++ b/src/climate/distributed.ts
@@ -0,0 +1,15 @@
+/** Climate Distributed module — tsb analytics library. */
+export interface Climate distributedOptions { tol?: number; maxIter?: number; }
+export interface Climate distributedResult { values: number[]; converged: boolean; }
+export function computeClimate distributed(data: number[], opts: Climate distributedOptions = {}): Climate distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeClimate distributed };
diff --git a/src/climate/enso.ts b/src/climate/enso.ts
new file mode 100644
index 00000000..c3190382
--- /dev/null
+++ b/src/climate/enso.ts
@@ -0,0 +1,22 @@
+/** Enso module — tsb analytics library. */
+
+/** Options for Enso. */
+export interface EnsoOptions { tol?: number; maxIter?: number; }
+
+/** Result from Enso. */
+export interface EnsoResult { values: number[]; converged: boolean; }
+
+/** Compute Enso. */
+export function computeEnso(data: number[], opts: EnsoOptions = {}): EnsoResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEnso };
diff --git a/src/climate/evapotranspiration.ts b/src/climate/evapotranspiration.ts
new file mode 100644
index 00000000..22e8100e
--- /dev/null
+++ b/src/climate/evapotranspiration.ts
@@ -0,0 +1,22 @@
+/** Evapotranspiration module — tsb analytics library. */
+
+/** Options for Evapotranspiration. */
+export interface EvapotranspirationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Evapotranspiration. */
+export interface EvapotranspirationResult { values: number[]; converged: boolean; }
+
+/** Compute Evapotranspiration. */
+export function computeEvapotranspiration(data: number[], opts: EvapotranspirationOptions = {}): EvapotranspirationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEvapotranspiration };
diff --git a/src/climate/experimental.ts b/src/climate/experimental.ts
new file mode 100644
index 00000000..dc212377
--- /dev/null
+++ b/src/climate/experimental.ts
@@ -0,0 +1,15 @@
+/** Climate Experimental module — tsb analytics library. */
+export interface Climate experimentalOptions { tol?: number; maxIter?: number; }
+export interface Climate experimentalResult { values: number[]; converged: boolean; }
+export function computeClimate experimental(data: number[], opts: Climate experimentalOptions = {}): Climate experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeClimate experimental };
diff --git a/src/climate/fast.ts b/src/climate/fast.ts
new file mode 100644
index 00000000..443cf63e
--- /dev/null
+++ b/src/climate/fast.ts
@@ -0,0 +1,15 @@
+/** Climate Fast module — tsb analytics library. */
+export interface Climate fastOptions { tol?: number; maxIter?: number; }
+export interface Climate fastResult { values: number[]; converged: boolean; }
+export function computeClimate fast(data: number[], opts: Climate fastOptions = {}): Climate fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeClimate fast };
diff --git a/src/climate/feedback.ts b/src/climate/feedback.ts
new file mode 100644
index 00000000..47d29cb6
--- /dev/null
+++ b/src/climate/feedback.ts
@@ -0,0 +1,22 @@
+/** Feedback module — tsb analytics library. */
+
+/** Options for Feedback. */
+export interface FeedbackOptions { tol?: number; maxIter?: number; }
+
+/** Result from Feedback. */
+export interface FeedbackResult { values: number[]; converged: boolean; }
+
+/** Compute Feedback. */
+export function computeFeedback(data: number[], opts: FeedbackOptions = {}): FeedbackResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFeedback };
diff --git a/src/climate/future.ts b/src/climate/future.ts
new file mode 100644
index 00000000..ca7442a8
--- /dev/null
+++ b/src/climate/future.ts
@@ -0,0 +1,15 @@
+/** Climate Future module — tsb analytics library. */
+export interface Climate futureOptions { tol?: number; maxIter?: number; }
+export interface Climate futureResult { values: number[]; converged: boolean; }
+export function computeClimate future(data: number[], opts: Climate futureOptions = {}): Climate futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeClimate future };
diff --git a/src/climate/gpu.ts b/src/climate/gpu.ts
new file mode 100644
index 00000000..91394fa4
--- /dev/null
+++ b/src/climate/gpu.ts
@@ -0,0 +1,15 @@
+/** Climate Gpu module — tsb analytics library. */
+export interface Climate gpuOptions { tol?: number; maxIter?: number; }
+export interface Climate gpuResult { values: number[]; converged: boolean; }
+export function computeClimate gpu(data: number[], opts: Climate gpuOptions = {}): Climate gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeClimate gpu };
diff --git a/src/climate/greenhouse.ts b/src/climate/greenhouse.ts
new file mode 100644
index 00000000..ab0f62b1
--- /dev/null
+++ b/src/climate/greenhouse.ts
@@ -0,0 +1,22 @@
+/** Greenhouse module — tsb analytics library. */
+
+/** Options for Greenhouse. */
+export interface GreenhouseOptions { tol?: number; maxIter?: number; }
+
+/** Result from Greenhouse. */
+export interface GreenhouseResult { values: number[]; converged: boolean; }
+
+/** Compute Greenhouse. */
+export function computeGreenhouse(data: number[], opts: GreenhouseOptions = {}): GreenhouseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGreenhouse };
diff --git a/src/climate/humidity.ts b/src/climate/humidity.ts
new file mode 100644
index 00000000..57075fe0
--- /dev/null
+++ b/src/climate/humidity.ts
@@ -0,0 +1,22 @@
+/** Humidity module — tsb analytics library. */
+
+/** Options for Humidity. */
+export interface HumidityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Humidity. */
+export interface HumidityResult { values: number[]; converged: boolean; }
+
+/** Compute Humidity. */
+export function computeHumidity(data: number[], opts: HumidityOptions = {}): HumidityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeHumidity };
diff --git a/src/climate/ice.ts b/src/climate/ice.ts
new file mode 100644
index 00000000..12cc09bb
--- /dev/null
+++ b/src/climate/ice.ts
@@ -0,0 +1,22 @@
+/** Ice module — tsb analytics library. */
+
+/** Options for Ice. */
+export interface IceOptions { tol?: number; maxIter?: number; }
+
+/** Result from Ice. */
+export interface IceResult { values: number[]; converged: boolean; }
+
+/** Compute Ice. */
+export function computeIce(data: number[], opts: IceOptions = {}): IceResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeIce };
diff --git a/src/climate/large.ts b/src/climate/large.ts
new file mode 100644
index 00000000..24ff80de
--- /dev/null
+++ b/src/climate/large.ts
@@ -0,0 +1,15 @@
+/** Climate Large module — tsb analytics library. */
+export interface Climate largeOptions { tol?: number; maxIter?: number; }
+export interface Climate largeResult { values: number[]; converged: boolean; }
+export function computeClimate large(data: number[], opts: Climate largeOptions = {}): Climate largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeClimate large };
diff --git a/src/climate/legacy.ts b/src/climate/legacy.ts
new file mode 100644
index 00000000..7652d3f2
--- /dev/null
+++ b/src/climate/legacy.ts
@@ -0,0 +1,15 @@
+/** Climate Legacy module — tsb analytics library. */
+export interface Climate legacyOptions { tol?: number; maxIter?: number; }
+export interface Climate legacyResult { values: number[]; converged: boolean; }
+export function computeClimate legacy(data: number[], opts: Climate legacyOptions = {}): Climate legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeClimate legacy };
diff --git a/src/climate/lite.ts b/src/climate/lite.ts
new file mode 100644
index 00000000..4beee705
--- /dev/null
+++ b/src/climate/lite.ts
@@ -0,0 +1,15 @@
+/** Climate Lite module — tsb analytics library. */
+export interface Climate liteOptions { tol?: number; maxIter?: number; }
+export interface Climate liteResult { values: number[]; converged: boolean; }
+export function computeClimate lite(data: number[], opts: Climate liteOptions = {}): Climate liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeClimate lite };
diff --git a/src/climate/methane.ts b/src/climate/methane.ts
new file mode 100644
index 00000000..f6ddc9df
--- /dev/null
+++ b/src/climate/methane.ts
@@ -0,0 +1,22 @@
+/** Methane module — tsb analytics library. */
+
+/** Options for Methane. */
+export interface MethaneOptions { tol?: number; maxIter?: number; }
+
+/** Result from Methane. */
+export interface MethaneResult { values: number[]; converged: boolean; }
+
+/** Compute Methane. */
+export function computeMethane(data: number[], opts: MethaneOptions = {}): MethaneResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMethane };
diff --git a/src/climate/mini.ts b/src/climate/mini.ts
new file mode 100644
index 00000000..02723cb2
--- /dev/null
+++ b/src/climate/mini.ts
@@ -0,0 +1,15 @@
+/** Climate Mini module — tsb analytics library. */
+export interface Climate miniOptions { tol?: number; maxIter?: number; }
+export interface Climate miniResult { values: number[]; converged: boolean; }
+export function computeClimate mini(data: number[], opts: Climate miniOptions = {}): Climate miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeClimate mini };
diff --git a/src/climate/nao.ts b/src/climate/nao.ts
new file mode 100644
index 00000000..bb21f923
--- /dev/null
+++ b/src/climate/nao.ts
@@ -0,0 +1,22 @@
+/** Nao module — tsb analytics library. */
+
+/** Options for Nao. */
+export interface NaoOptions { tol?: number; maxIter?: number; }
+
+/** Result from Nao. */
+export interface NaoResult { values: number[]; converged: boolean; }
+
+/** Compute Nao. */
+export function computeNao(data: number[], opts: NaoOptions = {}): NaoResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNao };
diff --git a/src/climate/next.ts b/src/climate/next.ts
new file mode 100644
index 00000000..9b362675
--- /dev/null
+++ b/src/climate/next.ts
@@ -0,0 +1,15 @@
+/** Climate Next module — tsb analytics library. */
+export interface Climate nextOptions { tol?: number; maxIter?: number; }
+export interface Climate nextResult { values: number[]; converged: boolean; }
+export function computeClimate next(data: number[], opts: Climate nextOptions = {}): Climate nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeClimate next };
diff --git a/src/climate/nitrous_oxide.ts b/src/climate/nitrous_oxide.ts
new file mode 100644
index 00000000..3f1326fd
--- /dev/null
+++ b/src/climate/nitrous_oxide.ts
@@ -0,0 +1,22 @@
+/** Nitrous Oxide module — tsb analytics library. */
+
+/** Options for Nitrous Oxide. */
+export interface NitrousOxideOptions { tol?: number; maxIter?: number; }
+
+/** Result from Nitrous Oxide. */
+export interface NitrousOxideResult { values: number[]; converged: boolean; }
+
+/** Compute Nitrous Oxide. */
+export function computeNitrousOxide(data: number[], opts: NitrousOxideOptions = {}): NitrousOxideResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNitrousOxide };
diff --git a/src/climate/ocean_heat.ts b/src/climate/ocean_heat.ts
new file mode 100644
index 00000000..2007a440
--- /dev/null
+++ b/src/climate/ocean_heat.ts
@@ -0,0 +1,22 @@
+/** Ocean Heat module — tsb analytics library. */
+
+/** Options for Ocean Heat. */
+export interface OceanHeatOptions { tol?: number; maxIter?: number; }
+
+/** Result from Ocean Heat. */
+export interface OceanHeatResult { values: number[]; converged: boolean; }
+
+/** Compute Ocean Heat. */
+export function computeOceanHeat(data: number[], opts: OceanHeatOptions = {}): OceanHeatResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeOceanHeat };
diff --git a/src/climate/online.ts b/src/climate/online.ts
new file mode 100644
index 00000000..ccee2692
--- /dev/null
+++ b/src/climate/online.ts
@@ -0,0 +1,15 @@
+/** Climate Online module — tsb analytics library. */
+export interface Climate onlineOptions { tol?: number; maxIter?: number; }
+export interface Climate onlineResult { values: number[]; converged: boolean; }
+export function computeClimate online(data: number[], opts: Climate onlineOptions = {}): Climate onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeClimate online };
diff --git a/src/climate/ozone.ts b/src/climate/ozone.ts
new file mode 100644
index 00000000..c2cfa377
--- /dev/null
+++ b/src/climate/ozone.ts
@@ -0,0 +1,22 @@
+/** Ozone module — tsb analytics library. */
+
+/** Options for Ozone. */
+export interface OzoneOptions { tol?: number; maxIter?: number; }
+
+/** Result from Ozone. */
+export interface OzoneResult { values: number[]; converged: boolean; }
+
+/** Compute Ozone. */
+export function computeOzone(data: number[], opts: OzoneOptions = {}): OzoneResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeOzone };
diff --git a/src/climate/parallel.ts b/src/climate/parallel.ts
new file mode 100644
index 00000000..93a6599f
--- /dev/null
+++ b/src/climate/parallel.ts
@@ -0,0 +1,15 @@
+/** Climate Parallel module — tsb analytics library. */
+export interface Climate parallelOptions { tol?: number; maxIter?: number; }
+export interface Climate parallelResult { values: number[]; converged: boolean; }
+export function computeClimate parallel(data: number[], opts: Climate parallelOptions = {}): Climate parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeClimate parallel };
diff --git a/src/climate/pdo.ts b/src/climate/pdo.ts
new file mode 100644
index 00000000..eb1c115d
--- /dev/null
+++ b/src/climate/pdo.ts
@@ -0,0 +1,22 @@
+/** Pdo module — tsb analytics library. */
+
+/** Options for Pdo. */
+export interface PdoOptions { tol?: number; maxIter?: number; }
+
+/** Result from Pdo. */
+export interface PdoResult { values: number[]; converged: boolean; }
+
+/** Compute Pdo. */
+export function computePdo(data: number[], opts: PdoOptions = {}): PdoResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePdo };
diff --git a/src/climate/plus.ts b/src/climate/plus.ts
new file mode 100644
index 00000000..f2b8ecf1
--- /dev/null
+++ b/src/climate/plus.ts
@@ -0,0 +1,15 @@
+/** Climate Plus module — tsb analytics library. */
+export interface Climate plusOptions { tol?: number; maxIter?: number; }
+export interface Climate plusResult { values: number[]; converged: boolean; }
+export function computeClimate plus(data: number[], opts: Climate plusOptions = {}): Climate plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeClimate plus };
diff --git a/src/climate/precipitation.ts b/src/climate/precipitation.ts
new file mode 100644
index 00000000..fcc4aaeb
--- /dev/null
+++ b/src/climate/precipitation.ts
@@ -0,0 +1,22 @@
+/** Precipitation module — tsb analytics library. */
+
+/** Options for Precipitation. */
+export interface PrecipitationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Precipitation. */
+export interface PrecipitationResult { values: number[]; converged: boolean; }
+
+/** Compute Precipitation. */
+export function computePrecipitation(data: number[], opts: PrecipitationOptions = {}): PrecipitationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePrecipitation };
diff --git a/src/climate/pressure.ts b/src/climate/pressure.ts
new file mode 100644
index 00000000..11d9535b
--- /dev/null
+++ b/src/climate/pressure.ts
@@ -0,0 +1,22 @@
+/** Pressure module — tsb analytics library. */
+
+/** Options for Pressure. */
+export interface PressureOptions { tol?: number; maxIter?: number; }
+
+/** Result from Pressure. */
+export interface PressureResult { values: number[]; converged: boolean; }
+
+/** Compute Pressure. */
+export function computePressure(data: number[], opts: PressureOptions = {}): PressureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePressure };
diff --git a/src/climate/pro.ts b/src/climate/pro.ts
new file mode 100644
index 00000000..b0045d08
--- /dev/null
+++ b/src/climate/pro.ts
@@ -0,0 +1,15 @@
+/** Climate Pro module — tsb analytics library. */
+export interface Climate proOptions { tol?: number; maxIter?: number; }
+export interface Climate proResult { values: number[]; converged: boolean; }
+export function computeClimate pro(data: number[], opts: Climate proOptions = {}): Climate proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeClimate pro };
diff --git a/src/climate/qbo.ts b/src/climate/qbo.ts
new file mode 100644
index 00000000..d1c6ea8d
--- /dev/null
+++ b/src/climate/qbo.ts
@@ -0,0 +1,22 @@
+/** Qbo module — tsb analytics library. */
+
+/** Options for Qbo. */
+export interface QboOptions { tol?: number; maxIter?: number; }
+
+/** Result from Qbo. */
+export interface QboResult { values: number[]; converged: boolean; }
+
+/** Compute Qbo. */
+export function computeQbo(data: number[], opts: QboOptions = {}): QboResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeQbo };
diff --git a/src/climate/radiation.ts b/src/climate/radiation.ts
new file mode 100644
index 00000000..aeb017db
--- /dev/null
+++ b/src/climate/radiation.ts
@@ -0,0 +1,22 @@
+/** Radiation module — tsb analytics library. */
+
+/** Options for Radiation. */
+export interface RadiationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Radiation. */
+export interface RadiationResult { values: number[]; converged: boolean; }
+
+/** Compute Radiation. */
+export function computeRadiation(data: number[], opts: RadiationOptions = {}): RadiationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRadiation };
diff --git a/src/climate/robust.ts b/src/climate/robust.ts
new file mode 100644
index 00000000..b906aaa7
--- /dev/null
+++ b/src/climate/robust.ts
@@ -0,0 +1,15 @@
+/** Climate Robust module — tsb analytics library. */
+export interface Climate robustOptions { tol?: number; maxIter?: number; }
+export interface Climate robustResult { values: number[]; converged: boolean; }
+export function computeClimate robust(data: number[], opts: Climate robustOptions = {}): Climate robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeClimate robust };
diff --git a/src/climate/sea_level.ts b/src/climate/sea_level.ts
new file mode 100644
index 00000000..9a518cf3
--- /dev/null
+++ b/src/climate/sea_level.ts
@@ -0,0 +1,22 @@
+/** Sea Level module — tsb analytics library. */
+
+/** Options for Sea Level. */
+export interface SeaLevelOptions { tol?: number; maxIter?: number; }
+
+/** Result from Sea Level. */
+export interface SeaLevelResult { values: number[]; converged: boolean; }
+
+/** Compute Sea Level. */
+export function computeSeaLevel(data: number[], opts: SeaLevelOptions = {}): SeaLevelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSeaLevel };
diff --git a/src/climate/sensitivity.ts b/src/climate/sensitivity.ts
new file mode 100644
index 00000000..2c021c56
--- /dev/null
+++ b/src/climate/sensitivity.ts
@@ -0,0 +1,22 @@
+/** Sensitivity module — tsb analytics library. */
+
+/** Options for Sensitivity. */
+export interface SensitivityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Sensitivity. */
+export interface SensitivityResult { values: number[]; converged: boolean; }
+
+/** Compute Sensitivity. */
+export function computeSensitivity(data: number[], opts: SensitivityOptions = {}): SensitivityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSensitivity };
diff --git a/src/climate/small.ts b/src/climate/small.ts
new file mode 100644
index 00000000..7009f980
--- /dev/null
+++ b/src/climate/small.ts
@@ -0,0 +1,15 @@
+/** Climate Small module — tsb analytics library. */
+export interface Climate smallOptions { tol?: number; maxIter?: number; }
+export interface Climate smallResult { values: number[]; converged: boolean; }
+export function computeClimate small(data: number[], opts: Climate smallOptions = {}): Climate smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeClimate small };
diff --git a/src/climate/snow.ts b/src/climate/snow.ts
new file mode 100644
index 00000000..047e1b4c
--- /dev/null
+++ b/src/climate/snow.ts
@@ -0,0 +1,22 @@
+/** Snow module — tsb analytics library. */
+
+/** Options for Snow. */
+export interface SnowOptions { tol?: number; maxIter?: number; }
+
+/** Result from Snow. */
+export interface SnowResult { values: number[]; converged: boolean; }
+
+/** Compute Snow. */
+export function computeSnow(data: number[], opts: SnowOptions = {}): SnowResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSnow };
diff --git a/src/climate/soil_moisture.ts b/src/climate/soil_moisture.ts
new file mode 100644
index 00000000..99acf570
--- /dev/null
+++ b/src/climate/soil_moisture.ts
@@ -0,0 +1,22 @@
+/** Soil Moisture module — tsb analytics library. */
+
+/** Options for Soil Moisture. */
+export interface SoilMoistureOptions { tol?: number; maxIter?: number; }
+
+/** Result from Soil Moisture. */
+export interface SoilMoistureResult { values: number[]; converged: boolean; }
+
+/** Compute Soil Moisture. */
+export function computeSoilMoisture(data: number[], opts: SoilMoistureOptions = {}): SoilMoistureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSoilMoisture };
diff --git a/src/climate/solar.ts b/src/climate/solar.ts
new file mode 100644
index 00000000..59e46f03
--- /dev/null
+++ b/src/climate/solar.ts
@@ -0,0 +1,22 @@
+/** Solar module — tsb analytics library. */
+
+/** Options for Solar. */
+export interface SolarOptions { tol?: number; maxIter?: number; }
+
+/** Result from Solar. */
+export interface SolarResult { values: number[]; converged: boolean; }
+
+/** Compute Solar. */
+export function computeSolar(data: number[], opts: SolarOptions = {}): SolarResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSolar };
diff --git a/src/climate/sparse.ts b/src/climate/sparse.ts
new file mode 100644
index 00000000..6d70c434
--- /dev/null
+++ b/src/climate/sparse.ts
@@ -0,0 +1,15 @@
+/** Climate Sparse module — tsb analytics library. */
+export interface Climate sparseOptions { tol?: number; maxIter?: number; }
+export interface Climate sparseResult { values: number[]; converged: boolean; }
+export function computeClimate sparse(data: number[], opts: Climate sparseOptions = {}): Climate sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeClimate sparse };
diff --git a/src/climate/stable.ts b/src/climate/stable.ts
new file mode 100644
index 00000000..e2360276
--- /dev/null
+++ b/src/climate/stable.ts
@@ -0,0 +1,15 @@
+/** Climate Stable module — tsb analytics library. */
+export interface Climate stableOptions { tol?: number; maxIter?: number; }
+export interface Climate stableResult { values: number[]; converged: boolean; }
+export function computeClimate stable(data: number[], opts: Climate stableOptions = {}): Climate stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeClimate stable };
diff --git a/src/climate/streaming.ts b/src/climate/streaming.ts
new file mode 100644
index 00000000..00f1ab81
--- /dev/null
+++ b/src/climate/streaming.ts
@@ -0,0 +1,15 @@
+/** Climate Streaming module — tsb analytics library. */
+export interface Climate streamingOptions { tol?: number; maxIter?: number; }
+export interface Climate streamingResult { values: number[]; converged: boolean; }
+export function computeClimate streaming(data: number[], opts: Climate streamingOptions = {}): Climate streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeClimate streaming };
diff --git a/src/climate/teleconnection.ts b/src/climate/teleconnection.ts
new file mode 100644
index 00000000..1f68d39f
--- /dev/null
+++ b/src/climate/teleconnection.ts
@@ -0,0 +1,22 @@
+/** Teleconnection module — tsb analytics library. */
+
+/** Options for Teleconnection. */
+export interface TeleconnectionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Teleconnection. */
+export interface TeleconnectionResult { values: number[]; converged: boolean; }
+
+/** Compute Teleconnection. */
+export function computeTeleconnection(data: number[], opts: TeleconnectionOptions = {}): TeleconnectionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTeleconnection };
diff --git a/src/climate/temperature.ts b/src/climate/temperature.ts
new file mode 100644
index 00000000..0b8c1efe
--- /dev/null
+++ b/src/climate/temperature.ts
@@ -0,0 +1,22 @@
+/** Temperature module — tsb analytics library. */
+
+/** Options for Temperature. */
+export interface TemperatureOptions { tol?: number; maxIter?: number; }
+
+/** Result from Temperature. */
+export interface TemperatureResult { values: number[]; converged: boolean; }
+
+/** Compute Temperature. */
+export function computeTemperature(data: number[], opts: TemperatureOptions = {}): TemperatureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTemperature };
diff --git a/src/climate/v2.ts b/src/climate/v2.ts
new file mode 100644
index 00000000..81b1b1f2
--- /dev/null
+++ b/src/climate/v2.ts
@@ -0,0 +1,15 @@
+/** Climate V2 module — tsb analytics library. */
+export interface Climate v2Options { tol?: number; maxIter?: number; }
+export interface Climate v2Result { values: number[]; converged: boolean; }
+export function computeClimate v2(data: number[], opts: Climate v2Options = {}): Climate v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeClimate v2 };
diff --git a/src/climate/v3.ts b/src/climate/v3.ts
new file mode 100644
index 00000000..39d3895d
--- /dev/null
+++ b/src/climate/v3.ts
@@ -0,0 +1,15 @@
+/** Climate V3 module — tsb analytics library. */
+export interface Climate v3Options { tol?: number; maxIter?: number; }
+export interface Climate v3Result { values: number[]; converged: boolean; }
+export function computeClimate v3(data: number[], opts: Climate v3Options = {}): Climate v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeClimate v3 };
diff --git a/src/climate/volcanic.ts b/src/climate/volcanic.ts
new file mode 100644
index 00000000..890e767a
--- /dev/null
+++ b/src/climate/volcanic.ts
@@ -0,0 +1,22 @@
+/** Volcanic module — tsb analytics library. */
+
+/** Options for Volcanic. */
+export interface VolcanicOptions { tol?: number; maxIter?: number; }
+
+/** Result from Volcanic. */
+export interface VolcanicResult { values: number[]; converged: boolean; }
+
+/** Compute Volcanic. */
+export function computeVolcanic(data: number[], opts: VolcanicOptions = {}): VolcanicResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeVolcanic };
diff --git a/src/climate/wasm.ts b/src/climate/wasm.ts
new file mode 100644
index 00000000..48a8af3d
--- /dev/null
+++ b/src/climate/wasm.ts
@@ -0,0 +1,15 @@
+/** Climate Wasm module — tsb analytics library. */
+export interface Climate wasmOptions { tol?: number; maxIter?: number; }
+export interface Climate wasmResult { values: number[]; converged: boolean; }
+export function computeClimate wasm(data: number[], opts: Climate wasmOptions = {}): Climate wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeClimate wasm };
diff --git a/src/climate/wind.ts b/src/climate/wind.ts
new file mode 100644
index 00000000..1b1abca4
--- /dev/null
+++ b/src/climate/wind.ts
@@ -0,0 +1,22 @@
+/** Wind module — tsb analytics library. */
+
+/** Options for Wind. */
+export interface WindOptions { tol?: number; maxIter?: number; }
+
+/** Result from Wind. */
+export interface WindResult { values: number[]; converged: boolean; }
+
+/** Compute Wind. */
+export function computeWind(data: number[], opts: WindOptions = {}): WindResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeWind };
diff --git a/src/climate/xlarge.ts b/src/climate/xlarge.ts
new file mode 100644
index 00000000..33f64b08
--- /dev/null
+++ b/src/climate/xlarge.ts
@@ -0,0 +1,15 @@
+/** Climate Xlarge module — tsb analytics library. */
+export interface Climate xlargeOptions { tol?: number; maxIter?: number; }
+export interface Climate xlargeResult { values: number[]; converged: boolean; }
+export function computeClimate xlarge(data: number[], opts: Climate xlargeOptions = {}): Climate xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeClimate xlarge };
diff --git a/src/core/frame.ts b/src/core/frame.ts
index e21c341e..5e13d577 100644
--- a/src/core/frame.ts
+++ b/src/core/frame.ts
@@ -626,7 +626,7 @@ export class DataFrame {
for (let i = 0; i < nRows; i++) {
const row: Record
= {};
if (index) {
- row["Index"] = this.index.at(i) as Scalar;
+ row.Index = this.index.at(i) as Scalar;
}
for (const name of colNames) {
row[name] = this.col(name).iat(i);
@@ -833,9 +833,7 @@ function isIndexLike(v: unknown): v is Index {
}
const rec = v as Record;
return (
- typeof rec["size"] === "number" &&
- typeof rec["at"] === "function" &&
- typeof rec["getLoc"] === "function"
+ typeof rec.size === "number" && typeof rec.at === "function" && typeof rec.getLoc === "function"
);
}
diff --git a/src/core/options.ts b/src/core/options.ts
index 628b5cce..188c2188 100644
--- a/src/core/options.ts
+++ b/src/core/options.ts
@@ -210,7 +210,7 @@ function _makeProxy(prefix: string): OptionsProxy {
// If there is a direct match, return its value
const k = _normalizeKey(key);
if (_registry.has(k)) {
- return _registry.get(k)!.currentValue;
+ return _registry.get(k)?.currentValue;
}
// Otherwise return a nested proxy for deeper access
return _makeProxy(k);
diff --git a/src/core/series.ts b/src/core/series.ts
index c34d2569..c1ad5ab5 100644
--- a/src/core/series.ts
+++ b/src/core/series.ts
@@ -1559,9 +1559,7 @@ function isIndexLike(v: unknown): v is Index {
}
const rec = v as Record;
return (
- typeof rec["size"] === "number" &&
- typeof rec["at"] === "function" &&
- typeof rec["getLoc"] === "function"
+ typeof rec.size === "number" && typeof rec.at === "function" && typeof rec.getLoc === "function"
);
}
diff --git a/src/cv/action.ts b/src/cv/action.ts
new file mode 100644
index 00000000..bc6ac57b
--- /dev/null
+++ b/src/cv/action.ts
@@ -0,0 +1,22 @@
+/** Action module — tsb analytics library. */
+
+/** Options for Action. */
+export interface ActionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Action. */
+export interface ActionResult { values: number[]; converged: boolean; }
+
+/** Compute Action. */
+export function computeAction(data: number[], opts: ActionOptions = {}): ActionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAction };
diff --git a/src/cv/advanced.ts b/src/cv/advanced.ts
new file mode 100644
index 00000000..c1057670
--- /dev/null
+++ b/src/cv/advanced.ts
@@ -0,0 +1,15 @@
+/** Cv Advanced module — tsb analytics library. */
+export interface Cv advancedOptions { tol?: number; maxIter?: number; }
+export interface Cv advancedResult { values: number[]; converged: boolean; }
+export function computeCv advanced(data: number[], opts: Cv advancedOptions = {}): Cv advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCv advanced };
diff --git a/src/cv/augmentation.ts b/src/cv/augmentation.ts
new file mode 100644
index 00000000..26e41eb8
--- /dev/null
+++ b/src/cv/augmentation.ts
@@ -0,0 +1,22 @@
+/** Augmentation module — tsb analytics library. */
+
+/** Options for Augmentation. */
+export interface AugmentationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Augmentation. */
+export interface AugmentationResult { values: number[]; converged: boolean; }
+
+/** Compute Augmentation. */
+export function computeAugmentation(data: number[], opts: AugmentationOptions = {}): AugmentationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAugmentation };
diff --git a/src/cv/base2.ts b/src/cv/base2.ts
new file mode 100644
index 00000000..7bfc9e0b
--- /dev/null
+++ b/src/cv/base2.ts
@@ -0,0 +1,15 @@
+/** Cv Base2 module — tsb analytics library. */
+export interface Cv base2Options { tol?: number; maxIter?: number; }
+export interface Cv base2Result { values: number[]; converged: boolean; }
+export function computeCv base2(data: number[], opts: Cv base2Options = {}): Cv base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCv base2 };
diff --git a/src/cv/batch.ts b/src/cv/batch.ts
new file mode 100644
index 00000000..9354ab3c
--- /dev/null
+++ b/src/cv/batch.ts
@@ -0,0 +1,15 @@
+/** Cv Batch module — tsb analytics library. */
+export interface Cv batchOptions { tol?: number; maxIter?: number; }
+export interface Cv batchResult { values: number[]; converged: boolean; }
+export function computeCv batch(data: number[], opts: Cv batchOptions = {}): Cv batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCv batch };
diff --git a/src/cv/beta.ts b/src/cv/beta.ts
new file mode 100644
index 00000000..2772222f
--- /dev/null
+++ b/src/cv/beta.ts
@@ -0,0 +1,15 @@
+/** Cv Beta module — tsb analytics library. */
+export interface Cv betaOptions { tol?: number; maxIter?: number; }
+export interface Cv betaResult { values: number[]; converged: boolean; }
+export function computeCv beta(data: number[], opts: Cv betaOptions = {}): Cv betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCv beta };
diff --git a/src/cv/captioning.ts b/src/cv/captioning.ts
new file mode 100644
index 00000000..9f477944
--- /dev/null
+++ b/src/cv/captioning.ts
@@ -0,0 +1,22 @@
+/** Captioning module — tsb analytics library. */
+
+/** Options for Captioning. */
+export interface CaptioningOptions { tol?: number; maxIter?: number; }
+
+/** Result from Captioning. */
+export interface CaptioningResult { values: number[]; converged: boolean; }
+
+/** Compute Captioning. */
+export function computeCaptioning(data: number[], opts: CaptioningOptions = {}): CaptioningResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCaptioning };
diff --git a/src/cv/classification.ts b/src/cv/classification.ts
new file mode 100644
index 00000000..4dd329db
--- /dev/null
+++ b/src/cv/classification.ts
@@ -0,0 +1,22 @@
+/** Classification module — tsb analytics library. */
+
+/** Options for Classification. */
+export interface ClassificationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Classification. */
+export interface ClassificationResult { values: number[]; converged: boolean; }
+
+/** Compute Classification. */
+export function computeClassification(data: number[], opts: ClassificationOptions = {}): ClassificationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeClassification };
diff --git a/src/cv/colorization.ts b/src/cv/colorization.ts
new file mode 100644
index 00000000..2bf1723a
--- /dev/null
+++ b/src/cv/colorization.ts
@@ -0,0 +1,22 @@
+/** Colorization module — tsb analytics library. */
+
+/** Options for Colorization. */
+export interface ColorizationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Colorization. */
+export interface ColorizationResult { values: number[]; converged: boolean; }
+
+/** Compute Colorization. */
+export function computeColorization(data: number[], opts: ColorizationOptions = {}): ColorizationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeColorization };
diff --git a/src/cv/convolution.ts b/src/cv/convolution.ts
new file mode 100644
index 00000000..570e9ffb
--- /dev/null
+++ b/src/cv/convolution.ts
@@ -0,0 +1,22 @@
+/** Convolution module — tsb analytics library. */
+
+/** Options for Convolution. */
+export interface ConvolutionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Convolution. */
+export interface ConvolutionResult { values: number[]; converged: boolean; }
+
+/** Compute Convolution. */
+export function computeConvolution(data: number[], opts: ConvolutionOptions = {}): ConvolutionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeConvolution };
diff --git a/src/cv/cpu.ts b/src/cv/cpu.ts
new file mode 100644
index 00000000..b9fe440e
--- /dev/null
+++ b/src/cv/cpu.ts
@@ -0,0 +1,15 @@
+/** Cv Cpu module — tsb analytics library. */
+export interface Cv cpuOptions { tol?: number; maxIter?: number; }
+export interface Cv cpuResult { values: number[]; converged: boolean; }
+export function computeCv cpu(data: number[], opts: Cv cpuOptions = {}): Cv cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCv cpu };
diff --git a/src/cv/denoising.ts b/src/cv/denoising.ts
new file mode 100644
index 00000000..67865bc2
--- /dev/null
+++ b/src/cv/denoising.ts
@@ -0,0 +1,22 @@
+/** Denoising module — tsb analytics library. */
+
+/** Options for Denoising. */
+export interface DenoisingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Denoising. */
+export interface DenoisingResult { values: number[]; converged: boolean; }
+
+/** Compute Denoising. */
+export function computeDenoising(data: number[], opts: DenoisingOptions = {}): DenoisingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDenoising };
diff --git a/src/cv/dense.ts b/src/cv/dense.ts
new file mode 100644
index 00000000..9185d467
--- /dev/null
+++ b/src/cv/dense.ts
@@ -0,0 +1,15 @@
+/** Cv Dense module — tsb analytics library. */
+export interface Cv denseOptions { tol?: number; maxIter?: number; }
+export interface Cv denseResult { values: number[]; converged: boolean; }
+export function computeCv dense(data: number[], opts: Cv denseOptions = {}): Cv denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCv dense };
diff --git a/src/cv/depth.ts b/src/cv/depth.ts
new file mode 100644
index 00000000..49de3ab3
--- /dev/null
+++ b/src/cv/depth.ts
@@ -0,0 +1,22 @@
+/** Depth module — tsb analytics library. */
+
+/** Options for Depth. */
+export interface DepthOptions { tol?: number; maxIter?: number; }
+
+/** Result from Depth. */
+export interface DepthResult { values: number[]; converged: boolean; }
+
+/** Compute Depth. */
+export function computeDepth(data: number[], opts: DepthOptions = {}): DepthResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDepth };
diff --git a/src/cv/detection.ts b/src/cv/detection.ts
new file mode 100644
index 00000000..63e15e44
--- /dev/null
+++ b/src/cv/detection.ts
@@ -0,0 +1,22 @@
+/** Detection module — tsb analytics library. */
+
+/** Options for Detection. */
+export interface DetectionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Detection. */
+export interface DetectionResult { values: number[]; converged: boolean; }
+
+/** Compute Detection. */
+export function computeDetection(data: number[], opts: DetectionOptions = {}): DetectionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDetection };
diff --git a/src/cv/distributed.ts b/src/cv/distributed.ts
new file mode 100644
index 00000000..21bf9eb5
--- /dev/null
+++ b/src/cv/distributed.ts
@@ -0,0 +1,15 @@
+/** Cv Distributed module — tsb analytics library. */
+export interface Cv distributedOptions { tol?: number; maxIter?: number; }
+export interface Cv distributedResult { values: number[]; converged: boolean; }
+export function computeCv distributed(data: number[], opts: Cv distributedOptions = {}): Cv distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCv distributed };
diff --git a/src/cv/enhancement.ts b/src/cv/enhancement.ts
new file mode 100644
index 00000000..1326017e
--- /dev/null
+++ b/src/cv/enhancement.ts
@@ -0,0 +1,22 @@
+/** Enhancement module — tsb analytics library. */
+
+/** Options for Enhancement. */
+export interface EnhancementOptions { tol?: number; maxIter?: number; }
+
+/** Result from Enhancement. */
+export interface EnhancementResult { values: number[]; converged: boolean; }
+
+/** Compute Enhancement. */
+export function computeEnhancement(data: number[], opts: EnhancementOptions = {}): EnhancementResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEnhancement };
diff --git a/src/cv/experimental.ts b/src/cv/experimental.ts
new file mode 100644
index 00000000..64a90683
--- /dev/null
+++ b/src/cv/experimental.ts
@@ -0,0 +1,15 @@
+/** Cv Experimental module — tsb analytics library. */
+export interface Cv experimentalOptions { tol?: number; maxIter?: number; }
+export interface Cv experimentalResult { values: number[]; converged: boolean; }
+export function computeCv experimental(data: number[], opts: Cv experimentalOptions = {}): Cv experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCv experimental };
diff --git a/src/cv/fast.ts b/src/cv/fast.ts
new file mode 100644
index 00000000..8d0bf64c
--- /dev/null
+++ b/src/cv/fast.ts
@@ -0,0 +1,15 @@
+/** Cv Fast module — tsb analytics library. */
+export interface Cv fastOptions { tol?: number; maxIter?: number; }
+export interface Cv fastResult { values: number[]; converged: boolean; }
+export function computeCv fast(data: number[], opts: Cv fastOptions = {}): Cv fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCv fast };
diff --git a/src/cv/future.ts b/src/cv/future.ts
new file mode 100644
index 00000000..4544db0b
--- /dev/null
+++ b/src/cv/future.ts
@@ -0,0 +1,15 @@
+/** Cv Future module — tsb analytics library. */
+export interface Cv futureOptions { tol?: number; maxIter?: number; }
+export interface Cv futureResult { values: number[]; converged: boolean; }
+export function computeCv future(data: number[], opts: Cv futureOptions = {}): Cv futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCv future };
diff --git a/src/cv/gan.ts b/src/cv/gan.ts
new file mode 100644
index 00000000..17be4da8
--- /dev/null
+++ b/src/cv/gan.ts
@@ -0,0 +1,22 @@
+/** Gan module — tsb analytics library. */
+
+/** Options for Gan. */
+export interface GanOptions { tol?: number; maxIter?: number; }
+
+/** Result from Gan. */
+export interface GanResult { values: number[]; converged: boolean; }
+
+/** Compute Gan. */
+export function computeGan(data: number[], opts: GanOptions = {}): GanResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGan };
diff --git a/src/cv/gpu.ts b/src/cv/gpu.ts
new file mode 100644
index 00000000..3aab7e73
--- /dev/null
+++ b/src/cv/gpu.ts
@@ -0,0 +1,15 @@
+/** Cv Gpu module — tsb analytics library. */
+export interface Cv gpuOptions { tol?: number; maxIter?: number; }
+export interface Cv gpuResult { values: number[]; converged: boolean; }
+export function computeCv gpu(data: number[], opts: Cv gpuOptions = {}): Cv gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCv gpu };
diff --git a/src/cv/inpainting.ts b/src/cv/inpainting.ts
new file mode 100644
index 00000000..99b683d2
--- /dev/null
+++ b/src/cv/inpainting.ts
@@ -0,0 +1,22 @@
+/** Inpainting module — tsb analytics library. */
+
+/** Options for Inpainting. */
+export interface InpaintingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Inpainting. */
+export interface InpaintingResult { values: number[]; converged: boolean; }
+
+/** Compute Inpainting. */
+export function computeInpainting(data: number[], opts: InpaintingOptions = {}): InpaintingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeInpainting };
diff --git a/src/cv/keypoints.ts b/src/cv/keypoints.ts
new file mode 100644
index 00000000..64272d5d
--- /dev/null
+++ b/src/cv/keypoints.ts
@@ -0,0 +1,22 @@
+/** Keypoints module — tsb analytics library. */
+
+/** Options for Keypoints. */
+export interface KeypointsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Keypoints. */
+export interface KeypointsResult { values: number[]; converged: boolean; }
+
+/** Compute Keypoints. */
+export function computeKeypoints(data: number[], opts: KeypointsOptions = {}): KeypointsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeKeypoints };
diff --git a/src/cv/large.ts b/src/cv/large.ts
new file mode 100644
index 00000000..28e99e86
--- /dev/null
+++ b/src/cv/large.ts
@@ -0,0 +1,15 @@
+/** Cv Large module — tsb analytics library. */
+export interface Cv largeOptions { tol?: number; maxIter?: number; }
+export interface Cv largeResult { values: number[]; converged: boolean; }
+export function computeCv large(data: number[], opts: Cv largeOptions = {}): Cv largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCv large };
diff --git a/src/cv/legacy.ts b/src/cv/legacy.ts
new file mode 100644
index 00000000..474bfeb2
--- /dev/null
+++ b/src/cv/legacy.ts
@@ -0,0 +1,15 @@
+/** Cv Legacy module — tsb analytics library. */
+export interface Cv legacyOptions { tol?: number; maxIter?: number; }
+export interface Cv legacyResult { values: number[]; converged: boolean; }
+export function computeCv legacy(data: number[], opts: Cv legacyOptions = {}): Cv legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCv legacy };
diff --git a/src/cv/lite.ts b/src/cv/lite.ts
new file mode 100644
index 00000000..72612124
--- /dev/null
+++ b/src/cv/lite.ts
@@ -0,0 +1,15 @@
+/** Cv Lite module — tsb analytics library. */
+export interface Cv liteOptions { tol?: number; maxIter?: number; }
+export interface Cv liteResult { values: number[]; converged: boolean; }
+export function computeCv lite(data: number[], opts: Cv liteOptions = {}): Cv liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCv lite };
diff --git a/src/cv/matting.ts b/src/cv/matting.ts
new file mode 100644
index 00000000..bacdae41
--- /dev/null
+++ b/src/cv/matting.ts
@@ -0,0 +1,22 @@
+/** Matting module — tsb analytics library. */
+
+/** Options for Matting. */
+export interface MattingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Matting. */
+export interface MattingResult { values: number[]; converged: boolean; }
+
+/** Compute Matting. */
+export function computeMatting(data: number[], opts: MattingOptions = {}): MattingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMatting };
diff --git a/src/cv/mini.ts b/src/cv/mini.ts
new file mode 100644
index 00000000..3403b58d
--- /dev/null
+++ b/src/cv/mini.ts
@@ -0,0 +1,15 @@
+/** Cv Mini module — tsb analytics library. */
+export interface Cv miniOptions { tol?: number; maxIter?: number; }
+export interface Cv miniResult { values: number[]; converged: boolean; }
+export function computeCv mini(data: number[], opts: Cv miniOptions = {}): Cv miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCv mini };
diff --git a/src/cv/next.ts b/src/cv/next.ts
new file mode 100644
index 00000000..5e4eaa48
--- /dev/null
+++ b/src/cv/next.ts
@@ -0,0 +1,15 @@
+/** Cv Next module — tsb analytics library. */
+export interface Cv nextOptions { tol?: number; maxIter?: number; }
+export interface Cv nextResult { values: number[]; converged: boolean; }
+export function computeCv next(data: number[], opts: Cv nextOptions = {}): Cv nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCv next };
diff --git a/src/cv/normalization.ts b/src/cv/normalization.ts
new file mode 100644
index 00000000..847a1696
--- /dev/null
+++ b/src/cv/normalization.ts
@@ -0,0 +1,22 @@
+/** Normalization module — tsb analytics library. */
+
+/** Options for Normalization. */
+export interface NormalizationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Normalization. */
+export interface NormalizationResult { values: number[]; converged: boolean; }
+
+/** Compute Normalization. */
+export function computeNormalization(data: number[], opts: NormalizationOptions = {}): NormalizationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNormalization };
diff --git a/src/cv/ocr.ts b/src/cv/ocr.ts
new file mode 100644
index 00000000..dab443fa
--- /dev/null
+++ b/src/cv/ocr.ts
@@ -0,0 +1,22 @@
+/** Ocr module — tsb analytics library. */
+
+/** Options for Ocr. */
+export interface OcrOptions { tol?: number; maxIter?: number; }
+
+/** Result from Ocr. */
+export interface OcrResult { values: number[]; converged: boolean; }
+
+/** Compute Ocr. */
+export function computeOcr(data: number[], opts: OcrOptions = {}): OcrResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeOcr };
diff --git a/src/cv/online.ts b/src/cv/online.ts
new file mode 100644
index 00000000..c23bc69c
--- /dev/null
+++ b/src/cv/online.ts
@@ -0,0 +1,15 @@
+/** Cv Online module — tsb analytics library. */
+export interface Cv onlineOptions { tol?: number; maxIter?: number; }
+export interface Cv onlineResult { values: number[]; converged: boolean; }
+export function computeCv online(data: number[], opts: Cv onlineOptions = {}): Cv onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCv online };
diff --git a/src/cv/optical_flow.ts b/src/cv/optical_flow.ts
new file mode 100644
index 00000000..96a0bb73
--- /dev/null
+++ b/src/cv/optical_flow.ts
@@ -0,0 +1,22 @@
+/** Optical Flow module — tsb analytics library. */
+
+/** Options for Optical Flow. */
+export interface OpticalFlowOptions { tol?: number; maxIter?: number; }
+
+/** Result from Optical Flow. */
+export interface OpticalFlowResult { values: number[]; converged: boolean; }
+
+/** Compute Optical Flow. */
+export function computeOpticalFlow(data: number[], opts: OpticalFlowOptions = {}): OpticalFlowResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeOpticalFlow };
diff --git a/src/cv/parallel.ts b/src/cv/parallel.ts
new file mode 100644
index 00000000..542d7141
--- /dev/null
+++ b/src/cv/parallel.ts
@@ -0,0 +1,15 @@
+/** Cv Parallel module — tsb analytics library. */
+export interface Cv parallelOptions { tol?: number; maxIter?: number; }
+export interface Cv parallelResult { values: number[]; converged: boolean; }
+export function computeCv parallel(data: number[], opts: Cv parallelOptions = {}): Cv parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCv parallel };
diff --git a/src/cv/plus.ts b/src/cv/plus.ts
new file mode 100644
index 00000000..bf2110c9
--- /dev/null
+++ b/src/cv/plus.ts
@@ -0,0 +1,15 @@
+/** Cv Plus module — tsb analytics library. */
+export interface Cv plusOptions { tol?: number; maxIter?: number; }
+export interface Cv plusResult { values: number[]; converged: boolean; }
+export function computeCv plus(data: number[], opts: Cv plusOptions = {}): Cv plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCv plus };
diff --git a/src/cv/pooling.ts b/src/cv/pooling.ts
new file mode 100644
index 00000000..99a42360
--- /dev/null
+++ b/src/cv/pooling.ts
@@ -0,0 +1,22 @@
+/** Pooling module — tsb analytics library. */
+
+/** Options for Pooling. */
+export interface PoolingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Pooling. */
+export interface PoolingResult { values: number[]; converged: boolean; }
+
+/** Compute Pooling. */
+export function computePooling(data: number[], opts: PoolingOptions = {}): PoolingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePooling };
diff --git a/src/cv/pose.ts b/src/cv/pose.ts
new file mode 100644
index 00000000..f7828251
--- /dev/null
+++ b/src/cv/pose.ts
@@ -0,0 +1,22 @@
+/** Pose module — tsb analytics library. */
+
+/** Options for Pose. */
+export interface PoseOptions { tol?: number; maxIter?: number; }
+
+/** Result from Pose. */
+export interface PoseResult { values: number[]; converged: boolean; }
+
+/** Compute Pose. */
+export function computePose(data: number[], opts: PoseOptions = {}): PoseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePose };
diff --git a/src/cv/pro.ts b/src/cv/pro.ts
new file mode 100644
index 00000000..87d12718
--- /dev/null
+++ b/src/cv/pro.ts
@@ -0,0 +1,15 @@
+/** Cv Pro module — tsb analytics library. */
+export interface Cv proOptions { tol?: number; maxIter?: number; }
+export interface Cv proResult { values: number[]; converged: boolean; }
+export function computeCv pro(data: number[], opts: Cv proOptions = {}): Cv proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCv pro };
diff --git a/src/cv/recognition.ts b/src/cv/recognition.ts
new file mode 100644
index 00000000..c6e5cd2d
--- /dev/null
+++ b/src/cv/recognition.ts
@@ -0,0 +1,22 @@
+/** Recognition module — tsb analytics library. */
+
+/** Options for Recognition. */
+export interface RecognitionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Recognition. */
+export interface RecognitionResult { values: number[]; converged: boolean; }
+
+/** Compute Recognition. */
+export function computeRecognition(data: number[], opts: RecognitionOptions = {}): RecognitionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRecognition };
diff --git a/src/cv/robust.ts b/src/cv/robust.ts
new file mode 100644
index 00000000..ad76fc67
--- /dev/null
+++ b/src/cv/robust.ts
@@ -0,0 +1,15 @@
+/** Cv Robust module — tsb analytics library. */
+export interface Cv robustOptions { tol?: number; maxIter?: number; }
+export interface Cv robustResult { values: number[]; converged: boolean; }
+export function computeCv robust(data: number[], opts: Cv robustOptions = {}): Cv robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCv robust };
diff --git a/src/cv/scene.ts b/src/cv/scene.ts
new file mode 100644
index 00000000..70118b8a
--- /dev/null
+++ b/src/cv/scene.ts
@@ -0,0 +1,22 @@
+/** Scene module — tsb analytics library. */
+
+/** Options for Scene. */
+export interface SceneOptions { tol?: number; maxIter?: number; }
+
+/** Result from Scene. */
+export interface SceneResult { values: number[]; converged: boolean; }
+
+/** Compute Scene. */
+export function computeScene(data: number[], opts: SceneOptions = {}): SceneResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeScene };
diff --git a/src/cv/segmentation.ts b/src/cv/segmentation.ts
new file mode 100644
index 00000000..c74e19ce
--- /dev/null
+++ b/src/cv/segmentation.ts
@@ -0,0 +1,22 @@
+/** Segmentation module — tsb analytics library. */
+
+/** Options for Segmentation. */
+export interface SegmentationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Segmentation. */
+export interface SegmentationResult { values: number[]; converged: boolean; }
+
+/** Compute Segmentation. */
+export function computeSegmentation(data: number[], opts: SegmentationOptions = {}): SegmentationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSegmentation };
diff --git a/src/cv/small.ts b/src/cv/small.ts
new file mode 100644
index 00000000..ee5c5d1f
--- /dev/null
+++ b/src/cv/small.ts
@@ -0,0 +1,15 @@
+/** Cv Small module — tsb analytics library. */
+export interface Cv smallOptions { tol?: number; maxIter?: number; }
+export interface Cv smallResult { values: number[]; converged: boolean; }
+export function computeCv small(data: number[], opts: Cv smallOptions = {}): Cv smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCv small };
diff --git a/src/cv/sparse.ts b/src/cv/sparse.ts
new file mode 100644
index 00000000..2337b37f
--- /dev/null
+++ b/src/cv/sparse.ts
@@ -0,0 +1,15 @@
+/** Cv Sparse module — tsb analytics library. */
+export interface Cv sparseOptions { tol?: number; maxIter?: number; }
+export interface Cv sparseResult { values: number[]; converged: boolean; }
+export function computeCv sparse(data: number[], opts: Cv sparseOptions = {}): Cv sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCv sparse };
diff --git a/src/cv/stable.ts b/src/cv/stable.ts
new file mode 100644
index 00000000..98297567
--- /dev/null
+++ b/src/cv/stable.ts
@@ -0,0 +1,15 @@
+/** Cv Stable module — tsb analytics library. */
+export interface Cv stableOptions { tol?: number; maxIter?: number; }
+export interface Cv stableResult { values: number[]; converged: boolean; }
+export function computeCv stable(data: number[], opts: Cv stableOptions = {}): Cv stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCv stable };
diff --git a/src/cv/stereo.ts b/src/cv/stereo.ts
new file mode 100644
index 00000000..ba22328c
--- /dev/null
+++ b/src/cv/stereo.ts
@@ -0,0 +1,22 @@
+/** Stereo module — tsb analytics library. */
+
+/** Options for Stereo. */
+export interface StereoOptions { tol?: number; maxIter?: number; }
+
+/** Result from Stereo. */
+export interface StereoResult { values: number[]; converged: boolean; }
+
+/** Compute Stereo. */
+export function computeStereo(data: number[], opts: StereoOptions = {}): StereoResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeStereo };
diff --git a/src/cv/streaming.ts b/src/cv/streaming.ts
new file mode 100644
index 00000000..b78e210a
--- /dev/null
+++ b/src/cv/streaming.ts
@@ -0,0 +1,15 @@
+/** Cv Streaming module — tsb analytics library. */
+export interface Cv streamingOptions { tol?: number; maxIter?: number; }
+export interface Cv streamingResult { values: number[]; converged: boolean; }
+export function computeCv streaming(data: number[], opts: Cv streamingOptions = {}): Cv streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCv streaming };
diff --git a/src/cv/style_transfer.ts b/src/cv/style_transfer.ts
new file mode 100644
index 00000000..11a2961b
--- /dev/null
+++ b/src/cv/style_transfer.ts
@@ -0,0 +1,22 @@
+/** Style Transfer module — tsb analytics library. */
+
+/** Options for Style Transfer. */
+export interface StyleTransferOptions { tol?: number; maxIter?: number; }
+
+/** Result from Style Transfer. */
+export interface StyleTransferResult { values: number[]; converged: boolean; }
+
+/** Compute Style Transfer. */
+export function computeStyleTransfer(data: number[], opts: StyleTransferOptions = {}): StyleTransferResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeStyleTransfer };
diff --git a/src/cv/super_resolution.ts b/src/cv/super_resolution.ts
new file mode 100644
index 00000000..8ca86122
--- /dev/null
+++ b/src/cv/super_resolution.ts
@@ -0,0 +1,22 @@
+/** Super Resolution module — tsb analytics library. */
+
+/** Options for Super Resolution. */
+export interface SuperResolutionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Super Resolution. */
+export interface SuperResolutionResult { values: number[]; converged: boolean; }
+
+/** Compute Super Resolution. */
+export function computeSuperResolution(data: number[], opts: SuperResolutionOptions = {}): SuperResolutionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSuperResolution };
diff --git a/src/cv/tracking.ts b/src/cv/tracking.ts
new file mode 100644
index 00000000..2e7304b5
--- /dev/null
+++ b/src/cv/tracking.ts
@@ -0,0 +1,22 @@
+/** Tracking module — tsb analytics library. */
+
+/** Options for Tracking. */
+export interface TrackingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Tracking. */
+export interface TrackingResult { values: number[]; converged: boolean; }
+
+/** Compute Tracking. */
+export function computeTracking(data: number[], opts: TrackingOptions = {}): TrackingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTracking };
diff --git a/src/cv/v2.ts b/src/cv/v2.ts
new file mode 100644
index 00000000..2d5f7208
--- /dev/null
+++ b/src/cv/v2.ts
@@ -0,0 +1,15 @@
+/** Cv V2 module — tsb analytics library. */
+export interface Cv v2Options { tol?: number; maxIter?: number; }
+export interface Cv v2Result { values: number[]; converged: boolean; }
+export function computeCv v2(data: number[], opts: Cv v2Options = {}): Cv v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCv v2 };
diff --git a/src/cv/v3.ts b/src/cv/v3.ts
new file mode 100644
index 00000000..385b8196
--- /dev/null
+++ b/src/cv/v3.ts
@@ -0,0 +1,15 @@
+/** Cv V3 module — tsb analytics library. */
+export interface Cv v3Options { tol?: number; maxIter?: number; }
+export interface Cv v3Result { values: number[]; converged: boolean; }
+export function computeCv v3(data: number[], opts: Cv v3Options = {}): Cv v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCv v3 };
diff --git a/src/cv/vae.ts b/src/cv/vae.ts
new file mode 100644
index 00000000..c763968d
--- /dev/null
+++ b/src/cv/vae.ts
@@ -0,0 +1,22 @@
+/** Vae module — tsb analytics library. */
+
+/** Options for Vae. */
+export interface VaeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Vae. */
+export interface VaeResult { values: number[]; converged: boolean; }
+
+/** Compute Vae. */
+export function computeVae(data: number[], opts: VaeOptions = {}): VaeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeVae };
diff --git a/src/cv/verification.ts b/src/cv/verification.ts
new file mode 100644
index 00000000..327fedec
--- /dev/null
+++ b/src/cv/verification.ts
@@ -0,0 +1,22 @@
+/** Verification module — tsb analytics library. */
+
+/** Options for Verification. */
+export interface VerificationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Verification. */
+export interface VerificationResult { values: number[]; converged: boolean; }
+
+/** Compute Verification. */
+export function computeVerification(data: number[], opts: VerificationOptions = {}): VerificationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeVerification };
diff --git a/src/cv/vqa.ts b/src/cv/vqa.ts
new file mode 100644
index 00000000..5ce2b61d
--- /dev/null
+++ b/src/cv/vqa.ts
@@ -0,0 +1,22 @@
+/** Vqa module — tsb analytics library. */
+
+/** Options for Vqa. */
+export interface VqaOptions { tol?: number; maxIter?: number; }
+
+/** Result from Vqa. */
+export interface VqaResult { values: number[]; converged: boolean; }
+
+/** Compute Vqa. */
+export function computeVqa(data: number[], opts: VqaOptions = {}): VqaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeVqa };
diff --git a/src/cv/wasm.ts b/src/cv/wasm.ts
new file mode 100644
index 00000000..f0599abd
--- /dev/null
+++ b/src/cv/wasm.ts
@@ -0,0 +1,15 @@
+/** Cv Wasm module — tsb analytics library. */
+export interface Cv wasmOptions { tol?: number; maxIter?: number; }
+export interface Cv wasmResult { values: number[]; converged: boolean; }
+export function computeCv wasm(data: number[], opts: Cv wasmOptions = {}): Cv wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCv wasm };
diff --git a/src/cv/xlarge.ts b/src/cv/xlarge.ts
new file mode 100644
index 00000000..ecf72bba
--- /dev/null
+++ b/src/cv/xlarge.ts
@@ -0,0 +1,15 @@
+/** Cv Xlarge module — tsb analytics library. */
+export interface Cv xlargeOptions { tol?: number; maxIter?: number; }
+export interface Cv xlargeResult { values: number[]; converged: boolean; }
+export function computeCv xlarge(data: number[], opts: Cv xlargeOptions = {}): Cv xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeCv xlarge };
diff --git a/src/demography/advanced.ts b/src/demography/advanced.ts
new file mode 100644
index 00000000..fb2c79b9
--- /dev/null
+++ b/src/demography/advanced.ts
@@ -0,0 +1,15 @@
+/** Demography Advanced module — tsb analytics library. */
+export interface Demography advancedOptions { tol?: number; maxIter?: number; }
+export interface Demography advancedResult { values: number[]; converged: boolean; }
+export function computeDemography advanced(data: number[], opts: Demography advancedOptions = {}): Demography advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeDemography advanced };
diff --git a/src/demography/aging.ts b/src/demography/aging.ts
new file mode 100644
index 00000000..46c1489b
--- /dev/null
+++ b/src/demography/aging.ts
@@ -0,0 +1,22 @@
+/** Aging module — tsb analytics library. */
+
+/** Options for Aging. */
+export interface AgingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Aging. */
+export interface AgingResult { values: number[]; converged: boolean; }
+
+/** Compute Aging. */
+export function computeAging(data: number[], opts: AgingOptions = {}): AgingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAging };
diff --git a/src/demography/asylum.ts b/src/demography/asylum.ts
new file mode 100644
index 00000000..0de715a4
--- /dev/null
+++ b/src/demography/asylum.ts
@@ -0,0 +1,22 @@
+/** Asylum module — tsb analytics library. */
+
+/** Options for Asylum. */
+export interface AsylumOptions { tol?: number; maxIter?: number; }
+
+/** Result from Asylum. */
+export interface AsylumResult { values: number[]; converged: boolean; }
+
+/** Compute Asylum. */
+export function computeAsylum(data: number[], opts: AsylumOptions = {}): AsylumResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAsylum };
diff --git a/src/demography/base2.ts b/src/demography/base2.ts
new file mode 100644
index 00000000..9a0ce16d
--- /dev/null
+++ b/src/demography/base2.ts
@@ -0,0 +1,15 @@
+/** Demography Base2 module — tsb analytics library. */
+export interface Demography base2Options { tol?: number; maxIter?: number; }
+export interface Demography base2Result { values: number[]; converged: boolean; }
+export function computeDemography base2(data: number[], opts: Demography base2Options = {}): Demography base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeDemography base2 };
diff --git a/src/demography/batch.ts b/src/demography/batch.ts
new file mode 100644
index 00000000..e864e778
--- /dev/null
+++ b/src/demography/batch.ts
@@ -0,0 +1,15 @@
+/** Demography Batch module — tsb analytics library. */
+export interface Demography batchOptions { tol?: number; maxIter?: number; }
+export interface Demography batchResult { values: number[]; converged: boolean; }
+export function computeDemography batch(data: number[], opts: Demography batchOptions = {}): Demography batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeDemography batch };
diff --git a/src/demography/beta.ts b/src/demography/beta.ts
new file mode 100644
index 00000000..ba7c2191
--- /dev/null
+++ b/src/demography/beta.ts
@@ -0,0 +1,15 @@
+/** Demography Beta module — tsb analytics library. */
+export interface Demography betaOptions { tol?: number; maxIter?: number; }
+export interface Demography betaResult { values: number[]; converged: boolean; }
+export function computeDemography beta(data: number[], opts: Demography betaOptions = {}): Demography betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeDemography beta };
diff --git a/src/demography/brain_drain.ts b/src/demography/brain_drain.ts
new file mode 100644
index 00000000..4e8be9e2
--- /dev/null
+++ b/src/demography/brain_drain.ts
@@ -0,0 +1,22 @@
+/** Brain Drain module — tsb analytics library. */
+
+/** Options for Brain Drain. */
+export interface BrainDrainOptions { tol?: number; maxIter?: number; }
+
+/** Result from Brain Drain. */
+export interface BrainDrainResult { values: number[]; converged: boolean; }
+
+/** Compute Brain Drain. */
+export function computeBrainDrain(data: number[], opts: BrainDrainOptions = {}): BrainDrainResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBrainDrain };
diff --git a/src/demography/cohort_component.ts b/src/demography/cohort_component.ts
new file mode 100644
index 00000000..59b6f573
--- /dev/null
+++ b/src/demography/cohort_component.ts
@@ -0,0 +1,22 @@
+/** Cohort Component module — tsb analytics library. */
+
+/** Options for Cohort Component. */
+export interface CohortComponentOptions { tol?: number; maxIter?: number; }
+
+/** Result from Cohort Component. */
+export interface CohortComponentResult { values: number[]; converged: boolean; }
+
+/** Compute Cohort Component. */
+export function computeCohortComponent(data: number[], opts: CohortComponentOptions = {}): CohortComponentResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCohortComponent };
diff --git a/src/demography/cpu.ts b/src/demography/cpu.ts
new file mode 100644
index 00000000..c68f9bb4
--- /dev/null
+++ b/src/demography/cpu.ts
@@ -0,0 +1,15 @@
+/** Demography Cpu module — tsb analytics library. */
+export interface Demography cpuOptions { tol?: number; maxIter?: number; }
+export interface Demography cpuResult { values: number[]; converged: boolean; }
+export function computeDemography cpu(data: number[], opts: Demography cpuOptions = {}): Demography cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeDemography cpu };
diff --git a/src/demography/demographic_dividend.ts b/src/demography/demographic_dividend.ts
new file mode 100644
index 00000000..af459981
--- /dev/null
+++ b/src/demography/demographic_dividend.ts
@@ -0,0 +1,22 @@
+/** Demographic Dividend module — tsb analytics library. */
+
+/** Options for Demographic Dividend. */
+export interface DemographicDividendOptions { tol?: number; maxIter?: number; }
+
+/** Result from Demographic Dividend. */
+export interface DemographicDividendResult { values: number[]; converged: boolean; }
+
+/** Compute Demographic Dividend. */
+export function computeDemographicDividend(data: number[], opts: DemographicDividendOptions = {}): DemographicDividendResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDemographicDividend };
diff --git a/src/demography/dense.ts b/src/demography/dense.ts
new file mode 100644
index 00000000..2b05b9da
--- /dev/null
+++ b/src/demography/dense.ts
@@ -0,0 +1,15 @@
+/** Demography Dense module — tsb analytics library. */
+export interface Demography denseOptions { tol?: number; maxIter?: number; }
+export interface Demography denseResult { values: number[]; converged: boolean; }
+export function computeDemography dense(data: number[], opts: Demography denseOptions = {}): Demography denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeDemography dense };
diff --git a/src/demography/dependency_ratio.ts b/src/demography/dependency_ratio.ts
new file mode 100644
index 00000000..9e9bd799
--- /dev/null
+++ b/src/demography/dependency_ratio.ts
@@ -0,0 +1,22 @@
+/** Dependency Ratio module — tsb analytics library. */
+
+/** Options for Dependency Ratio. */
+export interface DependencyRatioOptions { tol?: number; maxIter?: number; }
+
+/** Result from Dependency Ratio. */
+export interface DependencyRatioResult { values: number[]; converged: boolean; }
+
+/** Compute Dependency Ratio. */
+export function computeDependencyRatio(data: number[], opts: DependencyRatioOptions = {}): DependencyRatioResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDependencyRatio };
diff --git a/src/demography/diaspora.ts b/src/demography/diaspora.ts
new file mode 100644
index 00000000..4db1535b
--- /dev/null
+++ b/src/demography/diaspora.ts
@@ -0,0 +1,22 @@
+/** Diaspora module — tsb analytics library. */
+
+/** Options for Diaspora. */
+export interface DiasporaOptions { tol?: number; maxIter?: number; }
+
+/** Result from Diaspora. */
+export interface DiasporaResult { values: number[]; converged: boolean; }
+
+/** Compute Diaspora. */
+export function computeDiaspora(data: number[], opts: DiasporaOptions = {}): DiasporaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDiaspora };
diff --git a/src/demography/distributed.ts b/src/demography/distributed.ts
new file mode 100644
index 00000000..f78164aa
--- /dev/null
+++ b/src/demography/distributed.ts
@@ -0,0 +1,15 @@
+/** Demography Distributed module — tsb analytics library. */
+export interface Demography distributedOptions { tol?: number; maxIter?: number; }
+export interface Demography distributedResult { values: number[]; converged: boolean; }
+export function computeDemography distributed(data: number[], opts: Demography distributedOptions = {}): Demography distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeDemography distributed };
diff --git a/src/demography/epidemiological_transition.ts b/src/demography/epidemiological_transition.ts
new file mode 100644
index 00000000..4f4568a6
--- /dev/null
+++ b/src/demography/epidemiological_transition.ts
@@ -0,0 +1,22 @@
+/** Epidemiological Transition module — tsb analytics library. */
+
+/** Options for Epidemiological Transition. */
+export interface EpidemiologicalTransitionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Epidemiological Transition. */
+export interface EpidemiologicalTransitionResult { values: number[]; converged: boolean; }
+
+/** Compute Epidemiological Transition. */
+export function computeEpidemiologicalTransition(data: number[], opts: EpidemiologicalTransitionOptions = {}): EpidemiologicalTransitionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEpidemiologicalTransition };
diff --git a/src/demography/experimental.ts b/src/demography/experimental.ts
new file mode 100644
index 00000000..19f858f4
--- /dev/null
+++ b/src/demography/experimental.ts
@@ -0,0 +1,15 @@
+/** Demography Experimental module — tsb analytics library. */
+export interface Demography experimentalOptions { tol?: number; maxIter?: number; }
+export interface Demography experimentalResult { values: number[]; converged: boolean; }
+export function computeDemography experimental(data: number[], opts: Demography experimentalOptions = {}): Demography experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeDemography experimental };
diff --git a/src/demography/fast.ts b/src/demography/fast.ts
new file mode 100644
index 00000000..0ada1b48
--- /dev/null
+++ b/src/demography/fast.ts
@@ -0,0 +1,15 @@
+/** Demography Fast module — tsb analytics library. */
+export interface Demography fastOptions { tol?: number; maxIter?: number; }
+export interface Demography fastResult { values: number[]; converged: boolean; }
+export function computeDemography fast(data: number[], opts: Demography fastOptions = {}): Demography fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeDemography fast };
diff --git a/src/demography/fertility.ts b/src/demography/fertility.ts
new file mode 100644
index 00000000..f642ffc5
--- /dev/null
+++ b/src/demography/fertility.ts
@@ -0,0 +1,22 @@
+/** Fertility module — tsb analytics library. */
+
+/** Options for Fertility. */
+export interface FertilityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Fertility. */
+export interface FertilityResult { values: number[]; converged: boolean; }
+
+/** Compute Fertility. */
+export function computeFertility(data: number[], opts: FertilityOptions = {}): FertilityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFertility };
diff --git a/src/demography/fertility_transition.ts b/src/demography/fertility_transition.ts
new file mode 100644
index 00000000..0f6f690f
--- /dev/null
+++ b/src/demography/fertility_transition.ts
@@ -0,0 +1,22 @@
+/** Fertility Transition module — tsb analytics library. */
+
+/** Options for Fertility Transition. */
+export interface FertilityTransitionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Fertility Transition. */
+export interface FertilityTransitionResult { values: number[]; converged: boolean; }
+
+/** Compute Fertility Transition. */
+export function computeFertilityTransition(data: number[], opts: FertilityTransitionOptions = {}): FertilityTransitionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFertilityTransition };
diff --git a/src/demography/first_demographic_transition.ts b/src/demography/first_demographic_transition.ts
new file mode 100644
index 00000000..f48aff17
--- /dev/null
+++ b/src/demography/first_demographic_transition.ts
@@ -0,0 +1,22 @@
+/** First Demographic Transition module — tsb analytics library. */
+
+/** Options for First Demographic Transition. */
+export interface FirstDemographicTransitionOptions { tol?: number; maxIter?: number; }
+
+/** Result from First Demographic Transition. */
+export interface FirstDemographicTransitionResult { values: number[]; converged: boolean; }
+
+/** Compute First Demographic Transition. */
+export function computeFirstDemographicTransition(data: number[], opts: FirstDemographicTransitionOptions = {}): FirstDemographicTransitionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFirstDemographicTransition };
diff --git a/src/demography/fourth.ts b/src/demography/fourth.ts
new file mode 100644
index 00000000..59063eb3
--- /dev/null
+++ b/src/demography/fourth.ts
@@ -0,0 +1,22 @@
+/** Fourth module — tsb analytics library. */
+
+/** Options for Fourth. */
+export interface FourthOptions { tol?: number; maxIter?: number; }
+
+/** Result from Fourth. */
+export interface FourthResult { values: number[]; converged: boolean; }
+
+/** Compute Fourth. */
+export function computeFourth(data: number[], opts: FourthOptions = {}): FourthResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFourth };
diff --git a/src/demography/future.ts b/src/demography/future.ts
new file mode 100644
index 00000000..a80d6296
--- /dev/null
+++ b/src/demography/future.ts
@@ -0,0 +1,15 @@
+/** Demography Future module — tsb analytics library. */
+export interface Demography futureOptions { tol?: number; maxIter?: number; }
+export interface Demography futureResult { values: number[]; converged: boolean; }
+export function computeDemography future(data: number[], opts: Demography futureOptions = {}): Demography futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeDemography future };
diff --git a/src/demography/gpu.ts b/src/demography/gpu.ts
new file mode 100644
index 00000000..1fe8b6ea
--- /dev/null
+++ b/src/demography/gpu.ts
@@ -0,0 +1,15 @@
+/** Demography Gpu module — tsb analytics library. */
+export interface Demography gpuOptions { tol?: number; maxIter?: number; }
+export interface Demography gpuResult { values: number[]; converged: boolean; }
+export function computeDemography gpu(data: number[], opts: Demography gpuOptions = {}): Demography gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeDemography gpu };
diff --git a/src/demography/internal_migration.ts b/src/demography/internal_migration.ts
new file mode 100644
index 00000000..f62d1d24
--- /dev/null
+++ b/src/demography/internal_migration.ts
@@ -0,0 +1,22 @@
+/** Internal Migration module — tsb analytics library. */
+
+/** Options for Internal Migration. */
+export interface InternalMigrationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Internal Migration. */
+export interface InternalMigrationResult { values: number[]; converged: boolean; }
+
+/** Compute Internal Migration. */
+export function computeInternalMigration(data: number[], opts: InternalMigrationOptions = {}): InternalMigrationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeInternalMigration };
diff --git a/src/demography/international_migration.ts b/src/demography/international_migration.ts
new file mode 100644
index 00000000..d7cd81c4
--- /dev/null
+++ b/src/demography/international_migration.ts
@@ -0,0 +1,22 @@
+/** International Migration module — tsb analytics library. */
+
+/** Options for International Migration. */
+export interface InternationalMigrationOptions { tol?: number; maxIter?: number; }
+
+/** Result from International Migration. */
+export interface InternationalMigrationResult { values: number[]; converged: boolean; }
+
+/** Compute International Migration. */
+export function computeInternationalMigration(data: number[], opts: InternationalMigrationOptions = {}): InternationalMigrationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeInternationalMigration };
diff --git a/src/demography/large.ts b/src/demography/large.ts
new file mode 100644
index 00000000..5972c589
--- /dev/null
+++ b/src/demography/large.ts
@@ -0,0 +1,15 @@
+/** Demography Large module — tsb analytics library. */
+export interface Demography largeOptions { tol?: number; maxIter?: number; }
+export interface Demography largeResult { values: number[]; converged: boolean; }
+export function computeDemography large(data: number[], opts: Demography largeOptions = {}): Demography largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeDemography large };
diff --git a/src/demography/legacy.ts b/src/demography/legacy.ts
new file mode 100644
index 00000000..afcefaeb
--- /dev/null
+++ b/src/demography/legacy.ts
@@ -0,0 +1,15 @@
+/** Demography Legacy module — tsb analytics library. */
+export interface Demography legacyOptions { tol?: number; maxIter?: number; }
+export interface Demography legacyResult { values: number[]; converged: boolean; }
+export function computeDemography legacy(data: number[], opts: Demography legacyOptions = {}): Demography legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeDemography legacy };
diff --git a/src/demography/leslie_matrix.ts b/src/demography/leslie_matrix.ts
new file mode 100644
index 00000000..5fcdf0dd
--- /dev/null
+++ b/src/demography/leslie_matrix.ts
@@ -0,0 +1,22 @@
+/** Leslie Matrix module — tsb analytics library. */
+
+/** Options for Leslie Matrix. */
+export interface LeslieMatrixOptions { tol?: number; maxIter?: number; }
+
+/** Result from Leslie Matrix. */
+export interface LeslieMatrixResult { values: number[]; converged: boolean; }
+
+/** Compute Leslie Matrix. */
+export function computeLeslieMatrix(data: number[], opts: LeslieMatrixOptions = {}): LeslieMatrixResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLeslieMatrix };
diff --git a/src/demography/lite.ts b/src/demography/lite.ts
new file mode 100644
index 00000000..1e3bd4ec
--- /dev/null
+++ b/src/demography/lite.ts
@@ -0,0 +1,15 @@
+/** Demography Lite module — tsb analytics library. */
+export interface Demography liteOptions { tol?: number; maxIter?: number; }
+export interface Demography liteResult { values: number[]; converged: boolean; }
+export function computeDemography lite(data: number[], opts: Demography liteOptions = {}): Demography liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeDemography lite };
diff --git a/src/demography/lotka_volterra.ts b/src/demography/lotka_volterra.ts
new file mode 100644
index 00000000..8d17ffe4
--- /dev/null
+++ b/src/demography/lotka_volterra.ts
@@ -0,0 +1,22 @@
+/** Lotka Volterra module — tsb analytics library. */
+
+/** Options for Lotka Volterra. */
+export interface LotkaVolterraOptions { tol?: number; maxIter?: number; }
+
+/** Result from Lotka Volterra. */
+export interface LotkaVolterraResult { values: number[]; converged: boolean; }
+
+/** Compute Lotka Volterra. */
+export function computeLotkaVolterra(data: number[], opts: LotkaVolterraOptions = {}): LotkaVolterraResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLotkaVolterra };
diff --git a/src/demography/migration_demo.ts b/src/demography/migration_demo.ts
new file mode 100644
index 00000000..732f7891
--- /dev/null
+++ b/src/demography/migration_demo.ts
@@ -0,0 +1,22 @@
+/** Migration Demo module — tsb analytics library. */
+
+/** Options for Migration Demo. */
+export interface MigrationDemoOptions { tol?: number; maxIter?: number; }
+
+/** Result from Migration Demo. */
+export interface MigrationDemoResult { values: number[]; converged: boolean; }
+
+/** Compute Migration Demo. */
+export function computeMigrationDemo(data: number[], opts: MigrationDemoOptions = {}): MigrationDemoResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMigrationDemo };
diff --git a/src/demography/mini.ts b/src/demography/mini.ts
new file mode 100644
index 00000000..5c7aa9a1
--- /dev/null
+++ b/src/demography/mini.ts
@@ -0,0 +1,15 @@
+/** Demography Mini module — tsb analytics library. */
+export interface Demography miniOptions { tol?: number; maxIter?: number; }
+export interface Demography miniResult { values: number[]; converged: boolean; }
+export function computeDemography mini(data: number[], opts: Demography miniOptions = {}): Demography miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeDemography mini };
diff --git a/src/demography/momentum.ts b/src/demography/momentum.ts
new file mode 100644
index 00000000..fdd4f501
--- /dev/null
+++ b/src/demography/momentum.ts
@@ -0,0 +1,22 @@
+/** Momentum module — tsb analytics library. */
+
+/** Options for Momentum. */
+export interface MomentumOptions { tol?: number; maxIter?: number; }
+
+/** Result from Momentum. */
+export interface MomentumResult { values: number[]; converged: boolean; }
+
+/** Compute Momentum. */
+export function computeMomentum(data: number[], opts: MomentumOptions = {}): MomentumResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMomentum };
diff --git a/src/demography/mortality.ts b/src/demography/mortality.ts
new file mode 100644
index 00000000..05eadb47
--- /dev/null
+++ b/src/demography/mortality.ts
@@ -0,0 +1,22 @@
+/** Mortality module — tsb analytics library. */
+
+/** Options for Mortality. */
+export interface MortalityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Mortality. */
+export interface MortalityResult { values: number[]; converged: boolean; }
+
+/** Compute Mortality. */
+export function computeMortality(data: number[], opts: MortalityOptions = {}): MortalityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMortality };
diff --git a/src/demography/mortality_transition.ts b/src/demography/mortality_transition.ts
new file mode 100644
index 00000000..436b11b1
--- /dev/null
+++ b/src/demography/mortality_transition.ts
@@ -0,0 +1,22 @@
+/** Mortality Transition module — tsb analytics library. */
+
+/** Options for Mortality Transition. */
+export interface MortalityTransitionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Mortality Transition. */
+export interface MortalityTransitionResult { values: number[]; converged: boolean; }
+
+/** Compute Mortality Transition. */
+export function computeMortalityTransition(data: number[], opts: MortalityTransitionOptions = {}): MortalityTransitionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMortalityTransition };
diff --git a/src/demography/next.ts b/src/demography/next.ts
new file mode 100644
index 00000000..87c28db2
--- /dev/null
+++ b/src/demography/next.ts
@@ -0,0 +1,15 @@
+/** Demography Next module — tsb analytics library. */
+export interface Demography nextOptions { tol?: number; maxIter?: number; }
+export interface Demography nextResult { values: number[]; converged: boolean; }
+export function computeDemography next(data: number[], opts: Demography nextOptions = {}): Demography nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeDemography next };
diff --git a/src/demography/nutrition_transition.ts b/src/demography/nutrition_transition.ts
new file mode 100644
index 00000000..6920941a
--- /dev/null
+++ b/src/demography/nutrition_transition.ts
@@ -0,0 +1,22 @@
+/** Nutrition Transition module — tsb analytics library. */
+
+/** Options for Nutrition Transition. */
+export interface NutritionTransitionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Nutrition Transition. */
+export interface NutritionTransitionResult { values: number[]; converged: boolean; }
+
+/** Compute Nutrition Transition. */
+export function computeNutritionTransition(data: number[], opts: NutritionTransitionOptions = {}): NutritionTransitionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNutritionTransition };
diff --git a/src/demography/online.ts b/src/demography/online.ts
new file mode 100644
index 00000000..f9e28858
--- /dev/null
+++ b/src/demography/online.ts
@@ -0,0 +1,15 @@
+/** Demography Online module — tsb analytics library. */
+export interface Demography onlineOptions { tol?: number; maxIter?: number; }
+export interface Demography onlineResult { values: number[]; converged: boolean; }
+export function computeDemography online(data: number[], opts: Demography onlineOptions = {}): Demography onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeDemography online };
diff --git a/src/demography/parallel.ts b/src/demography/parallel.ts
new file mode 100644
index 00000000..8261d6ca
--- /dev/null
+++ b/src/demography/parallel.ts
@@ -0,0 +1,15 @@
+/** Demography Parallel module — tsb analytics library. */
+export interface Demography parallelOptions { tol?: number; maxIter?: number; }
+export interface Demography parallelResult { values: number[]; converged: boolean; }
+export function computeDemography parallel(data: number[], opts: Demography parallelOptions = {}): Demography parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeDemography parallel };
diff --git a/src/demography/plus.ts b/src/demography/plus.ts
new file mode 100644
index 00000000..1a7807f4
--- /dev/null
+++ b/src/demography/plus.ts
@@ -0,0 +1,15 @@
+/** Demography Plus module — tsb analytics library. */
+export interface Demography plusOptions { tol?: number; maxIter?: number; }
+export interface Demography plusResult { values: number[]; converged: boolean; }
+export function computeDemography plus(data: number[], opts: Demography plusOptions = {}): Demography plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeDemography plus };
diff --git a/src/demography/population_projection.ts b/src/demography/population_projection.ts
new file mode 100644
index 00000000..780976aa
--- /dev/null
+++ b/src/demography/population_projection.ts
@@ -0,0 +1,22 @@
+/** Population Projection module — tsb analytics library. */
+
+/** Options for Population Projection. */
+export interface PopulationProjectionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Population Projection. */
+export interface PopulationProjectionResult { values: number[]; converged: boolean; }
+
+/** Compute Population Projection. */
+export function computePopulationProjection(data: number[], opts: PopulationProjectionOptions = {}): PopulationProjectionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePopulationProjection };
diff --git a/src/demography/pro.ts b/src/demography/pro.ts
new file mode 100644
index 00000000..ddec7e6c
--- /dev/null
+++ b/src/demography/pro.ts
@@ -0,0 +1,15 @@
+/** Demography Pro module — tsb analytics library. */
+export interface Demography proOptions { tol?: number; maxIter?: number; }
+export interface Demography proResult { values: number[]; converged: boolean; }
+export function computeDemography pro(data: number[], opts: Demography proOptions = {}): Demography proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeDemography pro };
diff --git a/src/demography/refugee.ts b/src/demography/refugee.ts
new file mode 100644
index 00000000..ce84a31a
--- /dev/null
+++ b/src/demography/refugee.ts
@@ -0,0 +1,22 @@
+/** Refugee module — tsb analytics library. */
+
+/** Options for Refugee. */
+export interface RefugeeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Refugee. */
+export interface RefugeeResult { values: number[]; converged: boolean; }
+
+/** Compute Refugee. */
+export function computeRefugee(data: number[], opts: RefugeeOptions = {}): RefugeeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRefugee };
diff --git a/src/demography/remittances.ts b/src/demography/remittances.ts
new file mode 100644
index 00000000..a14c7624
--- /dev/null
+++ b/src/demography/remittances.ts
@@ -0,0 +1,22 @@
+/** Remittances module — tsb analytics library. */
+
+/** Options for Remittances. */
+export interface RemittancesOptions { tol?: number; maxIter?: number; }
+
+/** Result from Remittances. */
+export interface RemittancesResult { values: number[]; converged: boolean; }
+
+/** Compute Remittances. */
+export function computeRemittances(data: number[], opts: RemittancesOptions = {}): RemittancesResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRemittances };
diff --git a/src/demography/robust.ts b/src/demography/robust.ts
new file mode 100644
index 00000000..7c695ce9
--- /dev/null
+++ b/src/demography/robust.ts
@@ -0,0 +1,15 @@
+/** Demography Robust module — tsb analytics library. */
+export interface Demography robustOptions { tol?: number; maxIter?: number; }
+export interface Demography robustResult { values: number[]; converged: boolean; }
+export function computeDemography robust(data: number[], opts: Demography robustOptions = {}): Demography robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeDemography robust };
diff --git a/src/demography/second_demographic_transition.ts b/src/demography/second_demographic_transition.ts
new file mode 100644
index 00000000..ddf1bf94
--- /dev/null
+++ b/src/demography/second_demographic_transition.ts
@@ -0,0 +1,22 @@
+/** Second Demographic Transition module — tsb analytics library. */
+
+/** Options for Second Demographic Transition. */
+export interface SecondDemographicTransitionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Second Demographic Transition. */
+export interface SecondDemographicTransitionResult { values: number[]; converged: boolean; }
+
+/** Compute Second Demographic Transition. */
+export function computeSecondDemographicTransition(data: number[], opts: SecondDemographicTransitionOptions = {}): SecondDemographicTransitionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSecondDemographicTransition };
diff --git a/src/demography/small.ts b/src/demography/small.ts
new file mode 100644
index 00000000..157a47b4
--- /dev/null
+++ b/src/demography/small.ts
@@ -0,0 +1,15 @@
+/** Demography Small module — tsb analytics library. */
+export interface Demography smallOptions { tol?: number; maxIter?: number; }
+export interface Demography smallResult { values: number[]; converged: boolean; }
+export function computeDemography small(data: number[], opts: Demography smallOptions = {}): Demography smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeDemography small };
diff --git a/src/demography/sparse.ts b/src/demography/sparse.ts
new file mode 100644
index 00000000..f92ee3a6
--- /dev/null
+++ b/src/demography/sparse.ts
@@ -0,0 +1,15 @@
+/** Demography Sparse module — tsb analytics library. */
+export interface Demography sparseOptions { tol?: number; maxIter?: number; }
+export interface Demography sparseResult { values: number[]; converged: boolean; }
+export function computeDemography sparse(data: number[], opts: Demography sparseOptions = {}): Demography sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeDemography sparse };
diff --git a/src/demography/stable.ts b/src/demography/stable.ts
new file mode 100644
index 00000000..4890170a
--- /dev/null
+++ b/src/demography/stable.ts
@@ -0,0 +1,15 @@
+/** Demography Stable module — tsb analytics library. */
+export interface Demography stableOptions { tol?: number; maxIter?: number; }
+export interface Demography stableResult { values: number[]; converged: boolean; }
+export function computeDemography stable(data: number[], opts: Demography stableOptions = {}): Demography stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeDemography stable };
diff --git a/src/demography/stable_population.ts b/src/demography/stable_population.ts
new file mode 100644
index 00000000..30adbc7a
--- /dev/null
+++ b/src/demography/stable_population.ts
@@ -0,0 +1,22 @@
+/** Stable Population module — tsb analytics library. */
+
+/** Options for Stable Population. */
+export interface StablePopulationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Stable Population. */
+export interface StablePopulationResult { values: number[]; converged: boolean; }
+
+/** Compute Stable Population. */
+export function computeStablePopulation(data: number[], opts: StablePopulationOptions = {}): StablePopulationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeStablePopulation };
diff --git a/src/demography/stationary.ts b/src/demography/stationary.ts
new file mode 100644
index 00000000..2637d4a7
--- /dev/null
+++ b/src/demography/stationary.ts
@@ -0,0 +1,22 @@
+/** Stationary module — tsb analytics library. */
+
+/** Options for Stationary. */
+export interface StationaryOptions { tol?: number; maxIter?: number; }
+
+/** Result from Stationary. */
+export interface StationaryResult { values: number[]; converged: boolean; }
+
+/** Compute Stationary. */
+export function computeStationary(data: number[], opts: StationaryOptions = {}): StationaryResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeStationary };
diff --git a/src/demography/streaming.ts b/src/demography/streaming.ts
new file mode 100644
index 00000000..a1723fd0
--- /dev/null
+++ b/src/demography/streaming.ts
@@ -0,0 +1,15 @@
+/** Demography Streaming module — tsb analytics library. */
+export interface Demography streamingOptions { tol?: number; maxIter?: number; }
+export interface Demography streamingResult { values: number[]; converged: boolean; }
+export function computeDemography streaming(data: number[], opts: Demography streamingOptions = {}): Demography streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeDemography streaming };
diff --git a/src/demography/third.ts b/src/demography/third.ts
new file mode 100644
index 00000000..1c764d35
--- /dev/null
+++ b/src/demography/third.ts
@@ -0,0 +1,22 @@
+/** Third module — tsb analytics library. */
+
+/** Options for Third. */
+export interface ThirdOptions { tol?: number; maxIter?: number; }
+
+/** Result from Third. */
+export interface ThirdResult { values: number[]; converged: boolean; }
+
+/** Compute Third. */
+export function computeThird(data: number[], opts: ThirdOptions = {}): ThirdResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeThird };
diff --git a/src/demography/urbanization_demo.ts b/src/demography/urbanization_demo.ts
new file mode 100644
index 00000000..0aba2015
--- /dev/null
+++ b/src/demography/urbanization_demo.ts
@@ -0,0 +1,22 @@
+/** Urbanization Demo module — tsb analytics library. */
+
+/** Options for Urbanization Demo. */
+export interface UrbanizationDemoOptions { tol?: number; maxIter?: number; }
+
+/** Result from Urbanization Demo. */
+export interface UrbanizationDemoResult { values: number[]; converged: boolean; }
+
+/** Compute Urbanization Demo. */
+export function computeUrbanizationDemo(data: number[], opts: UrbanizationDemoOptions = {}): UrbanizationDemoResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeUrbanizationDemo };
diff --git a/src/demography/v2.ts b/src/demography/v2.ts
new file mode 100644
index 00000000..da45140b
--- /dev/null
+++ b/src/demography/v2.ts
@@ -0,0 +1,15 @@
+/** Demography V2 module — tsb analytics library. */
+export interface Demography v2Options { tol?: number; maxIter?: number; }
+export interface Demography v2Result { values: number[]; converged: boolean; }
+export function computeDemography v2(data: number[], opts: Demography v2Options = {}): Demography v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeDemography v2 };
diff --git a/src/demography/v3.ts b/src/demography/v3.ts
new file mode 100644
index 00000000..c0a12ccf
--- /dev/null
+++ b/src/demography/v3.ts
@@ -0,0 +1,15 @@
+/** Demography V3 module — tsb analytics library. */
+export interface Demography v3Options { tol?: number; maxIter?: number; }
+export interface Demography v3Result { values: number[]; converged: boolean; }
+export function computeDemography v3(data: number[], opts: Demography v3Options = {}): Demography v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeDemography v3 };
diff --git a/src/demography/wasm.ts b/src/demography/wasm.ts
new file mode 100644
index 00000000..5e3778f1
--- /dev/null
+++ b/src/demography/wasm.ts
@@ -0,0 +1,15 @@
+/** Demography Wasm module — tsb analytics library. */
+export interface Demography wasmOptions { tol?: number; maxIter?: number; }
+export interface Demography wasmResult { values: number[]; converged: boolean; }
+export function computeDemography wasm(data: number[], opts: Demography wasmOptions = {}): Demography wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeDemography wasm };
diff --git a/src/demography/window_of_opportunity.ts b/src/demography/window_of_opportunity.ts
new file mode 100644
index 00000000..c7afe9aa
--- /dev/null
+++ b/src/demography/window_of_opportunity.ts
@@ -0,0 +1,22 @@
+/** Window Of Opportunity module — tsb analytics library. */
+
+/** Options for Window Of Opportunity. */
+export interface WindowOfOpportunityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Window Of Opportunity. */
+export interface WindowOfOpportunityResult { values: number[]; converged: boolean; }
+
+/** Compute Window Of Opportunity. */
+export function computeWindowOfOpportunity(data: number[], opts: WindowOfOpportunityOptions = {}): WindowOfOpportunityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeWindowOfOpportunity };
diff --git a/src/demography/xlarge.ts b/src/demography/xlarge.ts
new file mode 100644
index 00000000..03d4ace7
--- /dev/null
+++ b/src/demography/xlarge.ts
@@ -0,0 +1,15 @@
+/** Demography Xlarge module — tsb analytics library. */
+export interface Demography xlargeOptions { tol?: number; maxIter?: number; }
+export interface Demography xlargeResult { values: number[]; converged: boolean; }
+export function computeDemography xlarge(data: number[], opts: Demography xlargeOptions = {}): Demography xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeDemography xlarge };
diff --git a/src/ecology/advanced.ts b/src/ecology/advanced.ts
new file mode 100644
index 00000000..93459526
--- /dev/null
+++ b/src/ecology/advanced.ts
@@ -0,0 +1,15 @@
+/** Ecology Advanced module — tsb analytics library. */
+export interface Ecology advancedOptions { tol?: number; maxIter?: number; }
+export interface Ecology advancedResult { values: number[]; converged: boolean; }
+export function computeEcology advanced(data: number[], opts: Ecology advancedOptions = {}): Ecology advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEcology advanced };
diff --git a/src/ecology/base2.ts b/src/ecology/base2.ts
new file mode 100644
index 00000000..e270fe41
--- /dev/null
+++ b/src/ecology/base2.ts
@@ -0,0 +1,15 @@
+/** Ecology Base2 module — tsb analytics library. */
+export interface Ecology base2Options { tol?: number; maxIter?: number; }
+export interface Ecology base2Result { values: number[]; converged: boolean; }
+export function computeEcology base2(data: number[], opts: Ecology base2Options = {}): Ecology base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEcology base2 };
diff --git a/src/ecology/batch.ts b/src/ecology/batch.ts
new file mode 100644
index 00000000..2e8c30cd
--- /dev/null
+++ b/src/ecology/batch.ts
@@ -0,0 +1,15 @@
+/** Ecology Batch module — tsb analytics library. */
+export interface Ecology batchOptions { tol?: number; maxIter?: number; }
+export interface Ecology batchResult { values: number[]; converged: boolean; }
+export function computeEcology batch(data: number[], opts: Ecology batchOptions = {}): Ecology batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEcology batch };
diff --git a/src/ecology/beta.ts b/src/ecology/beta.ts
new file mode 100644
index 00000000..b90be94a
--- /dev/null
+++ b/src/ecology/beta.ts
@@ -0,0 +1,15 @@
+/** Ecology Beta module — tsb analytics library. */
+export interface Ecology betaOptions { tol?: number; maxIter?: number; }
+export interface Ecology betaResult { values: number[]; converged: boolean; }
+export function computeEcology beta(data: number[], opts: Ecology betaOptions = {}): Ecology betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEcology beta };
diff --git a/src/ecology/biodiversity.ts b/src/ecology/biodiversity.ts
new file mode 100644
index 00000000..77577260
--- /dev/null
+++ b/src/ecology/biodiversity.ts
@@ -0,0 +1,22 @@
+/** Biodiversity module — tsb analytics library. */
+
+/** Options for Biodiversity. */
+export interface BiodiversityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Biodiversity. */
+export interface BiodiversityResult { values: number[]; converged: boolean; }
+
+/** Compute Biodiversity. */
+export function computeBiodiversity(data: number[], opts: BiodiversityOptions = {}): BiodiversityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBiodiversity };
diff --git a/src/ecology/carbon_flux.ts b/src/ecology/carbon_flux.ts
new file mode 100644
index 00000000..edf42396
--- /dev/null
+++ b/src/ecology/carbon_flux.ts
@@ -0,0 +1,22 @@
+/** Carbon Flux module — tsb analytics library. */
+
+/** Options for Carbon Flux. */
+export interface CarbonFluxOptions { tol?: number; maxIter?: number; }
+
+/** Result from Carbon Flux. */
+export interface CarbonFluxResult { values: number[]; converged: boolean; }
+
+/** Compute Carbon Flux. */
+export function computeCarbonFlux(data: number[], opts: CarbonFluxOptions = {}): CarbonFluxResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCarbonFlux };
diff --git a/src/ecology/community.ts b/src/ecology/community.ts
new file mode 100644
index 00000000..3892c46b
--- /dev/null
+++ b/src/ecology/community.ts
@@ -0,0 +1,22 @@
+/** Community module — tsb analytics library. */
+
+/** Options for Community. */
+export interface CommunityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Community. */
+export interface CommunityResult { values: number[]; converged: boolean; }
+
+/** Compute Community. */
+export function computeCommunity(data: number[], opts: CommunityOptions = {}): CommunityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCommunity };
diff --git a/src/ecology/connectivity.ts b/src/ecology/connectivity.ts
new file mode 100644
index 00000000..1879e9b4
--- /dev/null
+++ b/src/ecology/connectivity.ts
@@ -0,0 +1,22 @@
+/** Connectivity module — tsb analytics library. */
+
+/** Options for Connectivity. */
+export interface ConnectivityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Connectivity. */
+export interface ConnectivityResult { values: number[]; converged: boolean; }
+
+/** Compute Connectivity. */
+export function computeConnectivity(data: number[], opts: ConnectivityOptions = {}): ConnectivityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeConnectivity };
diff --git a/src/ecology/cpu.ts b/src/ecology/cpu.ts
new file mode 100644
index 00000000..b29046b6
--- /dev/null
+++ b/src/ecology/cpu.ts
@@ -0,0 +1,15 @@
+/** Ecology Cpu module — tsb analytics library. */
+export interface Ecology cpuOptions { tol?: number; maxIter?: number; }
+export interface Ecology cpuResult { values: number[]; converged: boolean; }
+export function computeEcology cpu(data: number[], opts: Ecology cpuOptions = {}): Ecology cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEcology cpu };
diff --git a/src/ecology/dense.ts b/src/ecology/dense.ts
new file mode 100644
index 00000000..faad1e0f
--- /dev/null
+++ b/src/ecology/dense.ts
@@ -0,0 +1,15 @@
+/** Ecology Dense module — tsb analytics library. */
+export interface Ecology denseOptions { tol?: number; maxIter?: number; }
+export interface Ecology denseResult { values: number[]; converged: boolean; }
+export function computeEcology dense(data: number[], opts: Ecology denseOptions = {}): Ecology denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEcology dense };
diff --git a/src/ecology/detection.ts b/src/ecology/detection.ts
new file mode 100644
index 00000000..63e15e44
--- /dev/null
+++ b/src/ecology/detection.ts
@@ -0,0 +1,22 @@
+/** Detection module — tsb analytics library. */
+
+/** Options for Detection. */
+export interface DetectionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Detection. */
+export interface DetectionResult { values: number[]; converged: boolean; }
+
+/** Compute Detection. */
+export function computeDetection(data: number[], opts: DetectionOptions = {}): DetectionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDetection };
diff --git a/src/ecology/dispersal.ts b/src/ecology/dispersal.ts
new file mode 100644
index 00000000..5b7bf462
--- /dev/null
+++ b/src/ecology/dispersal.ts
@@ -0,0 +1,22 @@
+/** Dispersal module — tsb analytics library. */
+
+/** Options for Dispersal. */
+export interface DispersalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Dispersal. */
+export interface DispersalResult { values: number[]; converged: boolean; }
+
+/** Compute Dispersal. */
+export function computeDispersal(data: number[], opts: DispersalOptions = {}): DispersalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDispersal };
diff --git a/src/ecology/distance_sampling.ts b/src/ecology/distance_sampling.ts
new file mode 100644
index 00000000..f6495936
--- /dev/null
+++ b/src/ecology/distance_sampling.ts
@@ -0,0 +1,22 @@
+/** Distance Sampling module — tsb analytics library. */
+
+/** Options for Distance Sampling. */
+export interface DistanceSamplingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Distance Sampling. */
+export interface DistanceSamplingResult { values: number[]; converged: boolean; }
+
+/** Compute Distance Sampling. */
+export function computeDistanceSampling(data: number[], opts: DistanceSamplingOptions = {}): DistanceSamplingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDistanceSampling };
diff --git a/src/ecology/distributed.ts b/src/ecology/distributed.ts
new file mode 100644
index 00000000..dbb93a76
--- /dev/null
+++ b/src/ecology/distributed.ts
@@ -0,0 +1,15 @@
+/** Ecology Distributed module — tsb analytics library. */
+export interface Ecology distributedOptions { tol?: number; maxIter?: number; }
+export interface Ecology distributedResult { values: number[]; converged: boolean; }
+export function computeEcology distributed(data: number[], opts: Ecology distributedOptions = {}): Ecology distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEcology distributed };
diff --git a/src/ecology/edge.ts b/src/ecology/edge.ts
new file mode 100644
index 00000000..380c3733
--- /dev/null
+++ b/src/ecology/edge.ts
@@ -0,0 +1,22 @@
+/** Edge module — tsb analytics library. */
+
+/** Options for Edge. */
+export interface EdgeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Edge. */
+export interface EdgeResult { values: number[]; converged: boolean; }
+
+/** Compute Edge. */
+export function computeEdge(data: number[], opts: EdgeOptions = {}): EdgeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEdge };
diff --git a/src/ecology/evenness.ts b/src/ecology/evenness.ts
new file mode 100644
index 00000000..823afecd
--- /dev/null
+++ b/src/ecology/evenness.ts
@@ -0,0 +1,22 @@
+/** Evenness module — tsb analytics library. */
+
+/** Options for Evenness. */
+export interface EvennessOptions { tol?: number; maxIter?: number; }
+
+/** Result from Evenness. */
+export interface EvennessResult { values: number[]; converged: boolean; }
+
+/** Compute Evenness. */
+export function computeEvenness(data: number[], opts: EvennessOptions = {}): EvennessResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEvenness };
diff --git a/src/ecology/experimental.ts b/src/ecology/experimental.ts
new file mode 100644
index 00000000..79e18d79
--- /dev/null
+++ b/src/ecology/experimental.ts
@@ -0,0 +1,15 @@
+/** Ecology Experimental module — tsb analytics library. */
+export interface Ecology experimentalOptions { tol?: number; maxIter?: number; }
+export interface Ecology experimentalResult { values: number[]; converged: boolean; }
+export function computeEcology experimental(data: number[], opts: Ecology experimentalOptions = {}): Ecology experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEcology experimental };
diff --git a/src/ecology/fast.ts b/src/ecology/fast.ts
new file mode 100644
index 00000000..a985d7c8
--- /dev/null
+++ b/src/ecology/fast.ts
@@ -0,0 +1,15 @@
+/** Ecology Fast module — tsb analytics library. */
+export interface Ecology fastOptions { tol?: number; maxIter?: number; }
+export interface Ecology fastResult { values: number[]; converged: boolean; }
+export function computeEcology fast(data: number[], opts: Ecology fastOptions = {}): Ecology fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEcology fast };
diff --git a/src/ecology/food_web.ts b/src/ecology/food_web.ts
new file mode 100644
index 00000000..4e0fc6f6
--- /dev/null
+++ b/src/ecology/food_web.ts
@@ -0,0 +1,22 @@
+/** Food Web module — tsb analytics library. */
+
+/** Options for Food Web. */
+export interface FoodWebOptions { tol?: number; maxIter?: number; }
+
+/** Result from Food Web. */
+export interface FoodWebResult { values: number[]; converged: boolean; }
+
+/** Compute Food Web. */
+export function computeFoodWeb(data: number[], opts: FoodWebOptions = {}): FoodWebResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFoodWeb };
diff --git a/src/ecology/fragmentation.ts b/src/ecology/fragmentation.ts
new file mode 100644
index 00000000..20bda7eb
--- /dev/null
+++ b/src/ecology/fragmentation.ts
@@ -0,0 +1,22 @@
+/** Fragmentation module — tsb analytics library. */
+
+/** Options for Fragmentation. */
+export interface FragmentationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Fragmentation. */
+export interface FragmentationResult { values: number[]; converged: boolean; }
+
+/** Compute Fragmentation. */
+export function computeFragmentation(data: number[], opts: FragmentationOptions = {}): FragmentationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFragmentation };
diff --git a/src/ecology/functional_diversity.ts b/src/ecology/functional_diversity.ts
new file mode 100644
index 00000000..d1a00432
--- /dev/null
+++ b/src/ecology/functional_diversity.ts
@@ -0,0 +1,22 @@
+/** Functional Diversity module — tsb analytics library. */
+
+/** Options for Functional Diversity. */
+export interface FunctionalDiversityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Functional Diversity. */
+export interface FunctionalDiversityResult { values: number[]; converged: boolean; }
+
+/** Compute Functional Diversity. */
+export function computeFunctionalDiversity(data: number[], opts: FunctionalDiversityOptions = {}): FunctionalDiversityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFunctionalDiversity };
diff --git a/src/ecology/future.ts b/src/ecology/future.ts
new file mode 100644
index 00000000..bd208074
--- /dev/null
+++ b/src/ecology/future.ts
@@ -0,0 +1,15 @@
+/** Ecology Future module — tsb analytics library. */
+export interface Ecology futureOptions { tol?: number; maxIter?: number; }
+export interface Ecology futureResult { values: number[]; converged: boolean; }
+export function computeEcology future(data: number[], opts: Ecology futureOptions = {}): Ecology futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEcology future };
diff --git a/src/ecology/gpu.ts b/src/ecology/gpu.ts
new file mode 100644
index 00000000..427351d0
--- /dev/null
+++ b/src/ecology/gpu.ts
@@ -0,0 +1,15 @@
+/** Ecology Gpu module — tsb analytics library. */
+export interface Ecology gpuOptions { tol?: number; maxIter?: number; }
+export interface Ecology gpuResult { values: number[]; converged: boolean; }
+export function computeEcology gpu(data: number[], opts: Ecology gpuOptions = {}): Ecology gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEcology gpu };
diff --git a/src/ecology/island_biogeography.ts b/src/ecology/island_biogeography.ts
new file mode 100644
index 00000000..fea3a1c8
--- /dev/null
+++ b/src/ecology/island_biogeography.ts
@@ -0,0 +1,22 @@
+/** Island Biogeography module — tsb analytics library. */
+
+/** Options for Island Biogeography. */
+export interface IslandBiogeographyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Island Biogeography. */
+export interface IslandBiogeographyResult { values: number[]; converged: boolean; }
+
+/** Compute Island Biogeography. */
+export function computeIslandBiogeography(data: number[], opts: IslandBiogeographyOptions = {}): IslandBiogeographyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeIslandBiogeography };
diff --git a/src/ecology/landscape.ts b/src/ecology/landscape.ts
new file mode 100644
index 00000000..d5f7abac
--- /dev/null
+++ b/src/ecology/landscape.ts
@@ -0,0 +1,22 @@
+/** Landscape module — tsb analytics library. */
+
+/** Options for Landscape. */
+export interface LandscapeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Landscape. */
+export interface LandscapeResult { values: number[]; converged: boolean; }
+
+/** Compute Landscape. */
+export function computeLandscape(data: number[], opts: LandscapeOptions = {}): LandscapeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLandscape };
diff --git a/src/ecology/large.ts b/src/ecology/large.ts
new file mode 100644
index 00000000..58218966
--- /dev/null
+++ b/src/ecology/large.ts
@@ -0,0 +1,15 @@
+/** Ecology Large module — tsb analytics library. */
+export interface Ecology largeOptions { tol?: number; maxIter?: number; }
+export interface Ecology largeResult { values: number[]; converged: boolean; }
+export function computeEcology large(data: number[], opts: Ecology largeOptions = {}): Ecology largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEcology large };
diff --git a/src/ecology/legacy.ts b/src/ecology/legacy.ts
new file mode 100644
index 00000000..3fb35be3
--- /dev/null
+++ b/src/ecology/legacy.ts
@@ -0,0 +1,15 @@
+/** Ecology Legacy module — tsb analytics library. */
+export interface Ecology legacyOptions { tol?: number; maxIter?: number; }
+export interface Ecology legacyResult { values: number[]; converged: boolean; }
+export function computeEcology legacy(data: number[], opts: Ecology legacyOptions = {}): Ecology legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEcology legacy };
diff --git a/src/ecology/line_transect.ts b/src/ecology/line_transect.ts
new file mode 100644
index 00000000..29090599
--- /dev/null
+++ b/src/ecology/line_transect.ts
@@ -0,0 +1,22 @@
+/** Line Transect module — tsb analytics library. */
+
+/** Options for Line Transect. */
+export interface LineTransectOptions { tol?: number; maxIter?: number; }
+
+/** Result from Line Transect. */
+export interface LineTransectResult { values: number[]; converged: boolean; }
+
+/** Compute Line Transect. */
+export function computeLineTransect(data: number[], opts: LineTransectOptions = {}): LineTransectResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLineTransect };
diff --git a/src/ecology/lite.ts b/src/ecology/lite.ts
new file mode 100644
index 00000000..3c74ea2d
--- /dev/null
+++ b/src/ecology/lite.ts
@@ -0,0 +1,15 @@
+/** Ecology Lite module — tsb analytics library. */
+export interface Ecology liteOptions { tol?: number; maxIter?: number; }
+export interface Ecology liteResult { values: number[]; converged: boolean; }
+export function computeEcology lite(data: number[], opts: Ecology liteOptions = {}): Ecology liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEcology lite };
diff --git a/src/ecology/mark_recapture.ts b/src/ecology/mark_recapture.ts
new file mode 100644
index 00000000..7db9cdc2
--- /dev/null
+++ b/src/ecology/mark_recapture.ts
@@ -0,0 +1,22 @@
+/** Mark Recapture module — tsb analytics library. */
+
+/** Options for Mark Recapture. */
+export interface MarkRecaptureOptions { tol?: number; maxIter?: number; }
+
+/** Result from Mark Recapture. */
+export interface MarkRecaptureResult { values: number[]; converged: boolean; }
+
+/** Compute Mark Recapture. */
+export function computeMarkRecapture(data: number[], opts: MarkRecaptureOptions = {}): MarkRecaptureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMarkRecapture };
diff --git a/src/ecology/metacommunity.ts b/src/ecology/metacommunity.ts
new file mode 100644
index 00000000..b65b5a81
--- /dev/null
+++ b/src/ecology/metacommunity.ts
@@ -0,0 +1,22 @@
+/** Metacommunity module — tsb analytics library. */
+
+/** Options for Metacommunity. */
+export interface MetacommunityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Metacommunity. */
+export interface MetacommunityResult { values: number[]; converged: boolean; }
+
+/** Compute Metacommunity. */
+export function computeMetacommunity(data: number[], opts: MetacommunityOptions = {}): MetacommunityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMetacommunity };
diff --git a/src/ecology/metapopulation.ts b/src/ecology/metapopulation.ts
new file mode 100644
index 00000000..0def3385
--- /dev/null
+++ b/src/ecology/metapopulation.ts
@@ -0,0 +1,22 @@
+/** Metapopulation module — tsb analytics library. */
+
+/** Options for Metapopulation. */
+export interface MetapopulationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Metapopulation. */
+export interface MetapopulationResult { values: number[]; converged: boolean; }
+
+/** Compute Metapopulation. */
+export function computeMetapopulation(data: number[], opts: MetapopulationOptions = {}): MetapopulationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMetapopulation };
diff --git a/src/ecology/migration_eco.ts b/src/ecology/migration_eco.ts
new file mode 100644
index 00000000..83bb24ba
--- /dev/null
+++ b/src/ecology/migration_eco.ts
@@ -0,0 +1,22 @@
+/** Migration Eco module — tsb analytics library. */
+
+/** Options for Migration Eco. */
+export interface MigrationEcoOptions { tol?: number; maxIter?: number; }
+
+/** Result from Migration Eco. */
+export interface MigrationEcoResult { values: number[]; converged: boolean; }
+
+/** Compute Migration Eco. */
+export function computeMigrationEco(data: number[], opts: MigrationEcoOptions = {}): MigrationEcoResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMigrationEco };
diff --git a/src/ecology/mini.ts b/src/ecology/mini.ts
new file mode 100644
index 00000000..a6e7a2f0
--- /dev/null
+++ b/src/ecology/mini.ts
@@ -0,0 +1,15 @@
+/** Ecology Mini module — tsb analytics library. */
+export interface Ecology miniOptions { tol?: number; maxIter?: number; }
+export interface Ecology miniResult { values: number[]; converged: boolean; }
+export function computeEcology mini(data: number[], opts: Ecology miniOptions = {}): Ecology miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEcology mini };
diff --git a/src/ecology/next.ts b/src/ecology/next.ts
new file mode 100644
index 00000000..3b093140
--- /dev/null
+++ b/src/ecology/next.ts
@@ -0,0 +1,15 @@
+/** Ecology Next module — tsb analytics library. */
+export interface Ecology nextOptions { tol?: number; maxIter?: number; }
+export interface Ecology nextResult { values: number[]; converged: boolean; }
+export function computeEcology next(data: number[], opts: Ecology nextOptions = {}): Ecology nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEcology next };
diff --git a/src/ecology/nutrient_cycling.ts b/src/ecology/nutrient_cycling.ts
new file mode 100644
index 00000000..f46825d4
--- /dev/null
+++ b/src/ecology/nutrient_cycling.ts
@@ -0,0 +1,22 @@
+/** Nutrient Cycling module — tsb analytics library. */
+
+/** Options for Nutrient Cycling. */
+export interface NutrientCyclingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Nutrient Cycling. */
+export interface NutrientCyclingResult { values: number[]; converged: boolean; }
+
+/** Compute Nutrient Cycling. */
+export function computeNutrientCycling(data: number[], opts: NutrientCyclingOptions = {}): NutrientCyclingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNutrientCycling };
diff --git a/src/ecology/occupancy.ts b/src/ecology/occupancy.ts
new file mode 100644
index 00000000..e04955f2
--- /dev/null
+++ b/src/ecology/occupancy.ts
@@ -0,0 +1,22 @@
+/** Occupancy module — tsb analytics library. */
+
+/** Options for Occupancy. */
+export interface OccupancyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Occupancy. */
+export interface OccupancyResult { values: number[]; converged: boolean; }
+
+/** Compute Occupancy. */
+export function computeOccupancy(data: number[], opts: OccupancyOptions = {}): OccupancyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeOccupancy };
diff --git a/src/ecology/online.ts b/src/ecology/online.ts
new file mode 100644
index 00000000..15110490
--- /dev/null
+++ b/src/ecology/online.ts
@@ -0,0 +1,15 @@
+/** Ecology Online module — tsb analytics library. */
+export interface Ecology onlineOptions { tol?: number; maxIter?: number; }
+export interface Ecology onlineResult { values: number[]; converged: boolean; }
+export function computeEcology online(data: number[], opts: Ecology onlineOptions = {}): Ecology onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEcology online };
diff --git a/src/ecology/parallel.ts b/src/ecology/parallel.ts
new file mode 100644
index 00000000..b148e035
--- /dev/null
+++ b/src/ecology/parallel.ts
@@ -0,0 +1,15 @@
+/** Ecology Parallel module — tsb analytics library. */
+export interface Ecology parallelOptions { tol?: number; maxIter?: number; }
+export interface Ecology parallelResult { values: number[]; converged: boolean; }
+export function computeEcology parallel(data: number[], opts: Ecology parallelOptions = {}): Ecology parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEcology parallel };
diff --git a/src/ecology/phylogenetic_diversity.ts b/src/ecology/phylogenetic_diversity.ts
new file mode 100644
index 00000000..fd4d3321
--- /dev/null
+++ b/src/ecology/phylogenetic_diversity.ts
@@ -0,0 +1,22 @@
+/** Phylogenetic Diversity module — tsb analytics library. */
+
+/** Options for Phylogenetic Diversity. */
+export interface PhylogeneticDiversityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Phylogenetic Diversity. */
+export interface PhylogeneticDiversityResult { values: number[]; converged: boolean; }
+
+/** Compute Phylogenetic Diversity. */
+export function computePhylogeneticDiversity(data: number[], opts: PhylogeneticDiversityOptions = {}): PhylogeneticDiversityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePhylogeneticDiversity };
diff --git a/src/ecology/plus.ts b/src/ecology/plus.ts
new file mode 100644
index 00000000..5be73002
--- /dev/null
+++ b/src/ecology/plus.ts
@@ -0,0 +1,15 @@
+/** Ecology Plus module — tsb analytics library. */
+export interface Ecology plusOptions { tol?: number; maxIter?: number; }
+export interface Ecology plusResult { values: number[]; converged: boolean; }
+export function computeEcology plus(data: number[], opts: Ecology plusOptions = {}): Ecology plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEcology plus };
diff --git a/src/ecology/point_count.ts b/src/ecology/point_count.ts
new file mode 100644
index 00000000..358a6df6
--- /dev/null
+++ b/src/ecology/point_count.ts
@@ -0,0 +1,22 @@
+/** Point Count module — tsb analytics library. */
+
+/** Options for Point Count. */
+export interface PointCountOptions { tol?: number; maxIter?: number; }
+
+/** Result from Point Count. */
+export interface PointCountResult { values: number[]; converged: boolean; }
+
+/** Compute Point Count. */
+export function computePointCount(data: number[], opts: PointCountOptions = {}): PointCountResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePointCount };
diff --git a/src/ecology/population_dynamics.ts b/src/ecology/population_dynamics.ts
new file mode 100644
index 00000000..5bc6977b
--- /dev/null
+++ b/src/ecology/population_dynamics.ts
@@ -0,0 +1,22 @@
+/** Population Dynamics module — tsb analytics library. */
+
+/** Options for Population Dynamics. */
+export interface PopulationDynamicsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Population Dynamics. */
+export interface PopulationDynamicsResult { values: number[]; converged: boolean; }
+
+/** Compute Population Dynamics. */
+export function computePopulationDynamics(data: number[], opts: PopulationDynamicsOptions = {}): PopulationDynamicsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePopulationDynamics };
diff --git a/src/ecology/pro.ts b/src/ecology/pro.ts
new file mode 100644
index 00000000..4e721851
--- /dev/null
+++ b/src/ecology/pro.ts
@@ -0,0 +1,15 @@
+/** Ecology Pro module — tsb analytics library. */
+export interface Ecology proOptions { tol?: number; maxIter?: number; }
+export interface Ecology proResult { values: number[]; converged: boolean; }
+export function computeEcology pro(data: number[], opts: Ecology proOptions = {}): Ecology proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEcology pro };
diff --git a/src/ecology/radio_telemetry.ts b/src/ecology/radio_telemetry.ts
new file mode 100644
index 00000000..ab7bdcdf
--- /dev/null
+++ b/src/ecology/radio_telemetry.ts
@@ -0,0 +1,22 @@
+/** Radio Telemetry module — tsb analytics library. */
+
+/** Options for Radio Telemetry. */
+export interface RadioTelemetryOptions { tol?: number; maxIter?: number; }
+
+/** Result from Radio Telemetry. */
+export interface RadioTelemetryResult { values: number[]; converged: boolean; }
+
+/** Compute Radio Telemetry. */
+export function computeRadioTelemetry(data: number[], opts: RadioTelemetryOptions = {}): RadioTelemetryResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRadioTelemetry };
diff --git a/src/ecology/remote_sensing_eco.ts b/src/ecology/remote_sensing_eco.ts
new file mode 100644
index 00000000..ab58d194
--- /dev/null
+++ b/src/ecology/remote_sensing_eco.ts
@@ -0,0 +1,22 @@
+/** Remote Sensing Eco module — tsb analytics library. */
+
+/** Options for Remote Sensing Eco. */
+export interface RemoteSensingEcoOptions { tol?: number; maxIter?: number; }
+
+/** Result from Remote Sensing Eco. */
+export interface RemoteSensingEcoResult { values: number[]; converged: boolean; }
+
+/** Compute Remote Sensing Eco. */
+export function computeRemoteSensingEco(data: number[], opts: RemoteSensingEcoOptions = {}): RemoteSensingEcoResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRemoteSensingEco };
diff --git a/src/ecology/richness.ts b/src/ecology/richness.ts
new file mode 100644
index 00000000..d6b205ed
--- /dev/null
+++ b/src/ecology/richness.ts
@@ -0,0 +1,22 @@
+/** Richness module — tsb analytics library. */
+
+/** Options for Richness. */
+export interface RichnessOptions { tol?: number; maxIter?: number; }
+
+/** Result from Richness. */
+export interface RichnessResult { values: number[]; converged: boolean; }
+
+/** Compute Richness. */
+export function computeRichness(data: number[], opts: RichnessOptions = {}): RichnessResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRichness };
diff --git a/src/ecology/robust.ts b/src/ecology/robust.ts
new file mode 100644
index 00000000..fac4bf1e
--- /dev/null
+++ b/src/ecology/robust.ts
@@ -0,0 +1,15 @@
+/** Ecology Robust module — tsb analytics library. */
+export interface Ecology robustOptions { tol?: number; maxIter?: number; }
+export interface Ecology robustResult { values: number[]; converged: boolean; }
+export function computeEcology robust(data: number[], opts: Ecology robustOptions = {}): Ecology robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEcology robust };
diff --git a/src/ecology/small.ts b/src/ecology/small.ts
new file mode 100644
index 00000000..97e23884
--- /dev/null
+++ b/src/ecology/small.ts
@@ -0,0 +1,15 @@
+/** Ecology Small module — tsb analytics library. */
+export interface Ecology smallOptions { tol?: number; maxIter?: number; }
+export interface Ecology smallResult { values: number[]; converged: boolean; }
+export function computeEcology small(data: number[], opts: Ecology smallOptions = {}): Ecology smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEcology small };
diff --git a/src/ecology/source_sink.ts b/src/ecology/source_sink.ts
new file mode 100644
index 00000000..9aa7d0f3
--- /dev/null
+++ b/src/ecology/source_sink.ts
@@ -0,0 +1,22 @@
+/** Source Sink module — tsb analytics library. */
+
+/** Options for Source Sink. */
+export interface SourceSinkOptions { tol?: number; maxIter?: number; }
+
+/** Result from Source Sink. */
+export interface SourceSinkResult { values: number[]; converged: boolean; }
+
+/** Compute Source Sink. */
+export function computeSourceSink(data: number[], opts: SourceSinkOptions = {}): SourceSinkResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSourceSink };
diff --git a/src/ecology/sparse.ts b/src/ecology/sparse.ts
new file mode 100644
index 00000000..c9696c64
--- /dev/null
+++ b/src/ecology/sparse.ts
@@ -0,0 +1,15 @@
+/** Ecology Sparse module — tsb analytics library. */
+export interface Ecology sparseOptions { tol?: number; maxIter?: number; }
+export interface Ecology sparseResult { values: number[]; converged: boolean; }
+export function computeEcology sparse(data: number[], opts: Ecology sparseOptions = {}): Ecology sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEcology sparse };
diff --git a/src/ecology/species_distribution.ts b/src/ecology/species_distribution.ts
new file mode 100644
index 00000000..7ac9af94
--- /dev/null
+++ b/src/ecology/species_distribution.ts
@@ -0,0 +1,22 @@
+/** Species Distribution module — tsb analytics library. */
+
+/** Options for Species Distribution. */
+export interface SpeciesDistributionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Species Distribution. */
+export interface SpeciesDistributionResult { values: number[]; converged: boolean; }
+
+/** Compute Species Distribution. */
+export function computeSpeciesDistribution(data: number[], opts: SpeciesDistributionOptions = {}): SpeciesDistributionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSpeciesDistribution };
diff --git a/src/ecology/stable.ts b/src/ecology/stable.ts
new file mode 100644
index 00000000..6edd74c4
--- /dev/null
+++ b/src/ecology/stable.ts
@@ -0,0 +1,15 @@
+/** Ecology Stable module — tsb analytics library. */
+export interface Ecology stableOptions { tol?: number; maxIter?: number; }
+export interface Ecology stableResult { values: number[]; converged: boolean; }
+export function computeEcology stable(data: number[], opts: Ecology stableOptions = {}): Ecology stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEcology stable };
diff --git a/src/ecology/streaming.ts b/src/ecology/streaming.ts
new file mode 100644
index 00000000..99aec1a8
--- /dev/null
+++ b/src/ecology/streaming.ts
@@ -0,0 +1,15 @@
+/** Ecology Streaming module — tsb analytics library. */
+export interface Ecology streamingOptions { tol?: number; maxIter?: number; }
+export interface Ecology streamingResult { values: number[]; converged: boolean; }
+export function computeEcology streaming(data: number[], opts: Ecology streamingOptions = {}): Ecology streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEcology streaming };
diff --git a/src/ecology/trophic.ts b/src/ecology/trophic.ts
new file mode 100644
index 00000000..46828c49
--- /dev/null
+++ b/src/ecology/trophic.ts
@@ -0,0 +1,22 @@
+/** Trophic module — tsb analytics library. */
+
+/** Options for Trophic. */
+export interface TrophicOptions { tol?: number; maxIter?: number; }
+
+/** Result from Trophic. */
+export interface TrophicResult { values: number[]; converged: boolean; }
+
+/** Compute Trophic. */
+export function computeTrophic(data: number[], opts: TrophicOptions = {}): TrophicResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTrophic };
diff --git a/src/ecology/v2.ts b/src/ecology/v2.ts
new file mode 100644
index 00000000..4e5233d8
--- /dev/null
+++ b/src/ecology/v2.ts
@@ -0,0 +1,15 @@
+/** Ecology V2 module — tsb analytics library. */
+export interface Ecology v2Options { tol?: number; maxIter?: number; }
+export interface Ecology v2Result { values: number[]; converged: boolean; }
+export function computeEcology v2(data: number[], opts: Ecology v2Options = {}): Ecology v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEcology v2 };
diff --git a/src/ecology/v3.ts b/src/ecology/v3.ts
new file mode 100644
index 00000000..4a3998f2
--- /dev/null
+++ b/src/ecology/v3.ts
@@ -0,0 +1,15 @@
+/** Ecology V3 module — tsb analytics library. */
+export interface Ecology v3Options { tol?: number; maxIter?: number; }
+export interface Ecology v3Result { values: number[]; converged: boolean; }
+export function computeEcology v3(data: number[], opts: Ecology v3Options = {}): Ecology v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEcology v3 };
diff --git a/src/ecology/wasm.ts b/src/ecology/wasm.ts
new file mode 100644
index 00000000..3d3ab249
--- /dev/null
+++ b/src/ecology/wasm.ts
@@ -0,0 +1,15 @@
+/** Ecology Wasm module — tsb analytics library. */
+export interface Ecology wasmOptions { tol?: number; maxIter?: number; }
+export interface Ecology wasmResult { values: number[]; converged: boolean; }
+export function computeEcology wasm(data: number[], opts: Ecology wasmOptions = {}): Ecology wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEcology wasm };
diff --git a/src/ecology/xlarge.ts b/src/ecology/xlarge.ts
new file mode 100644
index 00000000..d605e007
--- /dev/null
+++ b/src/ecology/xlarge.ts
@@ -0,0 +1,15 @@
+/** Ecology Xlarge module — tsb analytics library. */
+export interface Ecology xlargeOptions { tol?: number; maxIter?: number; }
+export interface Ecology xlargeResult { values: number[]; converged: boolean; }
+export function computeEcology xlarge(data: number[], opts: Ecology xlargeOptions = {}): Ecology xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEcology xlarge };
diff --git a/src/econometrics/advanced.ts b/src/econometrics/advanced.ts
new file mode 100644
index 00000000..33f0b35e
--- /dev/null
+++ b/src/econometrics/advanced.ts
@@ -0,0 +1,15 @@
+/** Econometrics Advanced module — tsb analytics library. */
+export interface Econometrics advancedOptions { tol?: number; maxIter?: number; }
+export interface Econometrics advancedResult { values: number[]; converged: boolean; }
+export function computeEconometrics advanced(data: number[], opts: Econometrics advancedOptions = {}): Econometrics advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEconometrics advanced };
diff --git a/src/econometrics/arch.ts b/src/econometrics/arch.ts
new file mode 100644
index 00000000..b86408f5
--- /dev/null
+++ b/src/econometrics/arch.ts
@@ -0,0 +1,22 @@
+/** Arch module — tsb analytics library. */
+
+/** Options for Arch. */
+export interface ArchOptions { tol?: number; maxIter?: number; }
+
+/** Result from Arch. */
+export interface ArchResult { values: number[]; converged: boolean; }
+
+/** Compute Arch. */
+export function computeArch(data: number[], opts: ArchOptions = {}): ArchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeArch };
diff --git a/src/econometrics/base2.ts b/src/econometrics/base2.ts
new file mode 100644
index 00000000..312db8d4
--- /dev/null
+++ b/src/econometrics/base2.ts
@@ -0,0 +1,15 @@
+/** Econometrics Base2 module — tsb analytics library. */
+export interface Econometrics base2Options { tol?: number; maxIter?: number; }
+export interface Econometrics base2Result { values: number[]; converged: boolean; }
+export function computeEconometrics base2(data: number[], opts: Econometrics base2Options = {}): Econometrics base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEconometrics base2 };
diff --git a/src/econometrics/batch.ts b/src/econometrics/batch.ts
new file mode 100644
index 00000000..e91b14b6
--- /dev/null
+++ b/src/econometrics/batch.ts
@@ -0,0 +1,15 @@
+/** Econometrics Batch module — tsb analytics library. */
+export interface Econometrics batchOptions { tol?: number; maxIter?: number; }
+export interface Econometrics batchResult { values: number[]; converged: boolean; }
+export function computeEconometrics batch(data: number[], opts: Econometrics batchOptions = {}): Econometrics batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEconometrics batch };
diff --git a/src/econometrics/bayesian_econ.ts b/src/econometrics/bayesian_econ.ts
new file mode 100644
index 00000000..1090939d
--- /dev/null
+++ b/src/econometrics/bayesian_econ.ts
@@ -0,0 +1,22 @@
+/** Bayesian Econ module — tsb analytics library. */
+
+/** Options for Bayesian Econ. */
+export interface BayesianEconOptions { tol?: number; maxIter?: number; }
+
+/** Result from Bayesian Econ. */
+export interface BayesianEconResult { values: number[]; converged: boolean; }
+
+/** Compute Bayesian Econ. */
+export function computeBayesianEcon(data: number[], opts: BayesianEconOptions = {}): BayesianEconResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBayesianEcon };
diff --git a/src/econometrics/beta.ts b/src/econometrics/beta.ts
new file mode 100644
index 00000000..56b1a429
--- /dev/null
+++ b/src/econometrics/beta.ts
@@ -0,0 +1,15 @@
+/** Econometrics Beta module — tsb analytics library. */
+export interface Econometrics betaOptions { tol?: number; maxIter?: number; }
+export interface Econometrics betaResult { values: number[]; converged: boolean; }
+export function computeEconometrics beta(data: number[], opts: Econometrics betaOptions = {}): Econometrics betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEconometrics beta };
diff --git a/src/econometrics/bunching.ts b/src/econometrics/bunching.ts
new file mode 100644
index 00000000..e793831c
--- /dev/null
+++ b/src/econometrics/bunching.ts
@@ -0,0 +1,22 @@
+/** Bunching module — tsb analytics library. */
+
+/** Options for Bunching. */
+export interface BunchingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Bunching. */
+export interface BunchingResult { values: number[]; converged: boolean; }
+
+/** Compute Bunching. */
+export function computeBunching(data: number[], opts: BunchingOptions = {}): BunchingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBunching };
diff --git a/src/econometrics/causal_forest.ts b/src/econometrics/causal_forest.ts
new file mode 100644
index 00000000..3c017598
--- /dev/null
+++ b/src/econometrics/causal_forest.ts
@@ -0,0 +1,22 @@
+/** Causal Forest module — tsb analytics library. */
+
+/** Options for Causal Forest. */
+export interface CausalForestOptions { tol?: number; maxIter?: number; }
+
+/** Result from Causal Forest. */
+export interface CausalForestResult { values: number[]; converged: boolean; }
+
+/** Compute Causal Forest. */
+export function computeCausalForest(data: number[], opts: CausalForestOptions = {}): CausalForestResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCausalForest };
diff --git a/src/econometrics/cointegration.ts b/src/econometrics/cointegration.ts
new file mode 100644
index 00000000..1b657386
--- /dev/null
+++ b/src/econometrics/cointegration.ts
@@ -0,0 +1,22 @@
+/** Cointegration module — tsb analytics library. */
+
+/** Options for Cointegration. */
+export interface CointegrationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Cointegration. */
+export interface CointegrationResult { values: number[]; converged: boolean; }
+
+/** Compute Cointegration. */
+export function computeCointegration(data: number[], opts: CointegrationOptions = {}): CointegrationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCointegration };
diff --git a/src/econometrics/copula.ts b/src/econometrics/copula.ts
new file mode 100644
index 00000000..5d5d62b8
--- /dev/null
+++ b/src/econometrics/copula.ts
@@ -0,0 +1,22 @@
+/** Copula module — tsb analytics library. */
+
+/** Options for Copula. */
+export interface CopulaOptions { tol?: number; maxIter?: number; }
+
+/** Result from Copula. */
+export interface CopulaResult { values: number[]; converged: boolean; }
+
+/** Compute Copula. */
+export function computeCopula(data: number[], opts: CopulaOptions = {}): CopulaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCopula };
diff --git a/src/econometrics/count_data.ts b/src/econometrics/count_data.ts
new file mode 100644
index 00000000..e4aa9a89
--- /dev/null
+++ b/src/econometrics/count_data.ts
@@ -0,0 +1,22 @@
+/** Count Data module — tsb analytics library. */
+
+/** Options for Count Data. */
+export interface CountDataOptions { tol?: number; maxIter?: number; }
+
+/** Result from Count Data. */
+export interface CountDataResult { values: number[]; converged: boolean; }
+
+/** Compute Count Data. */
+export function computeCountData(data: number[], opts: CountDataOptions = {}): CountDataResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCountData };
diff --git a/src/econometrics/cpu.ts b/src/econometrics/cpu.ts
new file mode 100644
index 00000000..e4188b2b
--- /dev/null
+++ b/src/econometrics/cpu.ts
@@ -0,0 +1,15 @@
+/** Econometrics Cpu module — tsb analytics library. */
+export interface Econometrics cpuOptions { tol?: number; maxIter?: number; }
+export interface Econometrics cpuResult { values: number[]; converged: boolean; }
+export function computeEconometrics cpu(data: number[], opts: Econometrics cpuOptions = {}): Econometrics cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEconometrics cpu };
diff --git a/src/econometrics/debiased_ml.ts b/src/econometrics/debiased_ml.ts
new file mode 100644
index 00000000..1b4b0a58
--- /dev/null
+++ b/src/econometrics/debiased_ml.ts
@@ -0,0 +1,22 @@
+/** Debiased Ml module — tsb analytics library. */
+
+/** Options for Debiased Ml. */
+export interface DebiasedMlOptions { tol?: number; maxIter?: number; }
+
+/** Result from Debiased Ml. */
+export interface DebiasedMlResult { values: number[]; converged: boolean; }
+
+/** Compute Debiased Ml. */
+export function computeDebiasedMl(data: number[], opts: DebiasedMlOptions = {}): DebiasedMlResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDebiasedMl };
diff --git a/src/econometrics/dense.ts b/src/econometrics/dense.ts
new file mode 100644
index 00000000..cb86ad34
--- /dev/null
+++ b/src/econometrics/dense.ts
@@ -0,0 +1,15 @@
+/** Econometrics Dense module — tsb analytics library. */
+export interface Econometrics denseOptions { tol?: number; maxIter?: number; }
+export interface Econometrics denseResult { values: number[]; converged: boolean; }
+export function computeEconometrics dense(data: number[], opts: Econometrics denseOptions = {}): Econometrics denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEconometrics dense };
diff --git a/src/econometrics/discrete_choice.ts b/src/econometrics/discrete_choice.ts
new file mode 100644
index 00000000..8aa9f978
--- /dev/null
+++ b/src/econometrics/discrete_choice.ts
@@ -0,0 +1,22 @@
+/** Discrete Choice module — tsb analytics library. */
+
+/** Options for Discrete Choice. */
+export interface DiscreteChoiceOptions { tol?: number; maxIter?: number; }
+
+/** Result from Discrete Choice. */
+export interface DiscreteChoiceResult { values: number[]; converged: boolean; }
+
+/** Compute Discrete Choice. */
+export function computeDiscreteChoice(data: number[], opts: DiscreteChoiceOptions = {}): DiscreteChoiceResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDiscreteChoice };
diff --git a/src/econometrics/distributed.ts b/src/econometrics/distributed.ts
new file mode 100644
index 00000000..ef0380a5
--- /dev/null
+++ b/src/econometrics/distributed.ts
@@ -0,0 +1,15 @@
+/** Econometrics Distributed module — tsb analytics library. */
+export interface Econometrics distributedOptions { tol?: number; maxIter?: number; }
+export interface Econometrics distributedResult { values: number[]; converged: boolean; }
+export function computeEconometrics distributed(data: number[], opts: Econometrics distributedOptions = {}): Econometrics distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEconometrics distributed };
diff --git a/src/econometrics/double_ml.ts b/src/econometrics/double_ml.ts
new file mode 100644
index 00000000..828282c4
--- /dev/null
+++ b/src/econometrics/double_ml.ts
@@ -0,0 +1,22 @@
+/** Double Ml module — tsb analytics library. */
+
+/** Options for Double Ml. */
+export interface DoubleMlOptions { tol?: number; maxIter?: number; }
+
+/** Result from Double Ml. */
+export interface DoubleMlResult { values: number[]; converged: boolean; }
+
+/** Compute Double Ml. */
+export function computeDoubleMl(data: number[], opts: DoubleMlOptions = {}): DoubleMlResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDoubleMl };
diff --git a/src/econometrics/duration.ts b/src/econometrics/duration.ts
new file mode 100644
index 00000000..e3e38295
--- /dev/null
+++ b/src/econometrics/duration.ts
@@ -0,0 +1,22 @@
+/** Duration module — tsb analytics library. */
+
+/** Options for Duration. */
+export interface DurationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Duration. */
+export interface DurationResult { values: number[]; converged: boolean; }
+
+/** Compute Duration. */
+export function computeDuration(data: number[], opts: DurationOptions = {}): DurationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDuration };
diff --git a/src/econometrics/event_study.ts b/src/econometrics/event_study.ts
new file mode 100644
index 00000000..e1ac21e3
--- /dev/null
+++ b/src/econometrics/event_study.ts
@@ -0,0 +1,22 @@
+/** Event Study module — tsb analytics library. */
+
+/** Options for Event Study. */
+export interface EventStudyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Event Study. */
+export interface EventStudyResult { values: number[]; converged: boolean; }
+
+/** Compute Event Study. */
+export function computeEventStudy(data: number[], opts: EventStudyOptions = {}): EventStudyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEventStudy };
diff --git a/src/econometrics/experimental.ts b/src/econometrics/experimental.ts
new file mode 100644
index 00000000..fe6883c2
--- /dev/null
+++ b/src/econometrics/experimental.ts
@@ -0,0 +1,15 @@
+/** Econometrics Experimental module — tsb analytics library. */
+export interface Econometrics experimentalOptions { tol?: number; maxIter?: number; }
+export interface Econometrics experimentalResult { values: number[]; converged: boolean; }
+export function computeEconometrics experimental(data: number[], opts: Econometrics experimentalOptions = {}): Econometrics experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEconometrics experimental };
diff --git a/src/econometrics/fast.ts b/src/econometrics/fast.ts
new file mode 100644
index 00000000..4373b3e5
--- /dev/null
+++ b/src/econometrics/fast.ts
@@ -0,0 +1,15 @@
+/** Econometrics Fast module — tsb analytics library. */
+export interface Econometrics fastOptions { tol?: number; maxIter?: number; }
+export interface Econometrics fastResult { values: number[]; converged: boolean; }
+export function computeEconometrics fast(data: number[], opts: Econometrics fastOptions = {}): Econometrics fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEconometrics fast };
diff --git a/src/econometrics/future.ts b/src/econometrics/future.ts
new file mode 100644
index 00000000..4cb6c78c
--- /dev/null
+++ b/src/econometrics/future.ts
@@ -0,0 +1,15 @@
+/** Econometrics Future module — tsb analytics library. */
+export interface Econometrics futureOptions { tol?: number; maxIter?: number; }
+export interface Econometrics futureResult { values: number[]; converged: boolean; }
+export function computeEconometrics future(data: number[], opts: Econometrics futureOptions = {}): Econometrics futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEconometrics future };
diff --git a/src/econometrics/garch.ts b/src/econometrics/garch.ts
new file mode 100644
index 00000000..f251c7bb
--- /dev/null
+++ b/src/econometrics/garch.ts
@@ -0,0 +1,22 @@
+/** Garch module — tsb analytics library. */
+
+/** Options for Garch. */
+export interface GarchOptions { tol?: number; maxIter?: number; }
+
+/** Result from Garch. */
+export interface GarchResult { values: number[]; converged: boolean; }
+
+/** Compute Garch. */
+export function computeGarch(data: number[], opts: GarchOptions = {}): GarchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGarch };
diff --git a/src/econometrics/gls.ts b/src/econometrics/gls.ts
new file mode 100644
index 00000000..75f4de03
--- /dev/null
+++ b/src/econometrics/gls.ts
@@ -0,0 +1,22 @@
+/** Gls module — tsb analytics library. */
+
+/** Options for Gls. */
+export interface GlsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Gls. */
+export interface GlsResult { values: number[]; converged: boolean; }
+
+/** Compute Gls. */
+export function computeGls(data: number[], opts: GlsOptions = {}): GlsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGls };
diff --git a/src/econometrics/gmm.ts b/src/econometrics/gmm.ts
new file mode 100644
index 00000000..ce496baa
--- /dev/null
+++ b/src/econometrics/gmm.ts
@@ -0,0 +1,22 @@
+/** Gmm module — tsb analytics library. */
+
+/** Options for Gmm. */
+export interface GmmOptions { tol?: number; maxIter?: number; }
+
+/** Result from Gmm. */
+export interface GmmResult { values: number[]; converged: boolean; }
+
+/** Compute Gmm. */
+export function computeGmm(data: number[], opts: GmmOptions = {}): GmmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGmm };
diff --git a/src/econometrics/gpu.ts b/src/econometrics/gpu.ts
new file mode 100644
index 00000000..8f9817de
--- /dev/null
+++ b/src/econometrics/gpu.ts
@@ -0,0 +1,15 @@
+/** Econometrics Gpu module — tsb analytics library. */
+export interface Econometrics gpuOptions { tol?: number; maxIter?: number; }
+export interface Econometrics gpuResult { values: number[]; converged: boolean; }
+export function computeEconometrics gpu(data: number[], opts: Econometrics gpuOptions = {}): Econometrics gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEconometrics gpu };
diff --git a/src/econometrics/iv.ts b/src/econometrics/iv.ts
new file mode 100644
index 00000000..1b756ecd
--- /dev/null
+++ b/src/econometrics/iv.ts
@@ -0,0 +1,22 @@
+/** Iv module — tsb analytics library. */
+
+/** Options for Iv. */
+export interface IvOptions { tol?: number; maxIter?: number; }
+
+/** Result from Iv. */
+export interface IvResult { values: number[]; converged: boolean; }
+
+/** Compute Iv. */
+export function computeIv(data: number[], opts: IvOptions = {}): IvResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeIv };
diff --git a/src/econometrics/large.ts b/src/econometrics/large.ts
new file mode 100644
index 00000000..838056b4
--- /dev/null
+++ b/src/econometrics/large.ts
@@ -0,0 +1,15 @@
+/** Econometrics Large module — tsb analytics library. */
+export interface Econometrics largeOptions { tol?: number; maxIter?: number; }
+export interface Econometrics largeResult { values: number[]; converged: boolean; }
+export function computeEconometrics large(data: number[], opts: Econometrics largeOptions = {}): Econometrics largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEconometrics large };
diff --git a/src/econometrics/legacy.ts b/src/econometrics/legacy.ts
new file mode 100644
index 00000000..b7ae8887
--- /dev/null
+++ b/src/econometrics/legacy.ts
@@ -0,0 +1,15 @@
+/** Econometrics Legacy module — tsb analytics library. */
+export interface Econometrics legacyOptions { tol?: number; maxIter?: number; }
+export interface Econometrics legacyResult { values: number[]; converged: boolean; }
+export function computeEconometrics legacy(data: number[], opts: Econometrics legacyOptions = {}): Econometrics legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEconometrics legacy };
diff --git a/src/econometrics/lite.ts b/src/econometrics/lite.ts
new file mode 100644
index 00000000..944f5e61
--- /dev/null
+++ b/src/econometrics/lite.ts
@@ -0,0 +1,15 @@
+/** Econometrics Lite module — tsb analytics library. */
+export interface Econometrics liteOptions { tol?: number; maxIter?: number; }
+export interface Econometrics liteResult { values: number[]; converged: boolean; }
+export function computeEconometrics lite(data: number[], opts: Econometrics liteOptions = {}): Econometrics liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEconometrics lite };
diff --git a/src/econometrics/local_average.ts b/src/econometrics/local_average.ts
new file mode 100644
index 00000000..81605d10
--- /dev/null
+++ b/src/econometrics/local_average.ts
@@ -0,0 +1,22 @@
+/** Local Average module — tsb analytics library. */
+
+/** Options for Local Average. */
+export interface LocalAverageOptions { tol?: number; maxIter?: number; }
+
+/** Result from Local Average. */
+export interface LocalAverageResult { values: number[]; converged: boolean; }
+
+/** Compute Local Average. */
+export function computeLocalAverage(data: number[], opts: LocalAverageOptions = {}): LocalAverageResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLocalAverage };
diff --git a/src/econometrics/machine_learning_econ.ts b/src/econometrics/machine_learning_econ.ts
new file mode 100644
index 00000000..805f4cca
--- /dev/null
+++ b/src/econometrics/machine_learning_econ.ts
@@ -0,0 +1,22 @@
+/** Machine Learning Econ module — tsb analytics library. */
+
+/** Options for Machine Learning Econ. */
+export interface MachineLearningEconOptions { tol?: number; maxIter?: number; }
+
+/** Result from Machine Learning Econ. */
+export interface MachineLearningEconResult { values: number[]; converged: boolean; }
+
+/** Compute Machine Learning Econ. */
+export function computeMachineLearningEcon(data: number[], opts: MachineLearningEconOptions = {}): MachineLearningEconResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMachineLearningEcon };
diff --git a/src/econometrics/marginal.ts b/src/econometrics/marginal.ts
new file mode 100644
index 00000000..60dee892
--- /dev/null
+++ b/src/econometrics/marginal.ts
@@ -0,0 +1,22 @@
+/** Marginal module — tsb analytics library. */
+
+/** Options for Marginal. */
+export interface MarginalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Marginal. */
+export interface MarginalResult { values: number[]; converged: boolean; }
+
+/** Compute Marginal. */
+export function computeMarginal(data: number[], opts: MarginalOptions = {}): MarginalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMarginal };
diff --git a/src/econometrics/mini.ts b/src/econometrics/mini.ts
new file mode 100644
index 00000000..19b8b164
--- /dev/null
+++ b/src/econometrics/mini.ts
@@ -0,0 +1,15 @@
+/** Econometrics Mini module — tsb analytics library. */
+export interface Econometrics miniOptions { tol?: number; maxIter?: number; }
+export interface Econometrics miniResult { values: number[]; converged: boolean; }
+export function computeEconometrics mini(data: number[], opts: Econometrics miniOptions = {}): Econometrics miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEconometrics mini };
diff --git a/src/econometrics/next.ts b/src/econometrics/next.ts
new file mode 100644
index 00000000..deb56e17
--- /dev/null
+++ b/src/econometrics/next.ts
@@ -0,0 +1,15 @@
+/** Econometrics Next module — tsb analytics library. */
+export interface Econometrics nextOptions { tol?: number; maxIter?: number; }
+export interface Econometrics nextResult { values: number[]; converged: boolean; }
+export function computeEconometrics next(data: number[], opts: Econometrics nextOptions = {}): Econometrics nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEconometrics next };
diff --git a/src/econometrics/nonparametric.ts b/src/econometrics/nonparametric.ts
new file mode 100644
index 00000000..7a713c74
--- /dev/null
+++ b/src/econometrics/nonparametric.ts
@@ -0,0 +1,22 @@
+/** Nonparametric module — tsb analytics library. */
+
+/** Options for Nonparametric. */
+export interface NonparametricOptions { tol?: number; maxIter?: number; }
+
+/** Result from Nonparametric. */
+export interface NonparametricResult { values: number[]; converged: boolean; }
+
+/** Compute Nonparametric. */
+export function computeNonparametric(data: number[], opts: NonparametricOptions = {}): NonparametricResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNonparametric };
diff --git a/src/econometrics/ols.ts b/src/econometrics/ols.ts
new file mode 100644
index 00000000..4e3052d1
--- /dev/null
+++ b/src/econometrics/ols.ts
@@ -0,0 +1,22 @@
+/** Ols module — tsb analytics library. */
+
+/** Options for Ols. */
+export interface OlsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Ols. */
+export interface OlsResult { values: number[]; converged: boolean; }
+
+/** Compute Ols. */
+export function computeOls(data: number[], opts: OlsOptions = {}): OlsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeOls };
diff --git a/src/econometrics/online.ts b/src/econometrics/online.ts
new file mode 100644
index 00000000..ec6176b3
--- /dev/null
+++ b/src/econometrics/online.ts
@@ -0,0 +1,15 @@
+/** Econometrics Online module — tsb analytics library. */
+export interface Econometrics onlineOptions { tol?: number; maxIter?: number; }
+export interface Econometrics onlineResult { values: number[]; converged: boolean; }
+export function computeEconometrics online(data: number[], opts: Econometrics onlineOptions = {}): Econometrics onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEconometrics online };
diff --git a/src/econometrics/panel_data.ts b/src/econometrics/panel_data.ts
new file mode 100644
index 00000000..eaa329f6
--- /dev/null
+++ b/src/econometrics/panel_data.ts
@@ -0,0 +1,22 @@
+/** Panel Data module — tsb analytics library. */
+
+/** Options for Panel Data. */
+export interface PanelDataOptions { tol?: number; maxIter?: number; }
+
+/** Result from Panel Data. */
+export interface PanelDataResult { values: number[]; converged: boolean; }
+
+/** Compute Panel Data. */
+export function computePanelData(data: number[], opts: PanelDataOptions = {}): PanelDataResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePanelData };
diff --git a/src/econometrics/parallel.ts b/src/econometrics/parallel.ts
new file mode 100644
index 00000000..165a1373
--- /dev/null
+++ b/src/econometrics/parallel.ts
@@ -0,0 +1,15 @@
+/** Econometrics Parallel module — tsb analytics library. */
+export interface Econometrics parallelOptions { tol?: number; maxIter?: number; }
+export interface Econometrics parallelResult { values: number[]; converged: boolean; }
+export function computeEconometrics parallel(data: number[], opts: Econometrics parallelOptions = {}): Econometrics parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEconometrics parallel };
diff --git a/src/econometrics/plus.ts b/src/econometrics/plus.ts
new file mode 100644
index 00000000..8c8d3597
--- /dev/null
+++ b/src/econometrics/plus.ts
@@ -0,0 +1,15 @@
+/** Econometrics Plus module — tsb analytics library. */
+export interface Econometrics plusOptions { tol?: number; maxIter?: number; }
+export interface Econometrics plusResult { values: number[]; converged: boolean; }
+export function computeEconometrics plus(data: number[], opts: Econometrics plusOptions = {}): Econometrics plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEconometrics plus };
diff --git a/src/econometrics/pro.ts b/src/econometrics/pro.ts
new file mode 100644
index 00000000..57e0b80a
--- /dev/null
+++ b/src/econometrics/pro.ts
@@ -0,0 +1,15 @@
+/** Econometrics Pro module — tsb analytics library. */
+export interface Econometrics proOptions { tol?: number; maxIter?: number; }
+export interface Econometrics proResult { values: number[]; converged: boolean; }
+export function computeEconometrics pro(data: number[], opts: Econometrics proOptions = {}): Econometrics proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEconometrics pro };
diff --git a/src/econometrics/quantile_reg.ts b/src/econometrics/quantile_reg.ts
new file mode 100644
index 00000000..e49e20e1
--- /dev/null
+++ b/src/econometrics/quantile_reg.ts
@@ -0,0 +1,22 @@
+/** Quantile Reg module — tsb analytics library. */
+
+/** Options for Quantile Reg. */
+export interface QuantileRegOptions { tol?: number; maxIter?: number; }
+
+/** Result from Quantile Reg. */
+export interface QuantileRegResult { values: number[]; converged: boolean; }
+
+/** Compute Quantile Reg. */
+export function computeQuantileReg(data: number[], opts: QuantileRegOptions = {}): QuantileRegResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeQuantileReg };
diff --git a/src/econometrics/regression_kink.ts b/src/econometrics/regression_kink.ts
new file mode 100644
index 00000000..454e83aa
--- /dev/null
+++ b/src/econometrics/regression_kink.ts
@@ -0,0 +1,22 @@
+/** Regression Kink module — tsb analytics library. */
+
+/** Options for Regression Kink. */
+export interface RegressionKinkOptions { tol?: number; maxIter?: number; }
+
+/** Result from Regression Kink. */
+export interface RegressionKinkResult { values: number[]; converged: boolean; }
+
+/** Compute Regression Kink. */
+export function computeRegressionKink(data: number[], opts: RegressionKinkOptions = {}): RegressionKinkResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRegressionKink };
diff --git a/src/econometrics/robust.ts b/src/econometrics/robust.ts
new file mode 100644
index 00000000..8bc3eeeb
--- /dev/null
+++ b/src/econometrics/robust.ts
@@ -0,0 +1,22 @@
+/** Robust module — tsb analytics library. */
+
+/** Options for Robust. */
+export interface RobustOptions { tol?: number; maxIter?: number; }
+
+/** Result from Robust. */
+export interface RobustResult { values: number[]; converged: boolean; }
+
+/** Compute Robust. */
+export function computeRobust(data: number[], opts: RobustOptions = {}): RobustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRobust };
diff --git a/src/econometrics/semiparametric.ts b/src/econometrics/semiparametric.ts
new file mode 100644
index 00000000..188997e5
--- /dev/null
+++ b/src/econometrics/semiparametric.ts
@@ -0,0 +1,22 @@
+/** Semiparametric module — tsb analytics library. */
+
+/** Options for Semiparametric. */
+export interface SemiparametricOptions { tol?: number; maxIter?: number; }
+
+/** Result from Semiparametric. */
+export interface SemiparametricResult { values: number[]; converged: boolean; }
+
+/** Compute Semiparametric. */
+export function computeSemiparametric(data: number[], opts: SemiparametricOptions = {}): SemiparametricResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSemiparametric };
diff --git a/src/econometrics/small.ts b/src/econometrics/small.ts
new file mode 100644
index 00000000..678c644f
--- /dev/null
+++ b/src/econometrics/small.ts
@@ -0,0 +1,15 @@
+/** Econometrics Small module — tsb analytics library. */
+export interface Econometrics smallOptions { tol?: number; maxIter?: number; }
+export interface Econometrics smallResult { values: number[]; converged: boolean; }
+export function computeEconometrics small(data: number[], opts: Econometrics smallOptions = {}): Econometrics smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEconometrics small };
diff --git a/src/econometrics/sparse.ts b/src/econometrics/sparse.ts
new file mode 100644
index 00000000..037531cb
--- /dev/null
+++ b/src/econometrics/sparse.ts
@@ -0,0 +1,15 @@
+/** Econometrics Sparse module — tsb analytics library. */
+export interface Econometrics sparseOptions { tol?: number; maxIter?: number; }
+export interface Econometrics sparseResult { values: number[]; converged: boolean; }
+export function computeEconometrics sparse(data: number[], opts: Econometrics sparseOptions = {}): Econometrics sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEconometrics sparse };
diff --git a/src/econometrics/stable.ts b/src/econometrics/stable.ts
new file mode 100644
index 00000000..4c6fd1a0
--- /dev/null
+++ b/src/econometrics/stable.ts
@@ -0,0 +1,15 @@
+/** Econometrics Stable module — tsb analytics library. */
+export interface Econometrics stableOptions { tol?: number; maxIter?: number; }
+export interface Econometrics stableResult { values: number[]; converged: boolean; }
+export function computeEconometrics stable(data: number[], opts: Econometrics stableOptions = {}): Econometrics stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEconometrics stable };
diff --git a/src/econometrics/staggered_did.ts b/src/econometrics/staggered_did.ts
new file mode 100644
index 00000000..7f7a2a39
--- /dev/null
+++ b/src/econometrics/staggered_did.ts
@@ -0,0 +1,22 @@
+/** Staggered Did module — tsb analytics library. */
+
+/** Options for Staggered Did. */
+export interface StaggeredDidOptions { tol?: number; maxIter?: number; }
+
+/** Result from Staggered Did. */
+export interface StaggeredDidResult { values: number[]; converged: boolean; }
+
+/** Compute Staggered Did. */
+export function computeStaggeredDid(data: number[], opts: StaggeredDidOptions = {}): StaggeredDidResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeStaggeredDid };
diff --git a/src/econometrics/streaming.ts b/src/econometrics/streaming.ts
new file mode 100644
index 00000000..7e8c551a
--- /dev/null
+++ b/src/econometrics/streaming.ts
@@ -0,0 +1,15 @@
+/** Econometrics Streaming module — tsb analytics library. */
+export interface Econometrics streamingOptions { tol?: number; maxIter?: number; }
+export interface Econometrics streamingResult { values: number[]; converged: boolean; }
+export function computeEconometrics streaming(data: number[], opts: Econometrics streamingOptions = {}): Econometrics streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEconometrics streaming };
diff --git a/src/econometrics/synthetic_diff_in_diff.ts b/src/econometrics/synthetic_diff_in_diff.ts
new file mode 100644
index 00000000..31fc1159
--- /dev/null
+++ b/src/econometrics/synthetic_diff_in_diff.ts
@@ -0,0 +1,22 @@
+/** Synthetic Diff In Diff module — tsb analytics library. */
+
+/** Options for Synthetic Diff In Diff. */
+export interface SyntheticDiffInDiffOptions { tol?: number; maxIter?: number; }
+
+/** Result from Synthetic Diff In Diff. */
+export interface SyntheticDiffInDiffResult { values: number[]; converged: boolean; }
+
+/** Compute Synthetic Diff In Diff. */
+export function computeSyntheticDiffInDiff(data: number[], opts: SyntheticDiffInDiffOptions = {}): SyntheticDiffInDiffResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSyntheticDiffInDiff };
diff --git a/src/econometrics/v2.ts b/src/econometrics/v2.ts
new file mode 100644
index 00000000..376def5d
--- /dev/null
+++ b/src/econometrics/v2.ts
@@ -0,0 +1,15 @@
+/** Econometrics V2 module — tsb analytics library. */
+export interface Econometrics v2Options { tol?: number; maxIter?: number; }
+export interface Econometrics v2Result { values: number[]; converged: boolean; }
+export function computeEconometrics v2(data: number[], opts: Econometrics v2Options = {}): Econometrics v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEconometrics v2 };
diff --git a/src/econometrics/v3.ts b/src/econometrics/v3.ts
new file mode 100644
index 00000000..cfdee4da
--- /dev/null
+++ b/src/econometrics/v3.ts
@@ -0,0 +1,15 @@
+/** Econometrics V3 module — tsb analytics library. */
+export interface Econometrics v3Options { tol?: number; maxIter?: number; }
+export interface Econometrics v3Result { values: number[]; converged: boolean; }
+export function computeEconometrics v3(data: number[], opts: Econometrics v3Options = {}): Econometrics v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEconometrics v3 };
diff --git a/src/econometrics/var.ts b/src/econometrics/var.ts
new file mode 100644
index 00000000..0377e36c
--- /dev/null
+++ b/src/econometrics/var.ts
@@ -0,0 +1,22 @@
+/** Var module — tsb analytics library. */
+
+/** Options for Var. */
+export interface VarOptions { tol?: number; maxIter?: number; }
+
+/** Result from Var. */
+export interface VarResult { values: number[]; converged: boolean; }
+
+/** Compute Var. */
+export function computeVar(data: number[], opts: VarOptions = {}): VarResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeVar };
diff --git a/src/econometrics/vecm.ts b/src/econometrics/vecm.ts
new file mode 100644
index 00000000..2b584c36
--- /dev/null
+++ b/src/econometrics/vecm.ts
@@ -0,0 +1,22 @@
+/** Vecm module — tsb analytics library. */
+
+/** Options for Vecm. */
+export interface VecmOptions { tol?: number; maxIter?: number; }
+
+/** Result from Vecm. */
+export interface VecmResult { values: number[]; converged: boolean; }
+
+/** Compute Vecm. */
+export function computeVecm(data: number[], opts: VecmOptions = {}): VecmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeVecm };
diff --git a/src/econometrics/wasm.ts b/src/econometrics/wasm.ts
new file mode 100644
index 00000000..00e4a7bd
--- /dev/null
+++ b/src/econometrics/wasm.ts
@@ -0,0 +1,15 @@
+/** Econometrics Wasm module — tsb analytics library. */
+export interface Econometrics wasmOptions { tol?: number; maxIter?: number; }
+export interface Econometrics wasmResult { values: number[]; converged: boolean; }
+export function computeEconometrics wasm(data: number[], opts: Econometrics wasmOptions = {}): Econometrics wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEconometrics wasm };
diff --git a/src/econometrics/xlarge.ts b/src/econometrics/xlarge.ts
new file mode 100644
index 00000000..33a0df17
--- /dev/null
+++ b/src/econometrics/xlarge.ts
@@ -0,0 +1,15 @@
+/** Econometrics Xlarge module — tsb analytics library. */
+export interface Econometrics xlargeOptions { tol?: number; maxIter?: number; }
+export interface Econometrics xlargeResult { values: number[]; converged: boolean; }
+export function computeEconometrics xlarge(data: number[], opts: Econometrics xlargeOptions = {}): Econometrics xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEconometrics xlarge };
diff --git a/src/education/advanced.ts b/src/education/advanced.ts
new file mode 100644
index 00000000..26a542eb
--- /dev/null
+++ b/src/education/advanced.ts
@@ -0,0 +1,15 @@
+/** Education Advanced module — tsb analytics library. */
+export interface Education advancedOptions { tol?: number; maxIter?: number; }
+export interface Education advancedResult { values: number[]; converged: boolean; }
+export function computeEducation advanced(data: number[], opts: Education advancedOptions = {}): Education advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEducation advanced };
diff --git a/src/education/assessment.ts b/src/education/assessment.ts
new file mode 100644
index 00000000..00a40a78
--- /dev/null
+++ b/src/education/assessment.ts
@@ -0,0 +1,22 @@
+/** Assessment module — tsb analytics library. */
+
+/** Options for Assessment. */
+export interface AssessmentOptions { tol?: number; maxIter?: number; }
+
+/** Result from Assessment. */
+export interface AssessmentResult { values: number[]; converged: boolean; }
+
+/** Compute Assessment. */
+export function computeAssessment(data: number[], opts: AssessmentOptions = {}): AssessmentResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAssessment };
diff --git a/src/education/base2.ts b/src/education/base2.ts
new file mode 100644
index 00000000..9c903518
--- /dev/null
+++ b/src/education/base2.ts
@@ -0,0 +1,15 @@
+/** Education Base2 module — tsb analytics library. */
+export interface Education base2Options { tol?: number; maxIter?: number; }
+export interface Education base2Result { values: number[]; converged: boolean; }
+export function computeEducation base2(data: number[], opts: Education base2Options = {}): Education base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEducation base2 };
diff --git a/src/education/batch.ts b/src/education/batch.ts
new file mode 100644
index 00000000..41b4b459
--- /dev/null
+++ b/src/education/batch.ts
@@ -0,0 +1,15 @@
+/** Education Batch module — tsb analytics library. */
+export interface Education batchOptions { tol?: number; maxIter?: number; }
+export interface Education batchResult { values: number[]; converged: boolean; }
+export function computeEducation batch(data: number[], opts: Education batchOptions = {}): Education batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEducation batch };
diff --git a/src/education/beta.ts b/src/education/beta.ts
new file mode 100644
index 00000000..1d61339e
--- /dev/null
+++ b/src/education/beta.ts
@@ -0,0 +1,15 @@
+/** Education Beta module — tsb analytics library. */
+export interface Education betaOptions { tol?: number; maxIter?: number; }
+export interface Education betaResult { values: number[]; converged: boolean; }
+export function computeEducation beta(data: number[], opts: Education betaOptions = {}): Education betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEducation beta };
diff --git a/src/education/bunching_edu.ts b/src/education/bunching_edu.ts
new file mode 100644
index 00000000..69d20150
--- /dev/null
+++ b/src/education/bunching_edu.ts
@@ -0,0 +1,22 @@
+/** Bunching Edu module — tsb analytics library. */
+
+/** Options for Bunching Edu. */
+export interface BunchingEduOptions { tol?: number; maxIter?: number; }
+
+/** Result from Bunching Edu. */
+export interface BunchingEduResult { values: number[]; converged: boolean; }
+
+/** Compute Bunching Edu. */
+export function computeBunchingEdu(data: number[], opts: BunchingEduOptions = {}): BunchingEduResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBunchingEdu };
diff --git a/src/education/causal_edu.ts b/src/education/causal_edu.ts
new file mode 100644
index 00000000..764c7574
--- /dev/null
+++ b/src/education/causal_edu.ts
@@ -0,0 +1,22 @@
+/** Causal Edu module — tsb analytics library. */
+
+/** Options for Causal Edu. */
+export interface CausalEduOptions { tol?: number; maxIter?: number; }
+
+/** Result from Causal Edu. */
+export interface CausalEduResult { values: number[]; converged: boolean; }
+
+/** Compute Causal Edu. */
+export function computeCausalEdu(data: number[], opts: CausalEduOptions = {}): CausalEduResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCausalEdu };
diff --git a/src/education/classical_test.ts b/src/education/classical_test.ts
new file mode 100644
index 00000000..0a5fd447
--- /dev/null
+++ b/src/education/classical_test.ts
@@ -0,0 +1,22 @@
+/** Classical Test module — tsb analytics library. */
+
+/** Options for Classical Test. */
+export interface ClassicalTestOptions { tol?: number; maxIter?: number; }
+
+/** Result from Classical Test. */
+export interface ClassicalTestResult { values: number[]; converged: boolean; }
+
+/** Compute Classical Test. */
+export function computeClassicalTest(data: number[], opts: ClassicalTestOptions = {}): ClassicalTestResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeClassicalTest };
diff --git a/src/education/cpu.ts b/src/education/cpu.ts
new file mode 100644
index 00000000..c2339ecb
--- /dev/null
+++ b/src/education/cpu.ts
@@ -0,0 +1,15 @@
+/** Education Cpu module — tsb analytics library. */
+export interface Education cpuOptions { tol?: number; maxIter?: number; }
+export interface Education cpuResult { values: number[]; converged: boolean; }
+export function computeEducation cpu(data: number[], opts: Education cpuOptions = {}): Education cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEducation cpu };
diff --git a/src/education/dense.ts b/src/education/dense.ts
new file mode 100644
index 00000000..46f251b0
--- /dev/null
+++ b/src/education/dense.ts
@@ -0,0 +1,15 @@
+/** Education Dense module — tsb analytics library. */
+export interface Education denseOptions { tol?: number; maxIter?: number; }
+export interface Education denseResult { values: number[]; converged: boolean; }
+export function computeEducation dense(data: number[], opts: Education denseOptions = {}): Education denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEducation dense };
diff --git a/src/education/difference_in_differences_edu.ts b/src/education/difference_in_differences_edu.ts
new file mode 100644
index 00000000..fee129a9
--- /dev/null
+++ b/src/education/difference_in_differences_edu.ts
@@ -0,0 +1,22 @@
+/** Difference In Differences Edu module — tsb analytics library. */
+
+/** Options for Difference In Differences Edu. */
+export interface DifferenceInDifferencesEduOptions { tol?: number; maxIter?: number; }
+
+/** Result from Difference In Differences Edu. */
+export interface DifferenceInDifferencesEduResult { values: number[]; converged: boolean; }
+
+/** Compute Difference In Differences Edu. */
+export function computeDifferenceInDifferencesEdu(data: number[], opts: DifferenceInDifferencesEduOptions = {}): DifferenceInDifferencesEduResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDifferenceInDifferencesEdu };
diff --git a/src/education/distributed.ts b/src/education/distributed.ts
new file mode 100644
index 00000000..df8eefe4
--- /dev/null
+++ b/src/education/distributed.ts
@@ -0,0 +1,15 @@
+/** Education Distributed module — tsb analytics library. */
+export interface Education distributedOptions { tol?: number; maxIter?: number; }
+export interface Education distributedResult { values: number[]; converged: boolean; }
+export function computeEducation distributed(data: number[], opts: Education distributedOptions = {}): Education distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEducation distributed };
diff --git a/src/education/equating_edu.ts b/src/education/equating_edu.ts
new file mode 100644
index 00000000..cd49211f
--- /dev/null
+++ b/src/education/equating_edu.ts
@@ -0,0 +1,22 @@
+/** Equating Edu module — tsb analytics library. */
+
+/** Options for Equating Edu. */
+export interface EquatingEduOptions { tol?: number; maxIter?: number; }
+
+/** Result from Equating Edu. */
+export interface EquatingEduResult { values: number[]; converged: boolean; }
+
+/** Compute Equating Edu. */
+export function computeEquatingEdu(data: number[], opts: EquatingEduOptions = {}): EquatingEduResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEquatingEdu };
diff --git a/src/education/experimental.ts b/src/education/experimental.ts
new file mode 100644
index 00000000..0169bf2d
--- /dev/null
+++ b/src/education/experimental.ts
@@ -0,0 +1,22 @@
+/** Experimental module — tsb analytics library. */
+
+/** Options for Experimental. */
+export interface ExperimentalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Experimental. */
+export interface ExperimentalResult { values: number[]; converged: boolean; }
+
+/** Compute Experimental. */
+export function computeExperimental(data: number[], opts: ExperimentalOptions = {}): ExperimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeExperimental };
diff --git a/src/education/factor_edu.ts b/src/education/factor_edu.ts
new file mode 100644
index 00000000..981d5ba3
--- /dev/null
+++ b/src/education/factor_edu.ts
@@ -0,0 +1,22 @@
+/** Factor Edu module — tsb analytics library. */
+
+/** Options for Factor Edu. */
+export interface FactorEduOptions { tol?: number; maxIter?: number; }
+
+/** Result from Factor Edu. */
+export interface FactorEduResult { values: number[]; converged: boolean; }
+
+/** Compute Factor Edu. */
+export function computeFactorEdu(data: number[], opts: FactorEduOptions = {}): FactorEduResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFactorEdu };
diff --git a/src/education/fast.ts b/src/education/fast.ts
new file mode 100644
index 00000000..1146f117
--- /dev/null
+++ b/src/education/fast.ts
@@ -0,0 +1,15 @@
+/** Education Fast module — tsb analytics library. */
+export interface Education fastOptions { tol?: number; maxIter?: number; }
+export interface Education fastResult { values: number[]; converged: boolean; }
+export function computeEducation fast(data: number[], opts: Education fastOptions = {}): Education fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEducation fast };
diff --git a/src/education/future.ts b/src/education/future.ts
new file mode 100644
index 00000000..7a0643bc
--- /dev/null
+++ b/src/education/future.ts
@@ -0,0 +1,15 @@
+/** Education Future module — tsb analytics library. */
+export interface Education futureOptions { tol?: number; maxIter?: number; }
+export interface Education futureResult { values: number[]; converged: boolean; }
+export function computeEducation future(data: number[], opts: Education futureOptions = {}): Education futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEducation future };
diff --git a/src/education/generalizability.ts b/src/education/generalizability.ts
new file mode 100644
index 00000000..e1ec0fe2
--- /dev/null
+++ b/src/education/generalizability.ts
@@ -0,0 +1,22 @@
+/** Generalizability module — tsb analytics library. */
+
+/** Options for Generalizability. */
+export interface GeneralizabilityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Generalizability. */
+export interface GeneralizabilityResult { values: number[]; converged: boolean; }
+
+/** Compute Generalizability. */
+export function computeGeneralizability(data: number[], opts: GeneralizabilityOptions = {}): GeneralizabilityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGeneralizability };
diff --git a/src/education/gpu.ts b/src/education/gpu.ts
new file mode 100644
index 00000000..280d6157
--- /dev/null
+++ b/src/education/gpu.ts
@@ -0,0 +1,15 @@
+/** Education Gpu module — tsb analytics library. */
+export interface Education gpuOptions { tol?: number; maxIter?: number; }
+export interface Education gpuResult { values: number[]; converged: boolean; }
+export function computeEducation gpu(data: number[], opts: Education gpuOptions = {}): Education gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEducation gpu };
diff --git a/src/education/growth_model.ts b/src/education/growth_model.ts
new file mode 100644
index 00000000..92707a2b
--- /dev/null
+++ b/src/education/growth_model.ts
@@ -0,0 +1,22 @@
+/** Growth Model module — tsb analytics library. */
+
+/** Options for Growth Model. */
+export interface GrowthModelOptions { tol?: number; maxIter?: number; }
+
+/** Result from Growth Model. */
+export interface GrowthModelResult { values: number[]; converged: boolean; }
+
+/** Compute Growth Model. */
+export function computeGrowthModel(data: number[], opts: GrowthModelOptions = {}): GrowthModelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGrowthModel };
diff --git a/src/education/instrumental_variable_edu.ts b/src/education/instrumental_variable_edu.ts
new file mode 100644
index 00000000..9200cd28
--- /dev/null
+++ b/src/education/instrumental_variable_edu.ts
@@ -0,0 +1,22 @@
+/** Instrumental Variable Edu module — tsb analytics library. */
+
+/** Options for Instrumental Variable Edu. */
+export interface InstrumentalVariableEduOptions { tol?: number; maxIter?: number; }
+
+/** Result from Instrumental Variable Edu. */
+export interface InstrumentalVariableEduResult { values: number[]; converged: boolean; }
+
+/** Compute Instrumental Variable Edu. */
+export function computeInstrumentalVariableEdu(data: number[], opts: InstrumentalVariableEduOptions = {}): InstrumentalVariableEduResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeInstrumentalVariableEdu };
diff --git a/src/education/interrupted_time_series_edu.ts b/src/education/interrupted_time_series_edu.ts
new file mode 100644
index 00000000..27fe31d7
--- /dev/null
+++ b/src/education/interrupted_time_series_edu.ts
@@ -0,0 +1,22 @@
+/** Interrupted Time Series Edu module — tsb analytics library. */
+
+/** Options for Interrupted Time Series Edu. */
+export interface InterruptedTimeSeriesEduOptions { tol?: number; maxIter?: number; }
+
+/** Result from Interrupted Time Series Edu. */
+export interface InterruptedTimeSeriesEduResult { values: number[]; converged: boolean; }
+
+/** Compute Interrupted Time Series Edu. */
+export function computeInterruptedTimeSeriesEdu(data: number[], opts: InterruptedTimeSeriesEduOptions = {}): InterruptedTimeSeriesEduResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeInterruptedTimeSeriesEdu };
diff --git a/src/education/item_response.ts b/src/education/item_response.ts
new file mode 100644
index 00000000..b1473c12
--- /dev/null
+++ b/src/education/item_response.ts
@@ -0,0 +1,22 @@
+/** Item Response module — tsb analytics library. */
+
+/** Options for Item Response. */
+export interface ItemResponseOptions { tol?: number; maxIter?: number; }
+
+/** Result from Item Response. */
+export interface ItemResponseResult { values: number[]; converged: boolean; }
+
+/** Compute Item Response. */
+export function computeItemResponse(data: number[], opts: ItemResponseOptions = {}): ItemResponseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeItemResponse };
diff --git a/src/education/large.ts b/src/education/large.ts
new file mode 100644
index 00000000..6463b255
--- /dev/null
+++ b/src/education/large.ts
@@ -0,0 +1,15 @@
+/** Education Large module — tsb analytics library. */
+export interface Education largeOptions { tol?: number; maxIter?: number; }
+export interface Education largeResult { values: number[]; converged: boolean; }
+export function computeEducation large(data: number[], opts: Education largeOptions = {}): Education largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEducation large };
diff --git a/src/education/legacy.ts b/src/education/legacy.ts
new file mode 100644
index 00000000..76587b77
--- /dev/null
+++ b/src/education/legacy.ts
@@ -0,0 +1,15 @@
+/** Education Legacy module — tsb analytics library. */
+export interface Education legacyOptions { tol?: number; maxIter?: number; }
+export interface Education legacyResult { values: number[]; converged: boolean; }
+export function computeEducation legacy(data: number[], opts: Education legacyOptions = {}): Education legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEducation legacy };
diff --git a/src/education/linking.ts b/src/education/linking.ts
new file mode 100644
index 00000000..01b661a6
--- /dev/null
+++ b/src/education/linking.ts
@@ -0,0 +1,22 @@
+/** Linking module — tsb analytics library. */
+
+/** Options for Linking. */
+export interface LinkingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Linking. */
+export interface LinkingResult { values: number[]; converged: boolean; }
+
+/** Compute Linking. */
+export function computeLinking(data: number[], opts: LinkingOptions = {}): LinkingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLinking };
diff --git a/src/education/lite.ts b/src/education/lite.ts
new file mode 100644
index 00000000..7bb56cf3
--- /dev/null
+++ b/src/education/lite.ts
@@ -0,0 +1,15 @@
+/** Education Lite module — tsb analytics library. */
+export interface Education liteOptions { tol?: number; maxIter?: number; }
+export interface Education liteResult { values: number[]; converged: boolean; }
+export function computeEducation lite(data: number[], opts: Education liteOptions = {}): Education liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEducation lite };
diff --git a/src/education/matching_edu.ts b/src/education/matching_edu.ts
new file mode 100644
index 00000000..a4fce656
--- /dev/null
+++ b/src/education/matching_edu.ts
@@ -0,0 +1,22 @@
+/** Matching Edu module — tsb analytics library. */
+
+/** Options for Matching Edu. */
+export interface MatchingEduOptions { tol?: number; maxIter?: number; }
+
+/** Result from Matching Edu. */
+export interface MatchingEduResult { values: number[]; converged: boolean; }
+
+/** Compute Matching Edu. */
+export function computeMatchingEdu(data: number[], opts: MatchingEduOptions = {}): MatchingEduResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMatchingEdu };
diff --git a/src/education/measurement_edu.ts b/src/education/measurement_edu.ts
new file mode 100644
index 00000000..ba92bb54
--- /dev/null
+++ b/src/education/measurement_edu.ts
@@ -0,0 +1,22 @@
+/** Measurement Edu module — tsb analytics library. */
+
+/** Options for Measurement Edu. */
+export interface MeasurementEduOptions { tol?: number; maxIter?: number; }
+
+/** Result from Measurement Edu. */
+export interface MeasurementEduResult { values: number[]; converged: boolean; }
+
+/** Compute Measurement Edu. */
+export function computeMeasurementEdu(data: number[], opts: MeasurementEduOptions = {}): MeasurementEduResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMeasurementEdu };
diff --git a/src/education/mini.ts b/src/education/mini.ts
new file mode 100644
index 00000000..0daaaa6b
--- /dev/null
+++ b/src/education/mini.ts
@@ -0,0 +1,15 @@
+/** Education Mini module — tsb analytics library. */
+export interface Education miniOptions { tol?: number; maxIter?: number; }
+export interface Education miniResult { values: number[]; converged: boolean; }
+export function computeEducation mini(data: number[], opts: Education miniOptions = {}): Education miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEducation mini };
diff --git a/src/education/multilevel.ts b/src/education/multilevel.ts
new file mode 100644
index 00000000..e0c3318f
--- /dev/null
+++ b/src/education/multilevel.ts
@@ -0,0 +1,22 @@
+/** Multilevel module — tsb analytics library. */
+
+/** Options for Multilevel. */
+export interface MultilevelOptions { tol?: number; maxIter?: number; }
+
+/** Result from Multilevel. */
+export interface MultilevelResult { values: number[]; converged: boolean; }
+
+/** Compute Multilevel. */
+export function computeMultilevel(data: number[], opts: MultilevelOptions = {}): MultilevelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMultilevel };
diff --git a/src/education/natural_experiment.ts b/src/education/natural_experiment.ts
new file mode 100644
index 00000000..5049d8a7
--- /dev/null
+++ b/src/education/natural_experiment.ts
@@ -0,0 +1,22 @@
+/** Natural Experiment module — tsb analytics library. */
+
+/** Options for Natural Experiment. */
+export interface NaturalExperimentOptions { tol?: number; maxIter?: number; }
+
+/** Result from Natural Experiment. */
+export interface NaturalExperimentResult { values: number[]; converged: boolean; }
+
+/** Compute Natural Experiment. */
+export function computeNaturalExperiment(data: number[], opts: NaturalExperimentOptions = {}): NaturalExperimentResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNaturalExperiment };
diff --git a/src/education/next.ts b/src/education/next.ts
new file mode 100644
index 00000000..dc757b3f
--- /dev/null
+++ b/src/education/next.ts
@@ -0,0 +1,15 @@
+/** Education Next module — tsb analytics library. */
+export interface Education nextOptions { tol?: number; maxIter?: number; }
+export interface Education nextResult { values: number[]; converged: boolean; }
+export function computeEducation next(data: number[], opts: Education nextOptions = {}): Education nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEducation next };
diff --git a/src/education/norming.ts b/src/education/norming.ts
new file mode 100644
index 00000000..4534795f
--- /dev/null
+++ b/src/education/norming.ts
@@ -0,0 +1,22 @@
+/** Norming module — tsb analytics library. */
+
+/** Options for Norming. */
+export interface NormingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Norming. */
+export interface NormingResult { values: number[]; converged: boolean; }
+
+/** Compute Norming. */
+export function computeNorming(data: number[], opts: NormingOptions = {}): NormingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNorming };
diff --git a/src/education/online.ts b/src/education/online.ts
new file mode 100644
index 00000000..13f49195
--- /dev/null
+++ b/src/education/online.ts
@@ -0,0 +1,15 @@
+/** Education Online module — tsb analytics library. */
+export interface Education onlineOptions { tol?: number; maxIter?: number; }
+export interface Education onlineResult { values: number[]; converged: boolean; }
+export function computeEducation online(data: number[], opts: Education onlineOptions = {}): Education onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEducation online };
diff --git a/src/education/parallel.ts b/src/education/parallel.ts
new file mode 100644
index 00000000..4a0136f0
--- /dev/null
+++ b/src/education/parallel.ts
@@ -0,0 +1,15 @@
+/** Education Parallel module — tsb analytics library. */
+export interface Education parallelOptions { tol?: number; maxIter?: number; }
+export interface Education parallelResult { values: number[]; converged: boolean; }
+export function computeEducation parallel(data: number[], opts: Education parallelOptions = {}): Education parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEducation parallel };
diff --git a/src/education/plus.ts b/src/education/plus.ts
new file mode 100644
index 00000000..c08ce857
--- /dev/null
+++ b/src/education/plus.ts
@@ -0,0 +1,15 @@
+/** Education Plus module — tsb analytics library. */
+export interface Education plusOptions { tol?: number; maxIter?: number; }
+export interface Education plusResult { values: number[]; converged: boolean; }
+export function computeEducation plus(data: number[], opts: Education plusOptions = {}): Education plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEducation plus };
diff --git a/src/education/pro.ts b/src/education/pro.ts
new file mode 100644
index 00000000..c621d534
--- /dev/null
+++ b/src/education/pro.ts
@@ -0,0 +1,15 @@
+/** Education Pro module — tsb analytics library. */
+export interface Education proOptions { tol?: number; maxIter?: number; }
+export interface Education proResult { values: number[]; converged: boolean; }
+export function computeEducation pro(data: number[], opts: Education proOptions = {}): Education proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEducation pro };
diff --git a/src/education/propensity_edu.ts b/src/education/propensity_edu.ts
new file mode 100644
index 00000000..d8c8be03
--- /dev/null
+++ b/src/education/propensity_edu.ts
@@ -0,0 +1,22 @@
+/** Propensity Edu module — tsb analytics library. */
+
+/** Options for Propensity Edu. */
+export interface PropensityEduOptions { tol?: number; maxIter?: number; }
+
+/** Result from Propensity Edu. */
+export interface PropensityEduResult { values: number[]; converged: boolean; }
+
+/** Compute Propensity Edu. */
+export function computePropensityEdu(data: number[], opts: PropensityEduOptions = {}): PropensityEduResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePropensityEdu };
diff --git a/src/education/quasi_experimental.ts b/src/education/quasi_experimental.ts
new file mode 100644
index 00000000..d2c7603e
--- /dev/null
+++ b/src/education/quasi_experimental.ts
@@ -0,0 +1,22 @@
+/** Quasi Experimental module — tsb analytics library. */
+
+/** Options for Quasi Experimental. */
+export interface QuasiExperimentalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Quasi Experimental. */
+export interface QuasiExperimentalResult { values: number[]; converged: boolean; }
+
+/** Compute Quasi Experimental. */
+export function computeQuasiExperimental(data: number[], opts: QuasiExperimentalOptions = {}): QuasiExperimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeQuasiExperimental };
diff --git a/src/education/rasch.ts b/src/education/rasch.ts
new file mode 100644
index 00000000..e09a5de0
--- /dev/null
+++ b/src/education/rasch.ts
@@ -0,0 +1,22 @@
+/** Rasch module — tsb analytics library. */
+
+/** Options for Rasch. */
+export interface RaschOptions { tol?: number; maxIter?: number; }
+
+/** Result from Rasch. */
+export interface RaschResult { values: number[]; converged: boolean; }
+
+/** Compute Rasch. */
+export function computeRasch(data: number[], opts: RaschOptions = {}): RaschResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRasch };
diff --git a/src/education/regression_discontinuity_edu.ts b/src/education/regression_discontinuity_edu.ts
new file mode 100644
index 00000000..c6c9abdb
--- /dev/null
+++ b/src/education/regression_discontinuity_edu.ts
@@ -0,0 +1,22 @@
+/** Regression Discontinuity Edu module — tsb analytics library. */
+
+/** Options for Regression Discontinuity Edu. */
+export interface RegressionDiscontinuityEduOptions { tol?: number; maxIter?: number; }
+
+/** Result from Regression Discontinuity Edu. */
+export interface RegressionDiscontinuityEduResult { values: number[]; converged: boolean; }
+
+/** Compute Regression Discontinuity Edu. */
+export function computeRegressionDiscontinuityEdu(data: number[], opts: RegressionDiscontinuityEduOptions = {}): RegressionDiscontinuityEduResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRegressionDiscontinuityEdu };
diff --git a/src/education/regression_kink_edu.ts b/src/education/regression_kink_edu.ts
new file mode 100644
index 00000000..9a9963a2
--- /dev/null
+++ b/src/education/regression_kink_edu.ts
@@ -0,0 +1,22 @@
+/** Regression Kink Edu module — tsb analytics library. */
+
+/** Options for Regression Kink Edu. */
+export interface RegressionKinkEduOptions { tol?: number; maxIter?: number; }
+
+/** Result from Regression Kink Edu. */
+export interface RegressionKinkEduResult { values: number[]; converged: boolean; }
+
+/** Compute Regression Kink Edu. */
+export function computeRegressionKinkEdu(data: number[], opts: RegressionKinkEduOptions = {}): RegressionKinkEduResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRegressionKinkEdu };
diff --git a/src/education/reliability_edu.ts b/src/education/reliability_edu.ts
new file mode 100644
index 00000000..7879a103
--- /dev/null
+++ b/src/education/reliability_edu.ts
@@ -0,0 +1,22 @@
+/** Reliability Edu module — tsb analytics library. */
+
+/** Options for Reliability Edu. */
+export interface ReliabilityEduOptions { tol?: number; maxIter?: number; }
+
+/** Result from Reliability Edu. */
+export interface ReliabilityEduResult { values: number[]; converged: boolean; }
+
+/** Compute Reliability Edu. */
+export function computeReliabilityEdu(data: number[], opts: ReliabilityEduOptions = {}): ReliabilityEduResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeReliabilityEdu };
diff --git a/src/education/robust.ts b/src/education/robust.ts
new file mode 100644
index 00000000..edc9a4ce
--- /dev/null
+++ b/src/education/robust.ts
@@ -0,0 +1,15 @@
+/** Education Robust module — tsb analytics library. */
+export interface Education robustOptions { tol?: number; maxIter?: number; }
+export interface Education robustResult { values: number[]; converged: boolean; }
+export function computeEducation robust(data: number[], opts: Education robustOptions = {}): Education robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEducation robust };
diff --git a/src/education/scaling.ts b/src/education/scaling.ts
new file mode 100644
index 00000000..0a07f233
--- /dev/null
+++ b/src/education/scaling.ts
@@ -0,0 +1,22 @@
+/** Scaling module — tsb analytics library. */
+
+/** Options for Scaling. */
+export interface ScalingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Scaling. */
+export interface ScalingResult { values: number[]; converged: boolean; }
+
+/** Compute Scaling. */
+export function computeScaling(data: number[], opts: ScalingOptions = {}): ScalingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeScaling };
diff --git a/src/education/sem_edu.ts b/src/education/sem_edu.ts
new file mode 100644
index 00000000..399ab0a4
--- /dev/null
+++ b/src/education/sem_edu.ts
@@ -0,0 +1,22 @@
+/** Sem Edu module — tsb analytics library. */
+
+/** Options for Sem Edu. */
+export interface SemEduOptions { tol?: number; maxIter?: number; }
+
+/** Result from Sem Edu. */
+export interface SemEduResult { values: number[]; converged: boolean; }
+
+/** Compute Sem Edu. */
+export function computeSemEdu(data: number[], opts: SemEduOptions = {}): SemEduResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSemEdu };
diff --git a/src/education/small.ts b/src/education/small.ts
new file mode 100644
index 00000000..c3131066
--- /dev/null
+++ b/src/education/small.ts
@@ -0,0 +1,15 @@
+/** Education Small module — tsb analytics library. */
+export interface Education smallOptions { tol?: number; maxIter?: number; }
+export interface Education smallResult { values: number[]; converged: boolean; }
+export function computeEducation small(data: number[], opts: Education smallOptions = {}): Education smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEducation small };
diff --git a/src/education/sparse.ts b/src/education/sparse.ts
new file mode 100644
index 00000000..e0148ed0
--- /dev/null
+++ b/src/education/sparse.ts
@@ -0,0 +1,15 @@
+/** Education Sparse module — tsb analytics library. */
+export interface Education sparseOptions { tol?: number; maxIter?: number; }
+export interface Education sparseResult { values: number[]; converged: boolean; }
+export function computeEducation sparse(data: number[], opts: Education sparseOptions = {}): Education sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEducation sparse };
diff --git a/src/education/stable.ts b/src/education/stable.ts
new file mode 100644
index 00000000..4a85ba5c
--- /dev/null
+++ b/src/education/stable.ts
@@ -0,0 +1,15 @@
+/** Education Stable module — tsb analytics library. */
+export interface Education stableOptions { tol?: number; maxIter?: number; }
+export interface Education stableResult { values: number[]; converged: boolean; }
+export function computeEducation stable(data: number[], opts: Education stableOptions = {}): Education stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEducation stable };
diff --git a/src/education/streaming.ts b/src/education/streaming.ts
new file mode 100644
index 00000000..72924662
--- /dev/null
+++ b/src/education/streaming.ts
@@ -0,0 +1,15 @@
+/** Education Streaming module — tsb analytics library. */
+export interface Education streamingOptions { tol?: number; maxIter?: number; }
+export interface Education streamingResult { values: number[]; converged: boolean; }
+export function computeEducation streaming(data: number[], opts: Education streamingOptions = {}): Education streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEducation streaming };
diff --git a/src/education/synthetic_control_edu.ts b/src/education/synthetic_control_edu.ts
new file mode 100644
index 00000000..cbee06bc
--- /dev/null
+++ b/src/education/synthetic_control_edu.ts
@@ -0,0 +1,22 @@
+/** Synthetic Control Edu module — tsb analytics library. */
+
+/** Options for Synthetic Control Edu. */
+export interface SyntheticControlEduOptions { tol?: number; maxIter?: number; }
+
+/** Result from Synthetic Control Edu. */
+export interface SyntheticControlEduResult { values: number[]; converged: boolean; }
+
+/** Compute Synthetic Control Edu. */
+export function computeSyntheticControlEdu(data: number[], opts: SyntheticControlEduOptions = {}): SyntheticControlEduResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSyntheticControlEdu };
diff --git a/src/education/v2.ts b/src/education/v2.ts
new file mode 100644
index 00000000..d790daef
--- /dev/null
+++ b/src/education/v2.ts
@@ -0,0 +1,15 @@
+/** Education V2 module — tsb analytics library. */
+export interface Education v2Options { tol?: number; maxIter?: number; }
+export interface Education v2Result { values: number[]; converged: boolean; }
+export function computeEducation v2(data: number[], opts: Education v2Options = {}): Education v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEducation v2 };
diff --git a/src/education/v3.ts b/src/education/v3.ts
new file mode 100644
index 00000000..7dcebf8b
--- /dev/null
+++ b/src/education/v3.ts
@@ -0,0 +1,15 @@
+/** Education V3 module — tsb analytics library. */
+export interface Education v3Options { tol?: number; maxIter?: number; }
+export interface Education v3Result { values: number[]; converged: boolean; }
+export function computeEducation v3(data: number[], opts: Education v3Options = {}): Education v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEducation v3 };
diff --git a/src/education/validity_edu.ts b/src/education/validity_edu.ts
new file mode 100644
index 00000000..b17b148b
--- /dev/null
+++ b/src/education/validity_edu.ts
@@ -0,0 +1,22 @@
+/** Validity Edu module — tsb analytics library. */
+
+/** Options for Validity Edu. */
+export interface ValidityEduOptions { tol?: number; maxIter?: number; }
+
+/** Result from Validity Edu. */
+export interface ValidityEduResult { values: number[]; converged: boolean; }
+
+/** Compute Validity Edu. */
+export function computeValidityEdu(data: number[], opts: ValidityEduOptions = {}): ValidityEduResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeValidityEdu };
diff --git a/src/education/value_added.ts b/src/education/value_added.ts
new file mode 100644
index 00000000..94011ac8
--- /dev/null
+++ b/src/education/value_added.ts
@@ -0,0 +1,22 @@
+/** Value Added module — tsb analytics library. */
+
+/** Options for Value Added. */
+export interface ValueAddedOptions { tol?: number; maxIter?: number; }
+
+/** Result from Value Added. */
+export interface ValueAddedResult { values: number[]; converged: boolean; }
+
+/** Compute Value Added. */
+export function computeValueAdded(data: number[], opts: ValueAddedOptions = {}): ValueAddedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeValueAdded };
diff --git a/src/education/wasm.ts b/src/education/wasm.ts
new file mode 100644
index 00000000..f6fffd88
--- /dev/null
+++ b/src/education/wasm.ts
@@ -0,0 +1,15 @@
+/** Education Wasm module — tsb analytics library. */
+export interface Education wasmOptions { tol?: number; maxIter?: number; }
+export interface Education wasmResult { values: number[]; converged: boolean; }
+export function computeEducation wasm(data: number[], opts: Education wasmOptions = {}): Education wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEducation wasm };
diff --git a/src/education/xlarge.ts b/src/education/xlarge.ts
new file mode 100644
index 00000000..35f39f64
--- /dev/null
+++ b/src/education/xlarge.ts
@@ -0,0 +1,15 @@
+/** Education Xlarge module — tsb analytics library. */
+export interface Education xlargeOptions { tol?: number; maxIter?: number; }
+export interface Education xlargeResult { values: number[]; converged: boolean; }
+export function computeEducation xlarge(data: number[], opts: Education xlargeOptions = {}): Education xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEducation xlarge };
diff --git a/src/energy/advanced.ts b/src/energy/advanced.ts
new file mode 100644
index 00000000..7d76a0c7
--- /dev/null
+++ b/src/energy/advanced.ts
@@ -0,0 +1,15 @@
+/** Energy Advanced module — tsb analytics library. */
+export interface Energy advancedOptions { tol?: number; maxIter?: number; }
+export interface Energy advancedResult { values: number[]; converged: boolean; }
+export function computeEnergy advanced(data: number[], opts: Energy advancedOptions = {}): Energy advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEnergy advanced };
diff --git a/src/energy/agriculture_energy.ts b/src/energy/agriculture_energy.ts
new file mode 100644
index 00000000..03ba1b5a
--- /dev/null
+++ b/src/energy/agriculture_energy.ts
@@ -0,0 +1,22 @@
+/** Agriculture Energy module — tsb analytics library. */
+
+/** Options for Agriculture Energy. */
+export interface AgricultureEnergyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Agriculture Energy. */
+export interface AgricultureEnergyResult { values: number[]; converged: boolean; }
+
+/** Compute Agriculture Energy. */
+export function computeAgricultureEnergy(data: number[], opts: AgricultureEnergyOptions = {}): AgricultureEnergyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAgricultureEnergy };
diff --git a/src/energy/base2.ts b/src/energy/base2.ts
new file mode 100644
index 00000000..b2fe944d
--- /dev/null
+++ b/src/energy/base2.ts
@@ -0,0 +1,15 @@
+/** Energy Base2 module — tsb analytics library. */
+export interface Energy base2Options { tol?: number; maxIter?: number; }
+export interface Energy base2Result { values: number[]; converged: boolean; }
+export function computeEnergy base2(data: number[], opts: Energy base2Options = {}): Energy base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEnergy base2 };
diff --git a/src/energy/batch.ts b/src/energy/batch.ts
new file mode 100644
index 00000000..ef9e4803
--- /dev/null
+++ b/src/energy/batch.ts
@@ -0,0 +1,15 @@
+/** Energy Batch module — tsb analytics library. */
+export interface Energy batchOptions { tol?: number; maxIter?: number; }
+export interface Energy batchResult { values: number[]; converged: boolean; }
+export function computeEnergy batch(data: number[], opts: Energy batchOptions = {}): Energy batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEnergy batch };
diff --git a/src/energy/beta.ts b/src/energy/beta.ts
new file mode 100644
index 00000000..74bc85a7
--- /dev/null
+++ b/src/energy/beta.ts
@@ -0,0 +1,15 @@
+/** Energy Beta module — tsb analytics library. */
+export interface Energy betaOptions { tol?: number; maxIter?: number; }
+export interface Energy betaResult { values: number[]; converged: boolean; }
+export function computeEnergy beta(data: number[], opts: Energy betaOptions = {}): Energy betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEnergy beta };
diff --git a/src/energy/biomass.ts b/src/energy/biomass.ts
new file mode 100644
index 00000000..e1ef7726
--- /dev/null
+++ b/src/energy/biomass.ts
@@ -0,0 +1,22 @@
+/** Biomass module — tsb analytics library. */
+
+/** Options for Biomass. */
+export interface BiomassOptions { tol?: number; maxIter?: number; }
+
+/** Result from Biomass. */
+export interface BiomassResult { values: number[]; converged: boolean; }
+
+/** Compute Biomass. */
+export function computeBiomass(data: number[], opts: BiomassOptions = {}): BiomassResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBiomass };
diff --git a/src/energy/building.ts b/src/energy/building.ts
new file mode 100644
index 00000000..bab5658c
--- /dev/null
+++ b/src/energy/building.ts
@@ -0,0 +1,22 @@
+/** Building module — tsb analytics library. */
+
+/** Options for Building. */
+export interface BuildingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Building. */
+export interface BuildingResult { values: number[]; converged: boolean; }
+
+/** Compute Building. */
+export function computeBuilding(data: number[], opts: BuildingOptions = {}): BuildingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBuilding };
diff --git a/src/energy/carbon_price.ts b/src/energy/carbon_price.ts
new file mode 100644
index 00000000..4656fe7e
--- /dev/null
+++ b/src/energy/carbon_price.ts
@@ -0,0 +1,22 @@
+/** Carbon Price module — tsb analytics library. */
+
+/** Options for Carbon Price. */
+export interface CarbonPriceOptions { tol?: number; maxIter?: number; }
+
+/** Result from Carbon Price. */
+export interface CarbonPriceResult { values: number[]; converged: boolean; }
+
+/** Compute Carbon Price. */
+export function computeCarbonPrice(data: number[], opts: CarbonPriceOptions = {}): CarbonPriceResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCarbonPrice };
diff --git a/src/energy/coal.ts b/src/energy/coal.ts
new file mode 100644
index 00000000..8777c1e6
--- /dev/null
+++ b/src/energy/coal.ts
@@ -0,0 +1,22 @@
+/** Coal module — tsb analytics library. */
+
+/** Options for Coal. */
+export interface CoalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Coal. */
+export interface CoalResult { values: number[]; converged: boolean; }
+
+/** Compute Coal. */
+export function computeCoal(data: number[], opts: CoalOptions = {}): CoalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCoal };
diff --git a/src/energy/conservation.ts b/src/energy/conservation.ts
new file mode 100644
index 00000000..2d38218a
--- /dev/null
+++ b/src/energy/conservation.ts
@@ -0,0 +1,22 @@
+/** Conservation module — tsb analytics library. */
+
+/** Options for Conservation. */
+export interface ConservationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Conservation. */
+export interface ConservationResult { values: number[]; converged: boolean; }
+
+/** Compute Conservation. */
+export function computeConservation(data: number[], opts: ConservationOptions = {}): ConservationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeConservation };
diff --git a/src/energy/cpu.ts b/src/energy/cpu.ts
new file mode 100644
index 00000000..9848ea05
--- /dev/null
+++ b/src/energy/cpu.ts
@@ -0,0 +1,15 @@
+/** Energy Cpu module — tsb analytics library. */
+export interface Energy cpuOptions { tol?: number; maxIter?: number; }
+export interface Energy cpuResult { values: number[]; converged: boolean; }
+export function computeEnergy cpu(data: number[], opts: Energy cpuOptions = {}): Energy cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEnergy cpu };
diff --git a/src/energy/demand_response.ts b/src/energy/demand_response.ts
new file mode 100644
index 00000000..7445cfc9
--- /dev/null
+++ b/src/energy/demand_response.ts
@@ -0,0 +1,22 @@
+/** Demand Response module — tsb analytics library. */
+
+/** Options for Demand Response. */
+export interface DemandResponseOptions { tol?: number; maxIter?: number; }
+
+/** Result from Demand Response. */
+export interface DemandResponseResult { values: number[]; converged: boolean; }
+
+/** Compute Demand Response. */
+export function computeDemandResponse(data: number[], opts: DemandResponseOptions = {}): DemandResponseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDemandResponse };
diff --git a/src/energy/dense.ts b/src/energy/dense.ts
new file mode 100644
index 00000000..d15c815f
--- /dev/null
+++ b/src/energy/dense.ts
@@ -0,0 +1,15 @@
+/** Energy Dense module — tsb analytics library. */
+export interface Energy denseOptions { tol?: number; maxIter?: number; }
+export interface Energy denseResult { values: number[]; converged: boolean; }
+export function computeEnergy dense(data: number[], opts: Energy denseOptions = {}): Energy denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEnergy dense };
diff --git a/src/energy/distributed.ts b/src/energy/distributed.ts
new file mode 100644
index 00000000..d74b455e
--- /dev/null
+++ b/src/energy/distributed.ts
@@ -0,0 +1,15 @@
+/** Energy Distributed module — tsb analytics library. */
+export interface Energy distributedOptions { tol?: number; maxIter?: number; }
+export interface Energy distributedResult { values: number[]; converged: boolean; }
+export function computeEnergy distributed(data: number[], opts: Energy distributedOptions = {}): Energy distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEnergy distributed };
diff --git a/src/energy/distribution.ts b/src/energy/distribution.ts
new file mode 100644
index 00000000..0d6b21d5
--- /dev/null
+++ b/src/energy/distribution.ts
@@ -0,0 +1,22 @@
+/** Distribution module — tsb analytics library. */
+
+/** Options for Distribution. */
+export interface DistributionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Distribution. */
+export interface DistributionResult { values: number[]; converged: boolean; }
+
+/** Compute Distribution. */
+export function computeDistribution(data: number[], opts: DistributionOptions = {}): DistributionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDistribution };
diff --git a/src/energy/efficiency.ts b/src/energy/efficiency.ts
new file mode 100644
index 00000000..02046069
--- /dev/null
+++ b/src/energy/efficiency.ts
@@ -0,0 +1,22 @@
+/** Efficiency module — tsb analytics library. */
+
+/** Options for Efficiency. */
+export interface EfficiencyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Efficiency. */
+export interface EfficiencyResult { values: number[]; converged: boolean; }
+
+/** Compute Efficiency. */
+export function computeEfficiency(data: number[], opts: EfficiencyOptions = {}): EfficiencyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEfficiency };
diff --git a/src/energy/emissions.ts b/src/energy/emissions.ts
new file mode 100644
index 00000000..eaa5e87e
--- /dev/null
+++ b/src/energy/emissions.ts
@@ -0,0 +1,22 @@
+/** Emissions module — tsb analytics library. */
+
+/** Options for Emissions. */
+export interface EmissionsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Emissions. */
+export interface EmissionsResult { values: number[]; converged: boolean; }
+
+/** Compute Emissions. */
+export function computeEmissions(data: number[], opts: EmissionsOptions = {}): EmissionsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEmissions };
diff --git a/src/energy/experimental.ts b/src/energy/experimental.ts
new file mode 100644
index 00000000..2adb03a2
--- /dev/null
+++ b/src/energy/experimental.ts
@@ -0,0 +1,15 @@
+/** Energy Experimental module — tsb analytics library. */
+export interface Energy experimentalOptions { tol?: number; maxIter?: number; }
+export interface Energy experimentalResult { values: number[]; converged: boolean; }
+export function computeEnergy experimental(data: number[], opts: Energy experimentalOptions = {}): Energy experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEnergy experimental };
diff --git a/src/energy/fast.ts b/src/energy/fast.ts
new file mode 100644
index 00000000..92e8b89e
--- /dev/null
+++ b/src/energy/fast.ts
@@ -0,0 +1,15 @@
+/** Energy Fast module — tsb analytics library. */
+export interface Energy fastOptions { tol?: number; maxIter?: number; }
+export interface Energy fastResult { values: number[]; converged: boolean; }
+export function computeEnergy fast(data: number[], opts: Energy fastOptions = {}): Energy fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEnergy fast };
diff --git a/src/energy/flexibility.ts b/src/energy/flexibility.ts
new file mode 100644
index 00000000..7a806b77
--- /dev/null
+++ b/src/energy/flexibility.ts
@@ -0,0 +1,22 @@
+/** Flexibility module — tsb analytics library. */
+
+/** Options for Flexibility. */
+export interface FlexibilityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Flexibility. */
+export interface FlexibilityResult { values: number[]; converged: boolean; }
+
+/** Compute Flexibility. */
+export function computeFlexibility(data: number[], opts: FlexibilityOptions = {}): FlexibilityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFlexibility };
diff --git a/src/energy/fossil_fuel.ts b/src/energy/fossil_fuel.ts
new file mode 100644
index 00000000..0447a77b
--- /dev/null
+++ b/src/energy/fossil_fuel.ts
@@ -0,0 +1,22 @@
+/** Fossil Fuel module — tsb analytics library. */
+
+/** Options for Fossil Fuel. */
+export interface FossilFuelOptions { tol?: number; maxIter?: number; }
+
+/** Result from Fossil Fuel. */
+export interface FossilFuelResult { values: number[]; converged: boolean; }
+
+/** Compute Fossil Fuel. */
+export function computeFossilFuel(data: number[], opts: FossilFuelOptions = {}): FossilFuelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFossilFuel };
diff --git a/src/energy/future.ts b/src/energy/future.ts
new file mode 100644
index 00000000..4b6e76d8
--- /dev/null
+++ b/src/energy/future.ts
@@ -0,0 +1,15 @@
+/** Energy Future module — tsb analytics library. */
+export interface Energy futureOptions { tol?: number; maxIter?: number; }
+export interface Energy futureResult { values: number[]; converged: boolean; }
+export function computeEnergy future(data: number[], opts: Energy futureOptions = {}): Energy futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEnergy future };
diff --git a/src/energy/gas.ts b/src/energy/gas.ts
new file mode 100644
index 00000000..b152d243
--- /dev/null
+++ b/src/energy/gas.ts
@@ -0,0 +1,22 @@
+/** Gas module — tsb analytics library. */
+
+/** Options for Gas. */
+export interface GasOptions { tol?: number; maxIter?: number; }
+
+/** Result from Gas. */
+export interface GasResult { values: number[]; converged: boolean; }
+
+/** Compute Gas. */
+export function computeGas(data: number[], opts: GasOptions = {}): GasResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGas };
diff --git a/src/energy/generation.ts b/src/energy/generation.ts
new file mode 100644
index 00000000..2b8e594c
--- /dev/null
+++ b/src/energy/generation.ts
@@ -0,0 +1,22 @@
+/** Generation module — tsb analytics library. */
+
+/** Options for Generation. */
+export interface GenerationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Generation. */
+export interface GenerationResult { values: number[]; converged: boolean; }
+
+/** Compute Generation. */
+export function computeGeneration(data: number[], opts: GenerationOptions = {}): GenerationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGeneration };
diff --git a/src/energy/geothermal.ts b/src/energy/geothermal.ts
new file mode 100644
index 00000000..386cb420
--- /dev/null
+++ b/src/energy/geothermal.ts
@@ -0,0 +1,22 @@
+/** Geothermal module — tsb analytics library. */
+
+/** Options for Geothermal. */
+export interface GeothermalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Geothermal. */
+export interface GeothermalResult { values: number[]; converged: boolean; }
+
+/** Compute Geothermal. */
+export function computeGeothermal(data: number[], opts: GeothermalOptions = {}): GeothermalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGeothermal };
diff --git a/src/energy/gpu.ts b/src/energy/gpu.ts
new file mode 100644
index 00000000..9671fcae
--- /dev/null
+++ b/src/energy/gpu.ts
@@ -0,0 +1,15 @@
+/** Energy Gpu module — tsb analytics library. */
+export interface Energy gpuOptions { tol?: number; maxIter?: number; }
+export interface Energy gpuResult { values: number[]; converged: boolean; }
+export function computeEnergy gpu(data: number[], opts: Energy gpuOptions = {}): Energy gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEnergy gpu };
diff --git a/src/energy/grid.ts b/src/energy/grid.ts
new file mode 100644
index 00000000..bedf0e6b
--- /dev/null
+++ b/src/energy/grid.ts
@@ -0,0 +1,22 @@
+/** Grid module — tsb analytics library. */
+
+/** Options for Grid. */
+export interface GridOptions { tol?: number; maxIter?: number; }
+
+/** Result from Grid. */
+export interface GridResult { values: number[]; converged: boolean; }
+
+/** Compute Grid. */
+export function computeGrid(data: number[], opts: GridOptions = {}): GridResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGrid };
diff --git a/src/energy/hydro.ts b/src/energy/hydro.ts
new file mode 100644
index 00000000..23424431
--- /dev/null
+++ b/src/energy/hydro.ts
@@ -0,0 +1,22 @@
+/** Hydro module — tsb analytics library. */
+
+/** Options for Hydro. */
+export interface HydroOptions { tol?: number; maxIter?: number; }
+
+/** Result from Hydro. */
+export interface HydroResult { values: number[]; converged: boolean; }
+
+/** Compute Hydro. */
+export function computeHydro(data: number[], opts: HydroOptions = {}): HydroResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeHydro };
diff --git a/src/energy/industry_energy.ts b/src/energy/industry_energy.ts
new file mode 100644
index 00000000..10d2347b
--- /dev/null
+++ b/src/energy/industry_energy.ts
@@ -0,0 +1,22 @@
+/** Industry Energy module — tsb analytics library. */
+
+/** Options for Industry Energy. */
+export interface IndustryEnergyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Industry Energy. */
+export interface IndustryEnergyResult { values: number[]; converged: boolean; }
+
+/** Compute Industry Energy. */
+export function computeIndustryEnergy(data: number[], opts: IndustryEnergyOptions = {}): IndustryEnergyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeIndustryEnergy };
diff --git a/src/energy/large.ts b/src/energy/large.ts
new file mode 100644
index 00000000..67944dc6
--- /dev/null
+++ b/src/energy/large.ts
@@ -0,0 +1,15 @@
+/** Energy Large module — tsb analytics library. */
+export interface Energy largeOptions { tol?: number; maxIter?: number; }
+export interface Energy largeResult { values: number[]; converged: boolean; }
+export function computeEnergy large(data: number[], opts: Energy largeOptions = {}): Energy largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEnergy large };
diff --git a/src/energy/legacy.ts b/src/energy/legacy.ts
new file mode 100644
index 00000000..d0f2009b
--- /dev/null
+++ b/src/energy/legacy.ts
@@ -0,0 +1,15 @@
+/** Energy Legacy module — tsb analytics library. */
+export interface Energy legacyOptions { tol?: number; maxIter?: number; }
+export interface Energy legacyResult { values: number[]; converged: boolean; }
+export function computeEnergy legacy(data: number[], opts: Energy legacyOptions = {}): Energy legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEnergy legacy };
diff --git a/src/energy/lite.ts b/src/energy/lite.ts
new file mode 100644
index 00000000..b207543f
--- /dev/null
+++ b/src/energy/lite.ts
@@ -0,0 +1,15 @@
+/** Energy Lite module — tsb analytics library. */
+export interface Energy liteOptions { tol?: number; maxIter?: number; }
+export interface Energy liteResult { values: number[]; converged: boolean; }
+export function computeEnergy lite(data: number[], opts: Energy liteOptions = {}): Energy liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEnergy lite };
diff --git a/src/energy/market_energy.ts b/src/energy/market_energy.ts
new file mode 100644
index 00000000..cedc6049
--- /dev/null
+++ b/src/energy/market_energy.ts
@@ -0,0 +1,22 @@
+/** Market Energy module — tsb analytics library. */
+
+/** Options for Market Energy. */
+export interface MarketEnergyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Market Energy. */
+export interface MarketEnergyResult { values: number[]; converged: boolean; }
+
+/** Compute Market Energy. */
+export function computeMarketEnergy(data: number[], opts: MarketEnergyOptions = {}): MarketEnergyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMarketEnergy };
diff --git a/src/energy/mini.ts b/src/energy/mini.ts
new file mode 100644
index 00000000..89841b55
--- /dev/null
+++ b/src/energy/mini.ts
@@ -0,0 +1,15 @@
+/** Energy Mini module — tsb analytics library. */
+export interface Energy miniOptions { tol?: number; maxIter?: number; }
+export interface Energy miniResult { values: number[]; converged: boolean; }
+export function computeEnergy mini(data: number[], opts: Energy miniOptions = {}): Energy miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEnergy mini };
diff --git a/src/energy/next.ts b/src/energy/next.ts
new file mode 100644
index 00000000..e44a0559
--- /dev/null
+++ b/src/energy/next.ts
@@ -0,0 +1,15 @@
+/** Energy Next module — tsb analytics library. */
+export interface Energy nextOptions { tol?: number; maxIter?: number; }
+export interface Energy nextResult { values: number[]; converged: boolean; }
+export function computeEnergy next(data: number[], opts: Energy nextOptions = {}): Energy nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEnergy next };
diff --git a/src/energy/nuclear_energy.ts b/src/energy/nuclear_energy.ts
new file mode 100644
index 00000000..1ebbb81e
--- /dev/null
+++ b/src/energy/nuclear_energy.ts
@@ -0,0 +1,22 @@
+/** Nuclear Energy module — tsb analytics library. */
+
+/** Options for Nuclear Energy. */
+export interface NuclearEnergyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Nuclear Energy. */
+export interface NuclearEnergyResult { values: number[]; converged: boolean; }
+
+/** Compute Nuclear Energy. */
+export function computeNuclearEnergy(data: number[], opts: NuclearEnergyOptions = {}): NuclearEnergyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNuclearEnergy };
diff --git a/src/energy/oil.ts b/src/energy/oil.ts
new file mode 100644
index 00000000..b343b363
--- /dev/null
+++ b/src/energy/oil.ts
@@ -0,0 +1,22 @@
+/** Oil module — tsb analytics library. */
+
+/** Options for Oil. */
+export interface OilOptions { tol?: number; maxIter?: number; }
+
+/** Result from Oil. */
+export interface OilResult { values: number[]; converged: boolean; }
+
+/** Compute Oil. */
+export function computeOil(data: number[], opts: OilOptions = {}): OilResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeOil };
diff --git a/src/energy/online.ts b/src/energy/online.ts
new file mode 100644
index 00000000..89e8fcf0
--- /dev/null
+++ b/src/energy/online.ts
@@ -0,0 +1,15 @@
+/** Energy Online module — tsb analytics library. */
+export interface Energy onlineOptions { tol?: number; maxIter?: number; }
+export interface Energy onlineResult { values: number[]; converged: boolean; }
+export function computeEnergy online(data: number[], opts: Energy onlineOptions = {}): Energy onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEnergy online };
diff --git a/src/energy/parallel.ts b/src/energy/parallel.ts
new file mode 100644
index 00000000..e9eafbb0
--- /dev/null
+++ b/src/energy/parallel.ts
@@ -0,0 +1,15 @@
+/** Energy Parallel module — tsb analytics library. */
+export interface Energy parallelOptions { tol?: number; maxIter?: number; }
+export interface Energy parallelResult { values: number[]; converged: boolean; }
+export function computeEnergy parallel(data: number[], opts: Energy parallelOptions = {}): Energy parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEnergy parallel };
diff --git a/src/energy/planning_energy.ts b/src/energy/planning_energy.ts
new file mode 100644
index 00000000..a37a58d6
--- /dev/null
+++ b/src/energy/planning_energy.ts
@@ -0,0 +1,22 @@
+/** Planning Energy module — tsb analytics library. */
+
+/** Options for Planning Energy. */
+export interface PlanningEnergyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Planning Energy. */
+export interface PlanningEnergyResult { values: number[]; converged: boolean; }
+
+/** Compute Planning Energy. */
+export function computePlanningEnergy(data: number[], opts: PlanningEnergyOptions = {}): PlanningEnergyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePlanningEnergy };
diff --git a/src/energy/plus.ts b/src/energy/plus.ts
new file mode 100644
index 00000000..3a4a201f
--- /dev/null
+++ b/src/energy/plus.ts
@@ -0,0 +1,15 @@
+/** Energy Plus module — tsb analytics library. */
+export interface Energy plusOptions { tol?: number; maxIter?: number; }
+export interface Energy plusResult { values: number[]; converged: boolean; }
+export function computeEnergy plus(data: number[], opts: Energy plusOptions = {}): Energy plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEnergy plus };
diff --git a/src/energy/policy_energy.ts b/src/energy/policy_energy.ts
new file mode 100644
index 00000000..9f15e635
--- /dev/null
+++ b/src/energy/policy_energy.ts
@@ -0,0 +1,22 @@
+/** Policy Energy module — tsb analytics library. */
+
+/** Options for Policy Energy. */
+export interface PolicyEnergyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Policy Energy. */
+export interface PolicyEnergyResult { values: number[]; converged: boolean; }
+
+/** Compute Policy Energy. */
+export function computePolicyEnergy(data: number[], opts: PolicyEnergyOptions = {}): PolicyEnergyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePolicyEnergy };
diff --git a/src/energy/pro.ts b/src/energy/pro.ts
new file mode 100644
index 00000000..38e0662e
--- /dev/null
+++ b/src/energy/pro.ts
@@ -0,0 +1,15 @@
+/** Energy Pro module — tsb analytics library. */
+export interface Energy proOptions { tol?: number; maxIter?: number; }
+export interface Energy proResult { values: number[]; converged: boolean; }
+export function computeEnergy pro(data: number[], opts: Energy proOptions = {}): Energy proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEnergy pro };
diff --git a/src/energy/renewable.ts b/src/energy/renewable.ts
new file mode 100644
index 00000000..71d62ad3
--- /dev/null
+++ b/src/energy/renewable.ts
@@ -0,0 +1,22 @@
+/** Renewable module — tsb analytics library. */
+
+/** Options for Renewable. */
+export interface RenewableOptions { tol?: number; maxIter?: number; }
+
+/** Result from Renewable. */
+export interface RenewableResult { values: number[]; converged: boolean; }
+
+/** Compute Renewable. */
+export function computeRenewable(data: number[], opts: RenewableOptions = {}): RenewableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRenewable };
diff --git a/src/energy/robust.ts b/src/energy/robust.ts
new file mode 100644
index 00000000..4fd15c74
--- /dev/null
+++ b/src/energy/robust.ts
@@ -0,0 +1,15 @@
+/** Energy Robust module — tsb analytics library. */
+export interface Energy robustOptions { tol?: number; maxIter?: number; }
+export interface Energy robustResult { values: number[]; converged: boolean; }
+export function computeEnergy robust(data: number[], opts: Energy robustOptions = {}): Energy robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEnergy robust };
diff --git a/src/energy/small.ts b/src/energy/small.ts
new file mode 100644
index 00000000..7bc6363b
--- /dev/null
+++ b/src/energy/small.ts
@@ -0,0 +1,15 @@
+/** Energy Small module — tsb analytics library. */
+export interface Energy smallOptions { tol?: number; maxIter?: number; }
+export interface Energy smallResult { values: number[]; converged: boolean; }
+export function computeEnergy small(data: number[], opts: Energy smallOptions = {}): Energy smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEnergy small };
diff --git a/src/energy/smart_grid.ts b/src/energy/smart_grid.ts
new file mode 100644
index 00000000..ccc4a144
--- /dev/null
+++ b/src/energy/smart_grid.ts
@@ -0,0 +1,22 @@
+/** Smart Grid module — tsb analytics library. */
+
+/** Options for Smart Grid. */
+export interface SmartGridOptions { tol?: number; maxIter?: number; }
+
+/** Result from Smart Grid. */
+export interface SmartGridResult { values: number[]; converged: boolean; }
+
+/** Compute Smart Grid. */
+export function computeSmartGrid(data: number[], opts: SmartGridOptions = {}): SmartGridResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSmartGrid };
diff --git a/src/energy/solar.ts b/src/energy/solar.ts
new file mode 100644
index 00000000..59e46f03
--- /dev/null
+++ b/src/energy/solar.ts
@@ -0,0 +1,22 @@
+/** Solar module — tsb analytics library. */
+
+/** Options for Solar. */
+export interface SolarOptions { tol?: number; maxIter?: number; }
+
+/** Result from Solar. */
+export interface SolarResult { values: number[]; converged: boolean; }
+
+/** Compute Solar. */
+export function computeSolar(data: number[], opts: SolarOptions = {}): SolarResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSolar };
diff --git a/src/energy/sparse.ts b/src/energy/sparse.ts
new file mode 100644
index 00000000..a68b1db1
--- /dev/null
+++ b/src/energy/sparse.ts
@@ -0,0 +1,15 @@
+/** Energy Sparse module — tsb analytics library. */
+export interface Energy sparseOptions { tol?: number; maxIter?: number; }
+export interface Energy sparseResult { values: number[]; converged: boolean; }
+export function computeEnergy sparse(data: number[], opts: Energy sparseOptions = {}): Energy sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEnergy sparse };
diff --git a/src/energy/stable.ts b/src/energy/stable.ts
new file mode 100644
index 00000000..e2ec4c0e
--- /dev/null
+++ b/src/energy/stable.ts
@@ -0,0 +1,15 @@
+/** Energy Stable module — tsb analytics library. */
+export interface Energy stableOptions { tol?: number; maxIter?: number; }
+export interface Energy stableResult { values: number[]; converged: boolean; }
+export function computeEnergy stable(data: number[], opts: Energy stableOptions = {}): Energy stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEnergy stable };
diff --git a/src/energy/storage.ts b/src/energy/storage.ts
new file mode 100644
index 00000000..64022863
--- /dev/null
+++ b/src/energy/storage.ts
@@ -0,0 +1,22 @@
+/** Storage module — tsb analytics library. */
+
+/** Options for Storage. */
+export interface StorageOptions { tol?: number; maxIter?: number; }
+
+/** Result from Storage. */
+export interface StorageResult { values: number[]; converged: boolean; }
+
+/** Compute Storage. */
+export function computeStorage(data: number[], opts: StorageOptions = {}): StorageResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeStorage };
diff --git a/src/energy/streaming.ts b/src/energy/streaming.ts
new file mode 100644
index 00000000..5b051919
--- /dev/null
+++ b/src/energy/streaming.ts
@@ -0,0 +1,15 @@
+/** Energy Streaming module — tsb analytics library. */
+export interface Energy streamingOptions { tol?: number; maxIter?: number; }
+export interface Energy streamingResult { values: number[]; converged: boolean; }
+export function computeEnergy streaming(data: number[], opts: Energy streamingOptions = {}): Energy streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEnergy streaming };
diff --git a/src/energy/transmission.ts b/src/energy/transmission.ts
new file mode 100644
index 00000000..ee1edbd3
--- /dev/null
+++ b/src/energy/transmission.ts
@@ -0,0 +1,22 @@
+/** Transmission module — tsb analytics library. */
+
+/** Options for Transmission. */
+export interface TransmissionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Transmission. */
+export interface TransmissionResult { values: number[]; converged: boolean; }
+
+/** Compute Transmission. */
+export function computeTransmission(data: number[], opts: TransmissionOptions = {}): TransmissionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTransmission };
diff --git a/src/energy/transport_energy.ts b/src/energy/transport_energy.ts
new file mode 100644
index 00000000..f564543f
--- /dev/null
+++ b/src/energy/transport_energy.ts
@@ -0,0 +1,22 @@
+/** Transport Energy module — tsb analytics library. */
+
+/** Options for Transport Energy. */
+export interface TransportEnergyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Transport Energy. */
+export interface TransportEnergyResult { values: number[]; converged: boolean; }
+
+/** Compute Transport Energy. */
+export function computeTransportEnergy(data: number[], opts: TransportEnergyOptions = {}): TransportEnergyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTransportEnergy };
diff --git a/src/energy/v2.ts b/src/energy/v2.ts
new file mode 100644
index 00000000..a42edf0e
--- /dev/null
+++ b/src/energy/v2.ts
@@ -0,0 +1,15 @@
+/** Energy V2 module — tsb analytics library. */
+export interface Energy v2Options { tol?: number; maxIter?: number; }
+export interface Energy v2Result { values: number[]; converged: boolean; }
+export function computeEnergy v2(data: number[], opts: Energy v2Options = {}): Energy v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEnergy v2 };
diff --git a/src/energy/v3.ts b/src/energy/v3.ts
new file mode 100644
index 00000000..9bdc00d0
--- /dev/null
+++ b/src/energy/v3.ts
@@ -0,0 +1,15 @@
+/** Energy V3 module — tsb analytics library. */
+export interface Energy v3Options { tol?: number; maxIter?: number; }
+export interface Energy v3Result { values: number[]; converged: boolean; }
+export function computeEnergy v3(data: number[], opts: Energy v3Options = {}): Energy v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEnergy v3 };
diff --git a/src/energy/wasm.ts b/src/energy/wasm.ts
new file mode 100644
index 00000000..86e83711
--- /dev/null
+++ b/src/energy/wasm.ts
@@ -0,0 +1,15 @@
+/** Energy Wasm module — tsb analytics library. */
+export interface Energy wasmOptions { tol?: number; maxIter?: number; }
+export interface Energy wasmResult { values: number[]; converged: boolean; }
+export function computeEnergy wasm(data: number[], opts: Energy wasmOptions = {}): Energy wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEnergy wasm };
diff --git a/src/energy/wind.ts b/src/energy/wind.ts
new file mode 100644
index 00000000..1b1abca4
--- /dev/null
+++ b/src/energy/wind.ts
@@ -0,0 +1,22 @@
+/** Wind module — tsb analytics library. */
+
+/** Options for Wind. */
+export interface WindOptions { tol?: number; maxIter?: number; }
+
+/** Result from Wind. */
+export interface WindResult { values: number[]; converged: boolean; }
+
+/** Compute Wind. */
+export function computeWind(data: number[], opts: WindOptions = {}): WindResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeWind };
diff --git a/src/energy/xlarge.ts b/src/energy/xlarge.ts
new file mode 100644
index 00000000..f24e966c
--- /dev/null
+++ b/src/energy/xlarge.ts
@@ -0,0 +1,15 @@
+/** Energy Xlarge module — tsb analytics library. */
+export interface Energy xlargeOptions { tol?: number; maxIter?: number; }
+export interface Energy xlargeResult { values: number[]; converged: boolean; }
+export function computeEnergy xlarge(data: number[], opts: Energy xlargeOptions = {}): Energy xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEnergy xlarge };
diff --git a/src/epidemiology/advanced.ts b/src/epidemiology/advanced.ts
new file mode 100644
index 00000000..cf561974
--- /dev/null
+++ b/src/epidemiology/advanced.ts
@@ -0,0 +1,15 @@
+/** Epidemiology Advanced module — tsb analytics library. */
+export interface Epidemiology advancedOptions { tol?: number; maxIter?: number; }
+export interface Epidemiology advancedResult { values: number[]; converged: boolean; }
+export function computeEpidemiology advanced(data: number[], opts: Epidemiology advancedOptions = {}): Epidemiology advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEpidemiology advanced };
diff --git a/src/epidemiology/age_structured.ts b/src/epidemiology/age_structured.ts
new file mode 100644
index 00000000..98dab4d7
--- /dev/null
+++ b/src/epidemiology/age_structured.ts
@@ -0,0 +1,22 @@
+/** Age Structured module — tsb analytics library. */
+
+/** Options for Age Structured. */
+export interface AgeStructuredOptions { tol?: number; maxIter?: number; }
+
+/** Result from Age Structured. */
+export interface AgeStructuredResult { values: number[]; converged: boolean; }
+
+/** Compute Age Structured. */
+export function computeAgeStructured(data: number[], opts: AgeStructuredOptions = {}): AgeStructuredResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAgeStructured };
diff --git a/src/epidemiology/airborne.ts b/src/epidemiology/airborne.ts
new file mode 100644
index 00000000..b97debf6
--- /dev/null
+++ b/src/epidemiology/airborne.ts
@@ -0,0 +1,22 @@
+/** Airborne module — tsb analytics library. */
+
+/** Options for Airborne. */
+export interface AirborneOptions { tol?: number; maxIter?: number; }
+
+/** Result from Airborne. */
+export interface AirborneResult { values: number[]; converged: boolean; }
+
+/** Compute Airborne. */
+export function computeAirborne(data: number[], opts: AirborneOptions = {}): AirborneResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAirborne };
diff --git a/src/epidemiology/attack_rate.ts b/src/epidemiology/attack_rate.ts
new file mode 100644
index 00000000..1f249a1f
--- /dev/null
+++ b/src/epidemiology/attack_rate.ts
@@ -0,0 +1,22 @@
+/** Attack Rate module — tsb analytics library. */
+
+/** Options for Attack Rate. */
+export interface AttackRateOptions { tol?: number; maxIter?: number; }
+
+/** Result from Attack Rate. */
+export interface AttackRateResult { values: number[]; converged: boolean; }
+
+/** Compute Attack Rate. */
+export function computeAttackRate(data: number[], opts: AttackRateOptions = {}): AttackRateResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAttackRate };
diff --git a/src/epidemiology/base2.ts b/src/epidemiology/base2.ts
new file mode 100644
index 00000000..8e9cc83f
--- /dev/null
+++ b/src/epidemiology/base2.ts
@@ -0,0 +1,15 @@
+/** Epidemiology Base2 module — tsb analytics library. */
+export interface Epidemiology base2Options { tol?: number; maxIter?: number; }
+export interface Epidemiology base2Result { values: number[]; converged: boolean; }
+export function computeEpidemiology base2(data: number[], opts: Epidemiology base2Options = {}): Epidemiology base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEpidemiology base2 };
diff --git a/src/epidemiology/batch.ts b/src/epidemiology/batch.ts
new file mode 100644
index 00000000..f6468d2c
--- /dev/null
+++ b/src/epidemiology/batch.ts
@@ -0,0 +1,15 @@
+/** Epidemiology Batch module — tsb analytics library. */
+export interface Epidemiology batchOptions { tol?: number; maxIter?: number; }
+export interface Epidemiology batchResult { values: number[]; converged: boolean; }
+export function computeEpidemiology batch(data: number[], opts: Epidemiology batchOptions = {}): Epidemiology batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEpidemiology batch };
diff --git a/src/epidemiology/beta.ts b/src/epidemiology/beta.ts
new file mode 100644
index 00000000..68c8f62b
--- /dev/null
+++ b/src/epidemiology/beta.ts
@@ -0,0 +1,15 @@
+/** Epidemiology Beta module — tsb analytics library. */
+export interface Epidemiology betaOptions { tol?: number; maxIter?: number; }
+export interface Epidemiology betaResult { values: number[]; converged: boolean; }
+export function computeEpidemiology beta(data: number[], opts: Epidemiology betaOptions = {}): Epidemiology betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEpidemiology beta };
diff --git a/src/epidemiology/boosting.ts b/src/epidemiology/boosting.ts
new file mode 100644
index 00000000..67dfc3f7
--- /dev/null
+++ b/src/epidemiology/boosting.ts
@@ -0,0 +1,22 @@
+/** Boosting module — tsb analytics library. */
+
+/** Options for Boosting. */
+export interface BoostingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Boosting. */
+export interface BoostingResult { values: number[]; converged: boolean; }
+
+/** Compute Boosting. */
+export function computeBoosting(data: number[], opts: BoostingOptions = {}): BoostingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBoosting };
diff --git a/src/epidemiology/case_fatality.ts b/src/epidemiology/case_fatality.ts
new file mode 100644
index 00000000..8c469462
--- /dev/null
+++ b/src/epidemiology/case_fatality.ts
@@ -0,0 +1,22 @@
+/** Case Fatality module — tsb analytics library. */
+
+/** Options for Case Fatality. */
+export interface CaseFatalityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Case Fatality. */
+export interface CaseFatalityResult { values: number[]; converged: boolean; }
+
+/** Compute Case Fatality. */
+export function computeCaseFatality(data: number[], opts: CaseFatalityOptions = {}): CaseFatalityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCaseFatality };
diff --git a/src/epidemiology/contact_tracing.ts b/src/epidemiology/contact_tracing.ts
new file mode 100644
index 00000000..3130eb48
--- /dev/null
+++ b/src/epidemiology/contact_tracing.ts
@@ -0,0 +1,22 @@
+/** Contact Tracing module — tsb analytics library. */
+
+/** Options for Contact Tracing. */
+export interface ContactTracingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Contact Tracing. */
+export interface ContactTracingResult { values: number[]; converged: boolean; }
+
+/** Compute Contact Tracing. */
+export function computeContactTracing(data: number[], opts: ContactTracingOptions = {}): ContactTracingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeContactTracing };
diff --git a/src/epidemiology/cpu.ts b/src/epidemiology/cpu.ts
new file mode 100644
index 00000000..2dfca675
--- /dev/null
+++ b/src/epidemiology/cpu.ts
@@ -0,0 +1,15 @@
+/** Epidemiology Cpu module — tsb analytics library. */
+export interface Epidemiology cpuOptions { tol?: number; maxIter?: number; }
+export interface Epidemiology cpuResult { values: number[]; converged: boolean; }
+export function computeEpidemiology cpu(data: number[], opts: Epidemiology cpuOptions = {}): Epidemiology cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEpidemiology cpu };
diff --git a/src/epidemiology/cross_immunity.ts b/src/epidemiology/cross_immunity.ts
new file mode 100644
index 00000000..a31c94fc
--- /dev/null
+++ b/src/epidemiology/cross_immunity.ts
@@ -0,0 +1,22 @@
+/** Cross Immunity module — tsb analytics library. */
+
+/** Options for Cross Immunity. */
+export interface CrossImmunityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Cross Immunity. */
+export interface CrossImmunityResult { values: number[]; converged: boolean; }
+
+/** Compute Cross Immunity. */
+export function computeCrossImmunity(data: number[], opts: CrossImmunityOptions = {}): CrossImmunityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCrossImmunity };
diff --git a/src/epidemiology/dense.ts b/src/epidemiology/dense.ts
new file mode 100644
index 00000000..10ac677b
--- /dev/null
+++ b/src/epidemiology/dense.ts
@@ -0,0 +1,15 @@
+/** Epidemiology Dense module — tsb analytics library. */
+export interface Epidemiology denseOptions { tol?: number; maxIter?: number; }
+export interface Epidemiology denseResult { values: number[]; converged: boolean; }
+export function computeEpidemiology dense(data: number[], opts: Epidemiology denseOptions = {}): Epidemiology denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEpidemiology dense };
diff --git a/src/epidemiology/deterministic.ts b/src/epidemiology/deterministic.ts
new file mode 100644
index 00000000..668b1358
--- /dev/null
+++ b/src/epidemiology/deterministic.ts
@@ -0,0 +1,22 @@
+/** Deterministic module — tsb analytics library. */
+
+/** Options for Deterministic. */
+export interface DeterministicOptions { tol?: number; maxIter?: number; }
+
+/** Result from Deterministic. */
+export interface DeterministicResult { values: number[]; converged: boolean; }
+
+/** Compute Deterministic. */
+export function computeDeterministic(data: number[], opts: DeterministicOptions = {}): DeterministicResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDeterministic };
diff --git a/src/epidemiology/distributed.ts b/src/epidemiology/distributed.ts
new file mode 100644
index 00000000..b97d17d3
--- /dev/null
+++ b/src/epidemiology/distributed.ts
@@ -0,0 +1,15 @@
+/** Epidemiology Distributed module — tsb analytics library. */
+export interface Epidemiology distributedOptions { tol?: number; maxIter?: number; }
+export interface Epidemiology distributedResult { values: number[]; converged: boolean; }
+export function computeEpidemiology distributed(data: number[], opts: Epidemiology distributedOptions = {}): Epidemiology distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEpidemiology distributed };
diff --git a/src/epidemiology/experimental.ts b/src/epidemiology/experimental.ts
new file mode 100644
index 00000000..6ccc642c
--- /dev/null
+++ b/src/epidemiology/experimental.ts
@@ -0,0 +1,15 @@
+/** Epidemiology Experimental module — tsb analytics library. */
+export interface Epidemiology experimentalOptions { tol?: number; maxIter?: number; }
+export interface Epidemiology experimentalResult { values: number[]; converged: boolean; }
+export function computeEpidemiology experimental(data: number[], opts: Epidemiology experimentalOptions = {}): Epidemiology experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEpidemiology experimental };
diff --git a/src/epidemiology/fast.ts b/src/epidemiology/fast.ts
new file mode 100644
index 00000000..33095caf
--- /dev/null
+++ b/src/epidemiology/fast.ts
@@ -0,0 +1,15 @@
+/** Epidemiology Fast module — tsb analytics library. */
+export interface Epidemiology fastOptions { tol?: number; maxIter?: number; }
+export interface Epidemiology fastResult { values: number[]; converged: boolean; }
+export function computeEpidemiology fast(data: number[], opts: Epidemiology fastOptions = {}): Epidemiology fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEpidemiology fast };
diff --git a/src/epidemiology/fomite.ts b/src/epidemiology/fomite.ts
new file mode 100644
index 00000000..f0621f85
--- /dev/null
+++ b/src/epidemiology/fomite.ts
@@ -0,0 +1,22 @@
+/** Fomite module — tsb analytics library. */
+
+/** Options for Fomite. */
+export interface FomiteOptions { tol?: number; maxIter?: number; }
+
+/** Result from Fomite. */
+export interface FomiteResult { values: number[]; converged: boolean; }
+
+/** Compute Fomite. */
+export function computeFomite(data: number[], opts: FomiteOptions = {}): FomiteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFomite };
diff --git a/src/epidemiology/future.ts b/src/epidemiology/future.ts
new file mode 100644
index 00000000..40753725
--- /dev/null
+++ b/src/epidemiology/future.ts
@@ -0,0 +1,15 @@
+/** Epidemiology Future module — tsb analytics library. */
+export interface Epidemiology futureOptions { tol?: number; maxIter?: number; }
+export interface Epidemiology futureResult { values: number[]; converged: boolean; }
+export function computeEpidemiology future(data: number[], opts: Epidemiology futureOptions = {}): Epidemiology futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEpidemiology future };
diff --git a/src/epidemiology/genomic_epi.ts b/src/epidemiology/genomic_epi.ts
new file mode 100644
index 00000000..9bb7b157
--- /dev/null
+++ b/src/epidemiology/genomic_epi.ts
@@ -0,0 +1,22 @@
+/** Genomic Epi module — tsb analytics library. */
+
+/** Options for Genomic Epi. */
+export interface GenomicEpiOptions { tol?: number; maxIter?: number; }
+
+/** Result from Genomic Epi. */
+export interface GenomicEpiResult { values: number[]; converged: boolean; }
+
+/** Compute Genomic Epi. */
+export function computeGenomicEpi(data: number[], opts: GenomicEpiOptions = {}): GenomicEpiResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGenomicEpi };
diff --git a/src/epidemiology/gpu.ts b/src/epidemiology/gpu.ts
new file mode 100644
index 00000000..b831ef18
--- /dev/null
+++ b/src/epidemiology/gpu.ts
@@ -0,0 +1,15 @@
+/** Epidemiology Gpu module — tsb analytics library. */
+export interface Epidemiology gpuOptions { tol?: number; maxIter?: number; }
+export interface Epidemiology gpuResult { values: number[]; converged: boolean; }
+export function computeEpidemiology gpu(data: number[], opts: Epidemiology gpuOptions = {}): Epidemiology gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEpidemiology gpu };
diff --git a/src/epidemiology/herd_immunity.ts b/src/epidemiology/herd_immunity.ts
new file mode 100644
index 00000000..bc117b2e
--- /dev/null
+++ b/src/epidemiology/herd_immunity.ts
@@ -0,0 +1,22 @@
+/** Herd Immunity module — tsb analytics library. */
+
+/** Options for Herd Immunity. */
+export interface HerdImmunityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Herd Immunity. */
+export interface HerdImmunityResult { values: number[]; converged: boolean; }
+
+/** Compute Herd Immunity. */
+export function computeHerdImmunity(data: number[], opts: HerdImmunityOptions = {}): HerdImmunityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeHerdImmunity };
diff --git a/src/epidemiology/infection_fatality.ts b/src/epidemiology/infection_fatality.ts
new file mode 100644
index 00000000..3b1071b0
--- /dev/null
+++ b/src/epidemiology/infection_fatality.ts
@@ -0,0 +1,22 @@
+/** Infection Fatality module — tsb analytics library. */
+
+/** Options for Infection Fatality. */
+export interface InfectionFatalityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Infection Fatality. */
+export interface InfectionFatalityResult { values: number[]; converged: boolean; }
+
+/** Compute Infection Fatality. */
+export function computeInfectionFatality(data: number[], opts: InfectionFatalityOptions = {}): InfectionFatalityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeInfectionFatality };
diff --git a/src/epidemiology/large.ts b/src/epidemiology/large.ts
new file mode 100644
index 00000000..dabb9289
--- /dev/null
+++ b/src/epidemiology/large.ts
@@ -0,0 +1,15 @@
+/** Epidemiology Large module — tsb analytics library. */
+export interface Epidemiology largeOptions { tol?: number; maxIter?: number; }
+export interface Epidemiology largeResult { values: number[]; converged: boolean; }
+export function computeEpidemiology large(data: number[], opts: Epidemiology largeOptions = {}): Epidemiology largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEpidemiology large };
diff --git a/src/epidemiology/legacy.ts b/src/epidemiology/legacy.ts
new file mode 100644
index 00000000..2c9e8cbd
--- /dev/null
+++ b/src/epidemiology/legacy.ts
@@ -0,0 +1,15 @@
+/** Epidemiology Legacy module — tsb analytics library. */
+export interface Epidemiology legacyOptions { tol?: number; maxIter?: number; }
+export interface Epidemiology legacyResult { values: number[]; converged: boolean; }
+export function computeEpidemiology legacy(data: number[], opts: Epidemiology legacyOptions = {}): Epidemiology legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEpidemiology legacy };
diff --git a/src/epidemiology/lite.ts b/src/epidemiology/lite.ts
new file mode 100644
index 00000000..76bec5cb
--- /dev/null
+++ b/src/epidemiology/lite.ts
@@ -0,0 +1,15 @@
+/** Epidemiology Lite module — tsb analytics library. */
+export interface Epidemiology liteOptions { tol?: number; maxIter?: number; }
+export interface Epidemiology liteResult { values: number[]; converged: boolean; }
+export function computeEpidemiology lite(data: number[], opts: Epidemiology liteOptions = {}): Epidemiology liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEpidemiology lite };
diff --git a/src/epidemiology/mini.ts b/src/epidemiology/mini.ts
new file mode 100644
index 00000000..e74719a9
--- /dev/null
+++ b/src/epidemiology/mini.ts
@@ -0,0 +1,15 @@
+/** Epidemiology Mini module — tsb analytics library. */
+export interface Epidemiology miniOptions { tol?: number; maxIter?: number; }
+export interface Epidemiology miniResult { values: number[]; converged: boolean; }
+export function computeEpidemiology mini(data: number[], opts: Epidemiology miniOptions = {}): Epidemiology miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEpidemiology mini };
diff --git a/src/epidemiology/network_epi.ts b/src/epidemiology/network_epi.ts
new file mode 100644
index 00000000..57ef7a6f
--- /dev/null
+++ b/src/epidemiology/network_epi.ts
@@ -0,0 +1,22 @@
+/** Network Epi module — tsb analytics library. */
+
+/** Options for Network Epi. */
+export interface NetworkEpiOptions { tol?: number; maxIter?: number; }
+
+/** Result from Network Epi. */
+export interface NetworkEpiResult { values: number[]; converged: boolean; }
+
+/** Compute Network Epi. */
+export function computeNetworkEpi(data: number[], opts: NetworkEpiOptions = {}): NetworkEpiResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNetworkEpi };
diff --git a/src/epidemiology/next.ts b/src/epidemiology/next.ts
new file mode 100644
index 00000000..24193d54
--- /dev/null
+++ b/src/epidemiology/next.ts
@@ -0,0 +1,15 @@
+/** Epidemiology Next module — tsb analytics library. */
+export interface Epidemiology nextOptions { tol?: number; maxIter?: number; }
+export interface Epidemiology nextResult { values: number[]; converged: boolean; }
+export function computeEpidemiology next(data: number[], opts: Epidemiology nextOptions = {}): Epidemiology nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEpidemiology next };
diff --git a/src/epidemiology/online.ts b/src/epidemiology/online.ts
new file mode 100644
index 00000000..800f3c69
--- /dev/null
+++ b/src/epidemiology/online.ts
@@ -0,0 +1,15 @@
+/** Epidemiology Online module — tsb analytics library. */
+export interface Epidemiology onlineOptions { tol?: number; maxIter?: number; }
+export interface Epidemiology onlineResult { values: number[]; converged: boolean; }
+export function computeEpidemiology online(data: number[], opts: Epidemiology onlineOptions = {}): Epidemiology onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEpidemiology online };
diff --git a/src/epidemiology/outbreak.ts b/src/epidemiology/outbreak.ts
new file mode 100644
index 00000000..060173ca
--- /dev/null
+++ b/src/epidemiology/outbreak.ts
@@ -0,0 +1,22 @@
+/** Outbreak module — tsb analytics library. */
+
+/** Options for Outbreak. */
+export interface OutbreakOptions { tol?: number; maxIter?: number; }
+
+/** Result from Outbreak. */
+export interface OutbreakResult { values: number[]; converged: boolean; }
+
+/** Compute Outbreak. */
+export function computeOutbreak(data: number[], opts: OutbreakOptions = {}): OutbreakResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeOutbreak };
diff --git a/src/epidemiology/parallel.ts b/src/epidemiology/parallel.ts
new file mode 100644
index 00000000..3392c07e
--- /dev/null
+++ b/src/epidemiology/parallel.ts
@@ -0,0 +1,15 @@
+/** Epidemiology Parallel module — tsb analytics library. */
+export interface Epidemiology parallelOptions { tol?: number; maxIter?: number; }
+export interface Epidemiology parallelResult { values: number[]; converged: boolean; }
+export function computeEpidemiology parallel(data: number[], opts: Epidemiology parallelOptions = {}): Epidemiology parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEpidemiology parallel };
diff --git a/src/epidemiology/plus.ts b/src/epidemiology/plus.ts
new file mode 100644
index 00000000..9e2565b6
--- /dev/null
+++ b/src/epidemiology/plus.ts
@@ -0,0 +1,15 @@
+/** Epidemiology Plus module — tsb analytics library. */
+export interface Epidemiology plusOptions { tol?: number; maxIter?: number; }
+export interface Epidemiology plusResult { values: number[]; converged: boolean; }
+export function computeEpidemiology plus(data: number[], opts: Epidemiology plusOptions = {}): Epidemiology plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEpidemiology plus };
diff --git a/src/epidemiology/pro.ts b/src/epidemiology/pro.ts
new file mode 100644
index 00000000..1e2ab4fb
--- /dev/null
+++ b/src/epidemiology/pro.ts
@@ -0,0 +1,15 @@
+/** Epidemiology Pro module — tsb analytics library. */
+export interface Epidemiology proOptions { tol?: number; maxIter?: number; }
+export interface Epidemiology proResult { values: number[]; converged: boolean; }
+export function computeEpidemiology pro(data: number[], opts: Epidemiology proOptions = {}): Epidemiology proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEpidemiology pro };
diff --git a/src/epidemiology/reproduction_number.ts b/src/epidemiology/reproduction_number.ts
new file mode 100644
index 00000000..bd90c917
--- /dev/null
+++ b/src/epidemiology/reproduction_number.ts
@@ -0,0 +1,22 @@
+/** Reproduction Number module — tsb analytics library. */
+
+/** Options for Reproduction Number. */
+export interface ReproductionNumberOptions { tol?: number; maxIter?: number; }
+
+/** Result from Reproduction Number. */
+export interface ReproductionNumberResult { values: number[]; converged: boolean; }
+
+/** Compute Reproduction Number. */
+export function computeReproductionNumber(data: number[], opts: ReproductionNumberOptions = {}): ReproductionNumberResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeReproductionNumber };
diff --git a/src/epidemiology/robust.ts b/src/epidemiology/robust.ts
new file mode 100644
index 00000000..39bc7d11
--- /dev/null
+++ b/src/epidemiology/robust.ts
@@ -0,0 +1,15 @@
+/** Epidemiology Robust module — tsb analytics library. */
+export interface Epidemiology robustOptions { tol?: number; maxIter?: number; }
+export interface Epidemiology robustResult { values: number[]; converged: boolean; }
+export function computeEpidemiology robust(data: number[], opts: Epidemiology robustOptions = {}): Epidemiology robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEpidemiology robust };
diff --git a/src/epidemiology/seir.ts b/src/epidemiology/seir.ts
new file mode 100644
index 00000000..55645292
--- /dev/null
+++ b/src/epidemiology/seir.ts
@@ -0,0 +1,22 @@
+/** Seir module — tsb analytics library. */
+
+/** Options for Seir. */
+export interface SeirOptions { tol?: number; maxIter?: number; }
+
+/** Result from Seir. */
+export interface SeirResult { values: number[]; converged: boolean; }
+
+/** Compute Seir. */
+export function computeSeir(data: number[], opts: SeirOptions = {}): SeirResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSeir };
diff --git a/src/epidemiology/seirs.ts b/src/epidemiology/seirs.ts
new file mode 100644
index 00000000..1e2d79b5
--- /dev/null
+++ b/src/epidemiology/seirs.ts
@@ -0,0 +1,22 @@
+/** Seirs module — tsb analytics library. */
+
+/** Options for Seirs. */
+export interface SeirsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Seirs. */
+export interface SeirsResult { values: number[]; converged: boolean; }
+
+/** Compute Seirs. */
+export function computeSeirs(data: number[], opts: SeirsOptions = {}): SeirsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSeirs };
diff --git a/src/epidemiology/sierd.ts b/src/epidemiology/sierd.ts
new file mode 100644
index 00000000..e63a4523
--- /dev/null
+++ b/src/epidemiology/sierd.ts
@@ -0,0 +1,22 @@
+/** Sierd module — tsb analytics library. */
+
+/** Options for Sierd. */
+export interface SierdOptions { tol?: number; maxIter?: number; }
+
+/** Result from Sierd. */
+export interface SierdResult { values: number[]; converged: boolean; }
+
+/** Compute Sierd. */
+export function computeSierd(data: number[], opts: SierdOptions = {}): SierdResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSierd };
diff --git a/src/epidemiology/sir.ts b/src/epidemiology/sir.ts
new file mode 100644
index 00000000..2b33a1e9
--- /dev/null
+++ b/src/epidemiology/sir.ts
@@ -0,0 +1,22 @@
+/** Sir module — tsb analytics library. */
+
+/** Options for Sir. */
+export interface SirOptions { tol?: number; maxIter?: number; }
+
+/** Result from Sir. */
+export interface SirResult { values: number[]; converged: boolean; }
+
+/** Compute Sir. */
+export function computeSir(data: number[], opts: SirOptions = {}): SirResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSir };
diff --git a/src/epidemiology/sird.ts b/src/epidemiology/sird.ts
new file mode 100644
index 00000000..072e971d
--- /dev/null
+++ b/src/epidemiology/sird.ts
@@ -0,0 +1,22 @@
+/** Sird module — tsb analytics library. */
+
+/** Options for Sird. */
+export interface SirdOptions { tol?: number; maxIter?: number; }
+
+/** Result from Sird. */
+export interface SirdResult { values: number[]; converged: boolean; }
+
+/** Compute Sird. */
+export function computeSird(data: number[], opts: SirdOptions = {}): SirdResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSird };
diff --git a/src/epidemiology/small.ts b/src/epidemiology/small.ts
new file mode 100644
index 00000000..08fd19c8
--- /dev/null
+++ b/src/epidemiology/small.ts
@@ -0,0 +1,15 @@
+/** Epidemiology Small module — tsb analytics library. */
+export interface Epidemiology smallOptions { tol?: number; maxIter?: number; }
+export interface Epidemiology smallResult { values: number[]; converged: boolean; }
+export function computeEpidemiology small(data: number[], opts: Epidemiology smallOptions = {}): Epidemiology smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEpidemiology small };
diff --git a/src/epidemiology/sparse.ts b/src/epidemiology/sparse.ts
new file mode 100644
index 00000000..3441ccb6
--- /dev/null
+++ b/src/epidemiology/sparse.ts
@@ -0,0 +1,15 @@
+/** Epidemiology Sparse module — tsb analytics library. */
+export interface Epidemiology sparseOptions { tol?: number; maxIter?: number; }
+export interface Epidemiology sparseResult { values: number[]; converged: boolean; }
+export function computeEpidemiology sparse(data: number[], opts: Epidemiology sparseOptions = {}): Epidemiology sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEpidemiology sparse };
diff --git a/src/epidemiology/spatial_epi.ts b/src/epidemiology/spatial_epi.ts
new file mode 100644
index 00000000..0395b432
--- /dev/null
+++ b/src/epidemiology/spatial_epi.ts
@@ -0,0 +1,22 @@
+/** Spatial Epi module — tsb analytics library. */
+
+/** Options for Spatial Epi. */
+export interface SpatialEpiOptions { tol?: number; maxIter?: number; }
+
+/** Result from Spatial Epi. */
+export interface SpatialEpiResult { values: number[]; converged: boolean; }
+
+/** Compute Spatial Epi. */
+export function computeSpatialEpi(data: number[], opts: SpatialEpiOptions = {}): SpatialEpiResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSpatialEpi };
diff --git a/src/epidemiology/spillover.ts b/src/epidemiology/spillover.ts
new file mode 100644
index 00000000..a9d12533
--- /dev/null
+++ b/src/epidemiology/spillover.ts
@@ -0,0 +1,22 @@
+/** Spillover module — tsb analytics library. */
+
+/** Options for Spillover. */
+export interface SpilloverOptions { tol?: number; maxIter?: number; }
+
+/** Result from Spillover. */
+export interface SpilloverResult { values: number[]; converged: boolean; }
+
+/** Compute Spillover. */
+export function computeSpillover(data: number[], opts: SpilloverOptions = {}): SpilloverResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSpillover };
diff --git a/src/epidemiology/stable.ts b/src/epidemiology/stable.ts
new file mode 100644
index 00000000..62fe7306
--- /dev/null
+++ b/src/epidemiology/stable.ts
@@ -0,0 +1,15 @@
+/** Epidemiology Stable module — tsb analytics library. */
+export interface Epidemiology stableOptions { tol?: number; maxIter?: number; }
+export interface Epidemiology stableResult { values: number[]; converged: boolean; }
+export function computeEpidemiology stable(data: number[], opts: Epidemiology stableOptions = {}): Epidemiology stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEpidemiology stable };
diff --git a/src/epidemiology/stochastic_epi.ts b/src/epidemiology/stochastic_epi.ts
new file mode 100644
index 00000000..218bcb38
--- /dev/null
+++ b/src/epidemiology/stochastic_epi.ts
@@ -0,0 +1,22 @@
+/** Stochastic Epi module — tsb analytics library. */
+
+/** Options for Stochastic Epi. */
+export interface StochasticEpiOptions { tol?: number; maxIter?: number; }
+
+/** Result from Stochastic Epi. */
+export interface StochasticEpiResult { values: number[]; converged: boolean; }
+
+/** Compute Stochastic Epi. */
+export function computeStochasticEpi(data: number[], opts: StochasticEpiOptions = {}): StochasticEpiResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeStochasticEpi };
diff --git a/src/epidemiology/streaming.ts b/src/epidemiology/streaming.ts
new file mode 100644
index 00000000..de2108e0
--- /dev/null
+++ b/src/epidemiology/streaming.ts
@@ -0,0 +1,15 @@
+/** Epidemiology Streaming module — tsb analytics library. */
+export interface Epidemiology streamingOptions { tol?: number; maxIter?: number; }
+export interface Epidemiology streamingResult { values: number[]; converged: boolean; }
+export function computeEpidemiology streaming(data: number[], opts: Epidemiology streamingOptions = {}): Epidemiology streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEpidemiology streaming };
diff --git a/src/epidemiology/surveillance.ts b/src/epidemiology/surveillance.ts
new file mode 100644
index 00000000..65a22f35
--- /dev/null
+++ b/src/epidemiology/surveillance.ts
@@ -0,0 +1,22 @@
+/** Surveillance module — tsb analytics library. */
+
+/** Options for Surveillance. */
+export interface SurveillanceOptions { tol?: number; maxIter?: number; }
+
+/** Result from Surveillance. */
+export interface SurveillanceResult { values: number[]; converged: boolean; }
+
+/** Compute Surveillance. */
+export function computeSurveillance(data: number[], opts: SurveillanceOptions = {}): SurveillanceResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSurveillance };
diff --git a/src/epidemiology/v2.ts b/src/epidemiology/v2.ts
new file mode 100644
index 00000000..ec2d5000
--- /dev/null
+++ b/src/epidemiology/v2.ts
@@ -0,0 +1,15 @@
+/** Epidemiology V2 module — tsb analytics library. */
+export interface Epidemiology v2Options { tol?: number; maxIter?: number; }
+export interface Epidemiology v2Result { values: number[]; converged: boolean; }
+export function computeEpidemiology v2(data: number[], opts: Epidemiology v2Options = {}): Epidemiology v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEpidemiology v2 };
diff --git a/src/epidemiology/v3.ts b/src/epidemiology/v3.ts
new file mode 100644
index 00000000..43cb15b8
--- /dev/null
+++ b/src/epidemiology/v3.ts
@@ -0,0 +1,15 @@
+/** Epidemiology V3 module — tsb analytics library. */
+export interface Epidemiology v3Options { tol?: number; maxIter?: number; }
+export interface Epidemiology v3Result { values: number[]; converged: boolean; }
+export function computeEpidemiology v3(data: number[], opts: Epidemiology v3Options = {}): Epidemiology v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEpidemiology v3 };
diff --git a/src/epidemiology/vaccine_efficacy.ts b/src/epidemiology/vaccine_efficacy.ts
new file mode 100644
index 00000000..14a95532
--- /dev/null
+++ b/src/epidemiology/vaccine_efficacy.ts
@@ -0,0 +1,22 @@
+/** Vaccine Efficacy module — tsb analytics library. */
+
+/** Options for Vaccine Efficacy. */
+export interface VaccineEfficacyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Vaccine Efficacy. */
+export interface VaccineEfficacyResult { values: number[]; converged: boolean; }
+
+/** Compute Vaccine Efficacy. */
+export function computeVaccineEfficacy(data: number[], opts: VaccineEfficacyOptions = {}): VaccineEfficacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeVaccineEfficacy };
diff --git a/src/epidemiology/vector_borne.ts b/src/epidemiology/vector_borne.ts
new file mode 100644
index 00000000..51f4006b
--- /dev/null
+++ b/src/epidemiology/vector_borne.ts
@@ -0,0 +1,22 @@
+/** Vector Borne module — tsb analytics library. */
+
+/** Options for Vector Borne. */
+export interface VectorBorneOptions { tol?: number; maxIter?: number; }
+
+/** Result from Vector Borne. */
+export interface VectorBorneResult { values: number[]; converged: boolean; }
+
+/** Compute Vector Borne. */
+export function computeVectorBorne(data: number[], opts: VectorBorneOptions = {}): VectorBorneResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeVectorBorne };
diff --git a/src/epidemiology/waning_immunity.ts b/src/epidemiology/waning_immunity.ts
new file mode 100644
index 00000000..d1666d47
--- /dev/null
+++ b/src/epidemiology/waning_immunity.ts
@@ -0,0 +1,22 @@
+/** Waning Immunity module — tsb analytics library. */
+
+/** Options for Waning Immunity. */
+export interface WaningImmunityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Waning Immunity. */
+export interface WaningImmunityResult { values: number[]; converged: boolean; }
+
+/** Compute Waning Immunity. */
+export function computeWaningImmunity(data: number[], opts: WaningImmunityOptions = {}): WaningImmunityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeWaningImmunity };
diff --git a/src/epidemiology/wasm.ts b/src/epidemiology/wasm.ts
new file mode 100644
index 00000000..67c56cdb
--- /dev/null
+++ b/src/epidemiology/wasm.ts
@@ -0,0 +1,15 @@
+/** Epidemiology Wasm module — tsb analytics library. */
+export interface Epidemiology wasmOptions { tol?: number; maxIter?: number; }
+export interface Epidemiology wasmResult { values: number[]; converged: boolean; }
+export function computeEpidemiology wasm(data: number[], opts: Epidemiology wasmOptions = {}): Epidemiology wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEpidemiology wasm };
diff --git a/src/epidemiology/waterborne.ts b/src/epidemiology/waterborne.ts
new file mode 100644
index 00000000..6066cf08
--- /dev/null
+++ b/src/epidemiology/waterborne.ts
@@ -0,0 +1,22 @@
+/** Waterborne module — tsb analytics library. */
+
+/** Options for Waterborne. */
+export interface WaterborneOptions { tol?: number; maxIter?: number; }
+
+/** Result from Waterborne. */
+export interface WaterborneResult { values: number[]; converged: boolean; }
+
+/** Compute Waterborne. */
+export function computeWaterborne(data: number[], opts: WaterborneOptions = {}): WaterborneResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeWaterborne };
diff --git a/src/epidemiology/xlarge.ts b/src/epidemiology/xlarge.ts
new file mode 100644
index 00000000..8f95a33a
--- /dev/null
+++ b/src/epidemiology/xlarge.ts
@@ -0,0 +1,15 @@
+/** Epidemiology Xlarge module — tsb analytics library. */
+export interface Epidemiology xlargeOptions { tol?: number; maxIter?: number; }
+export interface Epidemiology xlargeResult { values: number[]; converged: boolean; }
+export function computeEpidemiology xlarge(data: number[], opts: Epidemiology xlargeOptions = {}): Epidemiology xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeEpidemiology xlarge };
diff --git a/src/epidemiology/zoonotic.ts b/src/epidemiology/zoonotic.ts
new file mode 100644
index 00000000..d13cee7d
--- /dev/null
+++ b/src/epidemiology/zoonotic.ts
@@ -0,0 +1,22 @@
+/** Zoonotic module — tsb analytics library. */
+
+/** Options for Zoonotic. */
+export interface ZoonoticOptions { tol?: number; maxIter?: number; }
+
+/** Result from Zoonotic. */
+export interface ZoonoticResult { values: number[]; converged: boolean; }
+
+/** Compute Zoonotic. */
+export function computeZoonotic(data: number[], opts: ZoonoticOptions = {}): ZoonoticResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeZoonotic };
diff --git a/src/finance/advanced.ts b/src/finance/advanced.ts
new file mode 100644
index 00000000..8b34d5a2
--- /dev/null
+++ b/src/finance/advanced.ts
@@ -0,0 +1,15 @@
+/** Finance Advanced module — tsb analytics library. */
+export interface Finance advancedOptions { tol?: number; maxIter?: number; }
+export interface Finance advancedResult { values: number[]; converged: boolean; }
+export function computeFinance advanced(data: number[], opts: Finance advancedOptions = {}): Finance advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeFinance advanced };
diff --git a/src/finance/alpha.ts b/src/finance/alpha.ts
new file mode 100644
index 00000000..90a9c253
--- /dev/null
+++ b/src/finance/alpha.ts
@@ -0,0 +1,22 @@
+/** Alpha module — tsb analytics library. */
+
+/** Options for Alpha. */
+export interface AlphaOptions { tol?: number; maxIter?: number; }
+
+/** Result from Alpha. */
+export interface AlphaResult { values: number[]; converged: boolean; }
+
+/** Compute Alpha. */
+export function computeAlpha(data: number[], opts: AlphaOptions = {}): AlphaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAlpha };
diff --git a/src/finance/arbitrage.ts b/src/finance/arbitrage.ts
new file mode 100644
index 00000000..14f406b6
--- /dev/null
+++ b/src/finance/arbitrage.ts
@@ -0,0 +1,22 @@
+/** Arbitrage module — tsb analytics library. */
+
+/** Options for Arbitrage. */
+export interface ArbitrageOptions { tol?: number; maxIter?: number; }
+
+/** Result from Arbitrage. */
+export interface ArbitrageResult { values: number[]; converged: boolean; }
+
+/** Compute Arbitrage. */
+export function computeArbitrage(data: number[], opts: ArbitrageOptions = {}): ArbitrageResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeArbitrage };
diff --git a/src/finance/backtesting.ts b/src/finance/backtesting.ts
new file mode 100644
index 00000000..16933d86
--- /dev/null
+++ b/src/finance/backtesting.ts
@@ -0,0 +1,22 @@
+/** Backtesting module — tsb analytics library. */
+
+/** Options for Backtesting. */
+export interface BacktestingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Backtesting. */
+export interface BacktestingResult { values: number[]; converged: boolean; }
+
+/** Compute Backtesting. */
+export function computeBacktesting(data: number[], opts: BacktestingOptions = {}): BacktestingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBacktesting };
diff --git a/src/finance/base2.ts b/src/finance/base2.ts
new file mode 100644
index 00000000..076f3a58
--- /dev/null
+++ b/src/finance/base2.ts
@@ -0,0 +1,15 @@
+/** Finance Base2 module — tsb analytics library. */
+export interface Finance base2Options { tol?: number; maxIter?: number; }
+export interface Finance base2Result { values: number[]; converged: boolean; }
+export function computeFinance base2(data: number[], opts: Finance base2Options = {}): Finance base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeFinance base2 };
diff --git a/src/finance/batch.ts b/src/finance/batch.ts
new file mode 100644
index 00000000..ee40b42d
--- /dev/null
+++ b/src/finance/batch.ts
@@ -0,0 +1,15 @@
+/** Finance Batch module — tsb analytics library. */
+export interface Finance batchOptions { tol?: number; maxIter?: number; }
+export interface Finance batchResult { values: number[]; converged: boolean; }
+export function computeFinance batch(data: number[], opts: Finance batchOptions = {}): Finance batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeFinance batch };
diff --git a/src/finance/beta.ts b/src/finance/beta.ts
new file mode 100644
index 00000000..5310086e
--- /dev/null
+++ b/src/finance/beta.ts
@@ -0,0 +1,22 @@
+/** Beta module — tsb analytics library. */
+
+/** Options for Beta. */
+export interface BetaOptions { tol?: number; maxIter?: number; }
+
+/** Result from Beta. */
+export interface BetaResult { values: number[]; converged: boolean; }
+
+/** Compute Beta. */
+export function computeBeta(data: number[], opts: BetaOptions = {}): BetaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBeta };
diff --git a/src/finance/bonds.ts b/src/finance/bonds.ts
new file mode 100644
index 00000000..d5da170b
--- /dev/null
+++ b/src/finance/bonds.ts
@@ -0,0 +1,22 @@
+/** Bonds module — tsb analytics library. */
+
+/** Options for Bonds. */
+export interface BondsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Bonds. */
+export interface BondsResult { values: number[]; converged: boolean; }
+
+/** Compute Bonds. */
+export function computeBonds(data: number[], opts: BondsOptions = {}): BondsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBonds };
diff --git a/src/finance/commodities.ts b/src/finance/commodities.ts
new file mode 100644
index 00000000..a3c8a1d1
--- /dev/null
+++ b/src/finance/commodities.ts
@@ -0,0 +1,22 @@
+/** Commodities module — tsb analytics library. */
+
+/** Options for Commodities. */
+export interface CommoditiesOptions { tol?: number; maxIter?: number; }
+
+/** Result from Commodities. */
+export interface CommoditiesResult { values: number[]; converged: boolean; }
+
+/** Compute Commodities. */
+export function computeCommodities(data: number[], opts: CommoditiesOptions = {}): CommoditiesResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCommodities };
diff --git a/src/finance/cpu.ts b/src/finance/cpu.ts
new file mode 100644
index 00000000..d2a16d08
--- /dev/null
+++ b/src/finance/cpu.ts
@@ -0,0 +1,15 @@
+/** Finance Cpu module — tsb analytics library. */
+export interface Finance cpuOptions { tol?: number; maxIter?: number; }
+export interface Finance cpuResult { values: number[]; converged: boolean; }
+export function computeFinance cpu(data: number[], opts: Finance cpuOptions = {}): Finance cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeFinance cpu };
diff --git a/src/finance/credit.ts b/src/finance/credit.ts
new file mode 100644
index 00000000..16a38d2f
--- /dev/null
+++ b/src/finance/credit.ts
@@ -0,0 +1,22 @@
+/** Credit module — tsb analytics library. */
+
+/** Options for Credit. */
+export interface CreditOptions { tol?: number; maxIter?: number; }
+
+/** Result from Credit. */
+export interface CreditResult { values: number[]; converged: boolean; }
+
+/** Compute Credit. */
+export function computeCredit(data: number[], opts: CreditOptions = {}): CreditResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCredit };
diff --git a/src/finance/cvar.ts b/src/finance/cvar.ts
new file mode 100644
index 00000000..e885874e
--- /dev/null
+++ b/src/finance/cvar.ts
@@ -0,0 +1,22 @@
+/** Cvar module — tsb analytics library. */
+
+/** Options for Cvar. */
+export interface CvarOptions { tol?: number; maxIter?: number; }
+
+/** Result from Cvar. */
+export interface CvarResult { values: number[]; converged: boolean; }
+
+/** Compute Cvar. */
+export function computeCvar(data: number[], opts: CvarOptions = {}): CvarResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCvar };
diff --git a/src/finance/dense.ts b/src/finance/dense.ts
new file mode 100644
index 00000000..270b05f9
--- /dev/null
+++ b/src/finance/dense.ts
@@ -0,0 +1,15 @@
+/** Finance Dense module — tsb analytics library. */
+export interface Finance denseOptions { tol?: number; maxIter?: number; }
+export interface Finance denseResult { values: number[]; converged: boolean; }
+export function computeFinance dense(data: number[], opts: Finance denseOptions = {}): Finance denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeFinance dense };
diff --git a/src/finance/derivatives.ts b/src/finance/derivatives.ts
new file mode 100644
index 00000000..7694b7b7
--- /dev/null
+++ b/src/finance/derivatives.ts
@@ -0,0 +1,22 @@
+/** Derivatives module — tsb analytics library. */
+
+/** Options for Derivatives. */
+export interface DerivativesOptions { tol?: number; maxIter?: number; }
+
+/** Result from Derivatives. */
+export interface DerivativesResult { values: number[]; converged: boolean; }
+
+/** Compute Derivatives. */
+export function computeDerivatives(data: number[], opts: DerivativesOptions = {}): DerivativesResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDerivatives };
diff --git a/src/finance/distributed.ts b/src/finance/distributed.ts
new file mode 100644
index 00000000..3136c566
--- /dev/null
+++ b/src/finance/distributed.ts
@@ -0,0 +1,15 @@
+/** Finance Distributed module — tsb analytics library. */
+export interface Finance distributedOptions { tol?: number; maxIter?: number; }
+export interface Finance distributedResult { values: number[]; converged: boolean; }
+export function computeFinance distributed(data: number[], opts: Finance distributedOptions = {}): Finance distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeFinance distributed };
diff --git a/src/finance/drawdown.ts b/src/finance/drawdown.ts
new file mode 100644
index 00000000..24d9747f
--- /dev/null
+++ b/src/finance/drawdown.ts
@@ -0,0 +1,22 @@
+/** Drawdown module — tsb analytics library. */
+
+/** Options for Drawdown. */
+export interface DrawdownOptions { tol?: number; maxIter?: number; }
+
+/** Result from Drawdown. */
+export interface DrawdownResult { values: number[]; converged: boolean; }
+
+/** Compute Drawdown. */
+export function computeDrawdown(data: number[], opts: DrawdownOptions = {}): DrawdownResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDrawdown };
diff --git a/src/finance/equity.ts b/src/finance/equity.ts
new file mode 100644
index 00000000..b8f2761f
--- /dev/null
+++ b/src/finance/equity.ts
@@ -0,0 +1,22 @@
+/** Equity module — tsb analytics library. */
+
+/** Options for Equity. */
+export interface EquityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Equity. */
+export interface EquityResult { values: number[]; converged: boolean; }
+
+/** Compute Equity. */
+export function computeEquity(data: number[], opts: EquityOptions = {}): EquityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEquity };
diff --git a/src/finance/execution.ts b/src/finance/execution.ts
new file mode 100644
index 00000000..ea7ea188
--- /dev/null
+++ b/src/finance/execution.ts
@@ -0,0 +1,22 @@
+/** Execution module — tsb analytics library. */
+
+/** Options for Execution. */
+export interface ExecutionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Execution. */
+export interface ExecutionResult { values: number[]; converged: boolean; }
+
+/** Compute Execution. */
+export function computeExecution(data: number[], opts: ExecutionOptions = {}): ExecutionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeExecution };
diff --git a/src/finance/experimental.ts b/src/finance/experimental.ts
new file mode 100644
index 00000000..4c701f4d
--- /dev/null
+++ b/src/finance/experimental.ts
@@ -0,0 +1,15 @@
+/** Finance Experimental module — tsb analytics library. */
+export interface Finance experimentalOptions { tol?: number; maxIter?: number; }
+export interface Finance experimentalResult { values: number[]; converged: boolean; }
+export function computeFinance experimental(data: number[], opts: Finance experimentalOptions = {}): Finance experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeFinance experimental };
diff --git a/src/finance/fast.ts b/src/finance/fast.ts
new file mode 100644
index 00000000..f7b5668d
--- /dev/null
+++ b/src/finance/fast.ts
@@ -0,0 +1,15 @@
+/** Finance Fast module — tsb analytics library. */
+export interface Finance fastOptions { tol?: number; maxIter?: number; }
+export interface Finance fastResult { values: number[]; converged: boolean; }
+export function computeFinance fast(data: number[], opts: Finance fastOptions = {}): Finance fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeFinance fast };
diff --git a/src/finance/forex.ts b/src/finance/forex.ts
new file mode 100644
index 00000000..c573e99a
--- /dev/null
+++ b/src/finance/forex.ts
@@ -0,0 +1,22 @@
+/** Forex module — tsb analytics library. */
+
+/** Options for Forex. */
+export interface ForexOptions { tol?: number; maxIter?: number; }
+
+/** Result from Forex. */
+export interface ForexResult { values: number[]; converged: boolean; }
+
+/** Compute Forex. */
+export function computeForex(data: number[], opts: ForexOptions = {}): ForexResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeForex };
diff --git a/src/finance/future.ts b/src/finance/future.ts
new file mode 100644
index 00000000..0e1535c3
--- /dev/null
+++ b/src/finance/future.ts
@@ -0,0 +1,15 @@
+/** Finance Future module — tsb analytics library. */
+export interface Finance futureOptions { tol?: number; maxIter?: number; }
+export interface Finance futureResult { values: number[]; converged: boolean; }
+export function computeFinance future(data: number[], opts: Finance futureOptions = {}): Finance futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeFinance future };
diff --git a/src/finance/futures.ts b/src/finance/futures.ts
new file mode 100644
index 00000000..3a6ba5c5
--- /dev/null
+++ b/src/finance/futures.ts
@@ -0,0 +1,22 @@
+/** Futures module — tsb analytics library. */
+
+/** Options for Futures. */
+export interface FuturesOptions { tol?: number; maxIter?: number; }
+
+/** Result from Futures. */
+export interface FuturesResult { values: number[]; converged: boolean; }
+
+/** Compute Futures. */
+export function computeFutures(data: number[], opts: FuturesOptions = {}): FuturesResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFutures };
diff --git a/src/finance/gpu.ts b/src/finance/gpu.ts
new file mode 100644
index 00000000..6f4a0d8d
--- /dev/null
+++ b/src/finance/gpu.ts
@@ -0,0 +1,15 @@
+/** Finance Gpu module — tsb analytics library. */
+export interface Finance gpuOptions { tol?: number; maxIter?: number; }
+export interface Finance gpuResult { values: number[]; converged: boolean; }
+export function computeFinance gpu(data: number[], opts: Finance gpuOptions = {}): Finance gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeFinance gpu };
diff --git a/src/finance/hedging.ts b/src/finance/hedging.ts
new file mode 100644
index 00000000..06b699af
--- /dev/null
+++ b/src/finance/hedging.ts
@@ -0,0 +1,22 @@
+/** Hedging module — tsb analytics library. */
+
+/** Options for Hedging. */
+export interface HedgingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Hedging. */
+export interface HedgingResult { values: number[]; converged: boolean; }
+
+/** Compute Hedging. */
+export function computeHedging(data: number[], opts: HedgingOptions = {}): HedgingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeHedging };
diff --git a/src/finance/large.ts b/src/finance/large.ts
new file mode 100644
index 00000000..828aa146
--- /dev/null
+++ b/src/finance/large.ts
@@ -0,0 +1,15 @@
+/** Finance Large module — tsb analytics library. */
+export interface Finance largeOptions { tol?: number; maxIter?: number; }
+export interface Finance largeResult { values: number[]; converged: boolean; }
+export function computeFinance large(data: number[], opts: Finance largeOptions = {}): Finance largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeFinance large };
diff --git a/src/finance/legacy.ts b/src/finance/legacy.ts
new file mode 100644
index 00000000..2c3989be
--- /dev/null
+++ b/src/finance/legacy.ts
@@ -0,0 +1,15 @@
+/** Finance Legacy module — tsb analytics library. */
+export interface Finance legacyOptions { tol?: number; maxIter?: number; }
+export interface Finance legacyResult { values: number[]; converged: boolean; }
+export function computeFinance legacy(data: number[], opts: Finance legacyOptions = {}): Finance legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeFinance legacy };
diff --git a/src/finance/lite.ts b/src/finance/lite.ts
new file mode 100644
index 00000000..f17c26de
--- /dev/null
+++ b/src/finance/lite.ts
@@ -0,0 +1,15 @@
+/** Finance Lite module — tsb analytics library. */
+export interface Finance liteOptions { tol?: number; maxIter?: number; }
+export interface Finance liteResult { values: number[]; converged: boolean; }
+export function computeFinance lite(data: number[], opts: Finance liteOptions = {}): Finance liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeFinance lite };
diff --git a/src/finance/mean_reversion.ts b/src/finance/mean_reversion.ts
new file mode 100644
index 00000000..3569788a
--- /dev/null
+++ b/src/finance/mean_reversion.ts
@@ -0,0 +1,22 @@
+/** Mean Reversion module — tsb analytics library. */
+
+/** Options for Mean Reversion. */
+export interface MeanReversionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Mean Reversion. */
+export interface MeanReversionResult { values: number[]; converged: boolean; }
+
+/** Compute Mean Reversion. */
+export function computeMeanReversion(data: number[], opts: MeanReversionOptions = {}): MeanReversionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMeanReversion };
diff --git a/src/finance/mini.ts b/src/finance/mini.ts
new file mode 100644
index 00000000..a3e20ba9
--- /dev/null
+++ b/src/finance/mini.ts
@@ -0,0 +1,15 @@
+/** Finance Mini module — tsb analytics library. */
+export interface Finance miniOptions { tol?: number; maxIter?: number; }
+export interface Finance miniResult { values: number[]; converged: boolean; }
+export function computeFinance mini(data: number[], opts: Finance miniOptions = {}): Finance miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeFinance mini };
diff --git a/src/finance/momentum.ts b/src/finance/momentum.ts
new file mode 100644
index 00000000..fdd4f501
--- /dev/null
+++ b/src/finance/momentum.ts
@@ -0,0 +1,22 @@
+/** Momentum module — tsb analytics library. */
+
+/** Options for Momentum. */
+export interface MomentumOptions { tol?: number; maxIter?: number; }
+
+/** Result from Momentum. */
+export interface MomentumResult { values: number[]; converged: boolean; }
+
+/** Compute Momentum. */
+export function computeMomentum(data: number[], opts: MomentumOptions = {}): MomentumResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMomentum };
diff --git a/src/finance/next.ts b/src/finance/next.ts
new file mode 100644
index 00000000..91ec2e10
--- /dev/null
+++ b/src/finance/next.ts
@@ -0,0 +1,15 @@
+/** Finance Next module — tsb analytics library. */
+export interface Finance nextOptions { tol?: number; maxIter?: number; }
+export interface Finance nextResult { values: number[]; converged: boolean; }
+export function computeFinance next(data: number[], opts: Finance nextOptions = {}): Finance nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeFinance next };
diff --git a/src/finance/online.ts b/src/finance/online.ts
new file mode 100644
index 00000000..05739d4b
--- /dev/null
+++ b/src/finance/online.ts
@@ -0,0 +1,15 @@
+/** Finance Online module — tsb analytics library. */
+export interface Finance onlineOptions { tol?: number; maxIter?: number; }
+export interface Finance onlineResult { values: number[]; converged: boolean; }
+export function computeFinance online(data: number[], opts: Finance onlineOptions = {}): Finance onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeFinance online };
diff --git a/src/finance/options.ts b/src/finance/options.ts
new file mode 100644
index 00000000..dd147af5
--- /dev/null
+++ b/src/finance/options.ts
@@ -0,0 +1,22 @@
+/** Options module — tsb analytics library. */
+
+/** Options for Options. */
+export interface OptionsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Options. */
+export interface OptionsResult { values: number[]; converged: boolean; }
+
+/** Compute Options. */
+export function computeOptions(data: number[], opts: OptionsOptions = {}): OptionsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeOptions };
diff --git a/src/finance/pairs.ts b/src/finance/pairs.ts
new file mode 100644
index 00000000..8661b153
--- /dev/null
+++ b/src/finance/pairs.ts
@@ -0,0 +1,22 @@
+/** Pairs module — tsb analytics library. */
+
+/** Options for Pairs. */
+export interface PairsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Pairs. */
+export interface PairsResult { values: number[]; converged: boolean; }
+
+/** Compute Pairs. */
+export function computePairs(data: number[], opts: PairsOptions = {}): PairsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePairs };
diff --git a/src/finance/parallel.ts b/src/finance/parallel.ts
new file mode 100644
index 00000000..acf34bca
--- /dev/null
+++ b/src/finance/parallel.ts
@@ -0,0 +1,15 @@
+/** Finance Parallel module — tsb analytics library. */
+export interface Finance parallelOptions { tol?: number; maxIter?: number; }
+export interface Finance parallelResult { values: number[]; converged: boolean; }
+export function computeFinance parallel(data: number[], opts: Finance parallelOptions = {}): Finance parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeFinance parallel };
diff --git a/src/finance/plus.ts b/src/finance/plus.ts
new file mode 100644
index 00000000..b4020825
--- /dev/null
+++ b/src/finance/plus.ts
@@ -0,0 +1,15 @@
+/** Finance Plus module — tsb analytics library. */
+export interface Finance plusOptions { tol?: number; maxIter?: number; }
+export interface Finance plusResult { values: number[]; converged: boolean; }
+export function computeFinance plus(data: number[], opts: Finance plusOptions = {}): Finance plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeFinance plus };
diff --git a/src/finance/portfolio.ts b/src/finance/portfolio.ts
new file mode 100644
index 00000000..ebb8e355
--- /dev/null
+++ b/src/finance/portfolio.ts
@@ -0,0 +1,22 @@
+/** Portfolio module — tsb analytics library. */
+
+/** Options for Portfolio. */
+export interface PortfolioOptions { tol?: number; maxIter?: number; }
+
+/** Result from Portfolio. */
+export interface PortfolioResult { values: number[]; converged: boolean; }
+
+/** Compute Portfolio. */
+export function computePortfolio(data: number[], opts: PortfolioOptions = {}): PortfolioResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePortfolio };
diff --git a/src/finance/pricing.ts b/src/finance/pricing.ts
new file mode 100644
index 00000000..9e2ed935
--- /dev/null
+++ b/src/finance/pricing.ts
@@ -0,0 +1,22 @@
+/** Pricing module — tsb analytics library. */
+
+/** Options for Pricing. */
+export interface PricingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Pricing. */
+export interface PricingResult { values: number[]; converged: boolean; }
+
+/** Compute Pricing. */
+export function computePricing(data: number[], opts: PricingOptions = {}): PricingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePricing };
diff --git a/src/finance/pro.ts b/src/finance/pro.ts
new file mode 100644
index 00000000..4ee88dd9
--- /dev/null
+++ b/src/finance/pro.ts
@@ -0,0 +1,15 @@
+/** Finance Pro module — tsb analytics library. */
+export interface Finance proOptions { tol?: number; maxIter?: number; }
+export interface Finance proResult { values: number[]; converged: boolean; }
+export function computeFinance pro(data: number[], opts: Finance proOptions = {}): Finance proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeFinance pro };
diff --git a/src/finance/rates.ts b/src/finance/rates.ts
new file mode 100644
index 00000000..5d35dba7
--- /dev/null
+++ b/src/finance/rates.ts
@@ -0,0 +1,22 @@
+/** Rates module — tsb analytics library. */
+
+/** Options for Rates. */
+export interface RatesOptions { tol?: number; maxIter?: number; }
+
+/** Result from Rates. */
+export interface RatesResult { values: number[]; converged: boolean; }
+
+/** Compute Rates. */
+export function computeRates(data: number[], opts: RatesOptions = {}): RatesResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRates };
diff --git a/src/finance/risk.ts b/src/finance/risk.ts
new file mode 100644
index 00000000..5dffec3f
--- /dev/null
+++ b/src/finance/risk.ts
@@ -0,0 +1,22 @@
+/** Risk module — tsb analytics library. */
+
+/** Options for Risk. */
+export interface RiskOptions { tol?: number; maxIter?: number; }
+
+/** Result from Risk. */
+export interface RiskResult { values: number[]; converged: boolean; }
+
+/** Compute Risk. */
+export function computeRisk(data: number[], opts: RiskOptions = {}): RiskResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRisk };
diff --git a/src/finance/robust.ts b/src/finance/robust.ts
new file mode 100644
index 00000000..2033b0c9
--- /dev/null
+++ b/src/finance/robust.ts
@@ -0,0 +1,15 @@
+/** Finance Robust module — tsb analytics library. */
+export interface Finance robustOptions { tol?: number; maxIter?: number; }
+export interface Finance robustResult { values: number[]; converged: boolean; }
+export function computeFinance robust(data: number[], opts: Finance robustOptions = {}): Finance robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeFinance robust };
diff --git a/src/finance/scenario.ts b/src/finance/scenario.ts
new file mode 100644
index 00000000..6dfafa8b
--- /dev/null
+++ b/src/finance/scenario.ts
@@ -0,0 +1,22 @@
+/** Scenario module — tsb analytics library. */
+
+/** Options for Scenario. */
+export interface ScenarioOptions { tol?: number; maxIter?: number; }
+
+/** Result from Scenario. */
+export interface ScenarioResult { values: number[]; converged: boolean; }
+
+/** Compute Scenario. */
+export function computeScenario(data: number[], opts: ScenarioOptions = {}): ScenarioResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeScenario };
diff --git a/src/finance/sharpe.ts b/src/finance/sharpe.ts
new file mode 100644
index 00000000..830dc7ca
--- /dev/null
+++ b/src/finance/sharpe.ts
@@ -0,0 +1,22 @@
+/** Sharpe module — tsb analytics library. */
+
+/** Options for Sharpe. */
+export interface SharpeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Sharpe. */
+export interface SharpeResult { values: number[]; converged: boolean; }
+
+/** Compute Sharpe. */
+export function computeSharpe(data: number[], opts: SharpeOptions = {}): SharpeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSharpe };
diff --git a/src/finance/small.ts b/src/finance/small.ts
new file mode 100644
index 00000000..43e24c0d
--- /dev/null
+++ b/src/finance/small.ts
@@ -0,0 +1,15 @@
+/** Finance Small module — tsb analytics library. */
+export interface Finance smallOptions { tol?: number; maxIter?: number; }
+export interface Finance smallResult { values: number[]; converged: boolean; }
+export function computeFinance small(data: number[], opts: Finance smallOptions = {}): Finance smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeFinance small };
diff --git a/src/finance/sortino.ts b/src/finance/sortino.ts
new file mode 100644
index 00000000..74d23c8d
--- /dev/null
+++ b/src/finance/sortino.ts
@@ -0,0 +1,22 @@
+/** Sortino module — tsb analytics library. */
+
+/** Options for Sortino. */
+export interface SortinoOptions { tol?: number; maxIter?: number; }
+
+/** Result from Sortino. */
+export interface SortinoResult { values: number[]; converged: boolean; }
+
+/** Compute Sortino. */
+export function computeSortino(data: number[], opts: SortinoOptions = {}): SortinoResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSortino };
diff --git a/src/finance/sparse.ts b/src/finance/sparse.ts
new file mode 100644
index 00000000..828a88bc
--- /dev/null
+++ b/src/finance/sparse.ts
@@ -0,0 +1,15 @@
+/** Finance Sparse module — tsb analytics library. */
+export interface Finance sparseOptions { tol?: number; maxIter?: number; }
+export interface Finance sparseResult { values: number[]; converged: boolean; }
+export function computeFinance sparse(data: number[], opts: Finance sparseOptions = {}): Finance sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeFinance sparse };
diff --git a/src/finance/stable.ts b/src/finance/stable.ts
new file mode 100644
index 00000000..08de1b37
--- /dev/null
+++ b/src/finance/stable.ts
@@ -0,0 +1,15 @@
+/** Finance Stable module — tsb analytics library. */
+export interface Finance stableOptions { tol?: number; maxIter?: number; }
+export interface Finance stableResult { values: number[]; converged: boolean; }
+export function computeFinance stable(data: number[], opts: Finance stableOptions = {}): Finance stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeFinance stable };
diff --git a/src/finance/streaming.ts b/src/finance/streaming.ts
new file mode 100644
index 00000000..312b8293
--- /dev/null
+++ b/src/finance/streaming.ts
@@ -0,0 +1,15 @@
+/** Finance Streaming module — tsb analytics library. */
+export interface Finance streamingOptions { tol?: number; maxIter?: number; }
+export interface Finance streamingResult { values: number[]; converged: boolean; }
+export function computeFinance streaming(data: number[], opts: Finance streamingOptions = {}): Finance streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeFinance streaming };
diff --git a/src/finance/stress_test.ts b/src/finance/stress_test.ts
new file mode 100644
index 00000000..5faf1168
--- /dev/null
+++ b/src/finance/stress_test.ts
@@ -0,0 +1,22 @@
+/** Stress Test module — tsb analytics library. */
+
+/** Options for Stress Test. */
+export interface StressTestOptions { tol?: number; maxIter?: number; }
+
+/** Result from Stress Test. */
+export interface StressTestResult { values: number[]; converged: boolean; }
+
+/** Compute Stress Test. */
+export function computeStressTest(data: number[], opts: StressTestOptions = {}): StressTestResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeStressTest };
diff --git a/src/finance/v2.ts b/src/finance/v2.ts
new file mode 100644
index 00000000..f669727d
--- /dev/null
+++ b/src/finance/v2.ts
@@ -0,0 +1,15 @@
+/** Finance V2 module — tsb analytics library. */
+export interface Finance v2Options { tol?: number; maxIter?: number; }
+export interface Finance v2Result { values: number[]; converged: boolean; }
+export function computeFinance v2(data: number[], opts: Finance v2Options = {}): Finance v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeFinance v2 };
diff --git a/src/finance/v3.ts b/src/finance/v3.ts
new file mode 100644
index 00000000..71e90815
--- /dev/null
+++ b/src/finance/v3.ts
@@ -0,0 +1,15 @@
+/** Finance V3 module — tsb analytics library. */
+export interface Finance v3Options { tol?: number; maxIter?: number; }
+export interface Finance v3Result { values: number[]; converged: boolean; }
+export function computeFinance v3(data: number[], opts: Finance v3Options = {}): Finance v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeFinance v3 };
diff --git a/src/finance/var.ts b/src/finance/var.ts
new file mode 100644
index 00000000..0377e36c
--- /dev/null
+++ b/src/finance/var.ts
@@ -0,0 +1,22 @@
+/** Var module — tsb analytics library. */
+
+/** Options for Var. */
+export interface VarOptions { tol?: number; maxIter?: number; }
+
+/** Result from Var. */
+export interface VarResult { values: number[]; converged: boolean; }
+
+/** Compute Var. */
+export function computeVar(data: number[], opts: VarOptions = {}): VarResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeVar };
diff --git a/src/finance/volatility.ts b/src/finance/volatility.ts
new file mode 100644
index 00000000..4978c8aa
--- /dev/null
+++ b/src/finance/volatility.ts
@@ -0,0 +1,22 @@
+/** Volatility module — tsb analytics library. */
+
+/** Options for Volatility. */
+export interface VolatilityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Volatility. */
+export interface VolatilityResult { values: number[]; converged: boolean; }
+
+/** Compute Volatility. */
+export function computeVolatility(data: number[], opts: VolatilityOptions = {}): VolatilityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeVolatility };
diff --git a/src/finance/wasm.ts b/src/finance/wasm.ts
new file mode 100644
index 00000000..0df06e55
--- /dev/null
+++ b/src/finance/wasm.ts
@@ -0,0 +1,15 @@
+/** Finance Wasm module — tsb analytics library. */
+export interface Finance wasmOptions { tol?: number; maxIter?: number; }
+export interface Finance wasmResult { values: number[]; converged: boolean; }
+export function computeFinance wasm(data: number[], opts: Finance wasmOptions = {}): Finance wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeFinance wasm };
diff --git a/src/finance/xlarge.ts b/src/finance/xlarge.ts
new file mode 100644
index 00000000..fae36abe
--- /dev/null
+++ b/src/finance/xlarge.ts
@@ -0,0 +1,15 @@
+/** Finance Xlarge module — tsb analytics library. */
+export interface Finance xlargeOptions { tol?: number; maxIter?: number; }
+export interface Finance xlargeResult { values: number[]; converged: boolean; }
+export function computeFinance xlarge(data: number[], opts: Finance xlargeOptions = {}): Finance xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeFinance xlarge };
diff --git a/src/finance/yield_curve.ts b/src/finance/yield_curve.ts
new file mode 100644
index 00000000..6e838296
--- /dev/null
+++ b/src/finance/yield_curve.ts
@@ -0,0 +1,22 @@
+/** Yield Curve module — tsb analytics library. */
+
+/** Options for Yield Curve. */
+export interface YieldCurveOptions { tol?: number; maxIter?: number; }
+
+/** Result from Yield Curve. */
+export interface YieldCurveResult { values: number[]; converged: boolean; }
+
+/** Compute Yield Curve. */
+export function computeYieldCurve(data: number[], opts: YieldCurveOptions = {}): YieldCurveResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeYieldCurve };
diff --git a/src/geo/accessibility.ts b/src/geo/accessibility.ts
new file mode 100644
index 00000000..142037ed
--- /dev/null
+++ b/src/geo/accessibility.ts
@@ -0,0 +1,22 @@
+/** Accessibility module — tsb analytics library. */
+
+/** Options for Accessibility. */
+export interface AccessibilityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Accessibility. */
+export interface AccessibilityResult { values: number[]; converged: boolean; }
+
+/** Compute Accessibility. */
+export function computeAccessibility(data: number[], opts: AccessibilityOptions = {}): AccessibilityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAccessibility };
diff --git a/src/geo/advanced.ts b/src/geo/advanced.ts
new file mode 100644
index 00000000..7c4287c2
--- /dev/null
+++ b/src/geo/advanced.ts
@@ -0,0 +1,15 @@
+/** Geo Advanced module — tsb analytics library. */
+export interface Geo advancedOptions { tol?: number; maxIter?: number; }
+export interface Geo advancedResult { values: number[]; converged: boolean; }
+export function computeGeo advanced(data: number[], opts: Geo advancedOptions = {}): Geo advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeo advanced };
diff --git a/src/geo/base2.ts b/src/geo/base2.ts
new file mode 100644
index 00000000..190a68f7
--- /dev/null
+++ b/src/geo/base2.ts
@@ -0,0 +1,15 @@
+/** Geo Base2 module — tsb analytics library. */
+export interface Geo base2Options { tol?: number; maxIter?: number; }
+export interface Geo base2Result { values: number[]; converged: boolean; }
+export function computeGeo base2(data: number[], opts: Geo base2Options = {}): Geo base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeo base2 };
diff --git a/src/geo/batch.ts b/src/geo/batch.ts
new file mode 100644
index 00000000..6c984aa6
--- /dev/null
+++ b/src/geo/batch.ts
@@ -0,0 +1,15 @@
+/** Geo Batch module — tsb analytics library. */
+export interface Geo batchOptions { tol?: number; maxIter?: number; }
+export interface Geo batchResult { values: number[]; converged: boolean; }
+export function computeGeo batch(data: number[], opts: Geo batchOptions = {}): Geo batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeo batch };
diff --git a/src/geo/beta.ts b/src/geo/beta.ts
new file mode 100644
index 00000000..f2f9214a
--- /dev/null
+++ b/src/geo/beta.ts
@@ -0,0 +1,15 @@
+/** Geo Beta module — tsb analytics library. */
+export interface Geo betaOptions { tol?: number; maxIter?: number; }
+export interface Geo betaResult { values: number[]; converged: boolean; }
+export function computeGeo beta(data: number[], opts: Geo betaOptions = {}): Geo betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeo beta };
diff --git a/src/geo/boundaries.ts b/src/geo/boundaries.ts
new file mode 100644
index 00000000..e0fa507e
--- /dev/null
+++ b/src/geo/boundaries.ts
@@ -0,0 +1,22 @@
+/** Boundaries module — tsb analytics library. */
+
+/** Options for Boundaries. */
+export interface BoundariesOptions { tol?: number; maxIter?: number; }
+
+/** Result from Boundaries. */
+export interface BoundariesResult { values: number[]; converged: boolean; }
+
+/** Compute Boundaries. */
+export function computeBoundaries(data: number[], opts: BoundariesOptions = {}): BoundariesResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBoundaries };
diff --git a/src/geo/buffering.ts b/src/geo/buffering.ts
new file mode 100644
index 00000000..bdeaefcc
--- /dev/null
+++ b/src/geo/buffering.ts
@@ -0,0 +1,22 @@
+/** Buffering module — tsb analytics library. */
+
+/** Options for Buffering. */
+export interface BufferingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Buffering. */
+export interface BufferingResult { values: number[]; converged: boolean; }
+
+/** Compute Buffering. */
+export function computeBuffering(data: number[], opts: BufferingOptions = {}): BufferingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBuffering };
diff --git a/src/geo/catchment.ts b/src/geo/catchment.ts
new file mode 100644
index 00000000..a6e3c50f
--- /dev/null
+++ b/src/geo/catchment.ts
@@ -0,0 +1,22 @@
+/** Catchment module — tsb analytics library. */
+
+/** Options for Catchment. */
+export interface CatchmentOptions { tol?: number; maxIter?: number; }
+
+/** Result from Catchment. */
+export interface CatchmentResult { values: number[]; converged: boolean; }
+
+/** Compute Catchment. */
+export function computeCatchment(data: number[], opts: CatchmentOptions = {}): CatchmentResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCatchment };
diff --git a/src/geo/clustering.ts b/src/geo/clustering.ts
new file mode 100644
index 00000000..76db9a77
--- /dev/null
+++ b/src/geo/clustering.ts
@@ -0,0 +1,22 @@
+/** Clustering module — tsb analytics library. */
+
+/** Options for Clustering. */
+export interface ClusteringOptions { tol?: number; maxIter?: number; }
+
+/** Result from Clustering. */
+export interface ClusteringResult { values: number[]; converged: boolean; }
+
+/** Compute Clustering. */
+export function computeClustering(data: number[], opts: ClusteringOptions = {}): ClusteringResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeClustering };
diff --git a/src/geo/convex_hull.ts b/src/geo/convex_hull.ts
new file mode 100644
index 00000000..785058e2
--- /dev/null
+++ b/src/geo/convex_hull.ts
@@ -0,0 +1,22 @@
+/** Convex Hull module — tsb analytics library. */
+
+/** Options for Convex Hull. */
+export interface ConvexHullOptions { tol?: number; maxIter?: number; }
+
+/** Result from Convex Hull. */
+export interface ConvexHullResult { values: number[]; converged: boolean; }
+
+/** Compute Convex Hull. */
+export function computeConvexHull(data: number[], opts: ConvexHullOptions = {}): ConvexHullResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeConvexHull };
diff --git a/src/geo/coordinates.ts b/src/geo/coordinates.ts
new file mode 100644
index 00000000..42ab9d17
--- /dev/null
+++ b/src/geo/coordinates.ts
@@ -0,0 +1,22 @@
+/** Coordinates module — tsb analytics library. */
+
+/** Options for Coordinates. */
+export interface CoordinatesOptions { tol?: number; maxIter?: number; }
+
+/** Result from Coordinates. */
+export interface CoordinatesResult { values: number[]; converged: boolean; }
+
+/** Compute Coordinates. */
+export function computeCoordinates(data: number[], opts: CoordinatesOptions = {}): CoordinatesResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCoordinates };
diff --git a/src/geo/cpu.ts b/src/geo/cpu.ts
new file mode 100644
index 00000000..166e0fdf
--- /dev/null
+++ b/src/geo/cpu.ts
@@ -0,0 +1,15 @@
+/** Geo Cpu module — tsb analytics library. */
+export interface Geo cpuOptions { tol?: number; maxIter?: number; }
+export interface Geo cpuResult { values: number[]; converged: boolean; }
+export function computeGeo cpu(data: number[], opts: Geo cpuOptions = {}): Geo cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeo cpu };
diff --git a/src/geo/dense.ts b/src/geo/dense.ts
new file mode 100644
index 00000000..f8c765c1
--- /dev/null
+++ b/src/geo/dense.ts
@@ -0,0 +1,15 @@
+/** Geo Dense module — tsb analytics library. */
+export interface Geo denseOptions { tol?: number; maxIter?: number; }
+export interface Geo denseResult { values: number[]; converged: boolean; }
+export function computeGeo dense(data: number[], opts: Geo denseOptions = {}): Geo denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeo dense };
diff --git a/src/geo/distances.ts b/src/geo/distances.ts
new file mode 100644
index 00000000..06d696a4
--- /dev/null
+++ b/src/geo/distances.ts
@@ -0,0 +1,22 @@
+/** Distances module — tsb analytics library. */
+
+/** Options for Distances. */
+export interface DistancesOptions { tol?: number; maxIter?: number; }
+
+/** Result from Distances. */
+export interface DistancesResult { values: number[]; converged: boolean; }
+
+/** Compute Distances. */
+export function computeDistances(data: number[], opts: DistancesOptions = {}): DistancesResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDistances };
diff --git a/src/geo/distributed.ts b/src/geo/distributed.ts
new file mode 100644
index 00000000..3283733e
--- /dev/null
+++ b/src/geo/distributed.ts
@@ -0,0 +1,15 @@
+/** Geo Distributed module — tsb analytics library. */
+export interface Geo distributedOptions { tol?: number; maxIter?: number; }
+export interface Geo distributedResult { values: number[]; converged: boolean; }
+export function computeGeo distributed(data: number[], opts: Geo distributedOptions = {}): Geo distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeo distributed };
diff --git a/src/geo/elevation.ts b/src/geo/elevation.ts
new file mode 100644
index 00000000..4118099e
--- /dev/null
+++ b/src/geo/elevation.ts
@@ -0,0 +1,22 @@
+/** Elevation module — tsb analytics library. */
+
+/** Options for Elevation. */
+export interface ElevationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Elevation. */
+export interface ElevationResult { values: number[]; converged: boolean; }
+
+/** Compute Elevation. */
+export function computeElevation(data: number[], opts: ElevationOptions = {}): ElevationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeElevation };
diff --git a/src/geo/experimental.ts b/src/geo/experimental.ts
new file mode 100644
index 00000000..d393618f
--- /dev/null
+++ b/src/geo/experimental.ts
@@ -0,0 +1,15 @@
+/** Geo Experimental module — tsb analytics library. */
+export interface Geo experimentalOptions { tol?: number; maxIter?: number; }
+export interface Geo experimentalResult { values: number[]; converged: boolean; }
+export function computeGeo experimental(data: number[], opts: Geo experimentalOptions = {}): Geo experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeo experimental };
diff --git a/src/geo/fast.ts b/src/geo/fast.ts
new file mode 100644
index 00000000..1614c2c1
--- /dev/null
+++ b/src/geo/fast.ts
@@ -0,0 +1,15 @@
+/** Geo Fast module — tsb analytics library. */
+export interface Geo fastOptions { tol?: number; maxIter?: number; }
+export interface Geo fastResult { values: number[]; converged: boolean; }
+export function computeGeo fast(data: number[], opts: Geo fastOptions = {}): Geo fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeo fast };
diff --git a/src/geo/flow.ts b/src/geo/flow.ts
new file mode 100644
index 00000000..714346a3
--- /dev/null
+++ b/src/geo/flow.ts
@@ -0,0 +1,22 @@
+/** Flow module — tsb analytics library. */
+
+/** Options for Flow. */
+export interface FlowOptions { tol?: number; maxIter?: number; }
+
+/** Result from Flow. */
+export interface FlowResult { values: number[]; converged: boolean; }
+
+/** Compute Flow. */
+export function computeFlow(data: number[], opts: FlowOptions = {}): FlowResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFlow };
diff --git a/src/geo/future.ts b/src/geo/future.ts
new file mode 100644
index 00000000..328b40c2
--- /dev/null
+++ b/src/geo/future.ts
@@ -0,0 +1,15 @@
+/** Geo Future module — tsb analytics library. */
+export interface Geo futureOptions { tol?: number; maxIter?: number; }
+export interface Geo futureResult { values: number[]; converged: boolean; }
+export function computeGeo future(data: number[], opts: Geo futureOptions = {}): Geo futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeo future };
diff --git a/src/geo/geocoding.ts b/src/geo/geocoding.ts
new file mode 100644
index 00000000..94722d81
--- /dev/null
+++ b/src/geo/geocoding.ts
@@ -0,0 +1,22 @@
+/** Geocoding module — tsb analytics library. */
+
+/** Options for Geocoding. */
+export interface GeocodingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Geocoding. */
+export interface GeocodingResult { values: number[]; converged: boolean; }
+
+/** Compute Geocoding. */
+export function computeGeocoding(data: number[], opts: GeocodingOptions = {}): GeocodingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGeocoding };
diff --git a/src/geo/geographically_weighted.ts b/src/geo/geographically_weighted.ts
new file mode 100644
index 00000000..87ef0104
--- /dev/null
+++ b/src/geo/geographically_weighted.ts
@@ -0,0 +1,22 @@
+/** Geographically Weighted module — tsb analytics library. */
+
+/** Options for Geographically Weighted. */
+export interface GeographicallyWeightedOptions { tol?: number; maxIter?: number; }
+
+/** Result from Geographically Weighted. */
+export interface GeographicallyWeightedResult { values: number[]; converged: boolean; }
+
+/** Compute Geographically Weighted. */
+export function computeGeographicallyWeighted(data: number[], opts: GeographicallyWeightedOptions = {}): GeographicallyWeightedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGeographicallyWeighted };
diff --git a/src/geo/gpu.ts b/src/geo/gpu.ts
new file mode 100644
index 00000000..c0c1bedc
--- /dev/null
+++ b/src/geo/gpu.ts
@@ -0,0 +1,15 @@
+/** Geo Gpu module — tsb analytics library. */
+export interface Geo gpuOptions { tol?: number; maxIter?: number; }
+export interface Geo gpuResult { values: number[]; converged: boolean; }
+export function computeGeo gpu(data: number[], opts: Geo gpuOptions = {}): Geo gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeo gpu };
diff --git a/src/geo/heatmap.ts b/src/geo/heatmap.ts
new file mode 100644
index 00000000..72c4a17b
--- /dev/null
+++ b/src/geo/heatmap.ts
@@ -0,0 +1,22 @@
+/** Heatmap module — tsb analytics library. */
+
+/** Options for Heatmap. */
+export interface HeatmapOptions { tol?: number; maxIter?: number; }
+
+/** Result from Heatmap. */
+export interface HeatmapResult { values: number[]; converged: boolean; }
+
+/** Compute Heatmap. */
+export function computeHeatmap(data: number[], opts: HeatmapOptions = {}): HeatmapResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeHeatmap };
diff --git a/src/geo/interpolation.ts b/src/geo/interpolation.ts
new file mode 100644
index 00000000..ef7af6c1
--- /dev/null
+++ b/src/geo/interpolation.ts
@@ -0,0 +1,22 @@
+/** Interpolation module — tsb analytics library. */
+
+/** Options for Interpolation. */
+export interface InterpolationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Interpolation. */
+export interface InterpolationResult { values: number[]; converged: boolean; }
+
+/** Compute Interpolation. */
+export function computeInterpolation(data: number[], opts: InterpolationOptions = {}): InterpolationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeInterpolation };
diff --git a/src/geo/isochrone.ts b/src/geo/isochrone.ts
new file mode 100644
index 00000000..db327b75
--- /dev/null
+++ b/src/geo/isochrone.ts
@@ -0,0 +1,22 @@
+/** Isochrone module — tsb analytics library. */
+
+/** Options for Isochrone. */
+export interface IsochroneOptions { tol?: number; maxIter?: number; }
+
+/** Result from Isochrone. */
+export interface IsochroneResult { values: number[]; converged: boolean; }
+
+/** Compute Isochrone. */
+export function computeIsochrone(data: number[], opts: IsochroneOptions = {}): IsochroneResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeIsochrone };
diff --git a/src/geo/kriging.ts b/src/geo/kriging.ts
new file mode 100644
index 00000000..20ec7eee
--- /dev/null
+++ b/src/geo/kriging.ts
@@ -0,0 +1,22 @@
+/** Kriging module — tsb analytics library. */
+
+/** Options for Kriging. */
+export interface KrigingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Kriging. */
+export interface KrigingResult { values: number[]; converged: boolean; }
+
+/** Compute Kriging. */
+export function computeKriging(data: number[], opts: KrigingOptions = {}): KrigingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeKriging };
diff --git a/src/geo/large.ts b/src/geo/large.ts
new file mode 100644
index 00000000..27d0fd05
--- /dev/null
+++ b/src/geo/large.ts
@@ -0,0 +1,15 @@
+/** Geo Large module — tsb analytics library. */
+export interface Geo largeOptions { tol?: number; maxIter?: number; }
+export interface Geo largeResult { values: number[]; converged: boolean; }
+export function computeGeo large(data: number[], opts: Geo largeOptions = {}): Geo largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeo large };
diff --git a/src/geo/legacy.ts b/src/geo/legacy.ts
new file mode 100644
index 00000000..b3e9cd68
--- /dev/null
+++ b/src/geo/legacy.ts
@@ -0,0 +1,15 @@
+/** Geo Legacy module — tsb analytics library. */
+export interface Geo legacyOptions { tol?: number; maxIter?: number; }
+export interface Geo legacyResult { values: number[]; converged: boolean; }
+export function computeGeo legacy(data: number[], opts: Geo legacyOptions = {}): Geo legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeo legacy };
diff --git a/src/geo/lite.ts b/src/geo/lite.ts
new file mode 100644
index 00000000..8c57e01f
--- /dev/null
+++ b/src/geo/lite.ts
@@ -0,0 +1,15 @@
+/** Geo Lite module — tsb analytics library. */
+export interface Geo liteOptions { tol?: number; maxIter?: number; }
+export interface Geo liteResult { values: number[]; converged: boolean; }
+export function computeGeo lite(data: number[], opts: Geo liteOptions = {}): Geo liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeo lite };
diff --git a/src/geo/mini.ts b/src/geo/mini.ts
new file mode 100644
index 00000000..3963eb38
--- /dev/null
+++ b/src/geo/mini.ts
@@ -0,0 +1,15 @@
+/** Geo Mini module — tsb analytics library. */
+export interface Geo miniOptions { tol?: number; maxIter?: number; }
+export interface Geo miniResult { values: number[]; converged: boolean; }
+export function computeGeo mini(data: number[], opts: Geo miniOptions = {}): Geo miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeo mini };
diff --git a/src/geo/mobility.ts b/src/geo/mobility.ts
new file mode 100644
index 00000000..e4045767
--- /dev/null
+++ b/src/geo/mobility.ts
@@ -0,0 +1,22 @@
+/** Mobility module — tsb analytics library. */
+
+/** Options for Mobility. */
+export interface MobilityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Mobility. */
+export interface MobilityResult { values: number[]; converged: boolean; }
+
+/** Compute Mobility. */
+export function computeMobility(data: number[], opts: MobilityOptions = {}): MobilityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMobility };
diff --git a/src/geo/network_geo.ts b/src/geo/network_geo.ts
new file mode 100644
index 00000000..ac8f1db2
--- /dev/null
+++ b/src/geo/network_geo.ts
@@ -0,0 +1,22 @@
+/** Network Geo module — tsb analytics library. */
+
+/** Options for Network Geo. */
+export interface NetworkGeoOptions { tol?: number; maxIter?: number; }
+
+/** Result from Network Geo. */
+export interface NetworkGeoResult { values: number[]; converged: boolean; }
+
+/** Compute Network Geo. */
+export function computeNetworkGeo(data: number[], opts: NetworkGeoOptions = {}): NetworkGeoResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNetworkGeo };
diff --git a/src/geo/next.ts b/src/geo/next.ts
new file mode 100644
index 00000000..c08acc0e
--- /dev/null
+++ b/src/geo/next.ts
@@ -0,0 +1,15 @@
+/** Geo Next module — tsb analytics library. */
+export interface Geo nextOptions { tol?: number; maxIter?: number; }
+export interface Geo nextResult { values: number[]; converged: boolean; }
+export function computeGeo next(data: number[], opts: Geo nextOptions = {}): Geo nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeo next };
diff --git a/src/geo/online.ts b/src/geo/online.ts
new file mode 100644
index 00000000..44ab1501
--- /dev/null
+++ b/src/geo/online.ts
@@ -0,0 +1,15 @@
+/** Geo Online module — tsb analytics library. */
+export interface Geo onlineOptions { tol?: number; maxIter?: number; }
+export interface Geo onlineResult { values: number[]; converged: boolean; }
+export function computeGeo online(data: number[], opts: Geo onlineOptions = {}): Geo onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeo online };
diff --git a/src/geo/origin_destination.ts b/src/geo/origin_destination.ts
new file mode 100644
index 00000000..dc86c240
--- /dev/null
+++ b/src/geo/origin_destination.ts
@@ -0,0 +1,22 @@
+/** Origin Destination module — tsb analytics library. */
+
+/** Options for Origin Destination. */
+export interface OriginDestinationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Origin Destination. */
+export interface OriginDestinationResult { values: number[]; converged: boolean; }
+
+/** Compute Origin Destination. */
+export function computeOriginDestination(data: number[], opts: OriginDestinationOptions = {}): OriginDestinationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeOriginDestination };
diff --git a/src/geo/overlay.ts b/src/geo/overlay.ts
new file mode 100644
index 00000000..4e41f659
--- /dev/null
+++ b/src/geo/overlay.ts
@@ -0,0 +1,22 @@
+/** Overlay module — tsb analytics library. */
+
+/** Options for Overlay. */
+export interface OverlayOptions { tol?: number; maxIter?: number; }
+
+/** Result from Overlay. */
+export interface OverlayResult { values: number[]; converged: boolean; }
+
+/** Compute Overlay. */
+export function computeOverlay(data: number[], opts: OverlayOptions = {}): OverlayResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeOverlay };
diff --git a/src/geo/parallel.ts b/src/geo/parallel.ts
new file mode 100644
index 00000000..98039f46
--- /dev/null
+++ b/src/geo/parallel.ts
@@ -0,0 +1,15 @@
+/** Geo Parallel module — tsb analytics library. */
+export interface Geo parallelOptions { tol?: number; maxIter?: number; }
+export interface Geo parallelResult { values: number[]; converged: boolean; }
+export function computeGeo parallel(data: number[], opts: Geo parallelOptions = {}): Geo parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeo parallel };
diff --git a/src/geo/plus.ts b/src/geo/plus.ts
new file mode 100644
index 00000000..8d737843
--- /dev/null
+++ b/src/geo/plus.ts
@@ -0,0 +1,15 @@
+/** Geo Plus module — tsb analytics library. */
+export interface Geo plusOptions { tol?: number; maxIter?: number; }
+export interface Geo plusResult { values: number[]; converged: boolean; }
+export function computeGeo plus(data: number[], opts: Geo plusOptions = {}): Geo plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeo plus };
diff --git a/src/geo/point_pattern.ts b/src/geo/point_pattern.ts
new file mode 100644
index 00000000..9ee63b40
--- /dev/null
+++ b/src/geo/point_pattern.ts
@@ -0,0 +1,22 @@
+/** Point Pattern module — tsb analytics library. */
+
+/** Options for Point Pattern. */
+export interface PointPatternOptions { tol?: number; maxIter?: number; }
+
+/** Result from Point Pattern. */
+export interface PointPatternResult { values: number[]; converged: boolean; }
+
+/** Compute Point Pattern. */
+export function computePointPattern(data: number[], opts: PointPatternOptions = {}): PointPatternResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePointPattern };
diff --git a/src/geo/pro.ts b/src/geo/pro.ts
new file mode 100644
index 00000000..1229a258
--- /dev/null
+++ b/src/geo/pro.ts
@@ -0,0 +1,15 @@
+/** Geo Pro module — tsb analytics library. */
+export interface Geo proOptions { tol?: number; maxIter?: number; }
+export interface Geo proResult { values: number[]; converged: boolean; }
+export function computeGeo pro(data: number[], opts: Geo proOptions = {}): Geo proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeo pro };
diff --git a/src/geo/projections.ts b/src/geo/projections.ts
new file mode 100644
index 00000000..466079bd
--- /dev/null
+++ b/src/geo/projections.ts
@@ -0,0 +1,22 @@
+/** Projections module — tsb analytics library. */
+
+/** Options for Projections. */
+export interface ProjectionsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Projections. */
+export interface ProjectionsResult { values: number[]; converged: boolean; }
+
+/** Compute Projections. */
+export function computeProjections(data: number[], opts: ProjectionsOptions = {}): ProjectionsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeProjections };
diff --git a/src/geo/raster.ts b/src/geo/raster.ts
new file mode 100644
index 00000000..88023947
--- /dev/null
+++ b/src/geo/raster.ts
@@ -0,0 +1,22 @@
+/** Raster module — tsb analytics library. */
+
+/** Options for Raster. */
+export interface RasterOptions { tol?: number; maxIter?: number; }
+
+/** Result from Raster. */
+export interface RasterResult { values: number[]; converged: boolean; }
+
+/** Compute Raster. */
+export function computeRaster(data: number[], opts: RasterOptions = {}): RasterResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRaster };
diff --git a/src/geo/robust.ts b/src/geo/robust.ts
new file mode 100644
index 00000000..8999f80c
--- /dev/null
+++ b/src/geo/robust.ts
@@ -0,0 +1,15 @@
+/** Geo Robust module — tsb analytics library. */
+export interface Geo robustOptions { tol?: number; maxIter?: number; }
+export interface Geo robustResult { values: number[]; converged: boolean; }
+export function computeGeo robust(data: number[], opts: Geo robustOptions = {}): Geo robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeo robust };
diff --git a/src/geo/routing.ts b/src/geo/routing.ts
new file mode 100644
index 00000000..3de2357e
--- /dev/null
+++ b/src/geo/routing.ts
@@ -0,0 +1,22 @@
+/** Routing module — tsb analytics library. */
+
+/** Options for Routing. */
+export interface RoutingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Routing. */
+export interface RoutingResult { values: number[]; converged: boolean; }
+
+/** Compute Routing. */
+export function computeRouting(data: number[], opts: RoutingOptions = {}): RoutingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRouting };
diff --git a/src/geo/small.ts b/src/geo/small.ts
new file mode 100644
index 00000000..cef5499d
--- /dev/null
+++ b/src/geo/small.ts
@@ -0,0 +1,15 @@
+/** Geo Small module — tsb analytics library. */
+export interface Geo smallOptions { tol?: number; maxIter?: number; }
+export interface Geo smallResult { values: number[]; converged: boolean; }
+export function computeGeo small(data: number[], opts: Geo smallOptions = {}): Geo smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeo small };
diff --git a/src/geo/sparse.ts b/src/geo/sparse.ts
new file mode 100644
index 00000000..a245c3db
--- /dev/null
+++ b/src/geo/sparse.ts
@@ -0,0 +1,15 @@
+/** Geo Sparse module — tsb analytics library. */
+export interface Geo sparseOptions { tol?: number; maxIter?: number; }
+export interface Geo sparseResult { values: number[]; converged: boolean; }
+export function computeGeo sparse(data: number[], opts: Geo sparseOptions = {}): Geo sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeo sparse };
diff --git a/src/geo/spatial_autocorrelation.ts b/src/geo/spatial_autocorrelation.ts
new file mode 100644
index 00000000..9a96a67b
--- /dev/null
+++ b/src/geo/spatial_autocorrelation.ts
@@ -0,0 +1,22 @@
+/** Spatial Autocorrelation module — tsb analytics library. */
+
+/** Options for Spatial Autocorrelation. */
+export interface SpatialAutocorrelationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Spatial Autocorrelation. */
+export interface SpatialAutocorrelationResult { values: number[]; converged: boolean; }
+
+/** Compute Spatial Autocorrelation. */
+export function computeSpatialAutocorrelation(data: number[], opts: SpatialAutocorrelationOptions = {}): SpatialAutocorrelationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSpatialAutocorrelation };
diff --git a/src/geo/spatial_regression.ts b/src/geo/spatial_regression.ts
new file mode 100644
index 00000000..8bc147e3
--- /dev/null
+++ b/src/geo/spatial_regression.ts
@@ -0,0 +1,22 @@
+/** Spatial Regression module — tsb analytics library. */
+
+/** Options for Spatial Regression. */
+export interface SpatialRegressionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Spatial Regression. */
+export interface SpatialRegressionResult { values: number[]; converged: boolean; }
+
+/** Compute Spatial Regression. */
+export function computeSpatialRegression(data: number[], opts: SpatialRegressionOptions = {}): SpatialRegressionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSpatialRegression };
diff --git a/src/geo/stable.ts b/src/geo/stable.ts
new file mode 100644
index 00000000..ce41bb75
--- /dev/null
+++ b/src/geo/stable.ts
@@ -0,0 +1,15 @@
+/** Geo Stable module — tsb analytics library. */
+export interface Geo stableOptions { tol?: number; maxIter?: number; }
+export interface Geo stableResult { values: number[]; converged: boolean; }
+export function computeGeo stable(data: number[], opts: Geo stableOptions = {}): Geo stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeo stable };
diff --git a/src/geo/streaming.ts b/src/geo/streaming.ts
new file mode 100644
index 00000000..ab59bbed
--- /dev/null
+++ b/src/geo/streaming.ts
@@ -0,0 +1,15 @@
+/** Geo Streaming module — tsb analytics library. */
+export interface Geo streamingOptions { tol?: number; maxIter?: number; }
+export interface Geo streamingResult { values: number[]; converged: boolean; }
+export function computeGeo streaming(data: number[], opts: Geo streamingOptions = {}): Geo streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeo streaming };
diff --git a/src/geo/topology.ts b/src/geo/topology.ts
new file mode 100644
index 00000000..659cdcbb
--- /dev/null
+++ b/src/geo/topology.ts
@@ -0,0 +1,22 @@
+/** Topology module — tsb analytics library. */
+
+/** Options for Topology. */
+export interface TopologyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Topology. */
+export interface TopologyResult { values: number[]; converged: boolean; }
+
+/** Compute Topology. */
+export function computeTopology(data: number[], opts: TopologyOptions = {}): TopologyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTopology };
diff --git a/src/geo/trajectory.ts b/src/geo/trajectory.ts
new file mode 100644
index 00000000..fef02705
--- /dev/null
+++ b/src/geo/trajectory.ts
@@ -0,0 +1,22 @@
+/** Trajectory module — tsb analytics library. */
+
+/** Options for Trajectory. */
+export interface TrajectoryOptions { tol?: number; maxIter?: number; }
+
+/** Result from Trajectory. */
+export interface TrajectoryResult { values: number[]; converged: boolean; }
+
+/** Compute Trajectory. */
+export function computeTrajectory(data: number[], opts: TrajectoryOptions = {}): TrajectoryResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTrajectory };
diff --git a/src/geo/v2.ts b/src/geo/v2.ts
new file mode 100644
index 00000000..5cde5ddc
--- /dev/null
+++ b/src/geo/v2.ts
@@ -0,0 +1,15 @@
+/** Geo V2 module — tsb analytics library. */
+export interface Geo v2Options { tol?: number; maxIter?: number; }
+export interface Geo v2Result { values: number[]; converged: boolean; }
+export function computeGeo v2(data: number[], opts: Geo v2Options = {}): Geo v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeo v2 };
diff --git a/src/geo/v3.ts b/src/geo/v3.ts
new file mode 100644
index 00000000..26fb5149
--- /dev/null
+++ b/src/geo/v3.ts
@@ -0,0 +1,15 @@
+/** Geo V3 module — tsb analytics library. */
+export interface Geo v3Options { tol?: number; maxIter?: number; }
+export interface Geo v3Result { values: number[]; converged: boolean; }
+export function computeGeo v3(data: number[], opts: Geo v3Options = {}): Geo v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeo v3 };
diff --git a/src/geo/vector.ts b/src/geo/vector.ts
new file mode 100644
index 00000000..057a2af1
--- /dev/null
+++ b/src/geo/vector.ts
@@ -0,0 +1,22 @@
+/** Vector module — tsb analytics library. */
+
+/** Options for Vector. */
+export interface VectorOptions { tol?: number; maxIter?: number; }
+
+/** Result from Vector. */
+export interface VectorResult { values: number[]; converged: boolean; }
+
+/** Compute Vector. */
+export function computeVector(data: number[], opts: VectorOptions = {}): VectorResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeVector };
diff --git a/src/geo/voronoi.ts b/src/geo/voronoi.ts
new file mode 100644
index 00000000..9dd5f6cc
--- /dev/null
+++ b/src/geo/voronoi.ts
@@ -0,0 +1,22 @@
+/** Voronoi module — tsb analytics library. */
+
+/** Options for Voronoi. */
+export interface VoronoiOptions { tol?: number; maxIter?: number; }
+
+/** Result from Voronoi. */
+export interface VoronoiResult { values: number[]; converged: boolean; }
+
+/** Compute Voronoi. */
+export function computeVoronoi(data: number[], opts: VoronoiOptions = {}): VoronoiResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeVoronoi };
diff --git a/src/geo/wasm.ts b/src/geo/wasm.ts
new file mode 100644
index 00000000..42ac3d2d
--- /dev/null
+++ b/src/geo/wasm.ts
@@ -0,0 +1,15 @@
+/** Geo Wasm module — tsb analytics library. */
+export interface Geo wasmOptions { tol?: number; maxIter?: number; }
+export interface Geo wasmResult { values: number[]; converged: boolean; }
+export function computeGeo wasm(data: number[], opts: Geo wasmOptions = {}): Geo wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeo wasm };
diff --git a/src/geo/xlarge.ts b/src/geo/xlarge.ts
new file mode 100644
index 00000000..c948cdb7
--- /dev/null
+++ b/src/geo/xlarge.ts
@@ -0,0 +1,15 @@
+/** Geo Xlarge module — tsb analytics library. */
+export interface Geo xlargeOptions { tol?: number; maxIter?: number; }
+export interface Geo xlargeResult { values: number[]; converged: boolean; }
+export function computeGeo xlarge(data: number[], opts: Geo xlargeOptions = {}): Geo xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeo xlarge };
diff --git a/src/geology/advanced.ts b/src/geology/advanced.ts
new file mode 100644
index 00000000..a87a21e7
--- /dev/null
+++ b/src/geology/advanced.ts
@@ -0,0 +1,15 @@
+/** Geology Advanced module — tsb analytics library. */
+export interface Geology advancedOptions { tol?: number; maxIter?: number; }
+export interface Geology advancedResult { values: number[]; converged: boolean; }
+export function computeGeology advanced(data: number[], opts: Geology advancedOptions = {}): Geology advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeology advanced };
diff --git a/src/geology/base2.ts b/src/geology/base2.ts
new file mode 100644
index 00000000..22895a96
--- /dev/null
+++ b/src/geology/base2.ts
@@ -0,0 +1,15 @@
+/** Geology Base2 module — tsb analytics library. */
+export interface Geology base2Options { tol?: number; maxIter?: number; }
+export interface Geology base2Result { values: number[]; converged: boolean; }
+export function computeGeology base2(data: number[], opts: Geology base2Options = {}): Geology base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeology base2 };
diff --git a/src/geology/batch.ts b/src/geology/batch.ts
new file mode 100644
index 00000000..0116431f
--- /dev/null
+++ b/src/geology/batch.ts
@@ -0,0 +1,15 @@
+/** Geology Batch module — tsb analytics library. */
+export interface Geology batchOptions { tol?: number; maxIter?: number; }
+export interface Geology batchResult { values: number[]; converged: boolean; }
+export function computeGeology batch(data: number[], opts: Geology batchOptions = {}): Geology batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeology batch };
diff --git a/src/geology/beta.ts b/src/geology/beta.ts
new file mode 100644
index 00000000..411f9414
--- /dev/null
+++ b/src/geology/beta.ts
@@ -0,0 +1,15 @@
+/** Geology Beta module — tsb analytics library. */
+export interface Geology betaOptions { tol?: number; maxIter?: number; }
+export interface Geology betaResult { values: number[]; converged: boolean; }
+export function computeGeology beta(data: number[], opts: Geology betaOptions = {}): Geology betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeology beta };
diff --git a/src/geology/burial_history.ts b/src/geology/burial_history.ts
new file mode 100644
index 00000000..11da62e4
--- /dev/null
+++ b/src/geology/burial_history.ts
@@ -0,0 +1,22 @@
+/** Burial History module — tsb analytics library. */
+
+/** Options for Burial History. */
+export interface BurialHistoryOptions { tol?: number; maxIter?: number; }
+
+/** Result from Burial History. */
+export interface BurialHistoryResult { values: number[]; converged: boolean; }
+
+/** Compute Burial History. */
+export function computeBurialHistory(data: number[], opts: BurialHistoryOptions = {}): BurialHistoryResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBurialHistory };
diff --git a/src/geology/cosmogenic.ts b/src/geology/cosmogenic.ts
new file mode 100644
index 00000000..f45d7548
--- /dev/null
+++ b/src/geology/cosmogenic.ts
@@ -0,0 +1,22 @@
+/** Cosmogenic module — tsb analytics library. */
+
+/** Options for Cosmogenic. */
+export interface CosmogenicOptions { tol?: number; maxIter?: number; }
+
+/** Result from Cosmogenic. */
+export interface CosmogenicResult { values: number[]; converged: boolean; }
+
+/** Compute Cosmogenic. */
+export function computeCosmogenic(data: number[], opts: CosmogenicOptions = {}): CosmogenicResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCosmogenic };
diff --git a/src/geology/cpu.ts b/src/geology/cpu.ts
new file mode 100644
index 00000000..227c77fb
--- /dev/null
+++ b/src/geology/cpu.ts
@@ -0,0 +1,15 @@
+/** Geology Cpu module — tsb analytics library. */
+export interface Geology cpuOptions { tol?: number; maxIter?: number; }
+export interface Geology cpuResult { values: number[]; converged: boolean; }
+export function computeGeology cpu(data: number[], opts: Geology cpuOptions = {}): Geology cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeology cpu };
diff --git a/src/geology/dense.ts b/src/geology/dense.ts
new file mode 100644
index 00000000..3174365b
--- /dev/null
+++ b/src/geology/dense.ts
@@ -0,0 +1,15 @@
+/** Geology Dense module — tsb analytics library. */
+export interface Geology denseOptions { tol?: number; maxIter?: number; }
+export interface Geology denseResult { values: number[]; converged: boolean; }
+export function computeGeology dense(data: number[], opts: Geology denseOptions = {}): Geology denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeology dense };
diff --git a/src/geology/distributed.ts b/src/geology/distributed.ts
new file mode 100644
index 00000000..ca9dbe8f
--- /dev/null
+++ b/src/geology/distributed.ts
@@ -0,0 +1,15 @@
+/** Geology Distributed module — tsb analytics library. */
+export interface Geology distributedOptions { tol?: number; maxIter?: number; }
+export interface Geology distributedResult { values: number[]; converged: boolean; }
+export function computeGeology distributed(data: number[], opts: Geology distributedOptions = {}): Geology distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeology distributed };
diff --git a/src/geology/experimental.ts b/src/geology/experimental.ts
new file mode 100644
index 00000000..f00904c5
--- /dev/null
+++ b/src/geology/experimental.ts
@@ -0,0 +1,15 @@
+/** Geology Experimental module — tsb analytics library. */
+export interface Geology experimentalOptions { tol?: number; maxIter?: number; }
+export interface Geology experimentalResult { values: number[]; converged: boolean; }
+export function computeGeology experimental(data: number[], opts: Geology experimentalOptions = {}): Geology experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeology experimental };
diff --git a/src/geology/fast.ts b/src/geology/fast.ts
new file mode 100644
index 00000000..4b2ed390
--- /dev/null
+++ b/src/geology/fast.ts
@@ -0,0 +1,15 @@
+/** Geology Fast module — tsb analytics library. */
+export interface Geology fastOptions { tol?: number; maxIter?: number; }
+export interface Geology fastResult { values: number[]; converged: boolean; }
+export function computeGeology fast(data: number[], opts: Geology fastOptions = {}): Geology fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeology fast };
diff --git a/src/geology/future.ts b/src/geology/future.ts
new file mode 100644
index 00000000..99e28e8e
--- /dev/null
+++ b/src/geology/future.ts
@@ -0,0 +1,15 @@
+/** Geology Future module — tsb analytics library. */
+export interface Geology futureOptions { tol?: number; maxIter?: number; }
+export interface Geology futureResult { values: number[]; converged: boolean; }
+export function computeGeology future(data: number[], opts: Geology futureOptions = {}): Geology futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeology future };
diff --git a/src/geology/geochemistry.ts b/src/geology/geochemistry.ts
new file mode 100644
index 00000000..dbde7ede
--- /dev/null
+++ b/src/geology/geochemistry.ts
@@ -0,0 +1,22 @@
+/** Geochemistry module — tsb analytics library. */
+
+/** Options for Geochemistry. */
+export interface GeochemistryOptions { tol?: number; maxIter?: number; }
+
+/** Result from Geochemistry. */
+export interface GeochemistryResult { values: number[]; converged: boolean; }
+
+/** Compute Geochemistry. */
+export function computeGeochemistry(data: number[], opts: GeochemistryOptions = {}): GeochemistryResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGeochemistry };
diff --git a/src/geology/geochronology.ts b/src/geology/geochronology.ts
new file mode 100644
index 00000000..41272e88
--- /dev/null
+++ b/src/geology/geochronology.ts
@@ -0,0 +1,22 @@
+/** Geochronology module — tsb analytics library. */
+
+/** Options for Geochronology. */
+export interface GeochronologyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Geochronology. */
+export interface GeochronologyResult { values: number[]; converged: boolean; }
+
+/** Compute Geochronology. */
+export function computeGeochronology(data: number[], opts: GeochronologyOptions = {}): GeochronologyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGeochronology };
diff --git a/src/geology/geomorphology.ts b/src/geology/geomorphology.ts
new file mode 100644
index 00000000..42fb9192
--- /dev/null
+++ b/src/geology/geomorphology.ts
@@ -0,0 +1,22 @@
+/** Geomorphology module — tsb analytics library. */
+
+/** Options for Geomorphology. */
+export interface GeomorphologyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Geomorphology. */
+export interface GeomorphologyResult { values: number[]; converged: boolean; }
+
+/** Compute Geomorphology. */
+export function computeGeomorphology(data: number[], opts: GeomorphologyOptions = {}): GeomorphologyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGeomorphology };
diff --git a/src/geology/glaciology.ts b/src/geology/glaciology.ts
new file mode 100644
index 00000000..c21e41f1
--- /dev/null
+++ b/src/geology/glaciology.ts
@@ -0,0 +1,22 @@
+/** Glaciology module — tsb analytics library. */
+
+/** Options for Glaciology. */
+export interface GlaciologyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Glaciology. */
+export interface GlaciologyResult { values: number[]; converged: boolean; }
+
+/** Compute Glaciology. */
+export function computeGlaciology(data: number[], opts: GlaciologyOptions = {}): GlaciologyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGlaciology };
diff --git a/src/geology/gpu.ts b/src/geology/gpu.ts
new file mode 100644
index 00000000..bbc33fa8
--- /dev/null
+++ b/src/geology/gpu.ts
@@ -0,0 +1,15 @@
+/** Geology Gpu module — tsb analytics library. */
+export interface Geology gpuOptions { tol?: number; maxIter?: number; }
+export interface Geology gpuResult { values: number[]; converged: boolean; }
+export function computeGeology gpu(data: number[], opts: Geology gpuOptions = {}): Geology gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeology gpu };
diff --git a/src/geology/hydrogeology.ts b/src/geology/hydrogeology.ts
new file mode 100644
index 00000000..911214a2
--- /dev/null
+++ b/src/geology/hydrogeology.ts
@@ -0,0 +1,22 @@
+/** Hydrogeology module — tsb analytics library. */
+
+/** Options for Hydrogeology. */
+export interface HydrogeologyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Hydrogeology. */
+export interface HydrogeologyResult { values: number[]; converged: boolean; }
+
+/** Compute Hydrogeology. */
+export function computeHydrogeology(data: number[], opts: HydrogeologyOptions = {}): HydrogeologyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeHydrogeology };
diff --git a/src/geology/isotope.ts b/src/geology/isotope.ts
new file mode 100644
index 00000000..912e2ae5
--- /dev/null
+++ b/src/geology/isotope.ts
@@ -0,0 +1,22 @@
+/** Isotope module — tsb analytics library. */
+
+/** Options for Isotope. */
+export interface IsotopeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Isotope. */
+export interface IsotopeResult { values: number[]; converged: boolean; }
+
+/** Compute Isotope. */
+export function computeIsotope(data: number[], opts: IsotopeOptions = {}): IsotopeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeIsotope };
diff --git a/src/geology/large.ts b/src/geology/large.ts
new file mode 100644
index 00000000..2144a317
--- /dev/null
+++ b/src/geology/large.ts
@@ -0,0 +1,15 @@
+/** Geology Large module — tsb analytics library. */
+export interface Geology largeOptions { tol?: number; maxIter?: number; }
+export interface Geology largeResult { values: number[]; converged: boolean; }
+export function computeGeology large(data: number[], opts: Geology largeOptions = {}): Geology largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeology large };
diff --git a/src/geology/legacy.ts b/src/geology/legacy.ts
new file mode 100644
index 00000000..aa921691
--- /dev/null
+++ b/src/geology/legacy.ts
@@ -0,0 +1,15 @@
+/** Geology Legacy module — tsb analytics library. */
+export interface Geology legacyOptions { tol?: number; maxIter?: number; }
+export interface Geology legacyResult { values: number[]; converged: boolean; }
+export function computeGeology legacy(data: number[], opts: Geology legacyOptions = {}): Geology legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeology legacy };
diff --git a/src/geology/lite.ts b/src/geology/lite.ts
new file mode 100644
index 00000000..4bb06057
--- /dev/null
+++ b/src/geology/lite.ts
@@ -0,0 +1,15 @@
+/** Geology Lite module — tsb analytics library. */
+export interface Geology liteOptions { tol?: number; maxIter?: number; }
+export interface Geology liteResult { values: number[]; converged: boolean; }
+export function computeGeology lite(data: number[], opts: Geology liteOptions = {}): Geology liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeology lite };
diff --git a/src/geology/major_element.ts b/src/geology/major_element.ts
new file mode 100644
index 00000000..ce721ecc
--- /dev/null
+++ b/src/geology/major_element.ts
@@ -0,0 +1,22 @@
+/** Major Element module — tsb analytics library. */
+
+/** Options for Major Element. */
+export interface MajorElementOptions { tol?: number; maxIter?: number; }
+
+/** Result from Major Element. */
+export interface MajorElementResult { values: number[]; converged: boolean; }
+
+/** Compute Major Element. */
+export function computeMajorElement(data: number[], opts: MajorElementOptions = {}): MajorElementResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMajorElement };
diff --git a/src/geology/migration_geo.ts b/src/geology/migration_geo.ts
new file mode 100644
index 00000000..745a9d7d
--- /dev/null
+++ b/src/geology/migration_geo.ts
@@ -0,0 +1,22 @@
+/** Migration Geo module — tsb analytics library. */
+
+/** Options for Migration Geo. */
+export interface MigrationGeoOptions { tol?: number; maxIter?: number; }
+
+/** Result from Migration Geo. */
+export interface MigrationGeoResult { values: number[]; converged: boolean; }
+
+/** Compute Migration Geo. */
+export function computeMigrationGeo(data: number[], opts: MigrationGeoOptions = {}): MigrationGeoResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMigrationGeo };
diff --git a/src/geology/mineralogy.ts b/src/geology/mineralogy.ts
new file mode 100644
index 00000000..bc84c875
--- /dev/null
+++ b/src/geology/mineralogy.ts
@@ -0,0 +1,22 @@
+/** Mineralogy module — tsb analytics library. */
+
+/** Options for Mineralogy. */
+export interface MineralogyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Mineralogy. */
+export interface MineralogyResult { values: number[]; converged: boolean; }
+
+/** Compute Mineralogy. */
+export function computeMineralogy(data: number[], opts: MineralogyOptions = {}): MineralogyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMineralogy };
diff --git a/src/geology/mini.ts b/src/geology/mini.ts
new file mode 100644
index 00000000..fe5d5532
--- /dev/null
+++ b/src/geology/mini.ts
@@ -0,0 +1,15 @@
+/** Geology Mini module — tsb analytics library. */
+export interface Geology miniOptions { tol?: number; maxIter?: number; }
+export interface Geology miniResult { values: number[]; converged: boolean; }
+export function computeGeology mini(data: number[], opts: Geology miniOptions = {}): Geology miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeology mini };
diff --git a/src/geology/next.ts b/src/geology/next.ts
new file mode 100644
index 00000000..5bcddcdb
--- /dev/null
+++ b/src/geology/next.ts
@@ -0,0 +1,15 @@
+/** Geology Next module — tsb analytics library. */
+export interface Geology nextOptions { tol?: number; maxIter?: number; }
+export interface Geology nextResult { values: number[]; converged: boolean; }
+export function computeGeology next(data: number[], opts: Geology nextOptions = {}): Geology nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeology next };
diff --git a/src/geology/online.ts b/src/geology/online.ts
new file mode 100644
index 00000000..edceb611
--- /dev/null
+++ b/src/geology/online.ts
@@ -0,0 +1,15 @@
+/** Geology Online module — tsb analytics library. */
+export interface Geology onlineOptions { tol?: number; maxIter?: number; }
+export interface Geology onlineResult { values: number[]; converged: boolean; }
+export function computeGeology online(data: number[], opts: Geology onlineOptions = {}): Geology onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeology online };
diff --git a/src/geology/paleoclimatology.ts b/src/geology/paleoclimatology.ts
new file mode 100644
index 00000000..7bf44982
--- /dev/null
+++ b/src/geology/paleoclimatology.ts
@@ -0,0 +1,22 @@
+/** Paleoclimatology module — tsb analytics library. */
+
+/** Options for Paleoclimatology. */
+export interface PaleoclimatologyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Paleoclimatology. */
+export interface PaleoclimatologyResult { values: number[]; converged: boolean; }
+
+/** Compute Paleoclimatology. */
+export function computePaleoclimatology(data: number[], opts: PaleoclimatologyOptions = {}): PaleoclimatologyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePaleoclimatology };
diff --git a/src/geology/parallel.ts b/src/geology/parallel.ts
new file mode 100644
index 00000000..1a44e216
--- /dev/null
+++ b/src/geology/parallel.ts
@@ -0,0 +1,15 @@
+/** Geology Parallel module — tsb analytics library. */
+export interface Geology parallelOptions { tol?: number; maxIter?: number; }
+export interface Geology parallelResult { values: number[]; converged: boolean; }
+export function computeGeology parallel(data: number[], opts: Geology parallelOptions = {}): Geology parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeology parallel };
diff --git a/src/geology/petrology.ts b/src/geology/petrology.ts
new file mode 100644
index 00000000..83df7cac
--- /dev/null
+++ b/src/geology/petrology.ts
@@ -0,0 +1,22 @@
+/** Petrology module — tsb analytics library. */
+
+/** Options for Petrology. */
+export interface PetrologyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Petrology. */
+export interface PetrologyResult { values: number[]; converged: boolean; }
+
+/** Compute Petrology. */
+export function computePetrology(data: number[], opts: PetrologyOptions = {}): PetrologyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePetrology };
diff --git a/src/geology/plus.ts b/src/geology/plus.ts
new file mode 100644
index 00000000..d5edc651
--- /dev/null
+++ b/src/geology/plus.ts
@@ -0,0 +1,15 @@
+/** Geology Plus module — tsb analytics library. */
+export interface Geology plusOptions { tol?: number; maxIter?: number; }
+export interface Geology plusResult { values: number[]; converged: boolean; }
+export function computeGeology plus(data: number[], opts: Geology plusOptions = {}): Geology plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeology plus };
diff --git a/src/geology/pro.ts b/src/geology/pro.ts
new file mode 100644
index 00000000..1a2cea24
--- /dev/null
+++ b/src/geology/pro.ts
@@ -0,0 +1,15 @@
+/** Geology Pro module — tsb analytics library. */
+export interface Geology proOptions { tol?: number; maxIter?: number; }
+export interface Geology proResult { values: number[]; converged: boolean; }
+export function computeGeology pro(data: number[], opts: Geology proOptions = {}): Geology proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeology pro };
diff --git a/src/geology/quaternary.ts b/src/geology/quaternary.ts
new file mode 100644
index 00000000..678d175d
--- /dev/null
+++ b/src/geology/quaternary.ts
@@ -0,0 +1,22 @@
+/** Quaternary module — tsb analytics library. */
+
+/** Options for Quaternary. */
+export interface QuaternaryOptions { tol?: number; maxIter?: number; }
+
+/** Result from Quaternary. */
+export interface QuaternaryResult { values: number[]; converged: boolean; }
+
+/** Compute Quaternary. */
+export function computeQuaternary(data: number[], opts: QuaternaryOptions = {}): QuaternaryResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeQuaternary };
diff --git a/src/geology/radiogenic.ts b/src/geology/radiogenic.ts
new file mode 100644
index 00000000..b3a835f0
--- /dev/null
+++ b/src/geology/radiogenic.ts
@@ -0,0 +1,22 @@
+/** Radiogenic module — tsb analytics library. */
+
+/** Options for Radiogenic. */
+export interface RadiogenicOptions { tol?: number; maxIter?: number; }
+
+/** Result from Radiogenic. */
+export interface RadiogenicResult { values: number[]; converged: boolean; }
+
+/** Compute Radiogenic. */
+export function computeRadiogenic(data: number[], opts: RadiogenicOptions = {}): RadiogenicResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRadiogenic };
diff --git a/src/geology/rare_earth.ts b/src/geology/rare_earth.ts
new file mode 100644
index 00000000..bb90bf9f
--- /dev/null
+++ b/src/geology/rare_earth.ts
@@ -0,0 +1,22 @@
+/** Rare Earth module — tsb analytics library. */
+
+/** Options for Rare Earth. */
+export interface RareEarthOptions { tol?: number; maxIter?: number; }
+
+/** Result from Rare Earth. */
+export interface RareEarthResult { values: number[]; converged: boolean; }
+
+/** Compute Rare Earth. */
+export function computeRareEarth(data: number[], opts: RareEarthOptions = {}): RareEarthResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRareEarth };
diff --git a/src/geology/reservoir.ts b/src/geology/reservoir.ts
new file mode 100644
index 00000000..16572c13
--- /dev/null
+++ b/src/geology/reservoir.ts
@@ -0,0 +1,22 @@
+/** Reservoir module — tsb analytics library. */
+
+/** Options for Reservoir. */
+export interface ReservoirOptions { tol?: number; maxIter?: number; }
+
+/** Result from Reservoir. */
+export interface ReservoirResult { values: number[]; converged: boolean; }
+
+/** Compute Reservoir. */
+export function computeReservoir(data: number[], opts: ReservoirOptions = {}): ReservoirResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeReservoir };
diff --git a/src/geology/robust.ts b/src/geology/robust.ts
new file mode 100644
index 00000000..8cc916fb
--- /dev/null
+++ b/src/geology/robust.ts
@@ -0,0 +1,15 @@
+/** Geology Robust module — tsb analytics library. */
+export interface Geology robustOptions { tol?: number; maxIter?: number; }
+export interface Geology robustResult { values: number[]; converged: boolean; }
+export function computeGeology robust(data: number[], opts: Geology robustOptions = {}): Geology robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeology robust };
diff --git a/src/geology/seal.ts b/src/geology/seal.ts
new file mode 100644
index 00000000..e6575f9c
--- /dev/null
+++ b/src/geology/seal.ts
@@ -0,0 +1,22 @@
+/** Seal module — tsb analytics library. */
+
+/** Options for Seal. */
+export interface SealOptions { tol?: number; maxIter?: number; }
+
+/** Result from Seal. */
+export interface SealResult { values: number[]; converged: boolean; }
+
+/** Compute Seal. */
+export function computeSeal(data: number[], opts: SealOptions = {}): SealResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSeal };
diff --git a/src/geology/sedimentology.ts b/src/geology/sedimentology.ts
new file mode 100644
index 00000000..ae57728f
--- /dev/null
+++ b/src/geology/sedimentology.ts
@@ -0,0 +1,22 @@
+/** Sedimentology module — tsb analytics library. */
+
+/** Options for Sedimentology. */
+export interface SedimentologyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Sedimentology. */
+export interface SedimentologyResult { values: number[]; converged: boolean; }
+
+/** Compute Sedimentology. */
+export function computeSedimentology(data: number[], opts: SedimentologyOptions = {}): SedimentologyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSedimentology };
diff --git a/src/geology/seismology.ts b/src/geology/seismology.ts
new file mode 100644
index 00000000..de3044fc
--- /dev/null
+++ b/src/geology/seismology.ts
@@ -0,0 +1,22 @@
+/** Seismology module — tsb analytics library. */
+
+/** Options for Seismology. */
+export interface SeismologyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Seismology. */
+export interface SeismologyResult { values: number[]; converged: boolean; }
+
+/** Compute Seismology. */
+export function computeSeismology(data: number[], opts: SeismologyOptions = {}): SeismologyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSeismology };
diff --git a/src/geology/small.ts b/src/geology/small.ts
new file mode 100644
index 00000000..3d938158
--- /dev/null
+++ b/src/geology/small.ts
@@ -0,0 +1,15 @@
+/** Geology Small module — tsb analytics library. */
+export interface Geology smallOptions { tol?: number; maxIter?: number; }
+export interface Geology smallResult { values: number[]; converged: boolean; }
+export function computeGeology small(data: number[], opts: Geology smallOptions = {}): Geology smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeology small };
diff --git a/src/geology/source_rock.ts b/src/geology/source_rock.ts
new file mode 100644
index 00000000..bcd34680
--- /dev/null
+++ b/src/geology/source_rock.ts
@@ -0,0 +1,22 @@
+/** Source Rock module — tsb analytics library. */
+
+/** Options for Source Rock. */
+export interface SourceRockOptions { tol?: number; maxIter?: number; }
+
+/** Result from Source Rock. */
+export interface SourceRockResult { values: number[]; converged: boolean; }
+
+/** Compute Source Rock. */
+export function computeSourceRock(data: number[], opts: SourceRockOptions = {}): SourceRockResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSourceRock };
diff --git a/src/geology/sparse.ts b/src/geology/sparse.ts
new file mode 100644
index 00000000..313d889e
--- /dev/null
+++ b/src/geology/sparse.ts
@@ -0,0 +1,15 @@
+/** Geology Sparse module — tsb analytics library. */
+export interface Geology sparseOptions { tol?: number; maxIter?: number; }
+export interface Geology sparseResult { values: number[]; converged: boolean; }
+export function computeGeology sparse(data: number[], opts: Geology sparseOptions = {}): Geology sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeology sparse };
diff --git a/src/geology/stable.ts b/src/geology/stable.ts
new file mode 100644
index 00000000..53a7fce1
--- /dev/null
+++ b/src/geology/stable.ts
@@ -0,0 +1,15 @@
+/** Geology Stable module — tsb analytics library. */
+export interface Geology stableOptions { tol?: number; maxIter?: number; }
+export interface Geology stableResult { values: number[]; converged: boolean; }
+export function computeGeology stable(data: number[], opts: Geology stableOptions = {}): Geology stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeology stable };
diff --git a/src/geology/stable_isotope.ts b/src/geology/stable_isotope.ts
new file mode 100644
index 00000000..3ded34d9
--- /dev/null
+++ b/src/geology/stable_isotope.ts
@@ -0,0 +1,22 @@
+/** Stable Isotope module — tsb analytics library. */
+
+/** Options for Stable Isotope. */
+export interface StableIsotopeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Stable Isotope. */
+export interface StableIsotopeResult { values: number[]; converged: boolean; }
+
+/** Compute Stable Isotope. */
+export function computeStableIsotope(data: number[], opts: StableIsotopeOptions = {}): StableIsotopeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeStableIsotope };
diff --git a/src/geology/stratigraphy.ts b/src/geology/stratigraphy.ts
new file mode 100644
index 00000000..35371f10
--- /dev/null
+++ b/src/geology/stratigraphy.ts
@@ -0,0 +1,22 @@
+/** Stratigraphy module — tsb analytics library. */
+
+/** Options for Stratigraphy. */
+export interface StratigraphyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Stratigraphy. */
+export interface StratigraphyResult { values: number[]; converged: boolean; }
+
+/** Compute Stratigraphy. */
+export function computeStratigraphy(data: number[], opts: StratigraphyOptions = {}): StratigraphyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeStratigraphy };
diff --git a/src/geology/streaming.ts b/src/geology/streaming.ts
new file mode 100644
index 00000000..9ecc7ed7
--- /dev/null
+++ b/src/geology/streaming.ts
@@ -0,0 +1,15 @@
+/** Geology Streaming module — tsb analytics library. */
+export interface Geology streamingOptions { tol?: number; maxIter?: number; }
+export interface Geology streamingResult { values: number[]; converged: boolean; }
+export function computeGeology streaming(data: number[], opts: Geology streamingOptions = {}): Geology streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeology streaming };
diff --git a/src/geology/structural.ts b/src/geology/structural.ts
new file mode 100644
index 00000000..71be1bcc
--- /dev/null
+++ b/src/geology/structural.ts
@@ -0,0 +1,22 @@
+/** Structural module — tsb analytics library. */
+
+/** Options for Structural. */
+export interface StructuralOptions { tol?: number; maxIter?: number; }
+
+/** Result from Structural. */
+export interface StructuralResult { values: number[]; converged: boolean; }
+
+/** Compute Structural. */
+export function computeStructural(data: number[], opts: StructuralOptions = {}): StructuralResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeStructural };
diff --git a/src/geology/tectonics.ts b/src/geology/tectonics.ts
new file mode 100644
index 00000000..fa276bd7
--- /dev/null
+++ b/src/geology/tectonics.ts
@@ -0,0 +1,22 @@
+/** Tectonics module — tsb analytics library. */
+
+/** Options for Tectonics. */
+export interface TectonicsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Tectonics. */
+export interface TectonicsResult { values: number[]; converged: boolean; }
+
+/** Compute Tectonics. */
+export function computeTectonics(data: number[], opts: TectonicsOptions = {}): TectonicsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTectonics };
diff --git a/src/geology/thermal_maturation.ts b/src/geology/thermal_maturation.ts
new file mode 100644
index 00000000..b4bec26e
--- /dev/null
+++ b/src/geology/thermal_maturation.ts
@@ -0,0 +1,22 @@
+/** Thermal Maturation module — tsb analytics library. */
+
+/** Options for Thermal Maturation. */
+export interface ThermalMaturationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Thermal Maturation. */
+export interface ThermalMaturationResult { values: number[]; converged: boolean; }
+
+/** Compute Thermal Maturation. */
+export function computeThermalMaturation(data: number[], opts: ThermalMaturationOptions = {}): ThermalMaturationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeThermalMaturation };
diff --git a/src/geology/thermochronology.ts b/src/geology/thermochronology.ts
new file mode 100644
index 00000000..18773a0c
--- /dev/null
+++ b/src/geology/thermochronology.ts
@@ -0,0 +1,22 @@
+/** Thermochronology module — tsb analytics library. */
+
+/** Options for Thermochronology. */
+export interface ThermochronologyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Thermochronology. */
+export interface ThermochronologyResult { values: number[]; converged: boolean; }
+
+/** Compute Thermochronology. */
+export function computeThermochronology(data: number[], opts: ThermochronologyOptions = {}): ThermochronologyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeThermochronology };
diff --git a/src/geology/trace_element.ts b/src/geology/trace_element.ts
new file mode 100644
index 00000000..e645bbb7
--- /dev/null
+++ b/src/geology/trace_element.ts
@@ -0,0 +1,22 @@
+/** Trace Element module — tsb analytics library. */
+
+/** Options for Trace Element. */
+export interface TraceElementOptions { tol?: number; maxIter?: number; }
+
+/** Result from Trace Element. */
+export interface TraceElementResult { values: number[]; converged: boolean; }
+
+/** Compute Trace Element. */
+export function computeTraceElement(data: number[], opts: TraceElementOptions = {}): TraceElementResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTraceElement };
diff --git a/src/geology/trap.ts b/src/geology/trap.ts
new file mode 100644
index 00000000..62510caa
--- /dev/null
+++ b/src/geology/trap.ts
@@ -0,0 +1,22 @@
+/** Trap module — tsb analytics library. */
+
+/** Options for Trap. */
+export interface TrapOptions { tol?: number; maxIter?: number; }
+
+/** Result from Trap. */
+export interface TrapResult { values: number[]; converged: boolean; }
+
+/** Compute Trap. */
+export function computeTrap(data: number[], opts: TrapOptions = {}): TrapResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTrap };
diff --git a/src/geology/v2.ts b/src/geology/v2.ts
new file mode 100644
index 00000000..63b0580a
--- /dev/null
+++ b/src/geology/v2.ts
@@ -0,0 +1,15 @@
+/** Geology V2 module — tsb analytics library. */
+export interface Geology v2Options { tol?: number; maxIter?: number; }
+export interface Geology v2Result { values: number[]; converged: boolean; }
+export function computeGeology v2(data: number[], opts: Geology v2Options = {}): Geology v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeology v2 };
diff --git a/src/geology/v3.ts b/src/geology/v3.ts
new file mode 100644
index 00000000..bc560b46
--- /dev/null
+++ b/src/geology/v3.ts
@@ -0,0 +1,15 @@
+/** Geology V3 module — tsb analytics library. */
+export interface Geology v3Options { tol?: number; maxIter?: number; }
+export interface Geology v3Result { values: number[]; converged: boolean; }
+export function computeGeology v3(data: number[], opts: Geology v3Options = {}): Geology v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeology v3 };
diff --git a/src/geology/volcanology.ts b/src/geology/volcanology.ts
new file mode 100644
index 00000000..7c32ae45
--- /dev/null
+++ b/src/geology/volcanology.ts
@@ -0,0 +1,22 @@
+/** Volcanology module — tsb analytics library. */
+
+/** Options for Volcanology. */
+export interface VolcanologyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Volcanology. */
+export interface VolcanologyResult { values: number[]; converged: boolean; }
+
+/** Compute Volcanology. */
+export function computeVolcanology(data: number[], opts: VolcanologyOptions = {}): VolcanologyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeVolcanology };
diff --git a/src/geology/wasm.ts b/src/geology/wasm.ts
new file mode 100644
index 00000000..7eec9d43
--- /dev/null
+++ b/src/geology/wasm.ts
@@ -0,0 +1,15 @@
+/** Geology Wasm module — tsb analytics library. */
+export interface Geology wasmOptions { tol?: number; maxIter?: number; }
+export interface Geology wasmResult { values: number[]; converged: boolean; }
+export function computeGeology wasm(data: number[], opts: Geology wasmOptions = {}): Geology wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeology wasm };
diff --git a/src/geology/xlarge.ts b/src/geology/xlarge.ts
new file mode 100644
index 00000000..790d4d86
--- /dev/null
+++ b/src/geology/xlarge.ts
@@ -0,0 +1,15 @@
+/** Geology Xlarge module — tsb analytics library. */
+export interface Geology xlargeOptions { tol?: number; maxIter?: number; }
+export interface Geology xlargeResult { values: number[]; converged: boolean; }
+export function computeGeology xlarge(data: number[], opts: Geology xlargeOptions = {}): Geology xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeGeology xlarge };
diff --git a/src/health/advanced.ts b/src/health/advanced.ts
new file mode 100644
index 00000000..715e75d0
--- /dev/null
+++ b/src/health/advanced.ts
@@ -0,0 +1,15 @@
+/** Health Advanced module — tsb analytics library. */
+export interface Health advancedOptions { tol?: number; maxIter?: number; }
+export interface Health advancedResult { values: number[]; converged: boolean; }
+export function computeHealth advanced(data: number[], opts: Health advancedOptions = {}): Health advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHealth advanced };
diff --git a/src/health/base2.ts b/src/health/base2.ts
new file mode 100644
index 00000000..f18a8f2c
--- /dev/null
+++ b/src/health/base2.ts
@@ -0,0 +1,15 @@
+/** Health Base2 module — tsb analytics library. */
+export interface Health base2Options { tol?: number; maxIter?: number; }
+export interface Health base2Result { values: number[]; converged: boolean; }
+export function computeHealth base2(data: number[], opts: Health base2Options = {}): Health base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHealth base2 };
diff --git a/src/health/batch.ts b/src/health/batch.ts
new file mode 100644
index 00000000..09c545f9
--- /dev/null
+++ b/src/health/batch.ts
@@ -0,0 +1,15 @@
+/** Health Batch module — tsb analytics library. */
+export interface Health batchOptions { tol?: number; maxIter?: number; }
+export interface Health batchResult { values: number[]; converged: boolean; }
+export function computeHealth batch(data: number[], opts: Health batchOptions = {}): Health batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHealth batch };
diff --git a/src/health/beta.ts b/src/health/beta.ts
new file mode 100644
index 00000000..9cb0e763
--- /dev/null
+++ b/src/health/beta.ts
@@ -0,0 +1,15 @@
+/** Health Beta module — tsb analytics library. */
+export interface Health betaOptions { tol?: number; maxIter?: number; }
+export interface Health betaResult { values: number[]; converged: boolean; }
+export function computeHealth beta(data: number[], opts: Health betaOptions = {}): Health betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHealth beta };
diff --git a/src/health/biostatistics.ts b/src/health/biostatistics.ts
new file mode 100644
index 00000000..2e81c092
--- /dev/null
+++ b/src/health/biostatistics.ts
@@ -0,0 +1,22 @@
+/** Biostatistics module — tsb analytics library. */
+
+/** Options for Biostatistics. */
+export interface BiostatisticsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Biostatistics. */
+export interface BiostatisticsResult { values: number[]; converged: boolean; }
+
+/** Compute Biostatistics. */
+export function computeBiostatistics(data: number[], opts: BiostatisticsOptions = {}): BiostatisticsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBiostatistics };
diff --git a/src/health/burden.ts b/src/health/burden.ts
new file mode 100644
index 00000000..5781b792
--- /dev/null
+++ b/src/health/burden.ts
@@ -0,0 +1,22 @@
+/** Burden module — tsb analytics library. */
+
+/** Options for Burden. */
+export interface BurdenOptions { tol?: number; maxIter?: number; }
+
+/** Result from Burden. */
+export interface BurdenResult { values: number[]; converged: boolean; }
+
+/** Compute Burden. */
+export function computeBurden(data: number[], opts: BurdenOptions = {}): BurdenResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBurden };
diff --git a/src/health/case_control.ts b/src/health/case_control.ts
new file mode 100644
index 00000000..d1c58337
--- /dev/null
+++ b/src/health/case_control.ts
@@ -0,0 +1,22 @@
+/** Case Control module — tsb analytics library. */
+
+/** Options for Case Control. */
+export interface CaseControlOptions { tol?: number; maxIter?: number; }
+
+/** Result from Case Control. */
+export interface CaseControlResult { values: number[]; converged: boolean; }
+
+/** Compute Case Control. */
+export function computeCaseControl(data: number[], opts: CaseControlOptions = {}): CaseControlResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCaseControl };
diff --git a/src/health/clinical_trial.ts b/src/health/clinical_trial.ts
new file mode 100644
index 00000000..a3336cfa
--- /dev/null
+++ b/src/health/clinical_trial.ts
@@ -0,0 +1,22 @@
+/** Clinical Trial module — tsb analytics library. */
+
+/** Options for Clinical Trial. */
+export interface ClinicalTrialOptions { tol?: number; maxIter?: number; }
+
+/** Result from Clinical Trial. */
+export interface ClinicalTrialResult { values: number[]; converged: boolean; }
+
+/** Compute Clinical Trial. */
+export function computeClinicalTrial(data: number[], opts: ClinicalTrialOptions = {}): ClinicalTrialResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeClinicalTrial };
diff --git a/src/health/cohort_health.ts b/src/health/cohort_health.ts
new file mode 100644
index 00000000..6de83cf6
--- /dev/null
+++ b/src/health/cohort_health.ts
@@ -0,0 +1,22 @@
+/** Cohort Health module — tsb analytics library. */
+
+/** Options for Cohort Health. */
+export interface CohortHealthOptions { tol?: number; maxIter?: number; }
+
+/** Result from Cohort Health. */
+export interface CohortHealthResult { values: number[]; converged: boolean; }
+
+/** Compute Cohort Health. */
+export function computeCohortHealth(data: number[], opts: CohortHealthOptions = {}): CohortHealthResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCohortHealth };
diff --git a/src/health/competing_risks.ts b/src/health/competing_risks.ts
new file mode 100644
index 00000000..4a53f27c
--- /dev/null
+++ b/src/health/competing_risks.ts
@@ -0,0 +1,22 @@
+/** Competing Risks module — tsb analytics library. */
+
+/** Options for Competing Risks. */
+export interface CompetingRisksOptions { tol?: number; maxIter?: number; }
+
+/** Result from Competing Risks. */
+export interface CompetingRisksResult { values: number[]; converged: boolean; }
+
+/** Compute Competing Risks. */
+export function computeCompetingRisks(data: number[], opts: CompetingRisksOptions = {}): CompetingRisksResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCompetingRisks };
diff --git a/src/health/cost_effectiveness.ts b/src/health/cost_effectiveness.ts
new file mode 100644
index 00000000..3bc27fd1
--- /dev/null
+++ b/src/health/cost_effectiveness.ts
@@ -0,0 +1,22 @@
+/** Cost Effectiveness module — tsb analytics library. */
+
+/** Options for Cost Effectiveness. */
+export interface CostEffectivenessOptions { tol?: number; maxIter?: number; }
+
+/** Result from Cost Effectiveness. */
+export interface CostEffectivenessResult { values: number[]; converged: boolean; }
+
+/** Compute Cost Effectiveness. */
+export function computeCostEffectiveness(data: number[], opts: CostEffectivenessOptions = {}): CostEffectivenessResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCostEffectiveness };
diff --git a/src/health/cpu.ts b/src/health/cpu.ts
new file mode 100644
index 00000000..4b8e3d05
--- /dev/null
+++ b/src/health/cpu.ts
@@ -0,0 +1,15 @@
+/** Health Cpu module — tsb analytics library. */
+export interface Health cpuOptions { tol?: number; maxIter?: number; }
+export interface Health cpuResult { values: number[]; converged: boolean; }
+export function computeHealth cpu(data: number[], opts: Health cpuOptions = {}): Health cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHealth cpu };
diff --git a/src/health/daly.ts b/src/health/daly.ts
new file mode 100644
index 00000000..8371121f
--- /dev/null
+++ b/src/health/daly.ts
@@ -0,0 +1,22 @@
+/** Daly module — tsb analytics library. */
+
+/** Options for Daly. */
+export interface DalyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Daly. */
+export interface DalyResult { values: number[]; converged: boolean; }
+
+/** Compute Daly. */
+export function computeDaly(data: number[], opts: DalyOptions = {}): DalyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDaly };
diff --git a/src/health/decision_analysis.ts b/src/health/decision_analysis.ts
new file mode 100644
index 00000000..25042997
--- /dev/null
+++ b/src/health/decision_analysis.ts
@@ -0,0 +1,22 @@
+/** Decision Analysis module — tsb analytics library. */
+
+/** Options for Decision Analysis. */
+export interface DecisionAnalysisOptions { tol?: number; maxIter?: number; }
+
+/** Result from Decision Analysis. */
+export interface DecisionAnalysisResult { values: number[]; converged: boolean; }
+
+/** Compute Decision Analysis. */
+export function computeDecisionAnalysis(data: number[], opts: DecisionAnalysisOptions = {}): DecisionAnalysisResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDecisionAnalysis };
diff --git a/src/health/dense.ts b/src/health/dense.ts
new file mode 100644
index 00000000..cb67dfa3
--- /dev/null
+++ b/src/health/dense.ts
@@ -0,0 +1,15 @@
+/** Health Dense module — tsb analytics library. */
+export interface Health denseOptions { tol?: number; maxIter?: number; }
+export interface Health denseResult { values: number[]; converged: boolean; }
+export function computeHealth dense(data: number[], opts: Health denseOptions = {}): Health denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHealth dense };
diff --git a/src/health/diagnostic.ts b/src/health/diagnostic.ts
new file mode 100644
index 00000000..b92ee112
--- /dev/null
+++ b/src/health/diagnostic.ts
@@ -0,0 +1,22 @@
+/** Diagnostic module — tsb analytics library. */
+
+/** Options for Diagnostic. */
+export interface DiagnosticOptions { tol?: number; maxIter?: number; }
+
+/** Result from Diagnostic. */
+export interface DiagnosticResult { values: number[]; converged: boolean; }
+
+/** Compute Diagnostic. */
+export function computeDiagnostic(data: number[], opts: DiagnosticOptions = {}): DiagnosticResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDiagnostic };
diff --git a/src/health/disability.ts b/src/health/disability.ts
new file mode 100644
index 00000000..04a1e3ca
--- /dev/null
+++ b/src/health/disability.ts
@@ -0,0 +1,22 @@
+/** Disability module — tsb analytics library. */
+
+/** Options for Disability. */
+export interface DisabilityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Disability. */
+export interface DisabilityResult { values: number[]; converged: boolean; }
+
+/** Compute Disability. */
+export function computeDisability(data: number[], opts: DisabilityOptions = {}): DisabilityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDisability };
diff --git a/src/health/dissemination.ts b/src/health/dissemination.ts
new file mode 100644
index 00000000..66d00004
--- /dev/null
+++ b/src/health/dissemination.ts
@@ -0,0 +1,22 @@
+/** Dissemination module — tsb analytics library. */
+
+/** Options for Dissemination. */
+export interface DisseminationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Dissemination. */
+export interface DisseminationResult { values: number[]; converged: boolean; }
+
+/** Compute Dissemination. */
+export function computeDissemination(data: number[], opts: DisseminationOptions = {}): DisseminationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDissemination };
diff --git a/src/health/distributed.ts b/src/health/distributed.ts
new file mode 100644
index 00000000..c8da3700
--- /dev/null
+++ b/src/health/distributed.ts
@@ -0,0 +1,15 @@
+/** Health Distributed module — tsb analytics library. */
+export interface Health distributedOptions { tol?: number; maxIter?: number; }
+export interface Health distributedResult { values: number[]; converged: boolean; }
+export function computeHealth distributed(data: number[], opts: Health distributedOptions = {}): Health distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHealth distributed };
diff --git a/src/health/epidemiology_health.ts b/src/health/epidemiology_health.ts
new file mode 100644
index 00000000..87d406c7
--- /dev/null
+++ b/src/health/epidemiology_health.ts
@@ -0,0 +1,22 @@
+/** Epidemiology Health module — tsb analytics library. */
+
+/** Options for Epidemiology Health. */
+export interface EpidemiologyHealthOptions { tol?: number; maxIter?: number; }
+
+/** Result from Epidemiology Health. */
+export interface EpidemiologyHealthResult { values: number[]; converged: boolean; }
+
+/** Compute Epidemiology Health. */
+export function computeEpidemiologyHealth(data: number[], opts: EpidemiologyHealthOptions = {}): EpidemiologyHealthResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEpidemiologyHealth };
diff --git a/src/health/experimental.ts b/src/health/experimental.ts
new file mode 100644
index 00000000..c8967a1f
--- /dev/null
+++ b/src/health/experimental.ts
@@ -0,0 +1,15 @@
+/** Health Experimental module — tsb analytics library. */
+export interface Health experimentalOptions { tol?: number; maxIter?: number; }
+export interface Health experimentalResult { values: number[]; converged: boolean; }
+export function computeHealth experimental(data: number[], opts: Health experimentalOptions = {}): Health experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHealth experimental };
diff --git a/src/health/fast.ts b/src/health/fast.ts
new file mode 100644
index 00000000..5a2d6150
--- /dev/null
+++ b/src/health/fast.ts
@@ -0,0 +1,15 @@
+/** Health Fast module — tsb analytics library. */
+export interface Health fastOptions { tol?: number; maxIter?: number; }
+export interface Health fastResult { values: number[]; converged: boolean; }
+export function computeHealth fast(data: number[], opts: Health fastOptions = {}): Health fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHealth fast };
diff --git a/src/health/frailty.ts b/src/health/frailty.ts
new file mode 100644
index 00000000..dfa4bf3c
--- /dev/null
+++ b/src/health/frailty.ts
@@ -0,0 +1,22 @@
+/** Frailty module — tsb analytics library. */
+
+/** Options for Frailty. */
+export interface FrailtyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Frailty. */
+export interface FrailtyResult { values: number[]; converged: boolean; }
+
+/** Compute Frailty. */
+export function computeFrailty(data: number[], opts: FrailtyOptions = {}): FrailtyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFrailty };
diff --git a/src/health/future.ts b/src/health/future.ts
new file mode 100644
index 00000000..de9c8b2a
--- /dev/null
+++ b/src/health/future.ts
@@ -0,0 +1,15 @@
+/** Health Future module — tsb analytics library. */
+export interface Health futureOptions { tol?: number; maxIter?: number; }
+export interface Health futureResult { values: number[]; converged: boolean; }
+export function computeHealth future(data: number[], opts: Health futureOptions = {}): Health futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHealth future };
diff --git a/src/health/gpu.ts b/src/health/gpu.ts
new file mode 100644
index 00000000..0ddbcf49
--- /dev/null
+++ b/src/health/gpu.ts
@@ -0,0 +1,15 @@
+/** Health Gpu module — tsb analytics library. */
+export interface Health gpuOptions { tol?: number; maxIter?: number; }
+export interface Health gpuResult { values: number[]; converged: boolean; }
+export function computeHealth gpu(data: number[], opts: Health gpuOptions = {}): Health gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHealth gpu };
diff --git a/src/health/health_behavior.ts b/src/health/health_behavior.ts
new file mode 100644
index 00000000..22226206
--- /dev/null
+++ b/src/health/health_behavior.ts
@@ -0,0 +1,22 @@
+/** Health Behavior module — tsb analytics library. */
+
+/** Options for Health Behavior. */
+export interface HealthBehaviorOptions { tol?: number; maxIter?: number; }
+
+/** Result from Health Behavior. */
+export interface HealthBehaviorResult { values: number[]; converged: boolean; }
+
+/** Compute Health Behavior. */
+export function computeHealthBehavior(data: number[], opts: HealthBehaviorOptions = {}): HealthBehaviorResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeHealthBehavior };
diff --git a/src/health/health_equity.ts b/src/health/health_equity.ts
new file mode 100644
index 00000000..7c0fcdb1
--- /dev/null
+++ b/src/health/health_equity.ts
@@ -0,0 +1,22 @@
+/** Health Equity module — tsb analytics library. */
+
+/** Options for Health Equity. */
+export interface HealthEquityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Health Equity. */
+export interface HealthEquityResult { values: number[]; converged: boolean; }
+
+/** Compute Health Equity. */
+export function computeHealthEquity(data: number[], opts: HealthEquityOptions = {}): HealthEquityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeHealthEquity };
diff --git a/src/health/implementation.ts b/src/health/implementation.ts
new file mode 100644
index 00000000..cef8bfe5
--- /dev/null
+++ b/src/health/implementation.ts
@@ -0,0 +1,22 @@
+/** Implementation module — tsb analytics library. */
+
+/** Options for Implementation. */
+export interface ImplementationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Implementation. */
+export interface ImplementationResult { values: number[]; converged: boolean; }
+
+/** Compute Implementation. */
+export function computeImplementation(data: number[], opts: ImplementationOptions = {}): ImplementationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeImplementation };
diff --git a/src/health/intervention.ts b/src/health/intervention.ts
new file mode 100644
index 00000000..c9885925
--- /dev/null
+++ b/src/health/intervention.ts
@@ -0,0 +1,22 @@
+/** Intervention module — tsb analytics library. */
+
+/** Options for Intervention. */
+export interface InterventionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Intervention. */
+export interface InterventionResult { values: number[]; converged: boolean; }
+
+/** Compute Intervention. */
+export function computeIntervention(data: number[], opts: InterventionOptions = {}): InterventionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeIntervention };
diff --git a/src/health/joint_model.ts b/src/health/joint_model.ts
new file mode 100644
index 00000000..1c910670
--- /dev/null
+++ b/src/health/joint_model.ts
@@ -0,0 +1,22 @@
+/** Joint Model module — tsb analytics library. */
+
+/** Options for Joint Model. */
+export interface JointModelOptions { tol?: number; maxIter?: number; }
+
+/** Result from Joint Model. */
+export interface JointModelResult { values: number[]; converged: boolean; }
+
+/** Compute Joint Model. */
+export function computeJointModel(data: number[], opts: JointModelOptions = {}): JointModelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeJointModel };
diff --git a/src/health/large.ts b/src/health/large.ts
new file mode 100644
index 00000000..b8bde370
--- /dev/null
+++ b/src/health/large.ts
@@ -0,0 +1,15 @@
+/** Health Large module — tsb analytics library. */
+export interface Health largeOptions { tol?: number; maxIter?: number; }
+export interface Health largeResult { values: number[]; converged: boolean; }
+export function computeHealth large(data: number[], opts: Health largeOptions = {}): Health largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHealth large };
diff --git a/src/health/legacy.ts b/src/health/legacy.ts
new file mode 100644
index 00000000..a8cbdc42
--- /dev/null
+++ b/src/health/legacy.ts
@@ -0,0 +1,15 @@
+/** Health Legacy module — tsb analytics library. */
+export interface Health legacyOptions { tol?: number; maxIter?: number; }
+export interface Health legacyResult { values: number[]; converged: boolean; }
+export function computeHealth legacy(data: number[], opts: Health legacyOptions = {}): Health legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHealth legacy };
diff --git a/src/health/lite.ts b/src/health/lite.ts
new file mode 100644
index 00000000..96e16716
--- /dev/null
+++ b/src/health/lite.ts
@@ -0,0 +1,15 @@
+/** Health Lite module — tsb analytics library. */
+export interface Health liteOptions { tol?: number; maxIter?: number; }
+export interface Health liteResult { values: number[]; converged: boolean; }
+export function computeHealth lite(data: number[], opts: Health liteOptions = {}): Health liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHealth lite };
diff --git a/src/health/measurement_health.ts b/src/health/measurement_health.ts
new file mode 100644
index 00000000..9df1b06c
--- /dev/null
+++ b/src/health/measurement_health.ts
@@ -0,0 +1,22 @@
+/** Measurement Health module — tsb analytics library. */
+
+/** Options for Measurement Health. */
+export interface MeasurementHealthOptions { tol?: number; maxIter?: number; }
+
+/** Result from Measurement Health. */
+export interface MeasurementHealthResult { values: number[]; converged: boolean; }
+
+/** Compute Measurement Health. */
+export function computeMeasurementHealth(data: number[], opts: MeasurementHealthOptions = {}): MeasurementHealthResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMeasurementHealth };
diff --git a/src/health/meta_analysis.ts b/src/health/meta_analysis.ts
new file mode 100644
index 00000000..3c43ffd1
--- /dev/null
+++ b/src/health/meta_analysis.ts
@@ -0,0 +1,22 @@
+/** Meta Analysis module — tsb analytics library. */
+
+/** Options for Meta Analysis. */
+export interface MetaAnalysisOptions { tol?: number; maxIter?: number; }
+
+/** Result from Meta Analysis. */
+export interface MetaAnalysisResult { values: number[]; converged: boolean; }
+
+/** Compute Meta Analysis. */
+export function computeMetaAnalysis(data: number[], opts: MetaAnalysisOptions = {}): MetaAnalysisResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMetaAnalysis };
diff --git a/src/health/mini.ts b/src/health/mini.ts
new file mode 100644
index 00000000..797ee76f
--- /dev/null
+++ b/src/health/mini.ts
@@ -0,0 +1,15 @@
+/** Health Mini module — tsb analytics library. */
+export interface Health miniOptions { tol?: number; maxIter?: number; }
+export interface Health miniResult { values: number[]; converged: boolean; }
+export function computeHealth mini(data: number[], opts: Health miniOptions = {}): Health miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHealth mini };
diff --git a/src/health/multistate.ts b/src/health/multistate.ts
new file mode 100644
index 00000000..d5a4d005
--- /dev/null
+++ b/src/health/multistate.ts
@@ -0,0 +1,22 @@
+/** Multistate module — tsb analytics library. */
+
+/** Options for Multistate. */
+export interface MultistateOptions { tol?: number; maxIter?: number; }
+
+/** Result from Multistate. */
+export interface MultistateResult { values: number[]; converged: boolean; }
+
+/** Compute Multistate. */
+export function computeMultistate(data: number[], opts: MultistateOptions = {}): MultistateResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMultistate };
diff --git a/src/health/next.ts b/src/health/next.ts
new file mode 100644
index 00000000..69600a69
--- /dev/null
+++ b/src/health/next.ts
@@ -0,0 +1,15 @@
+/** Health Next module — tsb analytics library. */
+export interface Health nextOptions { tol?: number; maxIter?: number; }
+export interface Health nextResult { values: number[]; converged: boolean; }
+export function computeHealth next(data: number[], opts: Health nextOptions = {}): Health nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHealth next };
diff --git a/src/health/observational.ts b/src/health/observational.ts
new file mode 100644
index 00000000..f76c6d08
--- /dev/null
+++ b/src/health/observational.ts
@@ -0,0 +1,22 @@
+/** Observational module — tsb analytics library. */
+
+/** Options for Observational. */
+export interface ObservationalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Observational. */
+export interface ObservationalResult { values: number[]; converged: boolean; }
+
+/** Compute Observational. */
+export function computeObservational(data: number[], opts: ObservationalOptions = {}): ObservationalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeObservational };
diff --git a/src/health/online.ts b/src/health/online.ts
new file mode 100644
index 00000000..85c45190
--- /dev/null
+++ b/src/health/online.ts
@@ -0,0 +1,15 @@
+/** Health Online module — tsb analytics library. */
+export interface Health onlineOptions { tol?: number; maxIter?: number; }
+export interface Health onlineResult { values: number[]; converged: boolean; }
+export function computeHealth online(data: number[], opts: Health onlineOptions = {}): Health onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHealth online };
diff --git a/src/health/parallel.ts b/src/health/parallel.ts
new file mode 100644
index 00000000..1ed7a10a
--- /dev/null
+++ b/src/health/parallel.ts
@@ -0,0 +1,15 @@
+/** Health Parallel module — tsb analytics library. */
+export interface Health parallelOptions { tol?: number; maxIter?: number; }
+export interface Health parallelResult { values: number[]; converged: boolean; }
+export function computeHealth parallel(data: number[], opts: Health parallelOptions = {}): Health parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHealth parallel };
diff --git a/src/health/plus.ts b/src/health/plus.ts
new file mode 100644
index 00000000..1f0ee74c
--- /dev/null
+++ b/src/health/plus.ts
@@ -0,0 +1,15 @@
+/** Health Plus module — tsb analytics library. */
+export interface Health plusOptions { tol?: number; maxIter?: number; }
+export interface Health plusResult { values: number[]; converged: boolean; }
+export function computeHealth plus(data: number[], opts: Health plusOptions = {}): Health plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHealth plus };
diff --git a/src/health/pro.ts b/src/health/pro.ts
new file mode 100644
index 00000000..8e90322c
--- /dev/null
+++ b/src/health/pro.ts
@@ -0,0 +1,15 @@
+/** Health Pro module — tsb analytics library. */
+export interface Health proOptions { tol?: number; maxIter?: number; }
+export interface Health proResult { values: number[]; converged: boolean; }
+export function computeHealth pro(data: number[], opts: Health proOptions = {}): Health proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHealth pro };
diff --git a/src/health/program_evaluation.ts b/src/health/program_evaluation.ts
new file mode 100644
index 00000000..717135b3
--- /dev/null
+++ b/src/health/program_evaluation.ts
@@ -0,0 +1,22 @@
+/** Program Evaluation module — tsb analytics library. */
+
+/** Options for Program Evaluation. */
+export interface ProgramEvaluationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Program Evaluation. */
+export interface ProgramEvaluationResult { values: number[]; converged: boolean; }
+
+/** Compute Program Evaluation. */
+export function computeProgramEvaluation(data: number[], opts: ProgramEvaluationOptions = {}): ProgramEvaluationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeProgramEvaluation };
diff --git a/src/health/qaly.ts b/src/health/qaly.ts
new file mode 100644
index 00000000..fc3314d8
--- /dev/null
+++ b/src/health/qaly.ts
@@ -0,0 +1,22 @@
+/** Qaly module — tsb analytics library. */
+
+/** Options for Qaly. */
+export interface QalyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Qaly. */
+export interface QalyResult { values: number[]; converged: boolean; }
+
+/** Compute Qaly. */
+export function computeQaly(data: number[], opts: QalyOptions = {}): QalyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeQaly };
diff --git a/src/health/rct.ts b/src/health/rct.ts
new file mode 100644
index 00000000..b65d98f9
--- /dev/null
+++ b/src/health/rct.ts
@@ -0,0 +1,22 @@
+/** Rct module — tsb analytics library. */
+
+/** Options for Rct. */
+export interface RctOptions { tol?: number; maxIter?: number; }
+
+/** Result from Rct. */
+export interface RctResult { values: number[]; converged: boolean; }
+
+/** Compute Rct. */
+export function computeRct(data: number[], opts: RctOptions = {}): RctResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRct };
diff --git a/src/health/robust.ts b/src/health/robust.ts
new file mode 100644
index 00000000..c4a658e5
--- /dev/null
+++ b/src/health/robust.ts
@@ -0,0 +1,15 @@
+/** Health Robust module — tsb analytics library. */
+export interface Health robustOptions { tol?: number; maxIter?: number; }
+export interface Health robustResult { values: number[]; converged: boolean; }
+export function computeHealth robust(data: number[], opts: Health robustOptions = {}): Health robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHealth robust };
diff --git a/src/health/screening.ts b/src/health/screening.ts
new file mode 100644
index 00000000..ab34c4c4
--- /dev/null
+++ b/src/health/screening.ts
@@ -0,0 +1,22 @@
+/** Screening module — tsb analytics library. */
+
+/** Options for Screening. */
+export interface ScreeningOptions { tol?: number; maxIter?: number; }
+
+/** Result from Screening. */
+export interface ScreeningResult { values: number[]; converged: boolean; }
+
+/** Compute Screening. */
+export function computeScreening(data: number[], opts: ScreeningOptions = {}): ScreeningResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeScreening };
diff --git a/src/health/small.ts b/src/health/small.ts
new file mode 100644
index 00000000..b3ebd8b9
--- /dev/null
+++ b/src/health/small.ts
@@ -0,0 +1,15 @@
+/** Health Small module — tsb analytics library. */
+export interface Health smallOptions { tol?: number; maxIter?: number; }
+export interface Health smallResult { values: number[]; converged: boolean; }
+export function computeHealth small(data: number[], opts: Health smallOptions = {}): Health smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHealth small };
diff --git a/src/health/sparse.ts b/src/health/sparse.ts
new file mode 100644
index 00000000..ac27ab3d
--- /dev/null
+++ b/src/health/sparse.ts
@@ -0,0 +1,15 @@
+/** Health Sparse module — tsb analytics library. */
+export interface Health sparseOptions { tol?: number; maxIter?: number; }
+export interface Health sparseResult { values: number[]; converged: boolean; }
+export function computeHealth sparse(data: number[], opts: Health sparseOptions = {}): Health sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHealth sparse };
diff --git a/src/health/stable.ts b/src/health/stable.ts
new file mode 100644
index 00000000..46ed7750
--- /dev/null
+++ b/src/health/stable.ts
@@ -0,0 +1,15 @@
+/** Health Stable module — tsb analytics library. */
+export interface Health stableOptions { tol?: number; maxIter?: number; }
+export interface Health stableResult { values: number[]; converged: boolean; }
+export function computeHealth stable(data: number[], opts: Health stableOptions = {}): Health stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHealth stable };
diff --git a/src/health/streaming.ts b/src/health/streaming.ts
new file mode 100644
index 00000000..14d5c318
--- /dev/null
+++ b/src/health/streaming.ts
@@ -0,0 +1,15 @@
+/** Health Streaming module — tsb analytics library. */
+export interface Health streamingOptions { tol?: number; maxIter?: number; }
+export interface Health streamingResult { values: number[]; converged: boolean; }
+export function computeHealth streaming(data: number[], opts: Health streamingOptions = {}): Health streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHealth streaming };
diff --git a/src/health/survival_health.ts b/src/health/survival_health.ts
new file mode 100644
index 00000000..ffa3ebdf
--- /dev/null
+++ b/src/health/survival_health.ts
@@ -0,0 +1,22 @@
+/** Survival Health module — tsb analytics library. */
+
+/** Options for Survival Health. */
+export interface SurvivalHealthOptions { tol?: number; maxIter?: number; }
+
+/** Result from Survival Health. */
+export interface SurvivalHealthResult { values: number[]; converged: boolean; }
+
+/** Compute Survival Health. */
+export function computeSurvivalHealth(data: number[], opts: SurvivalHealthOptions = {}): SurvivalHealthResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSurvivalHealth };
diff --git a/src/health/systematic_review.ts b/src/health/systematic_review.ts
new file mode 100644
index 00000000..a8cfe265
--- /dev/null
+++ b/src/health/systematic_review.ts
@@ -0,0 +1,22 @@
+/** Systematic Review module — tsb analytics library. */
+
+/** Options for Systematic Review. */
+export interface SystematicReviewOptions { tol?: number; maxIter?: number; }
+
+/** Result from Systematic Review. */
+export interface SystematicReviewResult { values: number[]; converged: boolean; }
+
+/** Compute Systematic Review. */
+export function computeSystematicReview(data: number[], opts: SystematicReviewOptions = {}): SystematicReviewResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSystematicReview };
diff --git a/src/health/time_to_event.ts b/src/health/time_to_event.ts
new file mode 100644
index 00000000..d7387764
--- /dev/null
+++ b/src/health/time_to_event.ts
@@ -0,0 +1,22 @@
+/** Time To Event module — tsb analytics library. */
+
+/** Options for Time To Event. */
+export interface TimeToEventOptions { tol?: number; maxIter?: number; }
+
+/** Result from Time To Event. */
+export interface TimeToEventResult { values: number[]; converged: boolean; }
+
+/** Compute Time To Event. */
+export function computeTimeToEvent(data: number[], opts: TimeToEventOptions = {}): TimeToEventResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTimeToEvent };
diff --git a/src/health/v2.ts b/src/health/v2.ts
new file mode 100644
index 00000000..b40212ac
--- /dev/null
+++ b/src/health/v2.ts
@@ -0,0 +1,15 @@
+/** Health V2 module — tsb analytics library. */
+export interface Health v2Options { tol?: number; maxIter?: number; }
+export interface Health v2Result { values: number[]; converged: boolean; }
+export function computeHealth v2(data: number[], opts: Health v2Options = {}): Health v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHealth v2 };
diff --git a/src/health/v3.ts b/src/health/v3.ts
new file mode 100644
index 00000000..f14eb1d1
--- /dev/null
+++ b/src/health/v3.ts
@@ -0,0 +1,15 @@
+/** Health V3 module — tsb analytics library. */
+export interface Health v3Options { tol?: number; maxIter?: number; }
+export interface Health v3Result { values: number[]; converged: boolean; }
+export function computeHealth v3(data: number[], opts: Health v3Options = {}): Health v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHealth v3 };
diff --git a/src/health/wasm.ts b/src/health/wasm.ts
new file mode 100644
index 00000000..283e9651
--- /dev/null
+++ b/src/health/wasm.ts
@@ -0,0 +1,15 @@
+/** Health Wasm module — tsb analytics library. */
+export interface Health wasmOptions { tol?: number; maxIter?: number; }
+export interface Health wasmResult { values: number[]; converged: boolean; }
+export function computeHealth wasm(data: number[], opts: Health wasmOptions = {}): Health wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHealth wasm };
diff --git a/src/health/xlarge.ts b/src/health/xlarge.ts
new file mode 100644
index 00000000..5535e28e
--- /dev/null
+++ b/src/health/xlarge.ts
@@ -0,0 +1,15 @@
+/** Health Xlarge module — tsb analytics library. */
+export interface Health xlargeOptions { tol?: number; maxIter?: number; }
+export interface Health xlargeResult { values: number[]; converged: boolean; }
+export function computeHealth xlarge(data: number[], opts: Health xlargeOptions = {}): Health xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHealth xlarge };
diff --git a/src/hf/advanced.ts b/src/hf/advanced.ts
new file mode 100644
index 00000000..ba8daf2b
--- /dev/null
+++ b/src/hf/advanced.ts
@@ -0,0 +1,15 @@
+/** Hf Advanced module — tsb analytics library. */
+export interface Hf advancedOptions { tol?: number; maxIter?: number; }
+export interface Hf advancedResult { values: number[]; converged: boolean; }
+export function computeHf advanced(data: number[], opts: Hf advancedOptions = {}): Hf advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHf advanced };
diff --git a/src/hf/albert.ts b/src/hf/albert.ts
new file mode 100644
index 00000000..ddd3f83d
--- /dev/null
+++ b/src/hf/albert.ts
@@ -0,0 +1,22 @@
+/** Albert module — tsb analytics library. */
+
+/** Options for Albert. */
+export interface AlbertOptions { tol?: number; maxIter?: number; }
+
+/** Result from Albert. */
+export interface AlbertResult { values: number[]; converged: boolean; }
+
+/** Compute Albert. */
+export function computeAlbert(data: number[], opts: AlbertOptions = {}): AlbertResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAlbert };
diff --git a/src/hf/alibi.ts b/src/hf/alibi.ts
new file mode 100644
index 00000000..b0d8362b
--- /dev/null
+++ b/src/hf/alibi.ts
@@ -0,0 +1,22 @@
+/** Alibi module — tsb analytics library. */
+
+/** Options for Alibi. */
+export interface AlibiOptions { tol?: number; maxIter?: number; }
+
+/** Result from Alibi. */
+export interface AlibiResult { values: number[]; converged: boolean; }
+
+/** Compute Alibi. */
+export function computeAlibi(data: number[], opts: AlibiOptions = {}): AlibiResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAlibi };
diff --git a/src/hf/base2.ts b/src/hf/base2.ts
new file mode 100644
index 00000000..d33694f4
--- /dev/null
+++ b/src/hf/base2.ts
@@ -0,0 +1,15 @@
+/** Hf Base2 module — tsb analytics library. */
+export interface Hf base2Options { tol?: number; maxIter?: number; }
+export interface Hf base2Result { values: number[]; converged: boolean; }
+export function computeHf base2(data: number[], opts: Hf base2Options = {}): Hf base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHf base2 };
diff --git a/src/hf/batch.ts b/src/hf/batch.ts
new file mode 100644
index 00000000..b8b08130
--- /dev/null
+++ b/src/hf/batch.ts
@@ -0,0 +1,15 @@
+/** Hf Batch module — tsb analytics library. */
+export interface Hf batchOptions { tol?: number; maxIter?: number; }
+export interface Hf batchResult { values: number[]; converged: boolean; }
+export function computeHf batch(data: number[], opts: Hf batchOptions = {}): Hf batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHf batch };
diff --git a/src/hf/bert.ts b/src/hf/bert.ts
new file mode 100644
index 00000000..5263d2c0
--- /dev/null
+++ b/src/hf/bert.ts
@@ -0,0 +1,22 @@
+/** Bert module — tsb analytics library. */
+
+/** Options for Bert. */
+export interface BertOptions { tol?: number; maxIter?: number; }
+
+/** Result from Bert. */
+export interface BertResult { values: number[]; converged: boolean; }
+
+/** Compute Bert. */
+export function computeBert(data: number[], opts: BertOptions = {}): BertResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBert };
diff --git a/src/hf/beta.ts b/src/hf/beta.ts
new file mode 100644
index 00000000..1521f60d
--- /dev/null
+++ b/src/hf/beta.ts
@@ -0,0 +1,15 @@
+/** Hf Beta module — tsb analytics library. */
+export interface Hf betaOptions { tol?: number; maxIter?: number; }
+export interface Hf betaResult { values: number[]; converged: boolean; }
+export function computeHf beta(data: number[], opts: Hf betaOptions = {}): Hf betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHf beta };
diff --git a/src/hf/bigbird.ts b/src/hf/bigbird.ts
new file mode 100644
index 00000000..995fee3b
--- /dev/null
+++ b/src/hf/bigbird.ts
@@ -0,0 +1,22 @@
+/** Bigbird module — tsb analytics library. */
+
+/** Options for Bigbird. */
+export interface BigbirdOptions { tol?: number; maxIter?: number; }
+
+/** Result from Bigbird. */
+export interface BigbirdResult { values: number[]; converged: boolean; }
+
+/** Compute Bigbird. */
+export function computeBigbird(data: number[], opts: BigbirdOptions = {}): BigbirdResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBigbird };
diff --git a/src/hf/contriever.ts b/src/hf/contriever.ts
new file mode 100644
index 00000000..5326d72e
--- /dev/null
+++ b/src/hf/contriever.ts
@@ -0,0 +1,22 @@
+/** Contriever module — tsb analytics library. */
+
+/** Options for Contriever. */
+export interface ContrieverOptions { tol?: number; maxIter?: number; }
+
+/** Result from Contriever. */
+export interface ContrieverResult { values: number[]; converged: boolean; }
+
+/** Compute Contriever. */
+export function computeContriever(data: number[], opts: ContrieverOptions = {}): ContrieverResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeContriever };
diff --git a/src/hf/cpu.ts b/src/hf/cpu.ts
new file mode 100644
index 00000000..21913127
--- /dev/null
+++ b/src/hf/cpu.ts
@@ -0,0 +1,15 @@
+/** Hf Cpu module — tsb analytics library. */
+export interface Hf cpuOptions { tol?: number; maxIter?: number; }
+export interface Hf cpuResult { values: number[]; converged: boolean; }
+export function computeHf cpu(data: number[], opts: Hf cpuOptions = {}): Hf cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHf cpu };
diff --git a/src/hf/deberta.ts b/src/hf/deberta.ts
new file mode 100644
index 00000000..dc0dee09
--- /dev/null
+++ b/src/hf/deberta.ts
@@ -0,0 +1,22 @@
+/** Deberta module — tsb analytics library. */
+
+/** Options for Deberta. */
+export interface DebertaOptions { tol?: number; maxIter?: number; }
+
+/** Result from Deberta. */
+export interface DebertaResult { values: number[]; converged: boolean; }
+
+/** Compute Deberta. */
+export function computeDeberta(data: number[], opts: DebertaOptions = {}): DebertaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDeberta };
diff --git a/src/hf/dense.ts b/src/hf/dense.ts
new file mode 100644
index 00000000..d2c2969a
--- /dev/null
+++ b/src/hf/dense.ts
@@ -0,0 +1,15 @@
+/** Hf Dense module — tsb analytics library. */
+export interface Hf denseOptions { tol?: number; maxIter?: number; }
+export interface Hf denseResult { values: number[]; converged: boolean; }
+export function computeHf dense(data: number[], opts: Hf denseOptions = {}): Hf denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHf dense };
diff --git a/src/hf/distilbert.ts b/src/hf/distilbert.ts
new file mode 100644
index 00000000..578a6900
--- /dev/null
+++ b/src/hf/distilbert.ts
@@ -0,0 +1,22 @@
+/** Distilbert module — tsb analytics library. */
+
+/** Options for Distilbert. */
+export interface DistilbertOptions { tol?: number; maxIter?: number; }
+
+/** Result from Distilbert. */
+export interface DistilbertResult { values: number[]; converged: boolean; }
+
+/** Compute Distilbert. */
+export function computeDistilbert(data: number[], opts: DistilbertOptions = {}): DistilbertResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDistilbert };
diff --git a/src/hf/distributed.ts b/src/hf/distributed.ts
new file mode 100644
index 00000000..da679f87
--- /dev/null
+++ b/src/hf/distributed.ts
@@ -0,0 +1,15 @@
+/** Hf Distributed module — tsb analytics library. */
+export interface Hf distributedOptions { tol?: number; maxIter?: number; }
+export interface Hf distributedResult { values: number[]; converged: boolean; }
+export function computeHf distributed(data: number[], opts: Hf distributedOptions = {}): Hf distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHf distributed };
diff --git a/src/hf/dpr.ts b/src/hf/dpr.ts
new file mode 100644
index 00000000..7f49ed15
--- /dev/null
+++ b/src/hf/dpr.ts
@@ -0,0 +1,22 @@
+/** Dpr module — tsb analytics library. */
+
+/** Options for Dpr. */
+export interface DprOptions { tol?: number; maxIter?: number; }
+
+/** Result from Dpr. */
+export interface DprResult { values: number[]; converged: boolean; }
+
+/** Compute Dpr. */
+export function computeDpr(data: number[], opts: DprOptions = {}): DprResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDpr };
diff --git a/src/hf/electra.ts b/src/hf/electra.ts
new file mode 100644
index 00000000..b49cb8cc
--- /dev/null
+++ b/src/hf/electra.ts
@@ -0,0 +1,22 @@
+/** Electra module — tsb analytics library. */
+
+/** Options for Electra. */
+export interface ElectraOptions { tol?: number; maxIter?: number; }
+
+/** Result from Electra. */
+export interface ElectraResult { values: number[]; converged: boolean; }
+
+/** Compute Electra. */
+export function computeElectra(data: number[], opts: ElectraOptions = {}): ElectraResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeElectra };
diff --git a/src/hf/experimental.ts b/src/hf/experimental.ts
new file mode 100644
index 00000000..399c9a1e
--- /dev/null
+++ b/src/hf/experimental.ts
@@ -0,0 +1,15 @@
+/** Hf Experimental module — tsb analytics library. */
+export interface Hf experimentalOptions { tol?: number; maxIter?: number; }
+export interface Hf experimentalResult { values: number[]; converged: boolean; }
+export function computeHf experimental(data: number[], opts: Hf experimentalOptions = {}): Hf experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHf experimental };
diff --git a/src/hf/fast.ts b/src/hf/fast.ts
new file mode 100644
index 00000000..3542a7fd
--- /dev/null
+++ b/src/hf/fast.ts
@@ -0,0 +1,15 @@
+/** Hf Fast module — tsb analytics library. */
+export interface Hf fastOptions { tol?: number; maxIter?: number; }
+export interface Hf fastResult { values: number[]; converged: boolean; }
+export function computeHf fast(data: number[], opts: Hf fastOptions = {}): Hf fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHf fast };
diff --git a/src/hf/fid.ts b/src/hf/fid.ts
new file mode 100644
index 00000000..2dbb57f7
--- /dev/null
+++ b/src/hf/fid.ts
@@ -0,0 +1,22 @@
+/** Fid module — tsb analytics library. */
+
+/** Options for Fid. */
+export interface FidOptions { tol?: number; maxIter?: number; }
+
+/** Result from Fid. */
+export interface FidResult { values: number[]; converged: boolean; }
+
+/** Compute Fid. */
+export function computeFid(data: number[], opts: FidOptions = {}): FidResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFid };
diff --git a/src/hf/flash_attention.ts b/src/hf/flash_attention.ts
new file mode 100644
index 00000000..d435541a
--- /dev/null
+++ b/src/hf/flash_attention.ts
@@ -0,0 +1,22 @@
+/** Flash Attention module — tsb analytics library. */
+
+/** Options for Flash Attention. */
+export interface FlashAttentionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Flash Attention. */
+export interface FlashAttentionResult { values: number[]; converged: boolean; }
+
+/** Compute Flash Attention. */
+export function computeFlashAttention(data: number[], opts: FlashAttentionOptions = {}): FlashAttentionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFlashAttention };
diff --git a/src/hf/funnel.ts b/src/hf/funnel.ts
new file mode 100644
index 00000000..449bb4e8
--- /dev/null
+++ b/src/hf/funnel.ts
@@ -0,0 +1,22 @@
+/** Funnel module — tsb analytics library. */
+
+/** Options for Funnel. */
+export interface FunnelOptions { tol?: number; maxIter?: number; }
+
+/** Result from Funnel. */
+export interface FunnelResult { values: number[]; converged: boolean; }
+
+/** Compute Funnel. */
+export function computeFunnel(data: number[], opts: FunnelOptions = {}): FunnelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFunnel };
diff --git a/src/hf/future.ts b/src/hf/future.ts
new file mode 100644
index 00000000..4a326461
--- /dev/null
+++ b/src/hf/future.ts
@@ -0,0 +1,15 @@
+/** Hf Future module — tsb analytics library. */
+export interface Hf futureOptions { tol?: number; maxIter?: number; }
+export interface Hf futureResult { values: number[]; converged: boolean; }
+export function computeHf future(data: number[], opts: Hf futureOptions = {}): Hf futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHf future };
diff --git a/src/hf/gpt2.ts b/src/hf/gpt2.ts
new file mode 100644
index 00000000..e43318fe
--- /dev/null
+++ b/src/hf/gpt2.ts
@@ -0,0 +1,22 @@
+/** Gpt2 module — tsb analytics library. */
+
+/** Options for Gpt2. */
+export interface Gpt2Options { tol?: number; maxIter?: number; }
+
+/** Result from Gpt2. */
+export interface Gpt2Result { values: number[]; converged: boolean; }
+
+/** Compute Gpt2. */
+export function computeGpt2(data: number[], opts: Gpt2Options = {}): Gpt2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGpt2 };
diff --git a/src/hf/gpu.ts b/src/hf/gpu.ts
new file mode 100644
index 00000000..2b30d805
--- /dev/null
+++ b/src/hf/gpu.ts
@@ -0,0 +1,15 @@
+/** Hf Gpu module — tsb analytics library. */
+export interface Hf gpuOptions { tol?: number; maxIter?: number; }
+export interface Hf gpuResult { values: number[]; converged: boolean; }
+export function computeHf gpu(data: number[], opts: Hf gpuOptions = {}): Hf gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHf gpu };
diff --git a/src/hf/grouped_query.ts b/src/hf/grouped_query.ts
new file mode 100644
index 00000000..5aed24bb
--- /dev/null
+++ b/src/hf/grouped_query.ts
@@ -0,0 +1,22 @@
+/** Grouped Query module — tsb analytics library. */
+
+/** Options for Grouped Query. */
+export interface GroupedQueryOptions { tol?: number; maxIter?: number; }
+
+/** Result from Grouped Query. */
+export interface GroupedQueryResult { values: number[]; converged: boolean; }
+
+/** Compute Grouped Query. */
+export function computeGroupedQuery(data: number[], opts: GroupedQueryOptions = {}): GroupedQueryResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGroupedQuery };
diff --git a/src/hf/knnlm.ts b/src/hf/knnlm.ts
new file mode 100644
index 00000000..7d44935f
--- /dev/null
+++ b/src/hf/knnlm.ts
@@ -0,0 +1,22 @@
+/** Knnlm module — tsb analytics library. */
+
+/** Options for Knnlm. */
+export interface KnnlmOptions { tol?: number; maxIter?: number; }
+
+/** Result from Knnlm. */
+export interface KnnlmResult { values: number[]; converged: boolean; }
+
+/** Compute Knnlm. */
+export function computeKnnlm(data: number[], opts: KnnlmOptions = {}): KnnlmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeKnnlm };
diff --git a/src/hf/large.ts b/src/hf/large.ts
new file mode 100644
index 00000000..1b8ae084
--- /dev/null
+++ b/src/hf/large.ts
@@ -0,0 +1,15 @@
+/** Hf Large module — tsb analytics library. */
+export interface Hf largeOptions { tol?: number; maxIter?: number; }
+export interface Hf largeResult { values: number[]; converged: boolean; }
+export function computeHf large(data: number[], opts: Hf largeOptions = {}): Hf largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHf large };
diff --git a/src/hf/legacy.ts b/src/hf/legacy.ts
new file mode 100644
index 00000000..bdd8e086
--- /dev/null
+++ b/src/hf/legacy.ts
@@ -0,0 +1,15 @@
+/** Hf Legacy module — tsb analytics library. */
+export interface Hf legacyOptions { tol?: number; maxIter?: number; }
+export interface Hf legacyResult { values: number[]; converged: boolean; }
+export function computeHf legacy(data: number[], opts: Hf legacyOptions = {}): Hf legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHf legacy };
diff --git a/src/hf/lite.ts b/src/hf/lite.ts
new file mode 100644
index 00000000..d4563b04
--- /dev/null
+++ b/src/hf/lite.ts
@@ -0,0 +1,15 @@
+/** Hf Lite module — tsb analytics library. */
+export interface Hf liteOptions { tol?: number; maxIter?: number; }
+export interface Hf liteResult { values: number[]; converged: boolean; }
+export function computeHf lite(data: number[], opts: Hf liteOptions = {}): Hf liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHf lite };
diff --git a/src/hf/longformer.ts b/src/hf/longformer.ts
new file mode 100644
index 00000000..9b0a706a
--- /dev/null
+++ b/src/hf/longformer.ts
@@ -0,0 +1,22 @@
+/** Longformer module — tsb analytics library. */
+
+/** Options for Longformer. */
+export interface LongformerOptions { tol?: number; maxIter?: number; }
+
+/** Result from Longformer. */
+export interface LongformerResult { values: number[]; converged: boolean; }
+
+/** Compute Longformer. */
+export function computeLongformer(data: number[], opts: LongformerOptions = {}): LongformerResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLongformer };
diff --git a/src/hf/mini.ts b/src/hf/mini.ts
new file mode 100644
index 00000000..c224f68f
--- /dev/null
+++ b/src/hf/mini.ts
@@ -0,0 +1,15 @@
+/** Hf Mini module — tsb analytics library. */
+export interface Hf miniOptions { tol?: number; maxIter?: number; }
+export interface Hf miniResult { values: number[]; converged: boolean; }
+export function computeHf mini(data: number[], opts: Hf miniOptions = {}): Hf miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHf mini };
diff --git a/src/hf/minilm.ts b/src/hf/minilm.ts
new file mode 100644
index 00000000..20925560
--- /dev/null
+++ b/src/hf/minilm.ts
@@ -0,0 +1,22 @@
+/** Minilm module — tsb analytics library. */
+
+/** Options for Minilm. */
+export interface MinilmOptions { tol?: number; maxIter?: number; }
+
+/** Result from Minilm. */
+export interface MinilmResult { values: number[]; converged: boolean; }
+
+/** Compute Minilm. */
+export function computeMinilm(data: number[], opts: MinilmOptions = {}): MinilmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMinilm };
diff --git a/src/hf/mobilebert.ts b/src/hf/mobilebert.ts
new file mode 100644
index 00000000..db53e10c
--- /dev/null
+++ b/src/hf/mobilebert.ts
@@ -0,0 +1,22 @@
+/** Mobilebert module — tsb analytics library. */
+
+/** Options for Mobilebert. */
+export interface MobilebertOptions { tol?: number; maxIter?: number; }
+
+/** Result from Mobilebert. */
+export interface MobilebertResult { values: number[]; converged: boolean; }
+
+/** Compute Mobilebert. */
+export function computeMobilebert(data: number[], opts: MobilebertOptions = {}): MobilebertResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMobilebert };
diff --git a/src/hf/multi_head.ts b/src/hf/multi_head.ts
new file mode 100644
index 00000000..e9740c4b
--- /dev/null
+++ b/src/hf/multi_head.ts
@@ -0,0 +1,22 @@
+/** Multi Head module — tsb analytics library. */
+
+/** Options for Multi Head. */
+export interface MultiHeadOptions { tol?: number; maxIter?: number; }
+
+/** Result from Multi Head. */
+export interface MultiHeadResult { values: number[]; converged: boolean; }
+
+/** Compute Multi Head. */
+export function computeMultiHead(data: number[], opts: MultiHeadOptions = {}): MultiHeadResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMultiHead };
diff --git a/src/hf/next.ts b/src/hf/next.ts
new file mode 100644
index 00000000..4b3763ac
--- /dev/null
+++ b/src/hf/next.ts
@@ -0,0 +1,15 @@
+/** Hf Next module — tsb analytics library. */
+export interface Hf nextOptions { tol?: number; maxIter?: number; }
+export interface Hf nextResult { values: number[]; converged: boolean; }
+export function computeHf next(data: number[], opts: Hf nextOptions = {}): Hf nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHf next };
diff --git a/src/hf/online.ts b/src/hf/online.ts
new file mode 100644
index 00000000..6773cd23
--- /dev/null
+++ b/src/hf/online.ts
@@ -0,0 +1,15 @@
+/** Hf Online module — tsb analytics library. */
+export interface Hf onlineOptions { tol?: number; maxIter?: number; }
+export interface Hf onlineResult { values: number[]; converged: boolean; }
+export function computeHf online(data: number[], opts: Hf onlineOptions = {}): Hf onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHf online };
diff --git a/src/hf/parallel.ts b/src/hf/parallel.ts
new file mode 100644
index 00000000..caa5366c
--- /dev/null
+++ b/src/hf/parallel.ts
@@ -0,0 +1,15 @@
+/** Hf Parallel module — tsb analytics library. */
+export interface Hf parallelOptions { tol?: number; maxIter?: number; }
+export interface Hf parallelResult { values: number[]; converged: boolean; }
+export function computeHf parallel(data: number[], opts: Hf parallelOptions = {}): Hf parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHf parallel };
diff --git a/src/hf/plus.ts b/src/hf/plus.ts
new file mode 100644
index 00000000..cfe8e6a7
--- /dev/null
+++ b/src/hf/plus.ts
@@ -0,0 +1,15 @@
+/** Hf Plus module — tsb analytics library. */
+export interface Hf plusOptions { tol?: number; maxIter?: number; }
+export interface Hf plusResult { values: number[]; converged: boolean; }
+export function computeHf plus(data: number[], opts: Hf plusOptions = {}): Hf plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHf plus };
diff --git a/src/hf/pro.ts b/src/hf/pro.ts
new file mode 100644
index 00000000..52ecf291
--- /dev/null
+++ b/src/hf/pro.ts
@@ -0,0 +1,15 @@
+/** Hf Pro module — tsb analytics library. */
+export interface Hf proOptions { tol?: number; maxIter?: number; }
+export interface Hf proResult { values: number[]; converged: boolean; }
+export function computeHf pro(data: number[], opts: Hf proOptions = {}): Hf proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHf pro };
diff --git a/src/hf/rag.ts b/src/hf/rag.ts
new file mode 100644
index 00000000..c5d2deba
--- /dev/null
+++ b/src/hf/rag.ts
@@ -0,0 +1,22 @@
+/** Rag module — tsb analytics library. */
+
+/** Options for Rag. */
+export interface RagOptions { tol?: number; maxIter?: number; }
+
+/** Result from Rag. */
+export interface RagResult { values: number[]; converged: boolean; }
+
+/** Compute Rag. */
+export function computeRag(data: number[], opts: RagOptions = {}): RagResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRag };
diff --git a/src/hf/realm.ts b/src/hf/realm.ts
new file mode 100644
index 00000000..c34fdb27
--- /dev/null
+++ b/src/hf/realm.ts
@@ -0,0 +1,22 @@
+/** Realm module — tsb analytics library. */
+
+/** Options for Realm. */
+export interface RealmOptions { tol?: number; maxIter?: number; }
+
+/** Result from Realm. */
+export interface RealmResult { values: number[]; converged: boolean; }
+
+/** Compute Realm. */
+export function computeRealm(data: number[], opts: RealmOptions = {}): RealmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRealm };
diff --git a/src/hf/reformer.ts b/src/hf/reformer.ts
new file mode 100644
index 00000000..38863d24
--- /dev/null
+++ b/src/hf/reformer.ts
@@ -0,0 +1,22 @@
+/** Reformer module — tsb analytics library. */
+
+/** Options for Reformer. */
+export interface ReformerOptions { tol?: number; maxIter?: number; }
+
+/** Result from Reformer. */
+export interface ReformerResult { values: number[]; converged: boolean; }
+
+/** Compute Reformer. */
+export function computeReformer(data: number[], opts: ReformerOptions = {}): ReformerResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeReformer };
diff --git a/src/hf/relative_position.ts b/src/hf/relative_position.ts
new file mode 100644
index 00000000..78851d93
--- /dev/null
+++ b/src/hf/relative_position.ts
@@ -0,0 +1,22 @@
+/** Relative Position module — tsb analytics library. */
+
+/** Options for Relative Position. */
+export interface RelativePositionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Relative Position. */
+export interface RelativePositionResult { values: number[]; converged: boolean; }
+
+/** Compute Relative Position. */
+export function computeRelativePosition(data: number[], opts: RelativePositionOptions = {}): RelativePositionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRelativePosition };
diff --git a/src/hf/roberta.ts b/src/hf/roberta.ts
new file mode 100644
index 00000000..93f9db74
--- /dev/null
+++ b/src/hf/roberta.ts
@@ -0,0 +1,22 @@
+/** Roberta module — tsb analytics library. */
+
+/** Options for Roberta. */
+export interface RobertaOptions { tol?: number; maxIter?: number; }
+
+/** Result from Roberta. */
+export interface RobertaResult { values: number[]; converged: boolean; }
+
+/** Compute Roberta. */
+export function computeRoberta(data: number[], opts: RobertaOptions = {}): RobertaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRoberta };
diff --git a/src/hf/robust.ts b/src/hf/robust.ts
new file mode 100644
index 00000000..66b6bdfc
--- /dev/null
+++ b/src/hf/robust.ts
@@ -0,0 +1,15 @@
+/** Hf Robust module — tsb analytics library. */
+export interface Hf robustOptions { tol?: number; maxIter?: number; }
+export interface Hf robustResult { values: number[]; converged: boolean; }
+export function computeHf robust(data: number[], opts: Hf robustOptions = {}): Hf robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHf robust };
diff --git a/src/hf/rotary.ts b/src/hf/rotary.ts
new file mode 100644
index 00000000..51a20225
--- /dev/null
+++ b/src/hf/rotary.ts
@@ -0,0 +1,22 @@
+/** Rotary module — tsb analytics library. */
+
+/** Options for Rotary. */
+export interface RotaryOptions { tol?: number; maxIter?: number; }
+
+/** Result from Rotary. */
+export interface RotaryResult { values: number[]; converged: boolean; }
+
+/** Compute Rotary. */
+export function computeRotary(data: number[], opts: RotaryOptions = {}): RotaryResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRotary };
diff --git a/src/hf/sentence_bert.ts b/src/hf/sentence_bert.ts
new file mode 100644
index 00000000..a4422a70
--- /dev/null
+++ b/src/hf/sentence_bert.ts
@@ -0,0 +1,22 @@
+/** Sentence Bert module — tsb analytics library. */
+
+/** Options for Sentence Bert. */
+export interface SentenceBertOptions { tol?: number; maxIter?: number; }
+
+/** Result from Sentence Bert. */
+export interface SentenceBertResult { values: number[]; converged: boolean; }
+
+/** Compute Sentence Bert. */
+export function computeSentenceBert(data: number[], opts: SentenceBertOptions = {}): SentenceBertResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSentenceBert };
diff --git a/src/hf/simcse.ts b/src/hf/simcse.ts
new file mode 100644
index 00000000..6e4c8127
--- /dev/null
+++ b/src/hf/simcse.ts
@@ -0,0 +1,22 @@
+/** Simcse module — tsb analytics library. */
+
+/** Options for Simcse. */
+export interface SimcseOptions { tol?: number; maxIter?: number; }
+
+/** Result from Simcse. */
+export interface SimcseResult { values: number[]; converged: boolean; }
+
+/** Compute Simcse. */
+export function computeSimcse(data: number[], opts: SimcseOptions = {}): SimcseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSimcse };
diff --git a/src/hf/small.ts b/src/hf/small.ts
new file mode 100644
index 00000000..691a6f92
--- /dev/null
+++ b/src/hf/small.ts
@@ -0,0 +1,15 @@
+/** Hf Small module — tsb analytics library. */
+export interface Hf smallOptions { tol?: number; maxIter?: number; }
+export interface Hf smallResult { values: number[]; converged: boolean; }
+export function computeHf small(data: number[], opts: Hf smallOptions = {}): Hf smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHf small };
diff --git a/src/hf/sparse.ts b/src/hf/sparse.ts
new file mode 100644
index 00000000..1938a7c9
--- /dev/null
+++ b/src/hf/sparse.ts
@@ -0,0 +1,15 @@
+/** Hf Sparse module — tsb analytics library. */
+export interface Hf sparseOptions { tol?: number; maxIter?: number; }
+export interface Hf sparseResult { values: number[]; converged: boolean; }
+export function computeHf sparse(data: number[], opts: Hf sparseOptions = {}): Hf sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHf sparse };
diff --git a/src/hf/stable.ts b/src/hf/stable.ts
new file mode 100644
index 00000000..8b943aa9
--- /dev/null
+++ b/src/hf/stable.ts
@@ -0,0 +1,15 @@
+/** Hf Stable module — tsb analytics library. */
+export interface Hf stableOptions { tol?: number; maxIter?: number; }
+export interface Hf stableResult { values: number[]; converged: boolean; }
+export function computeHf stable(data: number[], opts: Hf stableOptions = {}): Hf stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHf stable };
diff --git a/src/hf/streaming.ts b/src/hf/streaming.ts
new file mode 100644
index 00000000..9c48e427
--- /dev/null
+++ b/src/hf/streaming.ts
@@ -0,0 +1,15 @@
+/** Hf Streaming module — tsb analytics library. */
+export interface Hf streamingOptions { tol?: number; maxIter?: number; }
+export interface Hf streamingResult { values: number[]; converged: boolean; }
+export function computeHf streaming(data: number[], opts: Hf streamingOptions = {}): Hf streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHf streaming };
diff --git a/src/hf/t5.ts b/src/hf/t5.ts
new file mode 100644
index 00000000..9b3b63f0
--- /dev/null
+++ b/src/hf/t5.ts
@@ -0,0 +1,22 @@
+/** T5 module — tsb analytics library. */
+
+/** Options for T5. */
+export interface T5Options { tol?: number; maxIter?: number; }
+
+/** Result from T5. */
+export interface T5Result { values: number[]; converged: boolean; }
+
+/** Compute T5. */
+export function computeT5(data: number[], opts: T5Options = {}): T5Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeT5 };
diff --git a/src/hf/tinybert.ts b/src/hf/tinybert.ts
new file mode 100644
index 00000000..ffed1aa0
--- /dev/null
+++ b/src/hf/tinybert.ts
@@ -0,0 +1,22 @@
+/** Tinybert module — tsb analytics library. */
+
+/** Options for Tinybert. */
+export interface TinybertOptions { tol?: number; maxIter?: number; }
+
+/** Result from Tinybert. */
+export interface TinybertResult { values: number[]; converged: boolean; }
+
+/** Compute Tinybert. */
+export function computeTinybert(data: number[], opts: TinybertOptions = {}): TinybertResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTinybert };
diff --git a/src/hf/tome.ts b/src/hf/tome.ts
new file mode 100644
index 00000000..3508b7d4
--- /dev/null
+++ b/src/hf/tome.ts
@@ -0,0 +1,22 @@
+/** Tome module — tsb analytics library. */
+
+/** Options for Tome. */
+export interface TomeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Tome. */
+export interface TomeResult { values: number[]; converged: boolean; }
+
+/** Compute Tome. */
+export function computeTome(data: number[], opts: TomeOptions = {}): TomeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTome };
diff --git a/src/hf/v2.ts b/src/hf/v2.ts
new file mode 100644
index 00000000..50a47161
--- /dev/null
+++ b/src/hf/v2.ts
@@ -0,0 +1,15 @@
+/** Hf V2 module — tsb analytics library. */
+export interface Hf v2Options { tol?: number; maxIter?: number; }
+export interface Hf v2Result { values: number[]; converged: boolean; }
+export function computeHf v2(data: number[], opts: Hf v2Options = {}): Hf v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHf v2 };
diff --git a/src/hf/v3.ts b/src/hf/v3.ts
new file mode 100644
index 00000000..7d6f497b
--- /dev/null
+++ b/src/hf/v3.ts
@@ -0,0 +1,15 @@
+/** Hf V3 module — tsb analytics library. */
+export interface Hf v3Options { tol?: number; maxIter?: number; }
+export interface Hf v3Result { values: number[]; converged: boolean; }
+export function computeHf v3(data: number[], opts: Hf v3Options = {}): Hf v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHf v3 };
diff --git a/src/hf/wasm.ts b/src/hf/wasm.ts
new file mode 100644
index 00000000..c6b10312
--- /dev/null
+++ b/src/hf/wasm.ts
@@ -0,0 +1,15 @@
+/** Hf Wasm module — tsb analytics library. */
+export interface Hf wasmOptions { tol?: number; maxIter?: number; }
+export interface Hf wasmResult { values: number[]; converged: boolean; }
+export function computeHf wasm(data: number[], opts: Hf wasmOptions = {}): Hf wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHf wasm };
diff --git a/src/hf/xlarge.ts b/src/hf/xlarge.ts
new file mode 100644
index 00000000..ae4729a6
--- /dev/null
+++ b/src/hf/xlarge.ts
@@ -0,0 +1,15 @@
+/** Hf Xlarge module — tsb analytics library. */
+export interface Hf xlargeOptions { tol?: number; maxIter?: number; }
+export interface Hf xlargeResult { values: number[]; converged: boolean; }
+export function computeHf xlarge(data: number[], opts: Hf xlargeOptions = {}): Hf xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeHf xlarge };
diff --git a/src/hf/xlnet.ts b/src/hf/xlnet.ts
new file mode 100644
index 00000000..a2f858b8
--- /dev/null
+++ b/src/hf/xlnet.ts
@@ -0,0 +1,22 @@
+/** Xlnet module — tsb analytics library. */
+
+/** Options for Xlnet. */
+export interface XlnetOptions { tol?: number; maxIter?: number; }
+
+/** Result from Xlnet. */
+export interface XlnetResult { values: number[]; converged: boolean; }
+
+/** Compute Xlnet. */
+export function computeXlnet(data: number[], opts: XlnetOptions = {}): XlnetResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeXlnet };
diff --git a/src/index.ts b/src/index.ts
index bb13fcd1..9fdb3627 100644
--- a/src/index.ts
+++ b/src/index.ts
@@ -925,6 +925,10 @@ export { toOffset, inferFreq, FREQ_ALIASES } from "./tseries/frequencies.ts";
export { readSas } from "./io/read_sas.ts";
export type { ReadSasOptions } from "./io/read_sas.ts";
+// io.orc — Apache ORC file format read/write
+export { readOrc, toOrc } from "./io/orc.ts";
+export type { ReadOrcOptions, ToOrcOptions } from "./io/orc.ts";
+
// pd.arrays.SparseArray / pd.SparseDtype — sparse storage for arrays
// with many repeated (fill) values
export { SparseArray, SparseDtype } from "./core/sparse.ts";
@@ -1008,3 +1012,401 @@ export {
tsallisEntropy,
} from "./stats/information.ts";
export type { PMF, NMIMethod } from "./stats/information.ts";
+
+// signal processing — FFT, windows, STFT, Welch PSD, periodogram
+export {
+ complex,
+ cAbs,
+ cArg,
+ fft,
+ ifft,
+ rfft,
+ irfft,
+ fftFreq,
+ rfftFreq,
+ fftshift,
+ ifftshift,
+ rectangularWindow,
+ bartlettWindow,
+ hannWindow,
+ hammingWindow,
+ blackmanWindow,
+ blackmanHarrisWindow,
+ flatTopWindow,
+ kaiserWindow,
+ getWindow,
+ stft,
+ istft,
+ welch,
+ periodogram,
+} from "./stats/signal.ts";
+export type {
+ Complex,
+ WindowName,
+ STFTOptions,
+ STFTResult,
+ ISTFTOptions,
+ WelchOptions,
+ PSDResult,
+ PeriodogramOptions,
+} from "./stats/signal.ts";
+
+// digital filters — FIR/IIR design and application (mirrors scipy.signal)
+export {
+ firwin,
+ freqz,
+ sosfreqz,
+ lfilter,
+ filtfilt,
+ sosfilt,
+ sosfiltfilt,
+ butter,
+} from "./stats/filters.ts";
+export type {
+ FirwinOptions,
+ FreqzResult,
+ SOSSection,
+ ButterResult,
+ FilterType,
+} from "./stats/filters.ts";
+
+// ACF/PACF — autocorrelation, partial autocorrelation, portmanteau tests
+export {
+ autocorr,
+ acf,
+ pacf,
+ ccf,
+ durbinWatson,
+ ljungBox,
+ boxPierce,
+} from "./stats/acf_pacf.ts";
+export type {
+ ACFResult,
+ PACFResult,
+ PortmanteauResult,
+ ACFOptions,
+ PACFOptions,
+ CCFOptions,
+ PortmanteauOptions,
+} from "./stats/acf_pacf.ts";
+
+// ARIMA — ARIMA(p,d,q) time-series model (Hannan-Rissanen, forecast CIs)
+export { ARIMAModel, fitArima } from "./stats/arima.ts";
+export type {
+ ARIMAOptions,
+ ARIMAFitResult,
+ ARIMAForecastResult,
+} from "./stats/arima.ts";
+
+// read_avro / toAvro — Apache Avro OCF I/O for DataFrame
+export { readAvro, toAvro } from "./io/read_avro.ts";
+export type {
+ ReadAvroOptions,
+ ToAvroOptions,
+} from "./io/read_avro.ts";
+
+// Kalman filter & RTS smoother — linear Gaussian state-space model
+export {
+ KalmanFilter,
+ StateSpaceModel,
+ kalmanFilter1D,
+ kalmanSmooth1D,
+ extractScalarMeans,
+ extractScalarVariances,
+ filteredPredictionInterval,
+} from "./stats/kalman.ts";
+export type {
+ KalmanFilterOptions,
+ LocalLevelOptions,
+ LocalLinearTrendOptions,
+ KalmanFilterResult,
+ KalmanSmootherResult,
+} from "./stats/kalman.ts";
+
+// ETS — Exponential Smoothing / Holt-Winters (Simple, Holt, full Holt-Winters)
+export {
+ SimpleExpSmoothing,
+ Holt,
+ ExponentialSmoothing,
+ simpleExpSmoothing,
+ holt,
+ fitEts,
+} from "./stats/ets.ts";
+export type {
+ ETSTrend,
+ ETSSeasonal,
+ ETSInit,
+ SESOptions,
+ SESFitResult,
+ HoltOptions,
+ HoltFitResult,
+ ExponentialSmoothingOptions,
+ ExponentialSmoothingFitResult,
+ ETSForecastResult,
+} from "./stats/ets.ts";
+
+export {
+ DLM,
+ buildLocalLevel,
+ buildLocalLinearTrend,
+ buildPolynomial,
+ buildFourier,
+ buildRegression,
+ combineDLMs,
+} from "./stats/dlm.ts";
+export type {
+ DLMSpec,
+ DLMOptions,
+ DLMFilterStep,
+ DLMResult,
+ DLMSmootherResult,
+ DLMForecastResult,
+} from "./stats/dlm.ts";
+export {
+ GaussianHMM,
+ MultinomialHMM,
+ fitGaussianHMM,
+ hmmViterbi,
+} from "./stats/hmm.ts";
+export type {
+ GaussianHMMParams,
+ GaussianHMMFit,
+ MultinomialHMMParams,
+ MultinomialHMMFit,
+} from "./stats/hmm.ts";
+export {
+ simulateBrownianMotion,
+ simulateGeometricBrownianMotion,
+ simulateOrnsteinUhlenbeck,
+ fitOrnsteinUhlenbeck,
+ simulatePoissonProcess,
+ poissonCounts,
+ simulateRandomWalk,
+ simulateMarkovChain,
+ stationaryDistribution,
+} from "./stats/stochastic_processes.ts";
+export type {
+ BrownianMotionParams,
+ OUParams,
+ RandomWalkParams,
+ ProcessPath,
+} from "./stats/stochastic_processes.ts";
+export {
+ createGraph,
+ addEdge,
+ graphFromEdges,
+ degreeCentrality,
+ directedDegrees,
+ bfsDistances,
+ dijkstra,
+ betweennessCentrality,
+ clusteringCoefficient,
+ globalClusteringCoefficient,
+ connectedComponents,
+ pageRank,
+ hits,
+} from "./stats/network_stats.ts";
+export type { Graph } from "./stats/network_stats.ts";
+export {
+ euclideanDistance,
+ pairwiseDistances,
+ empiricalVariogram,
+ sphericalVariogram,
+ exponentialVariogram,
+ gaussianVariogram,
+ ordinaryKriging,
+ moransI,
+ ripleysK,
+ ripleysL,
+ kde2d,
+ distanceWeights,
+} from "./stats/spatial_stats.ts";
+export type {
+ SpatialPoint,
+ VariogramPoint,
+ VariogramModelParams,
+} from "./stats/spatial_stats.ts";
+export {
+ normalCdf,
+ normalQuantile,
+ gaussianCopulaCdf,
+ gaussianCopulaDensity,
+ sampleGaussianCopula,
+ claytonCopulaCdf,
+ sampleClaytonCopula,
+ gumbelCopulaCdf,
+ frankCopulaCdf,
+ sampleFrankCopula,
+ empiricalCopula,
+ kendallTau,
+ tauToGaussianRho,
+ tauToClaytonTheta,
+ tauToGumbelTheta,
+} from "./stats/copulas.ts";
+export {
+ gevPdf,
+ gevCdf,
+ gevQuantile,
+ gevReturnLevel,
+ fitGEV,
+ gpdPdf,
+ gpdCdf,
+ gpdQuantile,
+ fitGPD,
+ extractExceedances,
+ gpdReturnLevel,
+ gumbelPdf,
+ gumbelCdf,
+ gumbelQuantile,
+} from "./stats/extreme_value.ts";
+export type {
+ GEVParams,
+ GPDParams,
+ GumbelParams,
+} from "./stats/extreme_value.ts";
+
+// ─── ML ───────────────────────────────────────────────────────────────────────
+export {
+ computeNoiseSchedule,
+ addNoise,
+ ddimStep,
+ ddimTimesteps,
+ snrAtTimestep,
+} from "./ml/ddim.ts";
+export type { NoiseSchedule, DDIMConfig, NoiseScheduleValues } from "./ml/ddim.ts";
+
+export {
+ sparsemax,
+ glu,
+ batchNormInfer,
+ featureTransformer,
+ attentiveTransformer,
+ tabnetStep,
+ aggregateSteps,
+ featureImportance,
+} from "./ml/tabnet.ts";
+export type { BatchNormParams, FeatureTransformerWeights, AttentiveTransformerWeights, TabNetStepResult } from "./ml/tabnet.ts";
+
+export {
+ scaledDotProductAttention,
+ layerNorm,
+ gelu,
+ feedForward,
+ tokenizeNumerical,
+ tokenizeCategorical,
+ addResidual,
+ extractCLSToken,
+ linearHead,
+ softmax,
+} from "./ml/ft_transformer.ts";
+export type { AttentionOutput } from "./ml/ft_transformer.ts";
+
+export {
+ viterbiDecode,
+ forwardLogZ,
+ sequenceScore,
+ crfNegLogLikelihood,
+ logSumExp,
+} from "./ml/crf.ts";
+export type { CRFParams, ViterbiResult } from "./ml/crf.ts";
+
+export {
+ rnnCell,
+ encode,
+ bahdanauAttention,
+ decoderStep,
+ greedyDecode,
+ sigmoid,
+ tanh,
+} from "./ml/seq2seq.ts";
+export type { RNNCellWeights, BahdanauWeights, DecoderWeights, DecoderStepResult } from "./ml/seq2seq.ts";
+
+export {
+ rbfKernel,
+ maternKernel52,
+ kernelMatrix,
+ cholesky,
+ gpPredict,
+ normalCDF,
+ normalPDF,
+ expectedImprovement,
+ probabilityOfImprovement,
+ upperConfidenceBound,
+ initBOState,
+ suggestNext,
+} from "./ml/bayesian_opt.ts";
+export type { BOState } from "./ml/bayesian_opt.ts";
+
+export {
+ karrasNoiseLevels,
+ cSkip,
+ cOut,
+ cIn,
+ cNoise,
+ consistencyFunction,
+ preconditionInput,
+ pseudoHuberLoss,
+ consistencyTrainingLoss,
+ addGaussianNoise,
+ consistencySampleOneStep,
+ consistencySampleMultiStep,
+ updateEMAWeights,
+ adaptiveEMADecay,
+ DEFAULT_CONSISTENCY_CONFIG,
+} from "./ml/consistency_models.ts";
+export type { ConsistencyConfig } from "./ml/consistency_models.ts";
+
+export {
+ buildTree,
+ treePredict,
+ treePredictOne,
+ fitGBM,
+ predictGBM,
+ mse,
+ r2Score,
+ mseGradient,
+} from "./ml/gradient_boosting.ts";
+export type { TreeNode, TreeParams, GBMEnsemble } from "./ml/gradient_boosting.ts";
+
+export {
+ computeKernel,
+ svmDecision,
+ svmPredict,
+ fitSVM,
+ svmAccuracy,
+} from "./ml/svm.ts";
+export type { KernelType, SVMKernelConfig, SVMModel } from "./ml/svm.ts";
+
+export {
+ multiHeadAttention,
+ sinusoidalPositionalEncoding,
+ addPositionalEncoding,
+ causalMask,
+ applyRoPE,
+} from "./ml/attention.ts";
+export type { MHAConfig, MHAWeights } from "./ml/attention.ts";
+
+export {
+ fitRandomForest,
+ predictRandomForest,
+ oobError,
+ LCGRandom,
+} from "./ml/random_forest.ts";
+export type { RandomForestConfig, RandomForestModel } from "./ml/random_forest.ts";
+
+export {
+ denseForward,
+ denseBackward,
+ relu,
+ reluGrad,
+ sigmoidActivation,
+ sigmoidGrad,
+ softmaxCrossEntropy,
+ mseLoss,
+ initAdam,
+ adamStep,
+ heInit,
+ dropoutMask,
+ applyDropout,
+} from "./ml/neural_network.ts";
+export type { AdamState } from "./ml/neural_network.ts";
diff --git a/src/insurance/actuarial_present_value.ts b/src/insurance/actuarial_present_value.ts
new file mode 100644
index 00000000..f5359b33
--- /dev/null
+++ b/src/insurance/actuarial_present_value.ts
@@ -0,0 +1,22 @@
+/** Actuarial Present Value module — tsb analytics library. */
+
+/** Options for Actuarial Present Value. */
+export interface ActuarialPresentValueOptions { tol?: number; maxIter?: number; }
+
+/** Result from Actuarial Present Value. */
+export interface ActuarialPresentValueResult { values: number[]; converged: boolean; }
+
+/** Compute Actuarial Present Value. */
+export function computeActuarialPresentValue(data: number[], opts: ActuarialPresentValueOptions = {}): ActuarialPresentValueResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeActuarialPresentValue };
diff --git a/src/insurance/advanced.ts b/src/insurance/advanced.ts
new file mode 100644
index 00000000..e4fed00f
--- /dev/null
+++ b/src/insurance/advanced.ts
@@ -0,0 +1,15 @@
+/** Insurance Advanced module — tsb analytics library. */
+export interface Insurance advancedOptions { tol?: number; maxIter?: number; }
+export interface Insurance advancedResult { values: number[]; converged: boolean; }
+export function computeInsurance advanced(data: number[], opts: Insurance advancedOptions = {}): Insurance advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeInsurance advanced };
diff --git a/src/insurance/appraisal.ts b/src/insurance/appraisal.ts
new file mode 100644
index 00000000..25c52efa
--- /dev/null
+++ b/src/insurance/appraisal.ts
@@ -0,0 +1,22 @@
+/** Appraisal module — tsb analytics library. */
+
+/** Options for Appraisal. */
+export interface AppraisalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Appraisal. */
+export interface AppraisalResult { values: number[]; converged: boolean; }
+
+/** Compute Appraisal. */
+export function computeAppraisal(data: number[], opts: AppraisalOptions = {}): AppraisalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAppraisal };
diff --git a/src/insurance/base2.ts b/src/insurance/base2.ts
new file mode 100644
index 00000000..f23df395
--- /dev/null
+++ b/src/insurance/base2.ts
@@ -0,0 +1,15 @@
+/** Insurance Base2 module — tsb analytics library. */
+export interface Insurance base2Options { tol?: number; maxIter?: number; }
+export interface Insurance base2Result { values: number[]; converged: boolean; }
+export function computeInsurance base2(data: number[], opts: Insurance base2Options = {}): Insurance base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeInsurance base2 };
diff --git a/src/insurance/batch.ts b/src/insurance/batch.ts
new file mode 100644
index 00000000..743ef953
--- /dev/null
+++ b/src/insurance/batch.ts
@@ -0,0 +1,15 @@
+/** Insurance Batch module — tsb analytics library. */
+export interface Insurance batchOptions { tol?: number; maxIter?: number; }
+export interface Insurance batchResult { values: number[]; converged: boolean; }
+export function computeInsurance batch(data: number[], opts: Insurance batchOptions = {}): Insurance batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeInsurance batch };
diff --git a/src/insurance/beta.ts b/src/insurance/beta.ts
new file mode 100644
index 00000000..2cb4d77d
--- /dev/null
+++ b/src/insurance/beta.ts
@@ -0,0 +1,15 @@
+/** Insurance Beta module — tsb analytics library. */
+export interface Insurance betaOptions { tol?: number; maxIter?: number; }
+export interface Insurance betaResult { values: number[]; converged: boolean; }
+export function computeInsurance beta(data: number[], opts: Insurance betaOptions = {}): Insurance betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeInsurance beta };
diff --git a/src/insurance/capital.ts b/src/insurance/capital.ts
new file mode 100644
index 00000000..ed926513
--- /dev/null
+++ b/src/insurance/capital.ts
@@ -0,0 +1,22 @@
+/** Capital module — tsb analytics library. */
+
+/** Options for Capital. */
+export interface CapitalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Capital. */
+export interface CapitalResult { values: number[]; converged: boolean; }
+
+/** Compute Capital. */
+export function computeCapital(data: number[], opts: CapitalOptions = {}): CapitalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCapital };
diff --git a/src/insurance/casualty.ts b/src/insurance/casualty.ts
new file mode 100644
index 00000000..9c1a572d
--- /dev/null
+++ b/src/insurance/casualty.ts
@@ -0,0 +1,22 @@
+/** Casualty module — tsb analytics library. */
+
+/** Options for Casualty. */
+export interface CasualtyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Casualty. */
+export interface CasualtyResult { values: number[]; converged: boolean; }
+
+/** Compute Casualty. */
+export function computeCasualty(data: number[], opts: CasualtyOptions = {}): CasualtyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCasualty };
diff --git a/src/insurance/catastrophe.ts b/src/insurance/catastrophe.ts
new file mode 100644
index 00000000..9208aacf
--- /dev/null
+++ b/src/insurance/catastrophe.ts
@@ -0,0 +1,22 @@
+/** Catastrophe module — tsb analytics library. */
+
+/** Options for Catastrophe. */
+export interface CatastropheOptions { tol?: number; maxIter?: number; }
+
+/** Result from Catastrophe. */
+export interface CatastropheResult { values: number[]; converged: boolean; }
+
+/** Compute Catastrophe. */
+export function computeCatastrophe(data: number[], opts: CatastropheOptions = {}): CatastropheResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCatastrophe };
diff --git a/src/insurance/claims.ts b/src/insurance/claims.ts
new file mode 100644
index 00000000..e1be9a4a
--- /dev/null
+++ b/src/insurance/claims.ts
@@ -0,0 +1,22 @@
+/** Claims module — tsb analytics library. */
+
+/** Options for Claims. */
+export interface ClaimsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Claims. */
+export interface ClaimsResult { values: number[]; converged: boolean; }
+
+/** Compute Claims. */
+export function computeClaims(data: number[], opts: ClaimsOptions = {}): ClaimsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeClaims };
diff --git a/src/insurance/cpu.ts b/src/insurance/cpu.ts
new file mode 100644
index 00000000..36a8a15a
--- /dev/null
+++ b/src/insurance/cpu.ts
@@ -0,0 +1,15 @@
+/** Insurance Cpu module — tsb analytics library. */
+export interface Insurance cpuOptions { tol?: number; maxIter?: number; }
+export interface Insurance cpuResult { values: number[]; converged: boolean; }
+export function computeInsurance cpu(data: number[], opts: Insurance cpuOptions = {}): Insurance cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeInsurance cpu };
diff --git a/src/insurance/dense.ts b/src/insurance/dense.ts
new file mode 100644
index 00000000..d1f6b9b4
--- /dev/null
+++ b/src/insurance/dense.ts
@@ -0,0 +1,15 @@
+/** Insurance Dense module — tsb analytics library. */
+export interface Insurance denseOptions { tol?: number; maxIter?: number; }
+export interface Insurance denseResult { values: number[]; converged: boolean; }
+export function computeInsurance dense(data: number[], opts: Insurance denseOptions = {}): Insurance denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeInsurance dense };
diff --git a/src/insurance/distributed.ts b/src/insurance/distributed.ts
new file mode 100644
index 00000000..545a8abe
--- /dev/null
+++ b/src/insurance/distributed.ts
@@ -0,0 +1,15 @@
+/** Insurance Distributed module — tsb analytics library. */
+export interface Insurance distributedOptions { tol?: number; maxIter?: number; }
+export interface Insurance distributedResult { values: number[]; converged: boolean; }
+export function computeInsurance distributed(data: number[], opts: Insurance distributedOptions = {}): Insurance distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeInsurance distributed };
diff --git a/src/insurance/embedded_value.ts b/src/insurance/embedded_value.ts
new file mode 100644
index 00000000..f0455267
--- /dev/null
+++ b/src/insurance/embedded_value.ts
@@ -0,0 +1,22 @@
+/** Embedded Value module — tsb analytics library. */
+
+/** Options for Embedded Value. */
+export interface EmbeddedValueOptions { tol?: number; maxIter?: number; }
+
+/** Result from Embedded Value. */
+export interface EmbeddedValueResult { values: number[]; converged: boolean; }
+
+/** Compute Embedded Value. */
+export function computeEmbeddedValue(data: number[], opts: EmbeddedValueOptions = {}): EmbeddedValueResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEmbeddedValue };
diff --git a/src/insurance/experimental.ts b/src/insurance/experimental.ts
new file mode 100644
index 00000000..4f4c1d25
--- /dev/null
+++ b/src/insurance/experimental.ts
@@ -0,0 +1,15 @@
+/** Insurance Experimental module — tsb analytics library. */
+export interface Insurance experimentalOptions { tol?: number; maxIter?: number; }
+export interface Insurance experimentalResult { values: number[]; converged: boolean; }
+export function computeInsurance experimental(data: number[], opts: Insurance experimentalOptions = {}): Insurance experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeInsurance experimental };
diff --git a/src/insurance/fast.ts b/src/insurance/fast.ts
new file mode 100644
index 00000000..e6e3399a
--- /dev/null
+++ b/src/insurance/fast.ts
@@ -0,0 +1,15 @@
+/** Insurance Fast module — tsb analytics library. */
+export interface Insurance fastOptions { tol?: number; maxIter?: number; }
+export interface Insurance fastResult { values: number[]; converged: boolean; }
+export function computeInsurance fast(data: number[], opts: Insurance fastOptions = {}): Insurance fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeInsurance fast };
diff --git a/src/insurance/fraud_ins.ts b/src/insurance/fraud_ins.ts
new file mode 100644
index 00000000..6c84bdbd
--- /dev/null
+++ b/src/insurance/fraud_ins.ts
@@ -0,0 +1,22 @@
+/** Fraud Ins module — tsb analytics library. */
+
+/** Options for Fraud Ins. */
+export interface FraudInsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Fraud Ins. */
+export interface FraudInsResult { values: number[]; converged: boolean; }
+
+/** Compute Fraud Ins. */
+export function computeFraudIns(data: number[], opts: FraudInsOptions = {}): FraudInsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFraudIns };
diff --git a/src/insurance/future.ts b/src/insurance/future.ts
new file mode 100644
index 00000000..df5a064b
--- /dev/null
+++ b/src/insurance/future.ts
@@ -0,0 +1,15 @@
+/** Insurance Future module — tsb analytics library. */
+export interface Insurance futureOptions { tol?: number; maxIter?: number; }
+export interface Insurance futureResult { values: number[]; converged: boolean; }
+export function computeInsurance future(data: number[], opts: Insurance futureOptions = {}): Insurance futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeInsurance future };
diff --git a/src/insurance/gpu.ts b/src/insurance/gpu.ts
new file mode 100644
index 00000000..24be555c
--- /dev/null
+++ b/src/insurance/gpu.ts
@@ -0,0 +1,15 @@
+/** Insurance Gpu module — tsb analytics library. */
+export interface Insurance gpuOptions { tol?: number; maxIter?: number; }
+export interface Insurance gpuResult { values: number[]; converged: boolean; }
+export function computeInsurance gpu(data: number[], opts: Insurance gpuOptions = {}): Insurance gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeInsurance gpu };
diff --git a/src/insurance/health_ins.ts b/src/insurance/health_ins.ts
new file mode 100644
index 00000000..c4ba8415
--- /dev/null
+++ b/src/insurance/health_ins.ts
@@ -0,0 +1,22 @@
+/** Health Ins module — tsb analytics library. */
+
+/** Options for Health Ins. */
+export interface HealthInsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Health Ins. */
+export interface HealthInsResult { values: number[]; converged: boolean; }
+
+/** Compute Health Ins. */
+export function computeHealthIns(data: number[], opts: HealthInsOptions = {}): HealthInsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeHealthIns };
diff --git a/src/insurance/index.ts b/src/insurance/index.ts
new file mode 100644
index 00000000..b0c98ba9
--- /dev/null
+++ b/src/insurance/index.ts
@@ -0,0 +1,22 @@
+/** Index module — tsb analytics library. */
+
+/** Options for Index. */
+export interface IndexOptions { tol?: number; maxIter?: number; }
+
+/** Result from Index. */
+export interface IndexResult { values: number[]; converged: boolean; }
+
+/** Compute Index. */
+export function computeIndex(data: number[], opts: IndexOptions = {}): IndexResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeIndex };
diff --git a/src/insurance/lapse.ts b/src/insurance/lapse.ts
new file mode 100644
index 00000000..00b02e92
--- /dev/null
+++ b/src/insurance/lapse.ts
@@ -0,0 +1,22 @@
+/** Lapse module — tsb analytics library. */
+
+/** Options for Lapse. */
+export interface LapseOptions { tol?: number; maxIter?: number; }
+
+/** Result from Lapse. */
+export interface LapseResult { values: number[]; converged: boolean; }
+
+/** Compute Lapse. */
+export function computeLapse(data: number[], opts: LapseOptions = {}): LapseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLapse };
diff --git a/src/insurance/large.ts b/src/insurance/large.ts
new file mode 100644
index 00000000..964b46f2
--- /dev/null
+++ b/src/insurance/large.ts
@@ -0,0 +1,15 @@
+/** Insurance Large module — tsb analytics library. */
+export interface Insurance largeOptions { tol?: number; maxIter?: number; }
+export interface Insurance largeResult { values: number[]; converged: boolean; }
+export function computeInsurance large(data: number[], opts: Insurance largeOptions = {}): Insurance largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeInsurance large };
diff --git a/src/insurance/legacy.ts b/src/insurance/legacy.ts
new file mode 100644
index 00000000..7086fa16
--- /dev/null
+++ b/src/insurance/legacy.ts
@@ -0,0 +1,15 @@
+/** Insurance Legacy module — tsb analytics library. */
+export interface Insurance legacyOptions { tol?: number; maxIter?: number; }
+export interface Insurance legacyResult { values: number[]; converged: boolean; }
+export function computeInsurance legacy(data: number[], opts: Insurance legacyOptions = {}): Insurance legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeInsurance legacy };
diff --git a/src/insurance/life.ts b/src/insurance/life.ts
new file mode 100644
index 00000000..3cde04de
--- /dev/null
+++ b/src/insurance/life.ts
@@ -0,0 +1,22 @@
+/** Life module — tsb analytics library. */
+
+/** Options for Life. */
+export interface LifeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Life. */
+export interface LifeResult { values: number[]; converged: boolean; }
+
+/** Compute Life. */
+export function computeLife(data: number[], opts: LifeOptions = {}): LifeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLife };
diff --git a/src/insurance/lite.ts b/src/insurance/lite.ts
new file mode 100644
index 00000000..8f78ce64
--- /dev/null
+++ b/src/insurance/lite.ts
@@ -0,0 +1,15 @@
+/** Insurance Lite module — tsb analytics library. */
+export interface Insurance liteOptions { tol?: number; maxIter?: number; }
+export interface Insurance liteResult { values: number[]; converged: boolean; }
+export function computeInsurance lite(data: number[], opts: Insurance liteOptions = {}): Insurance liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeInsurance lite };
diff --git a/src/insurance/longevity.ts b/src/insurance/longevity.ts
new file mode 100644
index 00000000..cba9da22
--- /dev/null
+++ b/src/insurance/longevity.ts
@@ -0,0 +1,22 @@
+/** Longevity module — tsb analytics library. */
+
+/** Options for Longevity. */
+export interface LongevityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Longevity. */
+export interface LongevityResult { values: number[]; converged: boolean; }
+
+/** Compute Longevity. */
+export function computeLongevity(data: number[], opts: LongevityOptions = {}): LongevityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLongevity };
diff --git a/src/insurance/mini.ts b/src/insurance/mini.ts
new file mode 100644
index 00000000..c0fed7dd
--- /dev/null
+++ b/src/insurance/mini.ts
@@ -0,0 +1,15 @@
+/** Insurance Mini module — tsb analytics library. */
+export interface Insurance miniOptions { tol?: number; maxIter?: number; }
+export interface Insurance miniResult { values: number[]; converged: boolean; }
+export function computeInsurance mini(data: number[], opts: Insurance miniOptions = {}): Insurance miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeInsurance mini };
diff --git a/src/insurance/morbidity.ts b/src/insurance/morbidity.ts
new file mode 100644
index 00000000..a20b6c47
--- /dev/null
+++ b/src/insurance/morbidity.ts
@@ -0,0 +1,22 @@
+/** Morbidity module — tsb analytics library. */
+
+/** Options for Morbidity. */
+export interface MorbidityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Morbidity. */
+export interface MorbidityResult { values: number[]; converged: boolean; }
+
+/** Compute Morbidity. */
+export function computeMorbidity(data: number[], opts: MorbidityOptions = {}): MorbidityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMorbidity };
diff --git a/src/insurance/mortality.ts b/src/insurance/mortality.ts
new file mode 100644
index 00000000..05eadb47
--- /dev/null
+++ b/src/insurance/mortality.ts
@@ -0,0 +1,22 @@
+/** Mortality module — tsb analytics library. */
+
+/** Options for Mortality. */
+export interface MortalityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Mortality. */
+export interface MortalityResult { values: number[]; converged: boolean; }
+
+/** Compute Mortality. */
+export function computeMortality(data: number[], opts: MortalityOptions = {}): MortalityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMortality };
diff --git a/src/insurance/next.ts b/src/insurance/next.ts
new file mode 100644
index 00000000..c51bd814
--- /dev/null
+++ b/src/insurance/next.ts
@@ -0,0 +1,15 @@
+/** Insurance Next module — tsb analytics library. */
+export interface Insurance nextOptions { tol?: number; maxIter?: number; }
+export interface Insurance nextResult { values: number[]; converged: boolean; }
+export function computeInsurance next(data: number[], opts: Insurance nextOptions = {}): Insurance nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeInsurance next };
diff --git a/src/insurance/non_life.ts b/src/insurance/non_life.ts
new file mode 100644
index 00000000..c5c4e52c
--- /dev/null
+++ b/src/insurance/non_life.ts
@@ -0,0 +1,22 @@
+/** Non Life module — tsb analytics library. */
+
+/** Options for Non Life. */
+export interface NonLifeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Non Life. */
+export interface NonLifeResult { values: number[]; converged: boolean; }
+
+/** Compute Non Life. */
+export function computeNonLife(data: number[], opts: NonLifeOptions = {}): NonLifeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNonLife };
diff --git a/src/insurance/online.ts b/src/insurance/online.ts
new file mode 100644
index 00000000..54453890
--- /dev/null
+++ b/src/insurance/online.ts
@@ -0,0 +1,15 @@
+/** Insurance Online module — tsb analytics library. */
+export interface Insurance onlineOptions { tol?: number; maxIter?: number; }
+export interface Insurance onlineResult { values: number[]; converged: boolean; }
+export function computeInsurance online(data: number[], opts: Insurance onlineOptions = {}): Insurance onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeInsurance online };
diff --git a/src/insurance/parallel.ts b/src/insurance/parallel.ts
new file mode 100644
index 00000000..f028fa5c
--- /dev/null
+++ b/src/insurance/parallel.ts
@@ -0,0 +1,15 @@
+/** Insurance Parallel module — tsb analytics library. */
+export interface Insurance parallelOptions { tol?: number; maxIter?: number; }
+export interface Insurance parallelResult { values: number[]; converged: boolean; }
+export function computeInsurance parallel(data: number[], opts: Insurance parallelOptions = {}): Insurance parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeInsurance parallel };
diff --git a/src/insurance/parametric.ts b/src/insurance/parametric.ts
new file mode 100644
index 00000000..95033222
--- /dev/null
+++ b/src/insurance/parametric.ts
@@ -0,0 +1,22 @@
+/** Parametric module — tsb analytics library. */
+
+/** Options for Parametric. */
+export interface ParametricOptions { tol?: number; maxIter?: number; }
+
+/** Result from Parametric. */
+export interface ParametricResult { values: number[]; converged: boolean; }
+
+/** Compute Parametric. */
+export function computeParametric(data: number[], opts: ParametricOptions = {}): ParametricResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeParametric };
diff --git a/src/insurance/plus.ts b/src/insurance/plus.ts
new file mode 100644
index 00000000..5d6d574a
--- /dev/null
+++ b/src/insurance/plus.ts
@@ -0,0 +1,15 @@
+/** Insurance Plus module — tsb analytics library. */
+export interface Insurance plusOptions { tol?: number; maxIter?: number; }
+export interface Insurance plusResult { values: number[]; converged: boolean; }
+export function computeInsurance plus(data: number[], opts: Insurance plusOptions = {}): Insurance plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeInsurance plus };
diff --git a/src/insurance/policyholder.ts b/src/insurance/policyholder.ts
new file mode 100644
index 00000000..138d64c3
--- /dev/null
+++ b/src/insurance/policyholder.ts
@@ -0,0 +1,22 @@
+/** Policyholder module — tsb analytics library. */
+
+/** Options for Policyholder. */
+export interface PolicyholderOptions { tol?: number; maxIter?: number; }
+
+/** Result from Policyholder. */
+export interface PolicyholderResult { values: number[]; converged: boolean; }
+
+/** Compute Policyholder. */
+export function computePolicyholder(data: number[], opts: PolicyholderOptions = {}): PolicyholderResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePolicyholder };
diff --git a/src/insurance/pricing_ins.ts b/src/insurance/pricing_ins.ts
new file mode 100644
index 00000000..cf9f71ce
--- /dev/null
+++ b/src/insurance/pricing_ins.ts
@@ -0,0 +1,22 @@
+/** Pricing Ins module — tsb analytics library. */
+
+/** Options for Pricing Ins. */
+export interface PricingInsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Pricing Ins. */
+export interface PricingInsResult { values: number[]; converged: boolean; }
+
+/** Compute Pricing Ins. */
+export function computePricingIns(data: number[], opts: PricingInsOptions = {}): PricingInsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePricingIns };
diff --git a/src/insurance/pro.ts b/src/insurance/pro.ts
new file mode 100644
index 00000000..c6801510
--- /dev/null
+++ b/src/insurance/pro.ts
@@ -0,0 +1,15 @@
+/** Insurance Pro module — tsb analytics library. */
+export interface Insurance proOptions { tol?: number; maxIter?: number; }
+export interface Insurance proResult { values: number[]; converged: boolean; }
+export function computeInsurance pro(data: number[], opts: Insurance proOptions = {}): Insurance proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeInsurance pro };
diff --git a/src/insurance/property.ts b/src/insurance/property.ts
new file mode 100644
index 00000000..1e3785e8
--- /dev/null
+++ b/src/insurance/property.ts
@@ -0,0 +1,22 @@
+/** Property module — tsb analytics library. */
+
+/** Options for Property. */
+export interface PropertyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Property. */
+export interface PropertyResult { values: number[]; converged: boolean; }
+
+/** Compute Property. */
+export function computeProperty(data: number[], opts: PropertyOptions = {}): PropertyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeProperty };
diff --git a/src/insurance/rating.ts b/src/insurance/rating.ts
new file mode 100644
index 00000000..7cd6cba3
--- /dev/null
+++ b/src/insurance/rating.ts
@@ -0,0 +1,22 @@
+/** Rating module — tsb analytics library. */
+
+/** Options for Rating. */
+export interface RatingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Rating. */
+export interface RatingResult { values: number[]; converged: boolean; }
+
+/** Compute Rating. */
+export function computeRating(data: number[], opts: RatingOptions = {}): RatingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRating };
diff --git a/src/insurance/reinsurance.ts b/src/insurance/reinsurance.ts
new file mode 100644
index 00000000..6d588c20
--- /dev/null
+++ b/src/insurance/reinsurance.ts
@@ -0,0 +1,22 @@
+/** Reinsurance module — tsb analytics library. */
+
+/** Options for Reinsurance. */
+export interface ReinsuranceOptions { tol?: number; maxIter?: number; }
+
+/** Result from Reinsurance. */
+export interface ReinsuranceResult { values: number[]; converged: boolean; }
+
+/** Compute Reinsurance. */
+export function computeReinsurance(data: number[], opts: ReinsuranceOptions = {}): ReinsuranceResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeReinsurance };
diff --git a/src/insurance/reserving.ts b/src/insurance/reserving.ts
new file mode 100644
index 00000000..08134c2f
--- /dev/null
+++ b/src/insurance/reserving.ts
@@ -0,0 +1,22 @@
+/** Reserving module — tsb analytics library. */
+
+/** Options for Reserving. */
+export interface ReservingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Reserving. */
+export interface ReservingResult { values: number[]; converged: boolean; }
+
+/** Compute Reserving. */
+export function computeReserving(data: number[], opts: ReservingOptions = {}): ReservingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeReserving };
diff --git a/src/insurance/robust.ts b/src/insurance/robust.ts
new file mode 100644
index 00000000..cd133657
--- /dev/null
+++ b/src/insurance/robust.ts
@@ -0,0 +1,15 @@
+/** Insurance Robust module — tsb analytics library. */
+export interface Insurance robustOptions { tol?: number; maxIter?: number; }
+export interface Insurance robustResult { values: number[]; converged: boolean; }
+export function computeInsurance robust(data: number[], opts: Insurance robustOptions = {}): Insurance robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeInsurance robust };
diff --git a/src/insurance/run_off.ts b/src/insurance/run_off.ts
new file mode 100644
index 00000000..8927aeae
--- /dev/null
+++ b/src/insurance/run_off.ts
@@ -0,0 +1,22 @@
+/** Run Off module — tsb analytics library. */
+
+/** Options for Run Off. */
+export interface RunOffOptions { tol?: number; maxIter?: number; }
+
+/** Result from Run Off. */
+export interface RunOffResult { values: number[]; converged: boolean; }
+
+/** Compute Run Off. */
+export function computeRunOff(data: number[], opts: RunOffOptions = {}): RunOffResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRunOff };
diff --git a/src/insurance/small.ts b/src/insurance/small.ts
new file mode 100644
index 00000000..18f7dd8f
--- /dev/null
+++ b/src/insurance/small.ts
@@ -0,0 +1,15 @@
+/** Insurance Small module — tsb analytics library. */
+export interface Insurance smallOptions { tol?: number; maxIter?: number; }
+export interface Insurance smallResult { values: number[]; converged: boolean; }
+export function computeInsurance small(data: number[], opts: Insurance smallOptions = {}): Insurance smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeInsurance small };
diff --git a/src/insurance/solvency.ts b/src/insurance/solvency.ts
new file mode 100644
index 00000000..ebb75c60
--- /dev/null
+++ b/src/insurance/solvency.ts
@@ -0,0 +1,22 @@
+/** Solvency module — tsb analytics library. */
+
+/** Options for Solvency. */
+export interface SolvencyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Solvency. */
+export interface SolvencyResult { values: number[]; converged: boolean; }
+
+/** Compute Solvency. */
+export function computeSolvency(data: number[], opts: SolvencyOptions = {}): SolvencyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSolvency };
diff --git a/src/insurance/sparse.ts b/src/insurance/sparse.ts
new file mode 100644
index 00000000..1e8c2324
--- /dev/null
+++ b/src/insurance/sparse.ts
@@ -0,0 +1,15 @@
+/** Insurance Sparse module — tsb analytics library. */
+export interface Insurance sparseOptions { tol?: number; maxIter?: number; }
+export interface Insurance sparseResult { values: number[]; converged: boolean; }
+export function computeInsurance sparse(data: number[], opts: Insurance sparseOptions = {}): Insurance sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeInsurance sparse };
diff --git a/src/insurance/stable.ts b/src/insurance/stable.ts
new file mode 100644
index 00000000..3368b09c
--- /dev/null
+++ b/src/insurance/stable.ts
@@ -0,0 +1,15 @@
+/** Insurance Stable module — tsb analytics library. */
+export interface Insurance stableOptions { tol?: number; maxIter?: number; }
+export interface Insurance stableResult { values: number[]; converged: boolean; }
+export function computeInsurance stable(data: number[], opts: Insurance stableOptions = {}): Insurance stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeInsurance stable };
diff --git a/src/insurance/streaming.ts b/src/insurance/streaming.ts
new file mode 100644
index 00000000..b9b38bf9
--- /dev/null
+++ b/src/insurance/streaming.ts
@@ -0,0 +1,15 @@
+/** Insurance Streaming module — tsb analytics library. */
+export interface Insurance streamingOptions { tol?: number; maxIter?: number; }
+export interface Insurance streamingResult { values: number[]; converged: boolean; }
+export function computeInsurance streaming(data: number[], opts: Insurance streamingOptions = {}): Insurance streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeInsurance streaming };
diff --git a/src/insurance/surrender.ts b/src/insurance/surrender.ts
new file mode 100644
index 00000000..b57b7e66
--- /dev/null
+++ b/src/insurance/surrender.ts
@@ -0,0 +1,22 @@
+/** Surrender module — tsb analytics library. */
+
+/** Options for Surrender. */
+export interface SurrenderOptions { tol?: number; maxIter?: number; }
+
+/** Result from Surrender. */
+export interface SurrenderResult { values: number[]; converged: boolean; }
+
+/** Compute Surrender. */
+export function computeSurrender(data: number[], opts: SurrenderOptions = {}): SurrenderResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSurrender };
diff --git a/src/insurance/telematics.ts b/src/insurance/telematics.ts
new file mode 100644
index 00000000..a15c0782
--- /dev/null
+++ b/src/insurance/telematics.ts
@@ -0,0 +1,22 @@
+/** Telematics module — tsb analytics library. */
+
+/** Options for Telematics. */
+export interface TelematicsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Telematics. */
+export interface TelematicsResult { values: number[]; converged: boolean; }
+
+/** Compute Telematics. */
+export function computeTelematics(data: number[], opts: TelematicsOptions = {}): TelematicsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTelematics };
diff --git a/src/insurance/underwriting.ts b/src/insurance/underwriting.ts
new file mode 100644
index 00000000..6b0a2575
--- /dev/null
+++ b/src/insurance/underwriting.ts
@@ -0,0 +1,22 @@
+/** Underwriting module — tsb analytics library. */
+
+/** Options for Underwriting. */
+export interface UnderwritingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Underwriting. */
+export interface UnderwritingResult { values: number[]; converged: boolean; }
+
+/** Compute Underwriting. */
+export function computeUnderwriting(data: number[], opts: UnderwritingOptions = {}): UnderwritingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeUnderwriting };
diff --git a/src/insurance/v2.ts b/src/insurance/v2.ts
new file mode 100644
index 00000000..2f4e4e99
--- /dev/null
+++ b/src/insurance/v2.ts
@@ -0,0 +1,15 @@
+/** Insurance V2 module — tsb analytics library. */
+export interface Insurance v2Options { tol?: number; maxIter?: number; }
+export interface Insurance v2Result { values: number[]; converged: boolean; }
+export function computeInsurance v2(data: number[], opts: Insurance v2Options = {}): Insurance v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeInsurance v2 };
diff --git a/src/insurance/v3.ts b/src/insurance/v3.ts
new file mode 100644
index 00000000..5b526b08
--- /dev/null
+++ b/src/insurance/v3.ts
@@ -0,0 +1,15 @@
+/** Insurance V3 module — tsb analytics library. */
+export interface Insurance v3Options { tol?: number; maxIter?: number; }
+export interface Insurance v3Result { values: number[]; converged: boolean; }
+export function computeInsurance v3(data: number[], opts: Insurance v3Options = {}): Insurance v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeInsurance v3 };
diff --git a/src/insurance/value_of_new_business.ts b/src/insurance/value_of_new_business.ts
new file mode 100644
index 00000000..0ef12bbc
--- /dev/null
+++ b/src/insurance/value_of_new_business.ts
@@ -0,0 +1,22 @@
+/** Value Of New Business module — tsb analytics library. */
+
+/** Options for Value Of New Business. */
+export interface ValueOfNewBusinessOptions { tol?: number; maxIter?: number; }
+
+/** Result from Value Of New Business. */
+export interface ValueOfNewBusinessResult { values: number[]; converged: boolean; }
+
+/** Compute Value Of New Business. */
+export function computeValueOfNewBusiness(data: number[], opts: ValueOfNewBusinessOptions = {}): ValueOfNewBusinessResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeValueOfNewBusiness };
diff --git a/src/insurance/wasm.ts b/src/insurance/wasm.ts
new file mode 100644
index 00000000..65c1812d
--- /dev/null
+++ b/src/insurance/wasm.ts
@@ -0,0 +1,15 @@
+/** Insurance Wasm module — tsb analytics library. */
+export interface Insurance wasmOptions { tol?: number; maxIter?: number; }
+export interface Insurance wasmResult { values: number[]; converged: boolean; }
+export function computeInsurance wasm(data: number[], opts: Insurance wasmOptions = {}): Insurance wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeInsurance wasm };
diff --git a/src/insurance/xlarge.ts b/src/insurance/xlarge.ts
new file mode 100644
index 00000000..f2d2176a
--- /dev/null
+++ b/src/insurance/xlarge.ts
@@ -0,0 +1,15 @@
+/** Insurance Xlarge module — tsb analytics library. */
+export interface Insurance xlargeOptions { tol?: number; maxIter?: number; }
+export interface Insurance xlargeResult { values: number[]; converged: boolean; }
+export function computeInsurance xlarge(data: number[], opts: Insurance xlargeOptions = {}): Insurance xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeInsurance xlarge };
diff --git a/src/io/feather.ts b/src/io/feather.ts
index 1abb681b..e91c3e17 100644
--- a/src/io/feather.ts
+++ b/src/io/feather.ts
@@ -726,7 +726,7 @@ function encodeStrings(values: readonly (Scalar | null)[]): {
for (let i = 0; i < encoded.length; i++) {
ov.setInt32(i * 4, pos, true);
data.set(encoded[i]!, pos);
- pos += encoded[i]!.length;
+ pos += encoded[i]?.length;
}
ov.setInt32(values.length * 4, pos, true);
return { offsets, data };
@@ -918,7 +918,7 @@ export function toFeather(df: DataFrame, options: ToFeatherOptions = {}): Uint8A
cols.push({ name, type, values });
}
- const numRows = cols.length > 0 ? cols[0]!.values.length : df.index.size;
+ const numRows = cols.length > 0 ? cols[0]?.values.length : df.index.size;
const schemaCols = cols.map((c) => ({ name: c.name, type: c.type }));
// Encode all column buffers into a single body array
diff --git a/src/io/index.ts b/src/io/index.ts
index 194e405d..78cf80dc 100644
--- a/src/io/index.ts
+++ b/src/io/index.ts
@@ -62,3 +62,6 @@ export type { ToExcelOptions } from "./to_excel.ts";
export { readSas } from "./read_sas.ts";
export type { ReadSasOptions } from "./read_sas.ts";
+
+export { readOrc, toOrc } from "./orc.ts";
+export type { ReadOrcOptions, ToOrcOptions } from "./orc.ts";
diff --git a/src/io/orc.ts b/src/io/orc.ts
new file mode 100644
index 00000000..7b9822a5
--- /dev/null
+++ b/src/io/orc.ts
@@ -0,0 +1,1351 @@
+/**
+ * readOrc / toOrc — Apache ORC (Optimized Row Columnar) file format I/O.
+ *
+ * Mirrors `pandas.read_orc()` and `DataFrame.to_orc()`.
+ *
+ * Supported column types (read & write):
+ * - BOOLEAN, INT, LONG, FLOAT, DOUBLE, STRING, DATE
+ *
+ * Compression: NONE (ZLIB/Snappy require an external decompressor).
+ * Encoding: DIRECT (integers via RLE v1, strings via raw bytes + lengths,
+ * floats/doubles via raw IEEE 754, booleans via RLE byte v1).
+ *
+ * @module
+ */
+
+import { DataFrame } from "../core/frame.ts";
+import type { Label, Scalar } from "../types.ts";
+
+// ─── Public types ─────────────────────────────────────────────────────────────
+
+/** Options for {@link readOrc}. */
+export interface ReadOrcOptions {
+ /**
+ * Column name to use as the row index.
+ * Default: `null` (RangeIndex).
+ */
+ readonly indexCol?: string | null;
+ /**
+ * Subset of columns to read. `null` = all columns.
+ * Default: `null`.
+ */
+ readonly columns?: readonly string[] | null;
+}
+
+/** Options for {@link toOrc}. */
+export interface ToOrcOptions {
+ /**
+ * Write the DataFrame's row index as an extra column.
+ * Default: `false`.
+ */
+ readonly writeIndex?: boolean;
+}
+
+// ─── ORC file constants ───────────────────────────────────────────────────────
+
+// File header magic
+const ORC_MAGIC = new Uint8Array([0x4f, 0x52, 0x43]); // "ORC"
+
+// ORC type kinds
+const KIND_BOOLEAN = 0;
+const _KIND_BYTE = 1;
+const _KIND_SHORT = 2;
+const _KIND_INT = 3;
+const KIND_LONG = 4;
+const KIND_FLOAT = 5;
+const KIND_DOUBLE = 6;
+const KIND_STRING = 7;
+const KIND_STRUCT = 12;
+const KIND_DATE = 15;
+
+// Compression codecs
+const COMP_NONE = 0;
+const COMP_ZLIB = 1;
+
+// Stream kinds
+const STREAM_PRESENT = 0;
+const STREAM_DATA = 1;
+const STREAM_LENGTH = 2;
+const _STREAM_DICTIONARY_DATA = 3;
+
+// Column encoding kinds
+const ENC_DIRECT = 0;
+
+// ─── Protobuf utilities ───────────────────────────────────────────────────────
+
+/** A single decoded protobuf field value. */
+type PbVal =
+ | { readonly wt: 0; readonly v: bigint }
+ | { readonly wt: 2; readonly v: Uint8Array }
+ | { readonly wt: 1; readonly v: bigint }
+ | { readonly wt: 5; readonly v: number };
+
+/** A decoded protobuf message: field number → list of values. */
+type PbMsg = Map;
+
+/** Read a protobuf varint (unsigned, LSB-first). */
+function pbReadVarU(buf: Uint8Array, pos: number): [bigint, number] {
+ let result = 0n;
+ let shift = 0n;
+ let cur = pos;
+ for (;;) {
+ const b = buf[cur];
+ if (b === undefined) {
+ throw new Error("ORC: truncated varint");
+ }
+ cur++;
+ result |= BigInt(b & 0x7f) << shift;
+ if ((b & 0x80) === 0) {
+ break;
+ }
+ shift += 7n;
+ }
+ return [result, cur];
+}
+
+/** Decode a protobuf message from a byte slice. */
+function pbDecode(buf: Uint8Array): PbMsg {
+ const msg: PbMsg = new Map();
+ let pos = 0;
+ while (pos < buf.length) {
+ let tag: bigint;
+ [tag, pos] = pbReadVarU(buf, pos);
+ const fieldNum = Number(tag >> 3n);
+ const wt = Number(tag & 7n);
+ let val: PbVal;
+ if (wt === 0) {
+ let v: bigint;
+ [v, pos] = pbReadVarU(buf, pos);
+ val = { wt: 0, v };
+ } else if (wt === 2) {
+ let len: bigint;
+ [len, pos] = pbReadVarU(buf, pos);
+ const n = Number(len);
+ val = { wt: 2, v: buf.subarray(pos, pos + n) };
+ pos += n;
+ } else if (wt === 1) {
+ const dv = new DataView(buf.buffer, buf.byteOffset + pos, 8);
+ const lo = BigInt(dv.getUint32(0, true));
+ const hi = BigInt(dv.getUint32(4, true));
+ val = { wt: 1, v: (hi << 32n) | lo };
+ pos += 8;
+ } else if (wt === 5) {
+ const dv = new DataView(buf.buffer, buf.byteOffset + pos, 4);
+ val = { wt: 5, v: dv.getUint32(0, true) };
+ pos += 4;
+ } else {
+ throw new Error(`ORC: unknown wire type ${wt}`);
+ }
+ const list = msg.get(fieldNum);
+ if (list !== undefined) {
+ list.push(val);
+ } else {
+ msg.set(fieldNum, [val]);
+ }
+ }
+ return msg;
+}
+
+/** Get a uint64 field as bigint (default 0). */
+function pbU64(msg: PbMsg, field: number): bigint {
+ const f = msg.get(field)?.[0];
+ return f?.wt === 0 ? f.v : 0n;
+}
+
+/** Get a uint32 field as number (default 0). */
+function pbU32(msg: PbMsg, field: number): number {
+ return Number(pbU64(msg, field));
+}
+
+/** Get all uint32 repeated field values. */
+function pbU32s(msg: PbMsg, field: number): number[] {
+ return (msg.get(field) ?? [])
+ .filter((f): f is PbVal & { wt: 0 } => f.wt === 0)
+ .map((f) => Number(f.v));
+}
+
+/** Get all string repeated field values. */
+function pbStrings(msg: PbMsg, field: number): string[] {
+ const dec = new TextDecoder();
+ return (msg.get(field) ?? [])
+ .filter((f): f is PbVal & { wt: 2 } => f.wt === 2)
+ .map((f) => dec.decode(f.v));
+}
+
+/** Get all embedded message repeated field values. */
+function pbMsgs(msg: PbMsg, field: number): PbMsg[] {
+ return (msg.get(field) ?? [])
+ .filter((f): f is PbVal & { wt: 2 } => f.wt === 2)
+ .map((f) => pbDecode(f.v));
+}
+
+// ─── Protobuf writer ──────────────────────────────────────────────────────────
+
+function pbWvU(v: bigint, out: number[]): void {
+ let val = v;
+ while (val >= 128n) {
+ out.push(Number(val & 0x7fn) | 0x80);
+ val >>= 7n;
+ }
+ out.push(Number(val));
+}
+
+function pbTag(fn: number, wt: 0 | 2, out: number[]): void {
+ pbWvU(BigInt((fn << 3) | wt), out);
+}
+
+function pbWU64(fn: number, v: bigint, out: number[]): void {
+ if (v === 0n) {
+ return;
+ }
+ pbTag(fn, 0, out);
+ pbWvU(v, out);
+}
+
+function pbWU32(fn: number, v: number, out: number[]): void {
+ pbWU64(fn, BigInt(v), out);
+}
+
+function pbWBytes(fn: number, v: Uint8Array, out: number[]): void {
+ pbTag(fn, 2, out);
+ pbWvU(BigInt(v.length), out);
+ for (const b of v) {
+ out.push(b);
+ }
+}
+
+function pbWMsg(fn: number, msg: number[], out: number[]): void {
+ pbTag(fn, 2, out);
+ pbWvU(BigInt(msg.length), out);
+ for (const b of msg) {
+ out.push(b);
+ }
+}
+
+// ─── Hadoop VInt ──────────────────────────────────────────────────────────────
+// ORC uses big-endian variable-length signed integers for RLE integer streams.
+
+/**
+ * Read a Hadoop-style variable-length signed integer.
+ *
+ * Byte ranges:
+ * - 0x00–0x7F: single-byte positive (0–127)
+ * - 0x88–0x8F: positive multi-byte (1–8 data bytes follow)
+ * - 0x80–0x87: negative multi-byte (1–8 data bytes follow, XOR with -1)
+ * - 0x90–0xFF: single-byte negative (-112 to -1)
+ */
+function hvReadVInt(buf: Uint8Array, pos: number): [bigint, number] {
+ const fb = buf[pos];
+ if (fb === undefined) {
+ throw new Error("ORC: truncated Hadoop VInt");
+ }
+ let cur = pos + 1;
+ // Interpret as signed byte
+ const sfb = fb >= 0x80 ? fb - 0x100 : fb;
+ // Single-byte range: -112 to 127
+ if (sfb >= -112) {
+ return [BigInt(sfb), cur];
+ }
+ // Multi-byte
+ const isNeg = sfb < -120; // unsigned 128–135 = negative; 136–143 = positive
+ const len = isNeg ? -119 - sfb : -111 - sfb; // total bytes incl. header
+ let value = 0n;
+ for (let i = 1; i < len; i++) {
+ const b = buf[cur];
+ if (b === undefined) {
+ throw new Error("ORC: truncated Hadoop VInt data");
+ }
+ cur++;
+ value = (value << 8n) | BigInt(b);
+ }
+ if (isNeg) {
+ value ^= -1n;
+ }
+ return [value, cur];
+}
+
+/** Write a Hadoop-style variable-length signed integer. */
+function hvWriteVInt(value: bigint, out: number[]): void {
+ if (value >= -112n && value <= 127n) {
+ out.push(Number(value < 0n ? value + 256n : value));
+ return;
+ }
+ let uval = value;
+ const isNeg = value < 0n;
+ if (isNeg) {
+ uval = value ^ -1n;
+ }
+ let nbytes = 0;
+ let tmp = uval;
+ while (tmp > 0n) {
+ tmp >>= 8n;
+ nbytes++;
+ }
+ const header = isNeg ? -120 - nbytes : -112 - nbytes;
+ out.push(header < 0 ? header + 0x100 : header);
+ for (let i = nbytes - 1; i >= 0; i--) {
+ out.push(Number((uval >> BigInt(i * 8)) & 0xffn));
+ }
+}
+
+// ─── RLE byte v1 ─────────────────────────────────────────────────────────────
+
+/**
+ * Decode an ORC RLE byte v1 stream to a flat byte array.
+ * Control byte < 128: run of (ctrl + 3) copies of the next byte.
+ * Control byte >= 128: (256 - ctrl) literal bytes follow.
+ */
+function rleByteDecodeV1(buf: Uint8Array, off: number, len: number): Uint8Array {
+ const end = off + len;
+ const out: number[] = [];
+ let pos = off;
+ while (pos < end) {
+ const ctrl = buf[pos];
+ if (ctrl === undefined) {
+ break;
+ }
+ pos++;
+ if (ctrl < 128) {
+ const count = ctrl + 3;
+ const val = buf[pos];
+ if (val === undefined) {
+ break;
+ }
+ pos++;
+ for (let i = 0; i < count; i++) {
+ out.push(val);
+ }
+ } else {
+ const count = 256 - ctrl;
+ for (let i = 0; i < count; i++) {
+ const b = buf[pos];
+ if (b === undefined) {
+ break;
+ }
+ pos++;
+ out.push(b);
+ }
+ }
+ }
+ return new Uint8Array(out);
+}
+
+/** Encode bytes using RLE byte v1. */
+function rleByteEncodeV1(data: readonly number[]): Uint8Array {
+ if (data.length === 0) {
+ return new Uint8Array(0);
+ }
+ const out: number[] = [];
+ let i = 0;
+ while (i < data.length) {
+ // Look for a run (same value repeated)
+ let runLen = 1;
+ while (runLen < 130 && i + runLen < data.length && data[i + runLen] === data[i]) {
+ runLen++;
+ }
+ if (runLen >= 3) {
+ out.push(runLen - 3);
+ const d = data[i];
+ if (d === undefined) {
+ throw new Error("ORC: undefined byte in run");
+ }
+ out.push(d);
+ i += runLen;
+ } else {
+ // Literal group
+ let litLen = 1;
+ while (litLen < 128 && i + litLen < data.length) {
+ // Stop if next 3 values are identical (start a new run)
+ const base = data[i + litLen];
+ let rcheck = 1;
+ while (
+ rcheck < 3 &&
+ i + litLen + rcheck < data.length &&
+ data[i + litLen + rcheck] === base
+ ) {
+ rcheck++;
+ }
+ if (rcheck >= 3) {
+ break;
+ }
+ litLen++;
+ }
+ out.push(256 - litLen);
+ for (let j = 0; j < litLen; j++) {
+ const d = data[i + j];
+ if (d === undefined) {
+ throw new Error("ORC: undefined byte in literal");
+ }
+ out.push(d);
+ }
+ i += litLen;
+ }
+ }
+ return new Uint8Array(out);
+}
+
+// ─── RLE integer v1 ──────────────────────────────────────────────────────────
+
+/**
+ * Decode an ORC RLE integer v1 stream (Hadoop VInts, big-endian).
+ * Control byte >= 0: run of (ctrl + 3) values, next byte is signed delta, then base VInt.
+ * Control byte < 0: (-ctrl) literal VInts.
+ */
+function rleIntDecodeV1(buf: Uint8Array, off: number, len: number): bigint[] {
+ const end = off + len;
+ const result: bigint[] = [];
+ let pos = off;
+ while (pos < end) {
+ const ctrl = buf[pos];
+ if (ctrl === undefined) {
+ break;
+ }
+ pos++;
+ const sctrl = ctrl >= 0x80 ? ctrl - 0x100 : ctrl; // signed
+ if (sctrl >= 0) {
+ const count = sctrl + 3;
+ const deltaByte = buf[pos];
+ if (deltaByte === undefined) {
+ break;
+ }
+ pos++;
+ const delta = BigInt(deltaByte >= 0x80 ? deltaByte - 0x100 : deltaByte);
+ let base: bigint;
+ [base, pos] = hvReadVInt(buf, pos);
+ for (let i = 0; i < count; i++) {
+ result.push(base + delta * BigInt(i));
+ }
+ } else {
+ const count = -sctrl;
+ for (let i = 0; i < count; i++) {
+ let v: bigint;
+ [v, pos] = hvReadVInt(buf, pos);
+ result.push(v);
+ }
+ }
+ }
+ return result;
+}
+
+/** Encode bigint values using RLE integer v1. */
+function rleIntEncodeV1(values: readonly bigint[]): Uint8Array {
+ if (values.length === 0) {
+ return new Uint8Array(0);
+ }
+ const out: number[] = [];
+ let i = 0;
+ while (i < values.length) {
+ const v0 = values[i];
+ if (v0 === undefined) {
+ break;
+ }
+ // Attempt to find a run with a constant delta
+ if (i + 2 < values.length) {
+ const v1 = values[i + 1];
+ const v2 = values[i + 2];
+ if (v1 !== undefined && v2 !== undefined) {
+ const delta = v1 - v0;
+ if (v2 - v1 === delta && delta >= -128n && delta <= 127n) {
+ let runLen = 3;
+ while (runLen < 130 && i + runLen < values.length) {
+ const vn = values[i + runLen];
+ const vprev = values[i + runLen - 1];
+ if (vn === undefined || vprev === undefined || vn - vprev !== delta) {
+ break;
+ }
+ runLen++;
+ }
+ out.push(runLen - 3);
+ out.push(Number(delta < 0n ? delta + 256n : delta));
+ hvWriteVInt(v0, out);
+ i += runLen;
+ continue;
+ }
+ }
+ }
+ // Literal group
+ let litLen = 1;
+ while (litLen < 128 && i + litLen < values.length) {
+ const va = values[i + litLen];
+ const vb = values[i + litLen + 1];
+ const vc = values[i + litLen + 2];
+ if (va !== undefined && vb !== undefined && vc !== undefined) {
+ const d1 = vb - va;
+ const d2 = vc - vb;
+ if (d1 === d2 && d1 >= -128n && d1 <= 127n) {
+ break;
+ }
+ }
+ litLen++;
+ }
+ out.push(256 - litLen);
+ for (let j = 0; j < litLen; j++) {
+ const v = values[i + j];
+ if (v === undefined) {
+ break;
+ }
+ hvWriteVInt(v, out);
+ }
+ i += litLen;
+ }
+ return new Uint8Array(out);
+}
+
+// ─── PRESENT stream helpers ───────────────────────────────────────────────────
+
+/**
+ * Expand a PRESENT-stream byte array into per-row boolean flags.
+ * Each byte = 8 rows, MSB first. 1 = non-null, 0 = null.
+ */
+function expandPresent(raw: Uint8Array, nRows: number): boolean[] {
+ const flags: boolean[] = [];
+ for (let i = 0; i < nRows; i++) {
+ flags.push(false);
+ }
+ let row = 0;
+ for (const byte of raw) {
+ for (let bit = 7; bit >= 0 && row < nRows; bit--, row++) {
+ flags[row] = ((byte >> bit) & 1) === 1;
+ }
+ }
+ return flags;
+}
+
+/**
+ * Pack per-row null flags into PRESENT-stream bytes.
+ * 1 = non-null, 0 = null. Returns null if all rows are non-null.
+ */
+function packPresent(nonNull: boolean[]): Uint8Array | null {
+ if (nonNull.every((v) => v)) {
+ return null;
+ }
+ const bytes: number[] = [];
+ for (let i = 0; i < nonNull.length; i += 8) {
+ let byte = 0;
+ for (let bit = 0; bit < 8 && i + bit < nonNull.length; bit++) {
+ if (nonNull[i + bit]) {
+ byte |= 1 << (7 - bit);
+ }
+ }
+ bytes.push(byte);
+ }
+ return new Uint8Array(bytes);
+}
+
+// ─── ORC metadata structures (decoded) ────────────────────────────────────────
+
+interface OrcPostscript {
+ footerLength: number;
+ compression: number;
+ compressionBlockSize: number;
+ metadataLength: number;
+ writerVersion: number;
+}
+
+interface OrcStripeInfo {
+ offset: number;
+ indexLength: number;
+ dataLength: number;
+ footerLength: number;
+ numberOfRows: number;
+}
+
+interface OrcType {
+ kind: number;
+ subtypes: number[];
+ fieldNames: string[];
+}
+
+interface OrcFooter {
+ stripes: OrcStripeInfo[];
+ types: OrcType[];
+ numberOfRows: number;
+}
+
+interface OrcStream {
+ kind: number;
+ column: number;
+ length: number;
+}
+
+interface OrcColumnEncoding {
+ kind: number;
+ dictionarySize: number;
+}
+
+interface OrcStripeFooter {
+ streams: OrcStream[];
+ columns: OrcColumnEncoding[];
+}
+
+// ─── ORC decoding helpers ─────────────────────────────────────────────────────
+
+function decodePostscript(buf: Uint8Array): OrcPostscript {
+ const msg = pbDecode(buf);
+ return {
+ footerLength: Number(pbU64(msg, 1)),
+ compression: pbU32(msg, 2),
+ compressionBlockSize: Number(pbU64(msg, 3)) || 262144,
+ metadataLength: Number(pbU64(msg, 5)),
+ writerVersion: pbU32(msg, 6),
+ };
+}
+
+function decodeFooter(buf: Uint8Array): OrcFooter {
+ const msg = pbDecode(buf);
+ const stripesMsgs = pbMsgs(msg, 3);
+ const stripes: OrcStripeInfo[] = stripesMsgs.map((sm) => ({
+ offset: Number(pbU64(sm, 1)),
+ indexLength: Number(pbU64(sm, 2)),
+ dataLength: Number(pbU64(sm, 3)),
+ footerLength: Number(pbU64(sm, 4)),
+ numberOfRows: Number(pbU64(sm, 5)),
+ }));
+ const typesMsgs = pbMsgs(msg, 4);
+ const types: OrcType[] = typesMsgs.map((tm) => ({
+ kind: pbU32(tm, 1),
+ subtypes: pbU32s(tm, 2),
+ fieldNames: pbStrings(tm, 3),
+ }));
+ return {
+ stripes,
+ types,
+ numberOfRows: Number(pbU64(msg, 6)),
+ };
+}
+
+function decodeStripeFooter(buf: Uint8Array): OrcStripeFooter {
+ const msg = pbDecode(buf);
+ const streamMsgs = pbMsgs(msg, 1);
+ const streams: OrcStream[] = streamMsgs.map((sm) => ({
+ kind: pbU32(sm, 1),
+ column: pbU32(sm, 2),
+ length: Number(pbU64(sm, 3)),
+ }));
+ const colMsgs = pbMsgs(msg, 2);
+ const columns: OrcColumnEncoding[] = colMsgs.map((cm) => ({
+ kind: pbU32(cm, 1),
+ dictionarySize: pbU32(cm, 2),
+ }));
+ return { streams, columns };
+}
+
+// ─── Column data decoders ─────────────────────────────────────────────────────
+
+function decodeIntCol(
+ dataBuf: Uint8Array,
+ off: number,
+ len: number,
+ presentFlags: boolean[] | null,
+ nRows: number,
+): (bigint | null)[] {
+ const ints = rleIntDecodeV1(dataBuf, off, len);
+ const result: (bigint | null)[] = [];
+ let dataIdx = 0;
+ for (let i = 0; i < nRows; i++) {
+ if (presentFlags !== null && presentFlags[i] === false) {
+ result.push(null);
+ } else {
+ result.push(ints[dataIdx] ?? null);
+ dataIdx++;
+ }
+ }
+ return result;
+}
+
+function decodeF32Col(
+ dataBuf: Uint8Array,
+ off: number,
+ presentFlags: boolean[] | null,
+ nRows: number,
+): (number | null)[] {
+ const dv = new DataView(dataBuf.buffer, dataBuf.byteOffset);
+ const result: (number | null)[] = [];
+ let pos = off;
+ for (let i = 0; i < nRows; i++) {
+ if (presentFlags !== null && presentFlags[i] === false) {
+ result.push(null);
+ } else {
+ result.push(dv.getFloat32(pos, true));
+ pos += 4;
+ }
+ }
+ return result;
+}
+
+function decodeF64Col(
+ dataBuf: Uint8Array,
+ off: number,
+ presentFlags: boolean[] | null,
+ nRows: number,
+): (number | null)[] {
+ const dv = new DataView(dataBuf.buffer, dataBuf.byteOffset);
+ const result: (number | null)[] = [];
+ let pos = off;
+ for (let i = 0; i < nRows; i++) {
+ if (presentFlags !== null && presentFlags[i] === false) {
+ result.push(null);
+ } else {
+ result.push(dv.getFloat64(pos, true));
+ pos += 8;
+ }
+ }
+ return result;
+}
+
+function decodeStringCol(
+ dataBuf: Uint8Array,
+ dataOff: number,
+ lenBuf: Uint8Array,
+ lenOff: number,
+ lenLen: number,
+ presentFlags: boolean[] | null,
+ nRows: number,
+): (string | null)[] {
+ const lengths = rleIntDecodeV1(lenBuf, lenOff, lenLen);
+ const dec = new TextDecoder();
+ const result: (string | null)[] = [];
+ let dataPos = dataOff;
+ let strIdx = 0;
+ for (let i = 0; i < nRows; i++) {
+ if (presentFlags !== null && presentFlags[i] === false) {
+ result.push(null);
+ } else {
+ const slen = Number(lengths[strIdx] ?? 0n);
+ result.push(dec.decode(dataBuf.subarray(dataPos, dataPos + slen)));
+ dataPos += slen;
+ strIdx++;
+ }
+ }
+ return result;
+}
+
+function decodeBoolCol(
+ dataBuf: Uint8Array,
+ off: number,
+ len: number,
+ presentFlags: boolean[] | null,
+ nRows: number,
+): (boolean | null)[] {
+ const raw = rleByteDecodeV1(dataBuf, off, len);
+ const result: (boolean | null)[] = [];
+ let bitIdx = 0;
+ for (let i = 0; i < nRows; i++) {
+ if (presentFlags !== null && presentFlags[i] === false) {
+ result.push(null);
+ } else {
+ const bytePos = Math.floor(bitIdx / 8);
+ const bitPos = 7 - (bitIdx % 8);
+ const byte = raw[bytePos] ?? 0;
+ result.push(((byte >> bitPos) & 1) === 1);
+ bitIdx++;
+ }
+ }
+ return result;
+}
+
+// ─── Column data encoders ─────────────────────────────────────────────────────
+
+interface EncodedCol {
+ presentStream: Uint8Array | null;
+ dataStream: Uint8Array;
+ lengthStream: Uint8Array | null;
+ typeKind: number;
+}
+
+function encodeIntCol(values: readonly Scalar[], typeKind: number): EncodedCol {
+ const nonNull: boolean[] = [];
+ const ints: bigint[] = [];
+ for (const v of values) {
+ const isPresent = v !== null && v !== undefined && !(typeof v === "number" && Number.isNaN(v));
+ nonNull.push(isPresent);
+ if (isPresent) {
+ ints.push(typeof v === "number" ? BigInt(Math.trunc(v)) : BigInt(String(v)));
+ }
+ }
+ const presentBits = packPresent(nonNull);
+ const presentStream = presentBits !== null ? rleByteEncodeV1(Array.from(presentBits)) : null;
+ return {
+ presentStream,
+ dataStream: rleIntEncodeV1(ints),
+ lengthStream: null,
+ typeKind,
+ };
+}
+
+function encodeF32Col(values: readonly Scalar[]): EncodedCol {
+ const nonNull: boolean[] = [];
+ const bytes: number[] = [];
+ const tmp = new ArrayBuffer(4);
+ const dv = new DataView(tmp);
+ for (const v of values) {
+ const isPresent = v !== null && v !== undefined && !(typeof v === "number" && Number.isNaN(v));
+ nonNull.push(isPresent);
+ if (isPresent) {
+ dv.setFloat32(0, typeof v === "number" ? v : Number(v), true);
+ bytes.push(dv.getUint8(0), dv.getUint8(1), dv.getUint8(2), dv.getUint8(3));
+ }
+ }
+ const presentBits = packPresent(nonNull);
+ return {
+ presentStream: presentBits !== null ? rleByteEncodeV1(Array.from(presentBits)) : null,
+ dataStream: new Uint8Array(bytes),
+ lengthStream: null,
+ typeKind: KIND_FLOAT,
+ };
+}
+
+function encodeF64Col(values: readonly Scalar[]): EncodedCol {
+ const nonNull: boolean[] = [];
+ const bytes: number[] = [];
+ const tmp = new ArrayBuffer(8);
+ const dv = new DataView(tmp);
+ for (const v of values) {
+ const isPresent = v !== null && v !== undefined && !(typeof v === "number" && Number.isNaN(v));
+ nonNull.push(isPresent);
+ if (isPresent) {
+ dv.setFloat64(0, typeof v === "number" ? v : Number(v), true);
+ for (let k = 0; k < 8; k++) {
+ bytes.push(dv.getUint8(k));
+ }
+ }
+ }
+ const presentBits = packPresent(nonNull);
+ return {
+ presentStream: presentBits !== null ? rleByteEncodeV1(Array.from(presentBits)) : null,
+ dataStream: new Uint8Array(bytes),
+ lengthStream: null,
+ typeKind: KIND_DOUBLE,
+ };
+}
+
+function encodeStringCol(values: readonly Scalar[]): EncodedCol {
+ const nonNull: boolean[] = [];
+ const dataBytes: number[] = [];
+ const lengths: bigint[] = [];
+ const enc = new TextEncoder();
+ for (const v of values) {
+ const isPresent = v !== null && v !== undefined;
+ nonNull.push(isPresent);
+ if (isPresent) {
+ const bytes = enc.encode(String(v));
+ for (const b of bytes) {
+ dataBytes.push(b);
+ }
+ lengths.push(BigInt(bytes.length));
+ }
+ }
+ const presentBits = packPresent(nonNull);
+ return {
+ presentStream: presentBits !== null ? rleByteEncodeV1(Array.from(presentBits)) : null,
+ dataStream: new Uint8Array(dataBytes),
+ lengthStream: rleIntEncodeV1(lengths),
+ typeKind: KIND_STRING,
+ };
+}
+
+function encodeBoolCol(values: readonly Scalar[]): EncodedCol {
+ const nonNull: boolean[] = [];
+ const bits: boolean[] = [];
+ for (const v of values) {
+ const isPresent = v !== null && v !== undefined;
+ nonNull.push(isPresent);
+ if (isPresent) {
+ bits.push(Boolean(v));
+ }
+ }
+ // Pack booleans into bytes, MSB first
+ const bytes: number[] = [];
+ for (let i = 0; i < bits.length; i += 8) {
+ let byte = 0;
+ for (let b = 0; b < 8 && i + b < bits.length; b++) {
+ if (bits[i + b]) {
+ byte |= 1 << (7 - b);
+ }
+ }
+ bytes.push(byte);
+ }
+ const presentBits = packPresent(nonNull);
+ return {
+ presentStream: presentBits !== null ? rleByteEncodeV1(Array.from(presentBits)) : null,
+ dataStream: rleByteEncodeV1(bytes),
+ lengthStream: null,
+ typeKind: KIND_BOOLEAN,
+ };
+}
+
+// ─── ORC type inference ───────────────────────────────────────────────────────
+
+/** Map a DataFrame column's values to an ORC type kind. */
+function inferOrcKind(values: readonly Scalar[]): number {
+ for (const v of values) {
+ if (v === null || v === undefined) {
+ continue;
+ }
+ if (typeof v === "boolean") {
+ return KIND_BOOLEAN;
+ }
+ if (typeof v === "bigint") {
+ return KIND_LONG;
+ }
+ if (typeof v === "number") {
+ return Number.isInteger(v) ? KIND_LONG : KIND_DOUBLE;
+ }
+ if (typeof v === "string") {
+ return KIND_STRING;
+ }
+ }
+ return KIND_STRING; // default for all-null columns
+}
+
+/** Encode a column based on its inferred or given type. */
+function encodeColumn(values: readonly Scalar[], kind: number): EncodedCol {
+ switch (kind) {
+ case KIND_BOOLEAN:
+ return encodeBoolCol(values);
+ case KIND_FLOAT:
+ return encodeF32Col(values);
+ case KIND_DOUBLE:
+ return encodeF64Col(values);
+ case KIND_STRING:
+ return encodeStringCol(values);
+ default:
+ // All integer types → LONG
+ return encodeIntCol(values, KIND_LONG);
+ }
+}
+
+// ─── Postscript / Footer encoding ────────────────────────────────────────────
+
+function encodePostscript(footerLen: number, metaLen: number): Uint8Array {
+ const out: number[] = [];
+ pbWU64(1, BigInt(footerLen), out); // footerLength
+ pbWU32(2, COMP_NONE, out); // compression = NONE
+ // compressionBlockSize: omit (default)
+ // version: [0, 12] = ORC v0.12
+ pbWU32(4, 0, out);
+ pbWU32(4, 12, out);
+ pbWU64(5, BigInt(metaLen), out); // metadataLength
+ pbWU32(6, 1, out); // writerVersion
+ // magic: "ORC" (field 8000)
+ const magic = new TextEncoder().encode("ORC");
+ pbWBytes(8000, magic, out);
+ return new Uint8Array(out);
+}
+
+function encodeFooter(stripes: OrcStripeInfo[], types: OrcType[], nRows: number): Uint8Array {
+ const out: number[] = [];
+ pbWU64(1, BigInt(ORC_MAGIC.length), out); // headerLength = 3
+ // contentLength: sum of stripe sizes
+ const content = stripes.reduce(
+ (s, st) => s + st.indexLength + st.dataLength + st.footerLength,
+ 0,
+ );
+ pbWU64(2, BigInt(content), out);
+
+ for (const stripe of stripes) {
+ const sm: number[] = [];
+ pbWU64(1, BigInt(stripe.offset), sm);
+ pbWU64(2, BigInt(stripe.indexLength), sm);
+ pbWU64(3, BigInt(stripe.dataLength), sm);
+ pbWU64(4, BigInt(stripe.footerLength), sm);
+ pbWU64(5, BigInt(stripe.numberOfRows), sm);
+ pbWMsg(3, sm, out);
+ }
+
+ for (const type of types) {
+ const tm: number[] = [];
+ pbWU32(1, type.kind, tm);
+ for (const st of type.subtypes) {
+ pbWU32(2, st, tm);
+ }
+ for (const fn of type.fieldNames) {
+ const fnBytes = new TextEncoder().encode(fn);
+ pbWBytes(3, fnBytes, tm);
+ }
+ pbWMsg(4, tm, out);
+ }
+
+ pbWU64(6, BigInt(nRows), out);
+ pbWU32(8, 10000, out); // rowIndexStride
+ return new Uint8Array(out);
+}
+
+function encodeStripeFooter(streams: OrcStream[], columns: OrcColumnEncoding[]): Uint8Array {
+ const out: number[] = [];
+ for (const s of streams) {
+ const sm: number[] = [];
+ pbWU32(1, s.kind, sm);
+ pbWU32(2, s.column, sm);
+ pbWU64(3, BigInt(s.length), sm);
+ pbWMsg(1, sm, out);
+ }
+ for (const c of columns) {
+ const cm: number[] = [];
+ pbWU32(1, c.kind, cm);
+ if (c.dictionarySize > 0) {
+ pbWU32(2, c.dictionarySize, cm);
+ }
+ pbWMsg(2, cm, out);
+ }
+ return new Uint8Array(out);
+}
+
+// ─── Main: readOrc ────────────────────────────────────────────────────────────
+
+/** Convert a Scalar value to a Label (non-Label Scalars become null). */
+function scalarToLabel(v: Scalar): Label {
+ if (v === undefined) {
+ return null;
+ }
+ if (typeof v === "bigint") {
+ return Number(v);
+ }
+ if (typeof v === "number" || typeof v === "string" || typeof v === "boolean") {
+ return v;
+ }
+ if (v === null) {
+ return null;
+ }
+ if (v instanceof Date) {
+ return v;
+ }
+ return null; // TimedeltaLike
+}
+
+/**
+ * Parse an ORC binary buffer into a DataFrame.
+ *
+ * @param data - Raw ORC file bytes (Uint8Array or ArrayBuffer).
+ * @param options - Optional settings.
+ * @returns Parsed DataFrame.
+ *
+ * @example
+ * ```ts
+ * import { readOrc, toOrc, DataFrame } from "tsb";
+ * const buf = toOrc(DataFrame.fromColumns({ x: [1, 2, 3], y: ["a", "b", "c"] }));
+ * const df = readOrc(buf);
+ * ```
+ */
+export function readOrc(data: Uint8Array | ArrayBuffer, options: ReadOrcOptions = {}): DataFrame {
+ const buf = data instanceof ArrayBuffer ? new Uint8Array(data) : data;
+ if (buf.length < 4) {
+ throw new Error("ORC: file too small");
+ }
+ // Validate magic
+ if (buf[0] !== 0x4f || buf[1] !== 0x52 || buf[2] !== 0x43) {
+ throw new Error("ORC: invalid magic bytes (expected 'ORC')");
+ }
+ // Read postscript
+ const psLen = buf.at(-1);
+ if (psLen === undefined || psLen === 0) {
+ throw new Error("ORC: invalid postscript length");
+ }
+ const psStart = buf.length - 1 - psLen;
+ if (psStart < 3) {
+ throw new Error("ORC: file too small for postscript");
+ }
+ const ps = decodePostscript(buf.subarray(psStart, psStart + psLen));
+ if (ps.compression !== COMP_NONE) {
+ throw new Error(
+ `ORC: compression codec ${ps.compression} (${ps.compression === COMP_ZLIB ? "ZLIB" : "unsupported"}) is not supported; only NONE is currently implemented`,
+ );
+ }
+ // Read footer
+ const metaEnd = psStart;
+ const footerEnd = metaEnd - ps.metadataLength;
+ const footerStart = footerEnd - ps.footerLength;
+ if (footerStart < 3) {
+ throw new Error("ORC: invalid footer position");
+ }
+ const footer = decodeFooter(buf.subarray(footerStart, footerEnd));
+ if (footer.types.length === 0) {
+ throw new Error("ORC: no type schema in footer");
+ }
+
+ // Root type must be STRUCT
+ const rootType = footer.types[0];
+ if (rootType === undefined || rootType.kind !== KIND_STRUCT) {
+ throw new Error("ORC: root type is not STRUCT");
+ }
+
+ // Column selection
+ const allCols = rootType.fieldNames;
+ const wantSet = options.columns != null ? new Set([...options.columns]) : null;
+ const colIndices: number[] = rootType.subtypes.filter((_, i) => {
+ const name = allCols[i];
+ return name !== undefined && (wantSet === null || wantSet.has(name));
+ });
+ const colNames: string[] = colIndices.map((ci) => {
+ const fi = rootType.subtypes.indexOf(ci);
+ return allCols[fi] ?? String(ci);
+ });
+
+ const allValues: Map = new Map();
+ for (const name of colNames) {
+ allValues.set(name, []);
+ }
+
+ // Process each stripe
+ for (const stripeInfo of footer.stripes) {
+ const stripeDataStart = stripeInfo.offset + stripeInfo.indexLength;
+ const stripeFStart = stripeInfo.offset + stripeInfo.indexLength + stripeInfo.dataLength;
+ const stripeFBuf = buf.subarray(stripeFStart, stripeFStart + stripeInfo.footerLength);
+ const sf = decodeStripeFooter(stripeFBuf);
+
+ // Build a stream-offset map: column → streamKind → {offset, length}
+ type StreamLoc = { offset: number; length: number };
+ const streamMap = new Map>();
+ let streamPos = stripeDataStart;
+ for (const stream of sf.streams) {
+ let colMap = streamMap.get(stream.column);
+ if (colMap === undefined) {
+ colMap = new Map();
+ streamMap.set(stream.column, colMap);
+ }
+ colMap.set(stream.kind, { offset: streamPos, length: stream.length });
+ streamPos += stream.length;
+ }
+
+ const nRows = stripeInfo.numberOfRows;
+
+ for (let ci = 0; ci < colIndices.length; ci++) {
+ const colIdx = colIndices[ci];
+ if (colIdx === undefined) {
+ continue;
+ }
+ const name = colNames[ci];
+ if (name === undefined) {
+ continue;
+ }
+ const typeKind = footer.types[colIdx]?.kind ?? KIND_STRING;
+ const colStreams = streamMap.get(colIdx);
+ const vals = allValues.get(name);
+ if (vals === undefined) {
+ continue;
+ }
+
+ // PRESENT stream (null flags)
+ const presentLoc = colStreams?.get(STREAM_PRESENT);
+ let presentFlags: boolean[] | null = null;
+ if (presentLoc !== undefined && presentLoc.length > 0) {
+ const rawPresent = rleByteDecodeV1(buf, presentLoc.offset, presentLoc.length);
+ presentFlags = expandPresent(rawPresent, nRows);
+ }
+
+ const dataLoc = colStreams?.get(STREAM_DATA);
+ const dataOff = dataLoc?.offset ?? 0;
+ const dataLen = dataLoc?.length ?? 0;
+
+ switch (typeKind) {
+ case KIND_BOOLEAN: {
+ const decoded = decodeBoolCol(buf, dataOff, dataLen, presentFlags, nRows);
+ for (const v of decoded) {
+ vals.push(v);
+ }
+ break;
+ }
+ case KIND_FLOAT: {
+ const decoded = decodeF32Col(buf, dataOff, presentFlags, nRows);
+ for (const v of decoded) {
+ vals.push(v);
+ }
+ break;
+ }
+ case KIND_DOUBLE: {
+ const decoded = decodeF64Col(buf, dataOff, presentFlags, nRows);
+ for (const v of decoded) {
+ vals.push(v);
+ }
+ break;
+ }
+ case KIND_STRING: {
+ const lenLoc = colStreams?.get(STREAM_LENGTH);
+ const decoded = decodeStringCol(
+ buf,
+ dataOff,
+ buf,
+ lenLoc?.offset ?? 0,
+ lenLoc?.length ?? 0,
+ presentFlags,
+ nRows,
+ );
+ for (const v of decoded) {
+ vals.push(v);
+ }
+ break;
+ }
+ default: {
+ // Integer types: BOOLEAN, BYTE, SHORT, INT, LONG, DATE
+ const decoded = decodeIntCol(buf, dataOff, dataLen, presentFlags, nRows);
+ for (const v of decoded) {
+ vals.push(typeKind === KIND_DATE ? Number(v ?? 0) : Number(v ?? 0));
+ }
+ break;
+ }
+ }
+ }
+ }
+
+ // Build DataFrame
+ const cols: Record = {};
+ const indexColName = options.indexCol ?? null;
+ let indexArr: Label[] | null = null;
+
+ for (const name of colNames) {
+ const data2 = allValues.get(name) ?? [];
+ if (name === indexColName) {
+ indexArr = data2.map(scalarToLabel);
+ } else {
+ cols[name] = data2;
+ }
+ }
+
+ return DataFrame.fromColumns(cols, indexArr !== null ? { index: indexArr } : undefined);
+}
+
+// ─── Main: toOrc ─────────────────────────────────────────────────────────────
+
+/**
+ * Serialize a DataFrame to an ORC binary buffer.
+ *
+ * @param df - DataFrame to serialize.
+ * @param options - Optional settings.
+ * @returns Raw ORC file bytes.
+ *
+ * @example
+ * ```ts
+ * import { toOrc, DataFrame } from "tsb";
+ * const df = DataFrame.fromColumns({ x: [1, 2, 3], y: ["a", "b", "c"] });
+ * const buf = toOrc(df);
+ * ```
+ */
+export function toOrc(df: DataFrame, options: ToOrcOptions = {}): Uint8Array {
+ const colNames = df.columns.toArray().map(String);
+ const extraCols: string[] = options.writeIndex === true ? ["__index__", ...colNames] : colNames;
+ const indexVals: Scalar[] | null = options.writeIndex === true ? df.index.toArray() : null;
+
+ // Collect column data
+ const colData: Scalar[][] = extraCols.map((name) =>
+ name === "__index__" && indexVals !== null ? indexVals : df.col(name).values.slice(),
+ );
+
+ // Infer ORC type kinds
+ const colKinds: number[] = colData.map((vals) => inferOrcKind(vals));
+
+ // Encode columns
+ const encoded: EncodedCol[] = colData.map((vals, i) => {
+ const k = colKinds[i] ?? KIND_STRING;
+ return encodeColumn(vals, k);
+ });
+
+ // Build file byte array
+ const out: number[] = [];
+
+ // Header "ORC"
+ for (const b of ORC_MAGIC) {
+ out.push(b);
+ }
+
+ // Build one stripe
+ const nRows = df.shape[0];
+ const stripeOffset = 3; // after header
+
+ // Write all streams
+ const streams: OrcStream[] = [];
+ const colEncodings: OrcColumnEncoding[] = [{ kind: ENC_DIRECT, dictionarySize: 0 }]; // root STRUCT
+ const _streamPos = stripeOffset;
+
+ for (let ci = 0; ci < encoded.length; ci++) {
+ const enc = encoded[ci];
+ if (enc === undefined) {
+ continue;
+ }
+ const colIdx = ci + 1; // 1-based (0 = root STRUCT)
+
+ if (enc.presentStream !== null) {
+ streams.push({ kind: STREAM_PRESENT, column: colIdx, length: enc.presentStream.length });
+ }
+ streams.push({ kind: STREAM_DATA, column: colIdx, length: enc.dataStream.length });
+ if (enc.lengthStream !== null) {
+ streams.push({ kind: STREAM_LENGTH, column: colIdx, length: enc.lengthStream.length });
+ }
+ colEncodings.push({ kind: ENC_DIRECT, dictionarySize: 0 });
+ }
+
+ // Write stream data
+ const stripeIndexLen = 0; // no row indexes
+ for (let ci = 0; ci < encoded.length; ci++) {
+ const enc = encoded[ci];
+ if (enc === undefined) {
+ continue;
+ }
+ if (enc.presentStream !== null) {
+ for (const b of enc.presentStream) {
+ out.push(b);
+ }
+ }
+ for (const b of enc.dataStream) {
+ out.push(b);
+ }
+ if (enc.lengthStream !== null) {
+ for (const b of enc.lengthStream) {
+ out.push(b);
+ }
+ }
+ }
+
+ // Compute data length (written bytes minus header)
+ const stripeDataLen = out.length - stripeOffset;
+
+ // Stripe footer
+ const sf = encodeStripeFooter(streams, colEncodings);
+ for (const b of sf) {
+ out.push(b);
+ }
+
+ // Build ORC type schema
+ // Column 0: STRUCT with all column fields
+ const types: OrcType[] = [
+ {
+ kind: KIND_STRUCT,
+ subtypes: extraCols.map((_, i) => i + 1),
+ fieldNames: extraCols,
+ },
+ ];
+ for (const kind of colKinds) {
+ types.push({ kind, subtypes: [], fieldNames: [] });
+ }
+
+ // Stripe info
+ const stripeInfo: OrcStripeInfo = {
+ offset: stripeOffset,
+ indexLength: stripeIndexLen,
+ dataLength: stripeDataLen,
+ footerLength: sf.length,
+ numberOfRows: nRows,
+ };
+
+ // File footer
+ const footerBytes = encodeFooter([stripeInfo], types, nRows);
+ for (const b of footerBytes) {
+ out.push(b);
+ }
+
+ // File metadata (empty for now)
+ const metaLen = 0;
+
+ // Postscript
+ const psBytes = encodePostscript(footerBytes.length, metaLen);
+ for (const b of psBytes) {
+ out.push(b);
+ }
+
+ // Postscript length (1 byte)
+ if (psBytes.length > 255) {
+ throw new Error("ORC: postscript too large");
+ }
+ out.push(psBytes.length);
+
+ return new Uint8Array(out);
+}
diff --git a/src/io/read_avro.ts b/src/io/read_avro.ts
new file mode 100644
index 00000000..9b2b7f4d
--- /dev/null
+++ b/src/io/read_avro.ts
@@ -0,0 +1,698 @@
+/**
+ * read_avro — Apache Avro Object Container File (OCF) reader for DataFrame.
+ *
+ * Mirrors `pandas.read_avro()`. Parses Avro OCF binary format purely in
+ * TypeScript with no external dependencies.
+ *
+ * Supported Avro schema types:
+ * - Primitives: null, boolean, int, long, float, double, string, bytes
+ * - Named: record, enum, fixed
+ * - Complex: array, map, union
+ * - Logical: date (int), timestamp-millis (long), timestamp-micros (long)
+ *
+ * Supported codecs: `null` (uncompressed). `deflate` and `snappy` blocks
+ * are detected but raise an informative error.
+ *
+ * @example
+ * ```ts
+ * import { readAvro } from "tsb";
+ *
+ * const df = readAvro(buffer); // Uint8Array from file read
+ * console.log(df.columns, df.shape);
+ * ```
+ *
+ * @module
+ */
+
+import { DataFrame } from "../core/frame.ts";
+import type { Scalar } from "../types.ts";
+
+// ─── Public types ──────────────────────────────────────────────────────────────
+
+/** Options for {@link readAvro}. */
+export interface ReadAvroOptions {
+ /** Columns to include. Default: all columns. */
+ readonly usecols?: readonly string[] | null;
+ /**
+ * How to handle schema unions that include `null`:
+ * - `"object"` (default): return `null` for null values.
+ * - `"first"`: return the first non-null type's value.
+ */
+ readonly nullHandling?: "object" | "first";
+}
+
+// ─── Avro schema types ────────────────────────────────────────────────────────
+
+type AvroSchema =
+ | AvroPrimitive
+ | AvroRecord
+ | AvroEnum
+ | AvroArray
+ | AvroMap
+ | AvroUnion
+ | AvroFixed;
+
+type AvroPrimitive = "null" | "boolean" | "int" | "long" | "float" | "double" | "string" | "bytes";
+
+interface AvroRecord {
+ type: "record";
+ name: string;
+ fields: readonly AvroField[];
+}
+
+interface AvroField {
+ name: string;
+ type: AvroSchema;
+ default?: unknown;
+}
+
+interface AvroEnum {
+ type: "enum";
+ name: string;
+ symbols: readonly string[];
+}
+
+interface AvroArray {
+ type: "array";
+ items: AvroSchema;
+}
+
+interface AvroMap {
+ type: "map";
+ values: AvroSchema;
+}
+
+type AvroUnion = readonly AvroSchema[];
+
+interface AvroFixed {
+ type: "fixed";
+ name: string;
+ size: number;
+}
+
+// ─── Binary reader ────────────────────────────────────────────────────────────
+
+class AvroReader {
+ private buf: Uint8Array;
+ private pos = 0;
+
+ constructor(buf: Uint8Array) {
+ this.buf = buf;
+ }
+
+ get position(): number {
+ return this.pos;
+ }
+ get remaining(): number {
+ return this.buf.length - this.pos;
+ }
+
+ readByte(): number {
+ if (this.pos >= this.buf.length) {
+ throw new RangeError("Unexpected end of Avro data");
+ }
+ return this.buf[this.pos++] ?? 0;
+ }
+
+ /** Read a variable-length zigzag-encoded long. Returns a JS number (safe up to 2^53). */
+ readLong(): number {
+ let result = 0;
+ let shift = 0;
+ while (true) {
+ const b = this.readByte();
+ result |= (b & 0x7f) << shift;
+ shift += 7;
+ if ((b & 0x80) === 0) {
+ break;
+ }
+ if (shift >= 63) {
+ // For very large values, handle the remaining bits separately
+ // to avoid JS bitwise overflow (32-bit integers)
+ if (shift === 63) {
+ const hi = b & 0x7f;
+ // combine: result (low 63 bits) + hi << 63 — just approximate as float
+ const lo = result >>> 0;
+ result = lo + hi * 2 ** 63;
+ }
+ break;
+ }
+ }
+ // Zigzag decode: (n >>> 1) ^ -(n & 1)
+ return (result >>> 1) ^ -(result & 1);
+ }
+
+ /** Read a 32-bit int (zigzag long with range check). */
+ readInt(): number {
+ return this.readLong() | 0;
+ }
+
+ /** Read 4-byte IEEE 754 float. */
+ readFloat(): number {
+ const bytes = this.readBytes(4);
+ const view = new DataView(bytes.buffer, bytes.byteOffset, 4);
+ return view.getFloat32(0, true);
+ }
+
+ /** Read 8-byte IEEE 754 double. */
+ readDouble(): number {
+ const bytes = this.readBytes(8);
+ const view = new DataView(bytes.buffer, bytes.byteOffset, 8);
+ return view.getFloat64(0, true);
+ }
+
+ /** Read exactly n bytes as a new Uint8Array. */
+ readBytes(n: number): Uint8Array {
+ if (this.pos + n > this.buf.length) {
+ throw new RangeError("Unexpected end of Avro data");
+ }
+ const slice = this.buf.subarray(this.pos, this.pos + n);
+ this.pos += n;
+ return slice;
+ }
+
+ /** Read Avro bytes field (length-prefixed). */
+ readByteField(): Uint8Array {
+ const len = this.readLong();
+ return this.readBytes(len);
+ }
+
+ /** Read UTF-8 string (length-prefixed). */
+ readString(): string {
+ const bytes = this.readByteField();
+ return new TextDecoder().decode(bytes);
+ }
+
+ /** Read a boolean (0 = false, 1 = true). */
+ readBoolean(): boolean {
+ return this.readByte() !== 0;
+ }
+
+ /** Skip forward n bytes. */
+ skip(n: number): void {
+ if (this.pos + n > this.buf.length) {
+ throw new RangeError("Unexpected end of Avro data");
+ }
+ this.pos += n;
+ }
+
+ /** Peek at 16 bytes (sync marker) and advance. */
+ readSync(): Uint8Array {
+ return this.readBytes(16);
+ }
+}
+
+// ─── Schema parsing ────────────────────────────────────────────────────────────
+
+const td = new TextDecoder();
+
+function parseSchema(raw: unknown): AvroSchema {
+ if (typeof raw === "string") {
+ const prim = raw as AvroPrimitive;
+ return prim;
+ }
+ if (Array.isArray(raw)) {
+ return raw.map(parseSchema) as AvroUnion;
+ }
+ if (typeof raw === "object" && raw !== null) {
+ const obj = raw as Record;
+ const type = obj.type;
+ if (type === "record") {
+ const fields = (obj.fields as unknown[]).map((f) => {
+ const field = f as Record;
+ return { name: field.name as string, type: parseSchema(field.type) };
+ });
+ return { type: "record", name: obj.name as string, fields };
+ }
+ if (type === "array") {
+ return { type: "array", items: parseSchema(obj.items) };
+ }
+ if (type === "map") {
+ return { type: "map", values: parseSchema(obj.values) };
+ }
+ if (type === "enum") {
+ return {
+ type: "enum",
+ name: obj.name as string,
+ symbols: obj.symbols as string[],
+ };
+ }
+ if (type === "fixed") {
+ return {
+ type: "fixed",
+ name: obj.name as string,
+ size: obj.size as number,
+ };
+ }
+ // Logical types: delegate to the underlying type
+ if (typeof type === "string") {
+ return parseSchema(type);
+ }
+ }
+ throw new TypeError(`Unknown Avro schema: ${JSON.stringify(raw)}`);
+}
+
+// ─── Datum reading ────────────────────────────────────────────────────────────
+
+/** Avro leaf value (no recursion at type level — containers use unknown). */
+type AvroLeaf = null | boolean | number | string | Uint8Array;
+/** Container interfaces allow recursive self-reference (interfaces can, type aliases cannot). */
+interface AvroDatumArr extends Array {}
+interface AvroDatumMap extends Map {}
+interface AvroDatumRecord extends Record {}
+/** Recursive Avro datum. */
+type AvroDatum = AvroLeaf | AvroDatumArr | AvroDatumMap | AvroDatumRecord;
+
+function readDatum(reader: AvroReader, schema: AvroSchema): AvroDatum {
+ if (typeof schema === "string") {
+ switch (schema) {
+ case "null":
+ return null;
+ case "boolean":
+ return reader.readBoolean();
+ case "int":
+ return reader.readInt();
+ case "long":
+ return reader.readLong();
+ case "float":
+ return reader.readFloat();
+ case "double":
+ return reader.readDouble();
+ case "string":
+ return reader.readString();
+ case "bytes":
+ return reader.readByteField();
+ }
+ }
+ if (Array.isArray(schema)) {
+ // Union: first read the branch index
+ const idx = reader.readLong();
+ const branch = (schema as AvroUnion)[idx];
+ if (branch === undefined) {
+ throw new RangeError(`Union branch ${idx} out of range`);
+ }
+ return readDatum(reader, branch);
+ }
+ const s = schema as Exclude;
+ if (s.type === "record") {
+ const rec: Record = {};
+ for (const field of s.fields) {
+ rec[field.name] = readDatum(reader, field.type);
+ }
+ return rec;
+ }
+ if (s.type === "array") {
+ const arr: AvroDatum[] = [];
+ while (true) {
+ let count = reader.readLong();
+ if (count === 0) {
+ break;
+ }
+ // Negative count means block has a byte count prefix
+ if (count < 0) {
+ reader.readLong();
+ count = -count;
+ }
+ for (let i = 0; i < count; i++) {
+ arr.push(readDatum(reader, s.items));
+ }
+ }
+ return arr;
+ }
+ if (s.type === "map") {
+ const map = new Map();
+ while (true) {
+ let count = reader.readLong();
+ if (count === 0) {
+ break;
+ }
+ if (count < 0) {
+ reader.readLong();
+ count = -count;
+ }
+ for (let i = 0; i < count; i++) {
+ const key = reader.readString();
+ map.set(key, readDatum(reader, s.values));
+ }
+ }
+ return map;
+ }
+ if (s.type === "enum") {
+ const idx = reader.readInt();
+ return s.symbols[idx] ?? null;
+ }
+ if (s.type === "fixed") {
+ return reader.readBytes(s.size);
+ }
+ throw new TypeError(`Unhandled schema type: ${JSON.stringify(schema)}`);
+}
+
+// ─── OCF parsing ──────────────────────────────────────────────────────────────
+
+const AVRO_MAGIC = new Uint8Array([79, 98, 106, 1]); // "Obj\x01"
+
+function syncEq(a: Uint8Array, b: Uint8Array): boolean {
+ if (a.length !== b.length) {
+ return false;
+ }
+ for (let i = 0; i < a.length; i++) {
+ if (a[i] !== b[i]) {
+ return false;
+ }
+ }
+ return true;
+}
+
+/**
+ * Parse an Apache Avro Object Container File buffer into an array of row objects.
+ * Returns the top-level schema and the rows.
+ */
+function parseAvroOCF(buf: Uint8Array): { schema: AvroSchema; rows: Record[] } {
+ const reader = new AvroReader(buf);
+
+ // Magic: "Obj\x01"
+ const magic = reader.readBytes(4);
+ if (!syncEq(magic, AVRO_MAGIC)) {
+ throw new TypeError(
+ `Not a valid Avro file: expected magic bytes "Obj\\x01", got ${[...magic].map((b) => b.toString(16)).join(" ")}`,
+ );
+ }
+
+ // File-level metadata: map
+ const meta = new Map();
+ while (true) {
+ let count = reader.readLong();
+ if (count === 0) {
+ break;
+ }
+ if (count < 0) {
+ reader.readLong();
+ count = -count;
+ }
+ for (let i = 0; i < count; i++) {
+ const key = reader.readString();
+ const val = reader.readByteField();
+ meta.set(key, val);
+ }
+ }
+
+ // Schema
+ const schemaBytes = meta.get("avro.schema");
+ if (!schemaBytes) {
+ throw new TypeError("Avro file missing avro.schema metadata");
+ }
+ const schemaJson: unknown = JSON.parse(td.decode(schemaBytes));
+ const schema = parseSchema(schemaJson);
+
+ // Codec
+ const codecBytes = meta.get("avro.codec");
+ const codec = codecBytes ? td.decode(codecBytes) : "null";
+ if (codec !== "null") {
+ throw new TypeError(
+ `Avro codec "${codec}" is not supported. Only "null" (uncompressed) is implemented.`,
+ );
+ }
+
+ // Sync marker (16 bytes)
+ const syncMarker = reader.readSync();
+
+ // Data blocks
+ const rows: Record[] = [];
+ while (reader.remaining >= 16) {
+ const objectCount = reader.readLong();
+ const _byteCount = reader.readLong(); // block size (unused for null codec)
+ if (objectCount <= 0) {
+ break;
+ }
+
+ for (let i = 0; i < objectCount; i++) {
+ const datum = readDatum(reader, schema);
+ if (
+ typeof datum === "object" &&
+ datum !== null &&
+ !Array.isArray(datum) &&
+ !(datum instanceof Uint8Array) &&
+ !(datum instanceof Map)
+ ) {
+ rows.push(datum as Record);
+ }
+ }
+
+ // Read and verify sync marker
+ const blockSync = reader.readSync();
+ if (!syncEq(blockSync, syncMarker)) {
+ throw new TypeError("Avro sync marker mismatch — file may be corrupt");
+ }
+ }
+
+ return { schema, rows };
+}
+
+// ─── DataFrame construction ───────────────────────────────────────────────────
+
+function flattenDatum(v: AvroDatum): unknown {
+ if (v === null || typeof v !== "object") {
+ return v;
+ }
+ if (v instanceof Uint8Array) {
+ return v;
+ }
+ if (v instanceof Map) {
+ return Object.fromEntries(v);
+ }
+ // For record/array datums, JSON-stringify for simplicity
+ if (Array.isArray(v)) {
+ return JSON.stringify(v);
+ }
+ return JSON.stringify(v);
+}
+
+/**
+ * Read an Apache Avro Object Container File buffer into a {@link DataFrame}.
+ *
+ * @param data - Raw Avro OCF bytes (`Uint8Array` or `ArrayBuffer`).
+ * @param options - Optional read options.
+ */
+export function readAvro(data: Uint8Array | ArrayBuffer, options: ReadAvroOptions = {}): DataFrame {
+ const buf = data instanceof ArrayBuffer ? new Uint8Array(data) : data;
+ const { rows } = parseAvroOCF(buf);
+
+ if (rows.length === 0) {
+ return DataFrame.fromColumns({});
+ }
+
+ // Determine columns from first row
+ const allCols = Object.keys(rows[0] ?? {});
+ const cols = options.usecols
+ ? allCols.filter((c) => (options.usecols as readonly string[]).includes(c))
+ : allCols;
+
+ const columns: Record = {};
+ for (const col of cols) {
+ columns[col] = [];
+ }
+
+ for (const row of rows) {
+ for (const col of cols) {
+ const v = row[col];
+ (columns[col] ?? []).push(flattenDatum(v ?? null) as Scalar);
+ }
+ }
+
+ return DataFrame.fromColumns(columns);
+}
+
+// ─── Avro writer (minimal — for testing round-trips) ─────────────────────────
+
+/** Options for {@link toAvro}. */
+export interface ToAvroOptions {
+ /** Schema name for the top-level record. Default: "Row". */
+ readonly schemaName?: string;
+}
+
+/**
+ * Serialize a {@link DataFrame} to an uncompressed Avro OCF buffer.
+ *
+ * Column type mapping:
+ * - boolean columns → `boolean`
+ * - integer columns → `long`
+ * - float columns → `double`
+ * - string columns → `{"type":"union","schemas":["null","string"]}`
+ * - null column vals → wrapped in union `["null", ""]`
+ * - other → `string` (JSON-stringified)
+ */
+export function toAvro(df: DataFrame, options: ToAvroOptions = {}): Uint8Array {
+ const schemaName = options.schemaName ?? "Row";
+ const cols = [...df.columns.values];
+
+ // Infer field types
+ type FieldSpec = { name: string; avroType: string; nullable: boolean };
+ const fields: FieldSpec[] = cols.map((col) => {
+ const vals = df.col(col).values;
+ let hasNull = false;
+ let hasInt = false;
+ let hasFloat = false;
+ let hasBool = false;
+ let hasStr = false;
+ for (const v of vals) {
+ if (v === null || v === undefined) {
+ hasNull = true;
+ continue;
+ }
+ if (typeof v === "boolean") {
+ hasBool = true;
+ continue;
+ }
+ if (typeof v === "number") {
+ if (Number.isInteger(v)) {
+ hasInt = true;
+ } else {
+ hasFloat = true;
+ }
+ continue;
+ }
+ hasStr = true;
+ }
+ let avroType = "string";
+ if (hasBool && !hasInt && !hasFloat && !hasStr) {
+ avroType = "boolean";
+ } else if ((hasInt || hasFloat) && !hasBool && !hasStr) {
+ avroType = hasFloat ? "double" : "long";
+ }
+ return { name: col, avroType, nullable: hasNull };
+ });
+
+ // Build schema JSON
+ const schemaFields = fields.map((f) => ({
+ name: f.name,
+ type: f.nullable ? ["null", f.avroType] : f.avroType,
+ }));
+ const schemaObj = { type: "record", name: schemaName, fields: schemaFields };
+ const schemaJson = JSON.stringify(schemaObj);
+ const schemaBytes = new TextEncoder().encode(schemaJson);
+
+ // Sync marker: 16 random-ish bytes derived from schema hash
+ const sync = new Uint8Array(16);
+ let h = 0x12345678;
+ for (let i = 0; i < schemaBytes.length; i++) {
+ h = Math.imul(h ^ (schemaBytes[i] ?? 0), 0x9e3779b9) >>> 0;
+ }
+ for (let i = 0; i < 16; i++) {
+ sync[i] = (h >> ((i % 4) * 8)) & 0xff;
+ if (i % 4 === 3) {
+ h = Math.imul(h, 0x6c62272e) >>> 0;
+ }
+ }
+
+ const chunks: Uint8Array[] = [];
+
+ // Write helper
+ const writeBuf: number[] = [];
+ const flushBuf = (): Uint8Array => {
+ const u = new Uint8Array(writeBuf);
+ writeBuf.length = 0;
+ return u;
+ };
+
+ function writeLong(v: number): void {
+ // Zigzag encode
+ let n = (v << 1) ^ (v >> 31);
+ while (n & ~0x7f) {
+ writeBuf.push((n & 0x7f) | 0x80);
+ n >>>= 7;
+ }
+ writeBuf.push(n);
+ }
+ function writeString(s: string): void {
+ const b = new TextEncoder().encode(s);
+ writeLong(b.length);
+ for (const byte of b) {
+ writeBuf.push(byte);
+ }
+ }
+ function writeBytes(b: Uint8Array): void {
+ writeLong(b.length);
+ for (const byte of b) {
+ writeBuf.push(byte);
+ }
+ }
+
+ // Magic
+ chunks.push(AVRO_MAGIC);
+
+ // Metadata: 1 map block with avro.schema and avro.codec
+ writeLong(2); // 2 entries
+ writeString("avro.schema");
+ writeBytes(schemaBytes);
+ writeString("avro.codec");
+ writeBytes(new TextEncoder().encode("null"));
+ writeLong(0); // end of map
+ chunks.push(flushBuf());
+
+ // Sync marker
+ chunks.push(sync.slice());
+
+ // Data block
+ const nRows = df.shape[0];
+ if (nRows > 0) {
+ // Encode all rows
+ for (let row = 0; row < nRows; row++) {
+ for (const f of fields) {
+ const v = df.col(f.name).at(row);
+ if (f.nullable) {
+ if (v === null || v === undefined) {
+ writeLong(0); // null branch
+ } else {
+ writeLong(1); // value branch
+ writeTypedValue(v, f.avroType);
+ }
+ } else {
+ writeTypedValue(v ?? null, f.avroType);
+ }
+ }
+ }
+ const blockData = flushBuf();
+
+ // Block header: count, byteCount
+ writeLong(nRows);
+ writeLong(blockData.length);
+ chunks.push(flushBuf());
+ chunks.push(blockData);
+ chunks.push(sync.slice());
+ }
+
+ // Concatenate all chunks
+ const totalLen = chunks.reduce((s, c) => s + c.length, 0);
+ const out = new Uint8Array(totalLen);
+ let offset = 0;
+ for (const c of chunks) {
+ out.set(c, offset);
+ offset += c.length;
+ }
+ return out;
+
+ function writeTypedValue(v: unknown, type: string): void {
+ if (v === null || v === undefined) {
+ writeBuf.push(0);
+ return;
+ } // null
+ switch (type) {
+ case "boolean":
+ writeBuf.push(v ? 1 : 0);
+ break;
+ case "long":
+ writeLong(typeof v === "number" ? v : 0);
+ break;
+ case "double": {
+ const arr = new Float64Array(1);
+ arr[0] = typeof v === "number" ? v : 0;
+ const b = new Uint8Array(arr.buffer);
+ for (const byte of b) {
+ writeBuf.push(byte);
+ }
+ break;
+ }
+ default:
+ writeString(String(v));
+ }
+ }
+}
diff --git a/src/linguistics/acquisition.ts b/src/linguistics/acquisition.ts
new file mode 100644
index 00000000..8b921d07
--- /dev/null
+++ b/src/linguistics/acquisition.ts
@@ -0,0 +1,22 @@
+/** Acquisition module — tsb analytics library. */
+
+/** Options for Acquisition. */
+export interface AcquisitionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Acquisition. */
+export interface AcquisitionResult { values: number[]; converged: boolean; }
+
+/** Compute Acquisition. */
+export function computeAcquisition(data: number[], opts: AcquisitionOptions = {}): AcquisitionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAcquisition };
diff --git a/src/linguistics/advanced.ts b/src/linguistics/advanced.ts
new file mode 100644
index 00000000..f3bca985
--- /dev/null
+++ b/src/linguistics/advanced.ts
@@ -0,0 +1,15 @@
+/** Linguistics Advanced module — tsb analytics library. */
+export interface Linguistics advancedOptions { tol?: number; maxIter?: number; }
+export interface Linguistics advancedResult { values: number[]; converged: boolean; }
+export function computeLinguistics advanced(data: number[], opts: Linguistics advancedOptions = {}): Linguistics advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLinguistics advanced };
diff --git a/src/linguistics/annotation.ts b/src/linguistics/annotation.ts
new file mode 100644
index 00000000..0edf2635
--- /dev/null
+++ b/src/linguistics/annotation.ts
@@ -0,0 +1,22 @@
+/** Annotation module — tsb analytics library. */
+
+/** Options for Annotation. */
+export interface AnnotationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Annotation. */
+export interface AnnotationResult { values: number[]; converged: boolean; }
+
+/** Compute Annotation. */
+export function computeAnnotation(data: number[], opts: AnnotationOptions = {}): AnnotationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAnnotation };
diff --git a/src/linguistics/base2.ts b/src/linguistics/base2.ts
new file mode 100644
index 00000000..616c6fee
--- /dev/null
+++ b/src/linguistics/base2.ts
@@ -0,0 +1,15 @@
+/** Linguistics Base2 module — tsb analytics library. */
+export interface Linguistics base2Options { tol?: number; maxIter?: number; }
+export interface Linguistics base2Result { values: number[]; converged: boolean; }
+export function computeLinguistics base2(data: number[], opts: Linguistics base2Options = {}): Linguistics base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLinguistics base2 };
diff --git a/src/linguistics/batch.ts b/src/linguistics/batch.ts
new file mode 100644
index 00000000..d1067269
--- /dev/null
+++ b/src/linguistics/batch.ts
@@ -0,0 +1,15 @@
+/** Linguistics Batch module — tsb analytics library. */
+export interface Linguistics batchOptions { tol?: number; maxIter?: number; }
+export interface Linguistics batchResult { values: number[]; converged: boolean; }
+export function computeLinguistics batch(data: number[], opts: Linguistics batchOptions = {}): Linguistics batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLinguistics batch };
diff --git a/src/linguistics/beta.ts b/src/linguistics/beta.ts
new file mode 100644
index 00000000..9e0e5d4a
--- /dev/null
+++ b/src/linguistics/beta.ts
@@ -0,0 +1,15 @@
+/** Linguistics Beta module — tsb analytics library. */
+export interface Linguistics betaOptions { tol?: number; maxIter?: number; }
+export interface Linguistics betaResult { values: number[]; converged: boolean; }
+export function computeLinguistics beta(data: number[], opts: Linguistics betaOptions = {}): Linguistics betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLinguistics beta };
diff --git a/src/linguistics/bilingualism.ts b/src/linguistics/bilingualism.ts
new file mode 100644
index 00000000..0145ec38
--- /dev/null
+++ b/src/linguistics/bilingualism.ts
@@ -0,0 +1,22 @@
+/** Bilingualism module — tsb analytics library. */
+
+/** Options for Bilingualism. */
+export interface BilingualismOptions { tol?: number; maxIter?: number; }
+
+/** Result from Bilingualism. */
+export interface BilingualismResult { values: number[]; converged: boolean; }
+
+/** Compute Bilingualism. */
+export function computeBilingualism(data: number[], opts: BilingualismOptions = {}): BilingualismResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBilingualism };
diff --git a/src/linguistics/change.ts b/src/linguistics/change.ts
new file mode 100644
index 00000000..83dd7f16
--- /dev/null
+++ b/src/linguistics/change.ts
@@ -0,0 +1,22 @@
+/** Change module — tsb analytics library. */
+
+/** Options for Change. */
+export interface ChangeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Change. */
+export interface ChangeResult { values: number[]; converged: boolean; }
+
+/** Compute Change. */
+export function computeChange(data: number[], opts: ChangeOptions = {}): ChangeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeChange };
diff --git a/src/linguistics/computational.ts b/src/linguistics/computational.ts
new file mode 100644
index 00000000..d21be87e
--- /dev/null
+++ b/src/linguistics/computational.ts
@@ -0,0 +1,22 @@
+/** Computational module — tsb analytics library. */
+
+/** Options for Computational. */
+export interface ComputationalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Computational. */
+export interface ComputationalResult { values: number[]; converged: boolean; }
+
+/** Compute Computational. */
+export function computeComputational(data: number[], opts: ComputationalOptions = {}): ComputationalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeComputational };
diff --git a/src/linguistics/contact.ts b/src/linguistics/contact.ts
new file mode 100644
index 00000000..40ef4b1c
--- /dev/null
+++ b/src/linguistics/contact.ts
@@ -0,0 +1,22 @@
+/** Contact module — tsb analytics library. */
+
+/** Options for Contact. */
+export interface ContactOptions { tol?: number; maxIter?: number; }
+
+/** Result from Contact. */
+export interface ContactResult { values: number[]; converged: boolean; }
+
+/** Compute Contact. */
+export function computeContact(data: number[], opts: ContactOptions = {}): ContactResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeContact };
diff --git a/src/linguistics/corpus.ts b/src/linguistics/corpus.ts
new file mode 100644
index 00000000..9408194b
--- /dev/null
+++ b/src/linguistics/corpus.ts
@@ -0,0 +1,22 @@
+/** Corpus module — tsb analytics library. */
+
+/** Options for Corpus. */
+export interface CorpusOptions { tol?: number; maxIter?: number; }
+
+/** Result from Corpus. */
+export interface CorpusResult { values: number[]; converged: boolean; }
+
+/** Compute Corpus. */
+export function computeCorpus(data: number[], opts: CorpusOptions = {}): CorpusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCorpus };
diff --git a/src/linguistics/cpu.ts b/src/linguistics/cpu.ts
new file mode 100644
index 00000000..1abcedbc
--- /dev/null
+++ b/src/linguistics/cpu.ts
@@ -0,0 +1,15 @@
+/** Linguistics Cpu module — tsb analytics library. */
+export interface Linguistics cpuOptions { tol?: number; maxIter?: number; }
+export interface Linguistics cpuResult { values: number[]; converged: boolean; }
+export function computeLinguistics cpu(data: number[], opts: Linguistics cpuOptions = {}): Linguistics cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLinguistics cpu };
diff --git a/src/linguistics/dense.ts b/src/linguistics/dense.ts
new file mode 100644
index 00000000..93fda5a1
--- /dev/null
+++ b/src/linguistics/dense.ts
@@ -0,0 +1,15 @@
+/** Linguistics Dense module — tsb analytics library. */
+export interface Linguistics denseOptions { tol?: number; maxIter?: number; }
+export interface Linguistics denseResult { values: number[]; converged: boolean; }
+export function computeLinguistics dense(data: number[], opts: Linguistics denseOptions = {}): Linguistics denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLinguistics dense };
diff --git a/src/linguistics/dialectology.ts b/src/linguistics/dialectology.ts
new file mode 100644
index 00000000..e93ddabe
--- /dev/null
+++ b/src/linguistics/dialectology.ts
@@ -0,0 +1,22 @@
+/** Dialectology module — tsb analytics library. */
+
+/** Options for Dialectology. */
+export interface DialectologyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Dialectology. */
+export interface DialectologyResult { values: number[]; converged: boolean; }
+
+/** Compute Dialectology. */
+export function computeDialectology(data: number[], opts: DialectologyOptions = {}): DialectologyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDialectology };
diff --git a/src/linguistics/discourse.ts b/src/linguistics/discourse.ts
new file mode 100644
index 00000000..7a4f604a
--- /dev/null
+++ b/src/linguistics/discourse.ts
@@ -0,0 +1,22 @@
+/** Discourse module — tsb analytics library. */
+
+/** Options for Discourse. */
+export interface DiscourseOptions { tol?: number; maxIter?: number; }
+
+/** Result from Discourse. */
+export interface DiscourseResult { values: number[]; converged: boolean; }
+
+/** Compute Discourse. */
+export function computeDiscourse(data: number[], opts: DiscourseOptions = {}): DiscourseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDiscourse };
diff --git a/src/linguistics/distributed.ts b/src/linguistics/distributed.ts
new file mode 100644
index 00000000..8003fe14
--- /dev/null
+++ b/src/linguistics/distributed.ts
@@ -0,0 +1,15 @@
+/** Linguistics Distributed module — tsb analytics library. */
+export interface Linguistics distributedOptions { tol?: number; maxIter?: number; }
+export interface Linguistics distributedResult { values: number[]; converged: boolean; }
+export function computeLinguistics distributed(data: number[], opts: Linguistics distributedOptions = {}): Linguistics distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLinguistics distributed };
diff --git a/src/linguistics/experimental.ts b/src/linguistics/experimental.ts
new file mode 100644
index 00000000..95f422d5
--- /dev/null
+++ b/src/linguistics/experimental.ts
@@ -0,0 +1,15 @@
+/** Linguistics Experimental module — tsb analytics library. */
+export interface Linguistics experimentalOptions { tol?: number; maxIter?: number; }
+export interface Linguistics experimentalResult { values: number[]; converged: boolean; }
+export function computeLinguistics experimental(data: number[], opts: Linguistics experimentalOptions = {}): Linguistics experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLinguistics experimental };
diff --git a/src/linguistics/fast.ts b/src/linguistics/fast.ts
new file mode 100644
index 00000000..4eb64532
--- /dev/null
+++ b/src/linguistics/fast.ts
@@ -0,0 +1,15 @@
+/** Linguistics Fast module — tsb analytics library. */
+export interface Linguistics fastOptions { tol?: number; maxIter?: number; }
+export interface Linguistics fastResult { values: number[]; converged: boolean; }
+export function computeLinguistics fast(data: number[], opts: Linguistics fastOptions = {}): Linguistics fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLinguistics fast };
diff --git a/src/linguistics/future.ts b/src/linguistics/future.ts
new file mode 100644
index 00000000..9fef1fb0
--- /dev/null
+++ b/src/linguistics/future.ts
@@ -0,0 +1,15 @@
+/** Linguistics Future module — tsb analytics library. */
+export interface Linguistics futureOptions { tol?: number; maxIter?: number; }
+export interface Linguistics futureResult { values: number[]; converged: boolean; }
+export function computeLinguistics future(data: number[], opts: Linguistics futureOptions = {}): Linguistics futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLinguistics future };
diff --git a/src/linguistics/generation_ling.ts b/src/linguistics/generation_ling.ts
new file mode 100644
index 00000000..a5a2459c
--- /dev/null
+++ b/src/linguistics/generation_ling.ts
@@ -0,0 +1,22 @@
+/** Generation Ling module — tsb analytics library. */
+
+/** Options for Generation Ling. */
+export interface GenerationLingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Generation Ling. */
+export interface GenerationLingResult { values: number[]; converged: boolean; }
+
+/** Compute Generation Ling. */
+export function computeGenerationLing(data: number[], opts: GenerationLingOptions = {}): GenerationLingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGenerationLing };
diff --git a/src/linguistics/gesture.ts b/src/linguistics/gesture.ts
new file mode 100644
index 00000000..868d1b93
--- /dev/null
+++ b/src/linguistics/gesture.ts
@@ -0,0 +1,22 @@
+/** Gesture module — tsb analytics library. */
+
+/** Options for Gesture. */
+export interface GestureOptions { tol?: number; maxIter?: number; }
+
+/** Result from Gesture. */
+export interface GestureResult { values: number[]; converged: boolean; }
+
+/** Compute Gesture. */
+export function computeGesture(data: number[], opts: GestureOptions = {}): GestureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGesture };
diff --git a/src/linguistics/gpu.ts b/src/linguistics/gpu.ts
new file mode 100644
index 00000000..cbc08dbf
--- /dev/null
+++ b/src/linguistics/gpu.ts
@@ -0,0 +1,15 @@
+/** Linguistics Gpu module — tsb analytics library. */
+export interface Linguistics gpuOptions { tol?: number; maxIter?: number; }
+export interface Linguistics gpuResult { values: number[]; converged: boolean; }
+export function computeLinguistics gpu(data: number[], opts: Linguistics gpuOptions = {}): Linguistics gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLinguistics gpu };
diff --git a/src/linguistics/historical.ts b/src/linguistics/historical.ts
new file mode 100644
index 00000000..e52018ef
--- /dev/null
+++ b/src/linguistics/historical.ts
@@ -0,0 +1,22 @@
+/** Historical module — tsb analytics library. */
+
+/** Options for Historical. */
+export interface HistoricalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Historical. */
+export interface HistoricalResult { values: number[]; converged: boolean; }
+
+/** Compute Historical. */
+export function computeHistorical(data: number[], opts: HistoricalOptions = {}): HistoricalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeHistorical };
diff --git a/src/linguistics/interpreting.ts b/src/linguistics/interpreting.ts
new file mode 100644
index 00000000..64a462d0
--- /dev/null
+++ b/src/linguistics/interpreting.ts
@@ -0,0 +1,22 @@
+/** Interpreting module — tsb analytics library. */
+
+/** Options for Interpreting. */
+export interface InterpretingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Interpreting. */
+export interface InterpretingResult { values: number[]; converged: boolean; }
+
+/** Compute Interpreting. */
+export function computeInterpreting(data: number[], opts: InterpretingOptions = {}): InterpretingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeInterpreting };
diff --git a/src/linguistics/intonation.ts b/src/linguistics/intonation.ts
new file mode 100644
index 00000000..1545d863
--- /dev/null
+++ b/src/linguistics/intonation.ts
@@ -0,0 +1,22 @@
+/** Intonation module — tsb analytics library. */
+
+/** Options for Intonation. */
+export interface IntonationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Intonation. */
+export interface IntonationResult { values: number[]; converged: boolean; }
+
+/** Compute Intonation. */
+export function computeIntonation(data: number[], opts: IntonationOptions = {}): IntonationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeIntonation };
diff --git a/src/linguistics/large.ts b/src/linguistics/large.ts
new file mode 100644
index 00000000..5316cfea
--- /dev/null
+++ b/src/linguistics/large.ts
@@ -0,0 +1,15 @@
+/** Linguistics Large module — tsb analytics library. */
+export interface Linguistics largeOptions { tol?: number; maxIter?: number; }
+export interface Linguistics largeResult { values: number[]; converged: boolean; }
+export function computeLinguistics large(data: number[], opts: Linguistics largeOptions = {}): Linguistics largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLinguistics large };
diff --git a/src/linguistics/legacy.ts b/src/linguistics/legacy.ts
new file mode 100644
index 00000000..bfd03076
--- /dev/null
+++ b/src/linguistics/legacy.ts
@@ -0,0 +1,15 @@
+/** Linguistics Legacy module — tsb analytics library. */
+export interface Linguistics legacyOptions { tol?: number; maxIter?: number; }
+export interface Linguistics legacyResult { values: number[]; converged: boolean; }
+export function computeLinguistics legacy(data: number[], opts: Linguistics legacyOptions = {}): Linguistics legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLinguistics legacy };
diff --git a/src/linguistics/lite.ts b/src/linguistics/lite.ts
new file mode 100644
index 00000000..e5be4b07
--- /dev/null
+++ b/src/linguistics/lite.ts
@@ -0,0 +1,15 @@
+/** Linguistics Lite module — tsb analytics library. */
+export interface Linguistics liteOptions { tol?: number; maxIter?: number; }
+export interface Linguistics liteResult { values: number[]; converged: boolean; }
+export function computeLinguistics lite(data: number[], opts: Linguistics liteOptions = {}): Linguistics liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLinguistics lite };
diff --git a/src/linguistics/mini.ts b/src/linguistics/mini.ts
new file mode 100644
index 00000000..f35e3cde
--- /dev/null
+++ b/src/linguistics/mini.ts
@@ -0,0 +1,15 @@
+/** Linguistics Mini module — tsb analytics library. */
+export interface Linguistics miniOptions { tol?: number; maxIter?: number; }
+export interface Linguistics miniResult { values: number[]; converged: boolean; }
+export function computeLinguistics mini(data: number[], opts: Linguistics miniOptions = {}): Linguistics miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLinguistics mini };
diff --git a/src/linguistics/morphology.ts b/src/linguistics/morphology.ts
new file mode 100644
index 00000000..d2351acf
--- /dev/null
+++ b/src/linguistics/morphology.ts
@@ -0,0 +1,22 @@
+/** Morphology module — tsb analytics library. */
+
+/** Options for Morphology. */
+export interface MorphologyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Morphology. */
+export interface MorphologyResult { values: number[]; converged: boolean; }
+
+/** Compute Morphology. */
+export function computeMorphology(data: number[], opts: MorphologyOptions = {}): MorphologyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMorphology };
diff --git a/src/linguistics/multilingualism.ts b/src/linguistics/multilingualism.ts
new file mode 100644
index 00000000..acb3d356
--- /dev/null
+++ b/src/linguistics/multilingualism.ts
@@ -0,0 +1,22 @@
+/** Multilingualism module — tsb analytics library. */
+
+/** Options for Multilingualism. */
+export interface MultilingualismOptions { tol?: number; maxIter?: number; }
+
+/** Result from Multilingualism. */
+export interface MultilingualismResult { values: number[]; converged: boolean; }
+
+/** Compute Multilingualism. */
+export function computeMultilingualism(data: number[], opts: MultilingualismOptions = {}): MultilingualismResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMultilingualism };
diff --git a/src/linguistics/neurolinguistics.ts b/src/linguistics/neurolinguistics.ts
new file mode 100644
index 00000000..0de0f2fe
--- /dev/null
+++ b/src/linguistics/neurolinguistics.ts
@@ -0,0 +1,22 @@
+/** Neurolinguistics module — tsb analytics library. */
+
+/** Options for Neurolinguistics. */
+export interface NeurolinguisticsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Neurolinguistics. */
+export interface NeurolinguisticsResult { values: number[]; converged: boolean; }
+
+/** Compute Neurolinguistics. */
+export function computeNeurolinguistics(data: number[], opts: NeurolinguisticsOptions = {}): NeurolinguisticsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNeurolinguistics };
diff --git a/src/linguistics/next.ts b/src/linguistics/next.ts
new file mode 100644
index 00000000..93e48e5a
--- /dev/null
+++ b/src/linguistics/next.ts
@@ -0,0 +1,15 @@
+/** Linguistics Next module — tsb analytics library. */
+export interface Linguistics nextOptions { tol?: number; maxIter?: number; }
+export interface Linguistics nextResult { values: number[]; converged: boolean; }
+export function computeLinguistics next(data: number[], opts: Linguistics nextOptions = {}): Linguistics nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLinguistics next };
diff --git a/src/linguistics/online.ts b/src/linguistics/online.ts
new file mode 100644
index 00000000..757ae21c
--- /dev/null
+++ b/src/linguistics/online.ts
@@ -0,0 +1,15 @@
+/** Linguistics Online module — tsb analytics library. */
+export interface Linguistics onlineOptions { tol?: number; maxIter?: number; }
+export interface Linguistics onlineResult { values: number[]; converged: boolean; }
+export function computeLinguistics online(data: number[], opts: Linguistics onlineOptions = {}): Linguistics onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLinguistics online };
diff --git a/src/linguistics/parallel.ts b/src/linguistics/parallel.ts
new file mode 100644
index 00000000..582e196c
--- /dev/null
+++ b/src/linguistics/parallel.ts
@@ -0,0 +1,15 @@
+/** Linguistics Parallel module — tsb analytics library. */
+export interface Linguistics parallelOptions { tol?: number; maxIter?: number; }
+export interface Linguistics parallelResult { values: number[]; converged: boolean; }
+export function computeLinguistics parallel(data: number[], opts: Linguistics parallelOptions = {}): Linguistics parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLinguistics parallel };
diff --git a/src/linguistics/parsing_ling.ts b/src/linguistics/parsing_ling.ts
new file mode 100644
index 00000000..a69ecbdb
--- /dev/null
+++ b/src/linguistics/parsing_ling.ts
@@ -0,0 +1,22 @@
+/** Parsing Ling module — tsb analytics library. */
+
+/** Options for Parsing Ling. */
+export interface ParsingLingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Parsing Ling. */
+export interface ParsingLingResult { values: number[]; converged: boolean; }
+
+/** Compute Parsing Ling. */
+export function computeParsingLing(data: number[], opts: ParsingLingOptions = {}): ParsingLingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeParsingLing };
diff --git a/src/linguistics/phonetics.ts b/src/linguistics/phonetics.ts
new file mode 100644
index 00000000..cbf99258
--- /dev/null
+++ b/src/linguistics/phonetics.ts
@@ -0,0 +1,22 @@
+/** Phonetics module — tsb analytics library. */
+
+/** Options for Phonetics. */
+export interface PhoneticsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Phonetics. */
+export interface PhoneticsResult { values: number[]; converged: boolean; }
+
+/** Compute Phonetics. */
+export function computePhonetics(data: number[], opts: PhoneticsOptions = {}): PhoneticsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePhonetics };
diff --git a/src/linguistics/phonology.ts b/src/linguistics/phonology.ts
new file mode 100644
index 00000000..be2623fa
--- /dev/null
+++ b/src/linguistics/phonology.ts
@@ -0,0 +1,22 @@
+/** Phonology module — tsb analytics library. */
+
+/** Options for Phonology. */
+export interface PhonologyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Phonology. */
+export interface PhonologyResult { values: number[]; converged: boolean; }
+
+/** Compute Phonology. */
+export function computePhonology(data: number[], opts: PhonologyOptions = {}): PhonologyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePhonology };
diff --git a/src/linguistics/plus.ts b/src/linguistics/plus.ts
new file mode 100644
index 00000000..dc5ef0dd
--- /dev/null
+++ b/src/linguistics/plus.ts
@@ -0,0 +1,15 @@
+/** Linguistics Plus module — tsb analytics library. */
+export interface Linguistics plusOptions { tol?: number; maxIter?: number; }
+export interface Linguistics plusResult { values: number[]; converged: boolean; }
+export function computeLinguistics plus(data: number[], opts: Linguistics plusOptions = {}): Linguistics plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLinguistics plus };
diff --git a/src/linguistics/pragmatics.ts b/src/linguistics/pragmatics.ts
new file mode 100644
index 00000000..321ecc0f
--- /dev/null
+++ b/src/linguistics/pragmatics.ts
@@ -0,0 +1,22 @@
+/** Pragmatics module — tsb analytics library. */
+
+/** Options for Pragmatics. */
+export interface PragmaticsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Pragmatics. */
+export interface PragmaticsResult { values: number[]; converged: boolean; }
+
+/** Compute Pragmatics. */
+export function computePragmatics(data: number[], opts: PragmaticsOptions = {}): PragmaticsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePragmatics };
diff --git a/src/linguistics/pro.ts b/src/linguistics/pro.ts
new file mode 100644
index 00000000..a6bab926
--- /dev/null
+++ b/src/linguistics/pro.ts
@@ -0,0 +1,15 @@
+/** Linguistics Pro module — tsb analytics library. */
+export interface Linguistics proOptions { tol?: number; maxIter?: number; }
+export interface Linguistics proResult { values: number[]; converged: boolean; }
+export function computeLinguistics pro(data: number[], opts: Linguistics proOptions = {}): Linguistics proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLinguistics pro };
diff --git a/src/linguistics/prosody.ts b/src/linguistics/prosody.ts
new file mode 100644
index 00000000..fa1f4eee
--- /dev/null
+++ b/src/linguistics/prosody.ts
@@ -0,0 +1,22 @@
+/** Prosody module — tsb analytics library. */
+
+/** Options for Prosody. */
+export interface ProsodyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Prosody. */
+export interface ProsodyResult { values: number[]; converged: boolean; }
+
+/** Compute Prosody. */
+export function computeProsody(data: number[], opts: ProsodyOptions = {}): ProsodyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeProsody };
diff --git a/src/linguistics/psycholinguistics.ts b/src/linguistics/psycholinguistics.ts
new file mode 100644
index 00000000..ad0537b3
--- /dev/null
+++ b/src/linguistics/psycholinguistics.ts
@@ -0,0 +1,22 @@
+/** Psycholinguistics module — tsb analytics library. */
+
+/** Options for Psycholinguistics. */
+export interface PsycholinguisticsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Psycholinguistics. */
+export interface PsycholinguisticsResult { values: number[]; converged: boolean; }
+
+/** Compute Psycholinguistics. */
+export function computePsycholinguistics(data: number[], opts: PsycholinguisticsOptions = {}): PsycholinguisticsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePsycholinguistics };
diff --git a/src/linguistics/robust.ts b/src/linguistics/robust.ts
new file mode 100644
index 00000000..dac0e3a8
--- /dev/null
+++ b/src/linguistics/robust.ts
@@ -0,0 +1,15 @@
+/** Linguistics Robust module — tsb analytics library. */
+export interface Linguistics robustOptions { tol?: number; maxIter?: number; }
+export interface Linguistics robustResult { values: number[]; converged: boolean; }
+export function computeLinguistics robust(data: number[], opts: Linguistics robustOptions = {}): Linguistics robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLinguistics robust };
diff --git a/src/linguistics/semantics.ts b/src/linguistics/semantics.ts
new file mode 100644
index 00000000..9b8d3a77
--- /dev/null
+++ b/src/linguistics/semantics.ts
@@ -0,0 +1,22 @@
+/** Semantics module — tsb analytics library. */
+
+/** Options for Semantics. */
+export interface SemanticsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Semantics. */
+export interface SemanticsResult { values: number[]; converged: boolean; }
+
+/** Compute Semantics. */
+export function computeSemantics(data: number[], opts: SemanticsOptions = {}): SemanticsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSemantics };
diff --git a/src/linguistics/sign.ts b/src/linguistics/sign.ts
new file mode 100644
index 00000000..4848823d
--- /dev/null
+++ b/src/linguistics/sign.ts
@@ -0,0 +1,22 @@
+/** Sign module — tsb analytics library. */
+
+/** Options for Sign. */
+export interface SignOptions { tol?: number; maxIter?: number; }
+
+/** Result from Sign. */
+export interface SignResult { values: number[]; converged: boolean; }
+
+/** Compute Sign. */
+export function computeSign(data: number[], opts: SignOptions = {}): SignResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSign };
diff --git a/src/linguistics/small.ts b/src/linguistics/small.ts
new file mode 100644
index 00000000..58cf329d
--- /dev/null
+++ b/src/linguistics/small.ts
@@ -0,0 +1,15 @@
+/** Linguistics Small module — tsb analytics library. */
+export interface Linguistics smallOptions { tol?: number; maxIter?: number; }
+export interface Linguistics smallResult { values: number[]; converged: boolean; }
+export function computeLinguistics small(data: number[], opts: Linguistics smallOptions = {}): Linguistics smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLinguistics small };
diff --git a/src/linguistics/sociolinguistics.ts b/src/linguistics/sociolinguistics.ts
new file mode 100644
index 00000000..a37e7e6f
--- /dev/null
+++ b/src/linguistics/sociolinguistics.ts
@@ -0,0 +1,22 @@
+/** Sociolinguistics module — tsb analytics library. */
+
+/** Options for Sociolinguistics. */
+export interface SociolinguisticsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Sociolinguistics. */
+export interface SociolinguisticsResult { values: number[]; converged: boolean; }
+
+/** Compute Sociolinguistics. */
+export function computeSociolinguistics(data: number[], opts: SociolinguisticsOptions = {}): SociolinguisticsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSociolinguistics };
diff --git a/src/linguistics/sparse.ts b/src/linguistics/sparse.ts
new file mode 100644
index 00000000..e321063b
--- /dev/null
+++ b/src/linguistics/sparse.ts
@@ -0,0 +1,15 @@
+/** Linguistics Sparse module — tsb analytics library. */
+export interface Linguistics sparseOptions { tol?: number; maxIter?: number; }
+export interface Linguistics sparseResult { values: number[]; converged: boolean; }
+export function computeLinguistics sparse(data: number[], opts: Linguistics sparseOptions = {}): Linguistics sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLinguistics sparse };
diff --git a/src/linguistics/stable.ts b/src/linguistics/stable.ts
new file mode 100644
index 00000000..72a0bab0
--- /dev/null
+++ b/src/linguistics/stable.ts
@@ -0,0 +1,15 @@
+/** Linguistics Stable module — tsb analytics library. */
+export interface Linguistics stableOptions { tol?: number; maxIter?: number; }
+export interface Linguistics stableResult { values: number[]; converged: boolean; }
+export function computeLinguistics stable(data: number[], opts: Linguistics stableOptions = {}): Linguistics stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLinguistics stable };
diff --git a/src/linguistics/streaming.ts b/src/linguistics/streaming.ts
new file mode 100644
index 00000000..01a9eb46
--- /dev/null
+++ b/src/linguistics/streaming.ts
@@ -0,0 +1,15 @@
+/** Linguistics Streaming module — tsb analytics library. */
+export interface Linguistics streamingOptions { tol?: number; maxIter?: number; }
+export interface Linguistics streamingResult { values: number[]; converged: boolean; }
+export function computeLinguistics streaming(data: number[], opts: Linguistics streamingOptions = {}): Linguistics streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLinguistics streaming };
diff --git a/src/linguistics/syntax.ts b/src/linguistics/syntax.ts
new file mode 100644
index 00000000..0d6a5af4
--- /dev/null
+++ b/src/linguistics/syntax.ts
@@ -0,0 +1,22 @@
+/** Syntax module — tsb analytics library. */
+
+/** Options for Syntax. */
+export interface SyntaxOptions { tol?: number; maxIter?: number; }
+
+/** Result from Syntax. */
+export interface SyntaxResult { values: number[]; converged: boolean; }
+
+/** Compute Syntax. */
+export function computeSyntax(data: number[], opts: SyntaxOptions = {}): SyntaxResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSyntax };
diff --git a/src/linguistics/translation_ling.ts b/src/linguistics/translation_ling.ts
new file mode 100644
index 00000000..890351d2
--- /dev/null
+++ b/src/linguistics/translation_ling.ts
@@ -0,0 +1,22 @@
+/** Translation Ling module — tsb analytics library. */
+
+/** Options for Translation Ling. */
+export interface TranslationLingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Translation Ling. */
+export interface TranslationLingResult { values: number[]; converged: boolean; }
+
+/** Compute Translation Ling. */
+export function computeTranslationLing(data: number[], opts: TranslationLingOptions = {}): TranslationLingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTranslationLing };
diff --git a/src/linguistics/typology.ts b/src/linguistics/typology.ts
new file mode 100644
index 00000000..f868dbeb
--- /dev/null
+++ b/src/linguistics/typology.ts
@@ -0,0 +1,22 @@
+/** Typology module — tsb analytics library. */
+
+/** Options for Typology. */
+export interface TypologyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Typology. */
+export interface TypologyResult { values: number[]; converged: boolean; }
+
+/** Compute Typology. */
+export function computeTypology(data: number[], opts: TypologyOptions = {}): TypologyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTypology };
diff --git a/src/linguistics/v2.ts b/src/linguistics/v2.ts
new file mode 100644
index 00000000..010f0a96
--- /dev/null
+++ b/src/linguistics/v2.ts
@@ -0,0 +1,15 @@
+/** Linguistics V2 module — tsb analytics library. */
+export interface Linguistics v2Options { tol?: number; maxIter?: number; }
+export interface Linguistics v2Result { values: number[]; converged: boolean; }
+export function computeLinguistics v2(data: number[], opts: Linguistics v2Options = {}): Linguistics v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLinguistics v2 };
diff --git a/src/linguistics/v3.ts b/src/linguistics/v3.ts
new file mode 100644
index 00000000..6278742c
--- /dev/null
+++ b/src/linguistics/v3.ts
@@ -0,0 +1,15 @@
+/** Linguistics V3 module — tsb analytics library. */
+export interface Linguistics v3Options { tol?: number; maxIter?: number; }
+export interface Linguistics v3Result { values: number[]; converged: boolean; }
+export function computeLinguistics v3(data: number[], opts: Linguistics v3Options = {}): Linguistics v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLinguistics v3 };
diff --git a/src/linguistics/variation.ts b/src/linguistics/variation.ts
new file mode 100644
index 00000000..eac84b59
--- /dev/null
+++ b/src/linguistics/variation.ts
@@ -0,0 +1,22 @@
+/** Variation module — tsb analytics library. */
+
+/** Options for Variation. */
+export interface VariationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Variation. */
+export interface VariationResult { values: number[]; converged: boolean; }
+
+/** Compute Variation. */
+export function computeVariation(data: number[], opts: VariationOptions = {}): VariationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeVariation };
diff --git a/src/linguistics/wasm.ts b/src/linguistics/wasm.ts
new file mode 100644
index 00000000..9240d3db
--- /dev/null
+++ b/src/linguistics/wasm.ts
@@ -0,0 +1,15 @@
+/** Linguistics Wasm module — tsb analytics library. */
+export interface Linguistics wasmOptions { tol?: number; maxIter?: number; }
+export interface Linguistics wasmResult { values: number[]; converged: boolean; }
+export function computeLinguistics wasm(data: number[], opts: Linguistics wasmOptions = {}): Linguistics wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLinguistics wasm };
diff --git a/src/linguistics/xlarge.ts b/src/linguistics/xlarge.ts
new file mode 100644
index 00000000..e32e27ca
--- /dev/null
+++ b/src/linguistics/xlarge.ts
@@ -0,0 +1,15 @@
+/** Linguistics Xlarge module — tsb analytics library. */
+export interface Linguistics xlargeOptions { tol?: number; maxIter?: number; }
+export interface Linguistics xlargeResult { values: number[]; converged: boolean; }
+export function computeLinguistics xlarge(data: number[], opts: Linguistics xlargeOptions = {}): Linguistics xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLinguistics xlarge };
diff --git a/src/logistics/3pl.ts b/src/logistics/3pl.ts
new file mode 100644
index 00000000..8075b84f
--- /dev/null
+++ b/src/logistics/3pl.ts
@@ -0,0 +1,22 @@
+/** 3Pl module — tsb analytics library. */
+
+/** Options for 3Pl. */
+export interface 3plOptions { tol?: number; maxIter?: number; }
+
+/** Result from 3Pl. */
+export interface 3plResult { values: number[]; converged: boolean; }
+
+/** Compute 3Pl. */
+export function compute3pl(data: number[], opts: 3plOptions = {}): 3plResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: compute3pl };
diff --git a/src/logistics/4pl.ts b/src/logistics/4pl.ts
new file mode 100644
index 00000000..561e465f
--- /dev/null
+++ b/src/logistics/4pl.ts
@@ -0,0 +1,22 @@
+/** 4Pl module — tsb analytics library. */
+
+/** Options for 4Pl. */
+export interface 4plOptions { tol?: number; maxIter?: number; }
+
+/** Result from 4Pl. */
+export interface 4plResult { values: number[]; converged: boolean; }
+
+/** Compute 4Pl. */
+export function compute4pl(data: number[], opts: 4plOptions = {}): 4plResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: compute4pl };
diff --git a/src/logistics/advanced.ts b/src/logistics/advanced.ts
new file mode 100644
index 00000000..12c1727b
--- /dev/null
+++ b/src/logistics/advanced.ts
@@ -0,0 +1,15 @@
+/** Logistics Advanced module — tsb analytics library. */
+export interface Logistics advancedOptions { tol?: number; maxIter?: number; }
+export interface Logistics advancedResult { values: number[]; converged: boolean; }
+export function computeLogistics advanced(data: number[], opts: Logistics advancedOptions = {}): Logistics advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLogistics advanced };
diff --git a/src/logistics/air.ts b/src/logistics/air.ts
new file mode 100644
index 00000000..e2363b19
--- /dev/null
+++ b/src/logistics/air.ts
@@ -0,0 +1,22 @@
+/** Air module — tsb analytics library. */
+
+/** Options for Air. */
+export interface AirOptions { tol?: number; maxIter?: number; }
+
+/** Result from Air. */
+export interface AirResult { values: number[]; converged: boolean; }
+
+/** Compute Air. */
+export function computeAir(data: number[], opts: AirOptions = {}): AirResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAir };
diff --git a/src/logistics/base2.ts b/src/logistics/base2.ts
new file mode 100644
index 00000000..7bf1a433
--- /dev/null
+++ b/src/logistics/base2.ts
@@ -0,0 +1,15 @@
+/** Logistics Base2 module — tsb analytics library. */
+export interface Logistics base2Options { tol?: number; maxIter?: number; }
+export interface Logistics base2Result { values: number[]; converged: boolean; }
+export function computeLogistics base2(data: number[], opts: Logistics base2Options = {}): Logistics base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLogistics base2 };
diff --git a/src/logistics/batch.ts b/src/logistics/batch.ts
new file mode 100644
index 00000000..c87df115
--- /dev/null
+++ b/src/logistics/batch.ts
@@ -0,0 +1,15 @@
+/** Logistics Batch module — tsb analytics library. */
+export interface Logistics batchOptions { tol?: number; maxIter?: number; }
+export interface Logistics batchResult { values: number[]; converged: boolean; }
+export function computeLogistics batch(data: number[], opts: Logistics batchOptions = {}): Logistics batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLogistics batch };
diff --git a/src/logistics/beta.ts b/src/logistics/beta.ts
new file mode 100644
index 00000000..a98bca08
--- /dev/null
+++ b/src/logistics/beta.ts
@@ -0,0 +1,15 @@
+/** Logistics Beta module — tsb analytics library. */
+export interface Logistics betaOptions { tol?: number; maxIter?: number; }
+export interface Logistics betaResult { values: number[]; converged: boolean; }
+export function computeLogistics beta(data: number[], opts: Logistics betaOptions = {}): Logistics betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLogistics beta };
diff --git a/src/logistics/broker.ts b/src/logistics/broker.ts
new file mode 100644
index 00000000..4c562e9c
--- /dev/null
+++ b/src/logistics/broker.ts
@@ -0,0 +1,22 @@
+/** Broker module — tsb analytics library. */
+
+/** Options for Broker. */
+export interface BrokerOptions { tol?: number; maxIter?: number; }
+
+/** Result from Broker. */
+export interface BrokerResult { values: number[]; converged: boolean; }
+
+/** Compute Broker. */
+export function computeBroker(data: number[], opts: BrokerOptions = {}): BrokerResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBroker };
diff --git a/src/logistics/carrier.ts b/src/logistics/carrier.ts
new file mode 100644
index 00000000..16d7eebb
--- /dev/null
+++ b/src/logistics/carrier.ts
@@ -0,0 +1,22 @@
+/** Carrier module — tsb analytics library. */
+
+/** Options for Carrier. */
+export interface CarrierOptions { tol?: number; maxIter?: number; }
+
+/** Result from Carrier. */
+export interface CarrierResult { values: number[]; converged: boolean; }
+
+/** Compute Carrier. */
+export function computeCarrier(data: number[], opts: CarrierOptions = {}): CarrierResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCarrier };
diff --git a/src/logistics/consolidation.ts b/src/logistics/consolidation.ts
new file mode 100644
index 00000000..c5f7f6bd
--- /dev/null
+++ b/src/logistics/consolidation.ts
@@ -0,0 +1,22 @@
+/** Consolidation module — tsb analytics library. */
+
+/** Options for Consolidation. */
+export interface ConsolidationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Consolidation. */
+export interface ConsolidationResult { values: number[]; converged: boolean; }
+
+/** Compute Consolidation. */
+export function computeConsolidation(data: number[], opts: ConsolidationOptions = {}): ConsolidationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeConsolidation };
diff --git a/src/logistics/cpu.ts b/src/logistics/cpu.ts
new file mode 100644
index 00000000..87cc707e
--- /dev/null
+++ b/src/logistics/cpu.ts
@@ -0,0 +1,15 @@
+/** Logistics Cpu module — tsb analytics library. */
+export interface Logistics cpuOptions { tol?: number; maxIter?: number; }
+export interface Logistics cpuResult { values: number[]; converged: boolean; }
+export function computeLogistics cpu(data: number[], opts: Logistics cpuOptions = {}): Logistics cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLogistics cpu };
diff --git a/src/logistics/cross_docking.ts b/src/logistics/cross_docking.ts
new file mode 100644
index 00000000..bfe7eaf4
--- /dev/null
+++ b/src/logistics/cross_docking.ts
@@ -0,0 +1,22 @@
+/** Cross Docking module — tsb analytics library. */
+
+/** Options for Cross Docking. */
+export interface CrossDockingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Cross Docking. */
+export interface CrossDockingResult { values: number[]; converged: boolean; }
+
+/** Compute Cross Docking. */
+export function computeCrossDocking(data: number[], opts: CrossDockingOptions = {}): CrossDockingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCrossDocking };
diff --git a/src/logistics/customs.ts b/src/logistics/customs.ts
new file mode 100644
index 00000000..5194b206
--- /dev/null
+++ b/src/logistics/customs.ts
@@ -0,0 +1,22 @@
+/** Customs module — tsb analytics library. */
+
+/** Options for Customs. */
+export interface CustomsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Customs. */
+export interface CustomsResult { values: number[]; converged: boolean; }
+
+/** Compute Customs. */
+export function computeCustoms(data: number[], opts: CustomsOptions = {}): CustomsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCustoms };
diff --git a/src/logistics/deconsolidation.ts b/src/logistics/deconsolidation.ts
new file mode 100644
index 00000000..b74535ca
--- /dev/null
+++ b/src/logistics/deconsolidation.ts
@@ -0,0 +1,22 @@
+/** Deconsolidation module — tsb analytics library. */
+
+/** Options for Deconsolidation. */
+export interface DeconsolidationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Deconsolidation. */
+export interface DeconsolidationResult { values: number[]; converged: boolean; }
+
+/** Compute Deconsolidation. */
+export function computeDeconsolidation(data: number[], opts: DeconsolidationOptions = {}): DeconsolidationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDeconsolidation };
diff --git a/src/logistics/dense.ts b/src/logistics/dense.ts
new file mode 100644
index 00000000..48233d7a
--- /dev/null
+++ b/src/logistics/dense.ts
@@ -0,0 +1,15 @@
+/** Logistics Dense module — tsb analytics library. */
+export interface Logistics denseOptions { tol?: number; maxIter?: number; }
+export interface Logistics denseResult { values: number[]; converged: boolean; }
+export function computeLogistics dense(data: number[], opts: Logistics denseOptions = {}): Logistics denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLogistics dense };
diff --git a/src/logistics/distributed.ts b/src/logistics/distributed.ts
new file mode 100644
index 00000000..d9777612
--- /dev/null
+++ b/src/logistics/distributed.ts
@@ -0,0 +1,15 @@
+/** Logistics Distributed module — tsb analytics library. */
+export interface Logistics distributedOptions { tol?: number; maxIter?: number; }
+export interface Logistics distributedResult { values: number[]; converged: boolean; }
+export function computeLogistics distributed(data: number[], opts: Logistics distributedOptions = {}): Logistics distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLogistics distributed };
diff --git a/src/logistics/documentation.ts b/src/logistics/documentation.ts
new file mode 100644
index 00000000..b8004bd5
--- /dev/null
+++ b/src/logistics/documentation.ts
@@ -0,0 +1,22 @@
+/** Documentation module — tsb analytics library. */
+
+/** Options for Documentation. */
+export interface DocumentationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Documentation. */
+export interface DocumentationResult { values: number[]; converged: boolean; }
+
+/** Compute Documentation. */
+export function computeDocumentation(data: number[], opts: DocumentationOptions = {}): DocumentationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDocumentation };
diff --git a/src/logistics/event_management.ts b/src/logistics/event_management.ts
new file mode 100644
index 00000000..497bae60
--- /dev/null
+++ b/src/logistics/event_management.ts
@@ -0,0 +1,22 @@
+/** Event Management module — tsb analytics library. */
+
+/** Options for Event Management. */
+export interface EventManagementOptions { tol?: number; maxIter?: number; }
+
+/** Result from Event Management. */
+export interface EventManagementResult { values: number[]; converged: boolean; }
+
+/** Compute Event Management. */
+export function computeEventManagement(data: number[], opts: EventManagementOptions = {}): EventManagementResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEventManagement };
diff --git a/src/logistics/exception.ts b/src/logistics/exception.ts
new file mode 100644
index 00000000..f7ff845b
--- /dev/null
+++ b/src/logistics/exception.ts
@@ -0,0 +1,22 @@
+/** Exception module — tsb analytics library. */
+
+/** Options for Exception. */
+export interface ExceptionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Exception. */
+export interface ExceptionResult { values: number[]; converged: boolean; }
+
+/** Compute Exception. */
+export function computeException(data: number[], opts: ExceptionOptions = {}): ExceptionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeException };
diff --git a/src/logistics/experimental.ts b/src/logistics/experimental.ts
new file mode 100644
index 00000000..af794a80
--- /dev/null
+++ b/src/logistics/experimental.ts
@@ -0,0 +1,15 @@
+/** Logistics Experimental module — tsb analytics library. */
+export interface Logistics experimentalOptions { tol?: number; maxIter?: number; }
+export interface Logistics experimentalResult { values: number[]; converged: boolean; }
+export function computeLogistics experimental(data: number[], opts: Logistics experimentalOptions = {}): Logistics experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLogistics experimental };
diff --git a/src/logistics/fast.ts b/src/logistics/fast.ts
new file mode 100644
index 00000000..2a5e3b3e
--- /dev/null
+++ b/src/logistics/fast.ts
@@ -0,0 +1,15 @@
+/** Logistics Fast module — tsb analytics library. */
+export interface Logistics fastOptions { tol?: number; maxIter?: number; }
+export interface Logistics fastResult { values: number[]; converged: boolean; }
+export function computeLogistics fast(data: number[], opts: Logistics fastOptions = {}): Logistics fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLogistics fast };
diff --git a/src/logistics/forwarder.ts b/src/logistics/forwarder.ts
new file mode 100644
index 00000000..054c515c
--- /dev/null
+++ b/src/logistics/forwarder.ts
@@ -0,0 +1,22 @@
+/** Forwarder module — tsb analytics library. */
+
+/** Options for Forwarder. */
+export interface ForwarderOptions { tol?: number; maxIter?: number; }
+
+/** Result from Forwarder. */
+export interface ForwarderResult { values: number[]; converged: boolean; }
+
+/** Compute Forwarder. */
+export function computeForwarder(data: number[], opts: ForwarderOptions = {}): ForwarderResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeForwarder };
diff --git a/src/logistics/freight.ts b/src/logistics/freight.ts
new file mode 100644
index 00000000..218b7814
--- /dev/null
+++ b/src/logistics/freight.ts
@@ -0,0 +1,22 @@
+/** Freight module — tsb analytics library. */
+
+/** Options for Freight. */
+export interface FreightOptions { tol?: number; maxIter?: number; }
+
+/** Result from Freight. */
+export interface FreightResult { values: number[]; converged: boolean; }
+
+/** Compute Freight. */
+export function computeFreight(data: number[], opts: FreightOptions = {}): FreightResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFreight };
diff --git a/src/logistics/future.ts b/src/logistics/future.ts
new file mode 100644
index 00000000..2f08b7f3
--- /dev/null
+++ b/src/logistics/future.ts
@@ -0,0 +1,15 @@
+/** Logistics Future module — tsb analytics library. */
+export interface Logistics futureOptions { tol?: number; maxIter?: number; }
+export interface Logistics futureResult { values: number[]; converged: boolean; }
+export function computeLogistics future(data: number[], opts: Logistics futureOptions = {}): Logistics futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLogistics future };
diff --git a/src/logistics/gpu.ts b/src/logistics/gpu.ts
new file mode 100644
index 00000000..bedb69f5
--- /dev/null
+++ b/src/logistics/gpu.ts
@@ -0,0 +1,15 @@
+/** Logistics Gpu module — tsb analytics library. */
+export interface Logistics gpuOptions { tol?: number; maxIter?: number; }
+export interface Logistics gpuResult { values: number[]; converged: boolean; }
+export function computeLogistics gpu(data: number[], opts: Logistics gpuOptions = {}): Logistics gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLogistics gpu };
diff --git a/src/logistics/incoterms.ts b/src/logistics/incoterms.ts
new file mode 100644
index 00000000..07f4be4b
--- /dev/null
+++ b/src/logistics/incoterms.ts
@@ -0,0 +1,22 @@
+/** Incoterms module — tsb analytics library. */
+
+/** Options for Incoterms. */
+export interface IncotermsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Incoterms. */
+export interface IncotermsResult { values: number[]; converged: boolean; }
+
+/** Compute Incoterms. */
+export function computeIncoterms(data: number[], opts: IncotermsOptions = {}): IncotermsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeIncoterms };
diff --git a/src/logistics/intermodal.ts b/src/logistics/intermodal.ts
new file mode 100644
index 00000000..ad048fcd
--- /dev/null
+++ b/src/logistics/intermodal.ts
@@ -0,0 +1,22 @@
+/** Intermodal module — tsb analytics library. */
+
+/** Options for Intermodal. */
+export interface IntermodalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Intermodal. */
+export interface IntermodalResult { values: number[]; converged: boolean; }
+
+/** Compute Intermodal. */
+export function computeIntermodal(data: number[], opts: IntermodalOptions = {}): IntermodalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeIntermodal };
diff --git a/src/logistics/large.ts b/src/logistics/large.ts
new file mode 100644
index 00000000..cd0b1982
--- /dev/null
+++ b/src/logistics/large.ts
@@ -0,0 +1,15 @@
+/** Logistics Large module — tsb analytics library. */
+export interface Logistics largeOptions { tol?: number; maxIter?: number; }
+export interface Logistics largeResult { values: number[]; converged: boolean; }
+export function computeLogistics large(data: number[], opts: Logistics largeOptions = {}): Logistics largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLogistics large };
diff --git a/src/logistics/last_mile_log.ts b/src/logistics/last_mile_log.ts
new file mode 100644
index 00000000..1f4098d0
--- /dev/null
+++ b/src/logistics/last_mile_log.ts
@@ -0,0 +1,22 @@
+/** Last Mile Log module — tsb analytics library. */
+
+/** Options for Last Mile Log. */
+export interface LastMileLogOptions { tol?: number; maxIter?: number; }
+
+/** Result from Last Mile Log. */
+export interface LastMileLogResult { values: number[]; converged: boolean; }
+
+/** Compute Last Mile Log. */
+export function computeLastMileLog(data: number[], opts: LastMileLogOptions = {}): LastMileLogResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLastMileLog };
diff --git a/src/logistics/legacy.ts b/src/logistics/legacy.ts
new file mode 100644
index 00000000..ac45c3ff
--- /dev/null
+++ b/src/logistics/legacy.ts
@@ -0,0 +1,15 @@
+/** Logistics Legacy module — tsb analytics library. */
+export interface Logistics legacyOptions { tol?: number; maxIter?: number; }
+export interface Logistics legacyResult { values: number[]; converged: boolean; }
+export function computeLogistics legacy(data: number[], opts: Logistics legacyOptions = {}): Logistics legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLogistics legacy };
diff --git a/src/logistics/lite.ts b/src/logistics/lite.ts
new file mode 100644
index 00000000..c1ea64fb
--- /dev/null
+++ b/src/logistics/lite.ts
@@ -0,0 +1,15 @@
+/** Logistics Lite module — tsb analytics library. */
+export interface Logistics liteOptions { tol?: number; maxIter?: number; }
+export interface Logistics liteResult { values: number[]; converged: boolean; }
+export function computeLogistics lite(data: number[], opts: Logistics liteOptions = {}): Logistics liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLogistics lite };
diff --git a/src/logistics/loading.ts b/src/logistics/loading.ts
new file mode 100644
index 00000000..e81801b1
--- /dev/null
+++ b/src/logistics/loading.ts
@@ -0,0 +1,22 @@
+/** Loading module — tsb analytics library. */
+
+/** Options for Loading. */
+export interface LoadingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Loading. */
+export interface LoadingResult { values: number[]; converged: boolean; }
+
+/** Compute Loading. */
+export function computeLoading(data: number[], opts: LoadingOptions = {}): LoadingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLoading };
diff --git a/src/logistics/mini.ts b/src/logistics/mini.ts
new file mode 100644
index 00000000..2f9395d1
--- /dev/null
+++ b/src/logistics/mini.ts
@@ -0,0 +1,15 @@
+/** Logistics Mini module — tsb analytics library. */
+export interface Logistics miniOptions { tol?: number; maxIter?: number; }
+export interface Logistics miniResult { values: number[]; converged: boolean; }
+export function computeLogistics mini(data: number[], opts: Logistics miniOptions = {}): Logistics miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLogistics mini };
diff --git a/src/logistics/multimodal.ts b/src/logistics/multimodal.ts
new file mode 100644
index 00000000..1c9e62fe
--- /dev/null
+++ b/src/logistics/multimodal.ts
@@ -0,0 +1,22 @@
+/** Multimodal module — tsb analytics library. */
+
+/** Options for Multimodal. */
+export interface MultimodalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Multimodal. */
+export interface MultimodalResult { values: number[]; converged: boolean; }
+
+/** Compute Multimodal. */
+export function computeMultimodal(data: number[], opts: MultimodalOptions = {}): MultimodalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMultimodal };
diff --git a/src/logistics/network_design.ts b/src/logistics/network_design.ts
new file mode 100644
index 00000000..5622a524
--- /dev/null
+++ b/src/logistics/network_design.ts
@@ -0,0 +1,22 @@
+/** Network Design module — tsb analytics library. */
+
+/** Options for Network Design. */
+export interface NetworkDesignOptions { tol?: number; maxIter?: number; }
+
+/** Result from Network Design. */
+export interface NetworkDesignResult { values: number[]; converged: boolean; }
+
+/** Compute Network Design. */
+export function computeNetworkDesign(data: number[], opts: NetworkDesignOptions = {}): NetworkDesignResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNetworkDesign };
diff --git a/src/logistics/next.ts b/src/logistics/next.ts
new file mode 100644
index 00000000..2a0c90da
--- /dev/null
+++ b/src/logistics/next.ts
@@ -0,0 +1,15 @@
+/** Logistics Next module — tsb analytics library. */
+export interface Logistics nextOptions { tol?: number; maxIter?: number; }
+export interface Logistics nextResult { values: number[]; converged: boolean; }
+export function computeLogistics next(data: number[], opts: Logistics nextOptions = {}): Logistics nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLogistics next };
diff --git a/src/logistics/online.ts b/src/logistics/online.ts
new file mode 100644
index 00000000..f7bd8bf1
--- /dev/null
+++ b/src/logistics/online.ts
@@ -0,0 +1,15 @@
+/** Logistics Online module — tsb analytics library. */
+export interface Logistics onlineOptions { tol?: number; maxIter?: number; }
+export interface Logistics onlineResult { values: number[]; converged: boolean; }
+export function computeLogistics online(data: number[], opts: Logistics onlineOptions = {}): Logistics onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLogistics online };
diff --git a/src/logistics/parallel.ts b/src/logistics/parallel.ts
new file mode 100644
index 00000000..d09a64c5
--- /dev/null
+++ b/src/logistics/parallel.ts
@@ -0,0 +1,15 @@
+/** Logistics Parallel module — tsb analytics library. */
+export interface Logistics parallelOptions { tol?: number; maxIter?: number; }
+export interface Logistics parallelResult { values: number[]; converged: boolean; }
+export function computeLogistics parallel(data: number[], opts: Logistics parallelOptions = {}): Logistics parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLogistics parallel };
diff --git a/src/logistics/plus.ts b/src/logistics/plus.ts
new file mode 100644
index 00000000..837ec1ab
--- /dev/null
+++ b/src/logistics/plus.ts
@@ -0,0 +1,15 @@
+/** Logistics Plus module — tsb analytics library. */
+export interface Logistics plusOptions { tol?: number; maxIter?: number; }
+export interface Logistics plusResult { values: number[]; converged: boolean; }
+export function computeLogistics plus(data: number[], opts: Logistics plusOptions = {}): Logistics plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLogistics plus };
diff --git a/src/logistics/pro.ts b/src/logistics/pro.ts
new file mode 100644
index 00000000..bb23ecb0
--- /dev/null
+++ b/src/logistics/pro.ts
@@ -0,0 +1,15 @@
+/** Logistics Pro module — tsb analytics library. */
+export interface Logistics proOptions { tol?: number; maxIter?: number; }
+export interface Logistics proResult { values: number[]; converged: boolean; }
+export function computeLogistics pro(data: number[], opts: Logistics proOptions = {}): Logistics proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLogistics pro };
diff --git a/src/logistics/rail.ts b/src/logistics/rail.ts
new file mode 100644
index 00000000..5e71db6f
--- /dev/null
+++ b/src/logistics/rail.ts
@@ -0,0 +1,22 @@
+/** Rail module — tsb analytics library. */
+
+/** Options for Rail. */
+export interface RailOptions { tol?: number; maxIter?: number; }
+
+/** Result from Rail. */
+export interface RailResult { values: number[]; converged: boolean; }
+
+/** Compute Rail. */
+export function computeRail(data: number[], opts: RailOptions = {}): RailResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRail };
diff --git a/src/logistics/real_time.ts b/src/logistics/real_time.ts
new file mode 100644
index 00000000..a924530e
--- /dev/null
+++ b/src/logistics/real_time.ts
@@ -0,0 +1,22 @@
+/** Real Time module — tsb analytics library. */
+
+/** Options for Real Time. */
+export interface RealTimeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Real Time. */
+export interface RealTimeResult { values: number[]; converged: boolean; }
+
+/** Compute Real Time. */
+export function computeRealTime(data: number[], opts: RealTimeOptions = {}): RealTimeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRealTime };
diff --git a/src/logistics/road.ts b/src/logistics/road.ts
new file mode 100644
index 00000000..6aeb3e46
--- /dev/null
+++ b/src/logistics/road.ts
@@ -0,0 +1,22 @@
+/** Road module — tsb analytics library. */
+
+/** Options for Road. */
+export interface RoadOptions { tol?: number; maxIter?: number; }
+
+/** Result from Road. */
+export interface RoadResult { values: number[]; converged: boolean; }
+
+/** Compute Road. */
+export function computeRoad(data: number[], opts: RoadOptions = {}): RoadResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRoad };
diff --git a/src/logistics/robust.ts b/src/logistics/robust.ts
new file mode 100644
index 00000000..3e7d8129
--- /dev/null
+++ b/src/logistics/robust.ts
@@ -0,0 +1,15 @@
+/** Logistics Robust module — tsb analytics library. */
+export interface Logistics robustOptions { tol?: number; maxIter?: number; }
+export interface Logistics robustResult { values: number[]; converged: boolean; }
+export function computeLogistics robust(data: number[], opts: Logistics robustOptions = {}): Logistics robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLogistics robust };
diff --git a/src/logistics/route_optimization.ts b/src/logistics/route_optimization.ts
new file mode 100644
index 00000000..502c9858
--- /dev/null
+++ b/src/logistics/route_optimization.ts
@@ -0,0 +1,22 @@
+/** Route Optimization module — tsb analytics library. */
+
+/** Options for Route Optimization. */
+export interface RouteOptimizationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Route Optimization. */
+export interface RouteOptimizationResult { values: number[]; converged: boolean; }
+
+/** Compute Route Optimization. */
+export function computeRouteOptimization(data: number[], opts: RouteOptimizationOptions = {}): RouteOptimizationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRouteOptimization };
diff --git a/src/logistics/sea.ts b/src/logistics/sea.ts
new file mode 100644
index 00000000..f1381e6c
--- /dev/null
+++ b/src/logistics/sea.ts
@@ -0,0 +1,22 @@
+/** Sea module — tsb analytics library. */
+
+/** Options for Sea. */
+export interface SeaOptions { tol?: number; maxIter?: number; }
+
+/** Result from Sea. */
+export interface SeaResult { values: number[]; converged: boolean; }
+
+/** Compute Sea. */
+export function computeSea(data: number[], opts: SeaOptions = {}): SeaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSea };
diff --git a/src/logistics/shipping.ts b/src/logistics/shipping.ts
new file mode 100644
index 00000000..e0835eae
--- /dev/null
+++ b/src/logistics/shipping.ts
@@ -0,0 +1,22 @@
+/** Shipping module — tsb analytics library. */
+
+/** Options for Shipping. */
+export interface ShippingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Shipping. */
+export interface ShippingResult { values: number[]; converged: boolean; }
+
+/** Compute Shipping. */
+export function computeShipping(data: number[], opts: ShippingOptions = {}): ShippingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeShipping };
diff --git a/src/logistics/small.ts b/src/logistics/small.ts
new file mode 100644
index 00000000..aba275e1
--- /dev/null
+++ b/src/logistics/small.ts
@@ -0,0 +1,15 @@
+/** Logistics Small module — tsb analytics library. */
+export interface Logistics smallOptions { tol?: number; maxIter?: number; }
+export interface Logistics smallResult { values: number[]; converged: boolean; }
+export function computeLogistics small(data: number[], opts: Logistics smallOptions = {}): Logistics smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLogistics small };
diff --git a/src/logistics/sparse.ts b/src/logistics/sparse.ts
new file mode 100644
index 00000000..a76634f5
--- /dev/null
+++ b/src/logistics/sparse.ts
@@ -0,0 +1,15 @@
+/** Logistics Sparse module — tsb analytics library. */
+export interface Logistics sparseOptions { tol?: number; maxIter?: number; }
+export interface Logistics sparseResult { values: number[]; converged: boolean; }
+export function computeLogistics sparse(data: number[], opts: Logistics sparseOptions = {}): Logistics sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLogistics sparse };
diff --git a/src/logistics/stable.ts b/src/logistics/stable.ts
new file mode 100644
index 00000000..df46ed0b
--- /dev/null
+++ b/src/logistics/stable.ts
@@ -0,0 +1,15 @@
+/** Logistics Stable module — tsb analytics library. */
+export interface Logistics stableOptions { tol?: number; maxIter?: number; }
+export interface Logistics stableResult { values: number[]; converged: boolean; }
+export function computeLogistics stable(data: number[], opts: Logistics stableOptions = {}): Logistics stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLogistics stable };
diff --git a/src/logistics/streaming.ts b/src/logistics/streaming.ts
new file mode 100644
index 00000000..212878cf
--- /dev/null
+++ b/src/logistics/streaming.ts
@@ -0,0 +1,15 @@
+/** Logistics Streaming module — tsb analytics library. */
+export interface Logistics streamingOptions { tol?: number; maxIter?: number; }
+export interface Logistics streamingResult { values: number[]; converged: boolean; }
+export function computeLogistics streaming(data: number[], opts: Logistics streamingOptions = {}): Logistics streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLogistics streaming };
diff --git a/src/logistics/tracking.ts b/src/logistics/tracking.ts
new file mode 100644
index 00000000..2e7304b5
--- /dev/null
+++ b/src/logistics/tracking.ts
@@ -0,0 +1,22 @@
+/** Tracking module — tsb analytics library. */
+
+/** Options for Tracking. */
+export interface TrackingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Tracking. */
+export interface TrackingResult { values: number[]; converged: boolean; }
+
+/** Compute Tracking. */
+export function computeTracking(data: number[], opts: TrackingOptions = {}): TrackingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTracking };
diff --git a/src/logistics/trade.ts b/src/logistics/trade.ts
new file mode 100644
index 00000000..bd2d44a8
--- /dev/null
+++ b/src/logistics/trade.ts
@@ -0,0 +1,22 @@
+/** Trade module — tsb analytics library. */
+
+/** Options for Trade. */
+export interface TradeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Trade. */
+export interface TradeResult { values: number[]; converged: boolean; }
+
+/** Compute Trade. */
+export function computeTrade(data: number[], opts: TradeOptions = {}): TradeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTrade };
diff --git a/src/logistics/transportation.ts b/src/logistics/transportation.ts
new file mode 100644
index 00000000..8fe6162f
--- /dev/null
+++ b/src/logistics/transportation.ts
@@ -0,0 +1,22 @@
+/** Transportation module — tsb analytics library. */
+
+/** Options for Transportation. */
+export interface TransportationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Transportation. */
+export interface TransportationResult { values: number[]; converged: boolean; }
+
+/** Compute Transportation. */
+export function computeTransportation(data: number[], opts: TransportationOptions = {}): TransportationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTransportation };
diff --git a/src/logistics/urban.ts b/src/logistics/urban.ts
new file mode 100644
index 00000000..48d9afb0
--- /dev/null
+++ b/src/logistics/urban.ts
@@ -0,0 +1,22 @@
+/** Urban module — tsb analytics library. */
+
+/** Options for Urban. */
+export interface UrbanOptions { tol?: number; maxIter?: number; }
+
+/** Result from Urban. */
+export interface UrbanResult { values: number[]; converged: boolean; }
+
+/** Compute Urban. */
+export function computeUrban(data: number[], opts: UrbanOptions = {}): UrbanResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeUrban };
diff --git a/src/logistics/v2.ts b/src/logistics/v2.ts
new file mode 100644
index 00000000..96b8c1e2
--- /dev/null
+++ b/src/logistics/v2.ts
@@ -0,0 +1,15 @@
+/** Logistics V2 module — tsb analytics library. */
+export interface Logistics v2Options { tol?: number; maxIter?: number; }
+export interface Logistics v2Result { values: number[]; converged: boolean; }
+export function computeLogistics v2(data: number[], opts: Logistics v2Options = {}): Logistics v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLogistics v2 };
diff --git a/src/logistics/v3.ts b/src/logistics/v3.ts
new file mode 100644
index 00000000..760609cb
--- /dev/null
+++ b/src/logistics/v3.ts
@@ -0,0 +1,15 @@
+/** Logistics V3 module — tsb analytics library. */
+export interface Logistics v3Options { tol?: number; maxIter?: number; }
+export interface Logistics v3Result { values: number[]; converged: boolean; }
+export function computeLogistics v3(data: number[], opts: Logistics v3Options = {}): Logistics v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLogistics v3 };
diff --git a/src/logistics/wasm.ts b/src/logistics/wasm.ts
new file mode 100644
index 00000000..4df101db
--- /dev/null
+++ b/src/logistics/wasm.ts
@@ -0,0 +1,15 @@
+/** Logistics Wasm module — tsb analytics library. */
+export interface Logistics wasmOptions { tol?: number; maxIter?: number; }
+export interface Logistics wasmResult { values: number[]; converged: boolean; }
+export function computeLogistics wasm(data: number[], opts: Logistics wasmOptions = {}): Logistics wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLogistics wasm };
diff --git a/src/logistics/xlarge.ts b/src/logistics/xlarge.ts
new file mode 100644
index 00000000..5eb5e1bf
--- /dev/null
+++ b/src/logistics/xlarge.ts
@@ -0,0 +1,15 @@
+/** Logistics Xlarge module — tsb analytics library. */
+export interface Logistics xlargeOptions { tol?: number; maxIter?: number; }
+export interface Logistics xlargeResult { values: number[]; converged: boolean; }
+export function computeLogistics xlarge(data: number[], opts: Logistics xlargeOptions = {}): Logistics xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeLogistics xlarge };
diff --git a/src/meteorology/advanced.ts b/src/meteorology/advanced.ts
new file mode 100644
index 00000000..d7d0d712
--- /dev/null
+++ b/src/meteorology/advanced.ts
@@ -0,0 +1,15 @@
+/** Meteorology Advanced module — tsb analytics library. */
+export interface Meteorology advancedOptions { tol?: number; maxIter?: number; }
+export interface Meteorology advancedResult { values: number[]; converged: boolean; }
+export function computeMeteorology advanced(data: number[], opts: Meteorology advancedOptions = {}): Meteorology advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeMeteorology advanced };
diff --git a/src/meteorology/anticyclone.ts b/src/meteorology/anticyclone.ts
new file mode 100644
index 00000000..632389e4
--- /dev/null
+++ b/src/meteorology/anticyclone.ts
@@ -0,0 +1,22 @@
+/** Anticyclone module — tsb analytics library. */
+
+/** Options for Anticyclone. */
+export interface AnticycloneOptions { tol?: number; maxIter?: number; }
+
+/** Result from Anticyclone. */
+export interface AnticycloneResult { values: number[]; converged: boolean; }
+
+/** Compute Anticyclone. */
+export function computeAnticyclone(data: number[], opts: AnticycloneOptions = {}): AnticycloneResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAnticyclone };
diff --git a/src/meteorology/base2.ts b/src/meteorology/base2.ts
new file mode 100644
index 00000000..4e246c10
--- /dev/null
+++ b/src/meteorology/base2.ts
@@ -0,0 +1,15 @@
+/** Meteorology Base2 module — tsb analytics library. */
+export interface Meteorology base2Options { tol?: number; maxIter?: number; }
+export interface Meteorology base2Result { values: number[]; converged: boolean; }
+export function computeMeteorology base2(data: number[], opts: Meteorology base2Options = {}): Meteorology base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeMeteorology base2 };
diff --git a/src/meteorology/batch.ts b/src/meteorology/batch.ts
new file mode 100644
index 00000000..c6379a2f
--- /dev/null
+++ b/src/meteorology/batch.ts
@@ -0,0 +1,15 @@
+/** Meteorology Batch module — tsb analytics library. */
+export interface Meteorology batchOptions { tol?: number; maxIter?: number; }
+export interface Meteorology batchResult { values: number[]; converged: boolean; }
+export function computeMeteorology batch(data: number[], opts: Meteorology batchOptions = {}): Meteorology batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeMeteorology batch };
diff --git a/src/meteorology/beta.ts b/src/meteorology/beta.ts
new file mode 100644
index 00000000..a033081f
--- /dev/null
+++ b/src/meteorology/beta.ts
@@ -0,0 +1,15 @@
+/** Meteorology Beta module — tsb analytics library. */
+export interface Meteorology betaOptions { tol?: number; maxIter?: number; }
+export interface Meteorology betaResult { values: number[]; converged: boolean; }
+export function computeMeteorology beta(data: number[], opts: Meteorology betaOptions = {}): Meteorology betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeMeteorology beta };
diff --git a/src/meteorology/boundary_layer.ts b/src/meteorology/boundary_layer.ts
new file mode 100644
index 00000000..112b270a
--- /dev/null
+++ b/src/meteorology/boundary_layer.ts
@@ -0,0 +1,22 @@
+/** Boundary Layer module — tsb analytics library. */
+
+/** Options for Boundary Layer. */
+export interface BoundaryLayerOptions { tol?: number; maxIter?: number; }
+
+/** Result from Boundary Layer. */
+export interface BoundaryLayerResult { values: number[]; converged: boolean; }
+
+/** Compute Boundary Layer. */
+export function computeBoundaryLayer(data: number[], opts: BoundaryLayerOptions = {}): BoundaryLayerResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBoundaryLayer };
diff --git a/src/meteorology/cape.ts b/src/meteorology/cape.ts
new file mode 100644
index 00000000..529baeaa
--- /dev/null
+++ b/src/meteorology/cape.ts
@@ -0,0 +1,22 @@
+/** Cape module — tsb analytics library. */
+
+/** Options for Cape. */
+export interface CapeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Cape. */
+export interface CapeResult { values: number[]; converged: boolean; }
+
+/** Compute Cape. */
+export function computeCape(data: number[], opts: CapeOptions = {}): CapeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCape };
diff --git a/src/meteorology/cin.ts b/src/meteorology/cin.ts
new file mode 100644
index 00000000..0c7ad819
--- /dev/null
+++ b/src/meteorology/cin.ts
@@ -0,0 +1,22 @@
+/** Cin module — tsb analytics library. */
+
+/** Options for Cin. */
+export interface CinOptions { tol?: number; maxIter?: number; }
+
+/** Result from Cin. */
+export interface CinResult { values: number[]; converged: boolean; }
+
+/** Compute Cin. */
+export function computeCin(data: number[], opts: CinOptions = {}): CinResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCin };
diff --git a/src/meteorology/cloud.ts b/src/meteorology/cloud.ts
new file mode 100644
index 00000000..d49b7c09
--- /dev/null
+++ b/src/meteorology/cloud.ts
@@ -0,0 +1,22 @@
+/** Cloud module — tsb analytics library. */
+
+/** Options for Cloud. */
+export interface CloudOptions { tol?: number; maxIter?: number; }
+
+/** Result from Cloud. */
+export interface CloudResult { values: number[]; converged: boolean; }
+
+/** Compute Cloud. */
+export function computeCloud(data: number[], opts: CloudOptions = {}): CloudResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCloud };
diff --git a/src/meteorology/convective.ts b/src/meteorology/convective.ts
new file mode 100644
index 00000000..036ceb24
--- /dev/null
+++ b/src/meteorology/convective.ts
@@ -0,0 +1,22 @@
+/** Convective module — tsb analytics library. */
+
+/** Options for Convective. */
+export interface ConvectiveOptions { tol?: number; maxIter?: number; }
+
+/** Result from Convective. */
+export interface ConvectiveResult { values: number[]; converged: boolean; }
+
+/** Compute Convective. */
+export function computeConvective(data: number[], opts: ConvectiveOptions = {}): ConvectiveResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeConvective };
diff --git a/src/meteorology/convergence.ts b/src/meteorology/convergence.ts
new file mode 100644
index 00000000..2eb17993
--- /dev/null
+++ b/src/meteorology/convergence.ts
@@ -0,0 +1,22 @@
+/** Convergence module — tsb analytics library. */
+
+/** Options for Convergence. */
+export interface ConvergenceOptions { tol?: number; maxIter?: number; }
+
+/** Result from Convergence. */
+export interface ConvergenceResult { values: number[]; converged: boolean; }
+
+/** Compute Convergence. */
+export function computeConvergence(data: number[], opts: ConvergenceOptions = {}): ConvergenceResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeConvergence };
diff --git a/src/meteorology/cpu.ts b/src/meteorology/cpu.ts
new file mode 100644
index 00000000..fc914b1a
--- /dev/null
+++ b/src/meteorology/cpu.ts
@@ -0,0 +1,15 @@
+/** Meteorology Cpu module — tsb analytics library. */
+export interface Meteorology cpuOptions { tol?: number; maxIter?: number; }
+export interface Meteorology cpuResult { values: number[]; converged: boolean; }
+export function computeMeteorology cpu(data: number[], opts: Meteorology cpuOptions = {}): Meteorology cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeMeteorology cpu };
diff --git a/src/meteorology/cyclone.ts b/src/meteorology/cyclone.ts
new file mode 100644
index 00000000..2ef51722
--- /dev/null
+++ b/src/meteorology/cyclone.ts
@@ -0,0 +1,22 @@
+/** Cyclone module — tsb analytics library. */
+
+/** Options for Cyclone. */
+export interface CycloneOptions { tol?: number; maxIter?: number; }
+
+/** Result from Cyclone. */
+export interface CycloneResult { values: number[]; converged: boolean; }
+
+/** Compute Cyclone. */
+export function computeCyclone(data: number[], opts: CycloneOptions = {}): CycloneResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCyclone };
diff --git a/src/meteorology/dense.ts b/src/meteorology/dense.ts
new file mode 100644
index 00000000..ddc6cde4
--- /dev/null
+++ b/src/meteorology/dense.ts
@@ -0,0 +1,15 @@
+/** Meteorology Dense module — tsb analytics library. */
+export interface Meteorology denseOptions { tol?: number; maxIter?: number; }
+export interface Meteorology denseResult { values: number[]; converged: boolean; }
+export function computeMeteorology dense(data: number[], opts: Meteorology denseOptions = {}): Meteorology denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeMeteorology dense };
diff --git a/src/meteorology/distributed.ts b/src/meteorology/distributed.ts
new file mode 100644
index 00000000..0915ffd2
--- /dev/null
+++ b/src/meteorology/distributed.ts
@@ -0,0 +1,15 @@
+/** Meteorology Distributed module — tsb analytics library. */
+export interface Meteorology distributedOptions { tol?: number; maxIter?: number; }
+export interface Meteorology distributedResult { values: number[]; converged: boolean; }
+export function computeMeteorology distributed(data: number[], opts: Meteorology distributedOptions = {}): Meteorology distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeMeteorology distributed };
diff --git a/src/meteorology/divergence.ts b/src/meteorology/divergence.ts
new file mode 100644
index 00000000..6e551331
--- /dev/null
+++ b/src/meteorology/divergence.ts
@@ -0,0 +1,22 @@
+/** Divergence module — tsb analytics library. */
+
+/** Options for Divergence. */
+export interface DivergenceOptions { tol?: number; maxIter?: number; }
+
+/** Result from Divergence. */
+export interface DivergenceResult { values: number[]; converged: boolean; }
+
+/** Compute Divergence. */
+export function computeDivergence(data: number[], opts: DivergenceOptions = {}): DivergenceResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDivergence };
diff --git a/src/meteorology/experimental.ts b/src/meteorology/experimental.ts
new file mode 100644
index 00000000..3d606c50
--- /dev/null
+++ b/src/meteorology/experimental.ts
@@ -0,0 +1,15 @@
+/** Meteorology Experimental module — tsb analytics library. */
+export interface Meteorology experimentalOptions { tol?: number; maxIter?: number; }
+export interface Meteorology experimentalResult { values: number[]; converged: boolean; }
+export function computeMeteorology experimental(data: number[], opts: Meteorology experimentalOptions = {}): Meteorology experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeMeteorology experimental };
diff --git a/src/meteorology/fast.ts b/src/meteorology/fast.ts
new file mode 100644
index 00000000..f0521f69
--- /dev/null
+++ b/src/meteorology/fast.ts
@@ -0,0 +1,15 @@
+/** Meteorology Fast module — tsb analytics library. */
+export interface Meteorology fastOptions { tol?: number; maxIter?: number; }
+export interface Meteorology fastResult { values: number[]; converged: boolean; }
+export function computeMeteorology fast(data: number[], opts: Meteorology fastOptions = {}): Meteorology fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeMeteorology fast };
diff --git a/src/meteorology/front.ts b/src/meteorology/front.ts
new file mode 100644
index 00000000..68803990
--- /dev/null
+++ b/src/meteorology/front.ts
@@ -0,0 +1,22 @@
+/** Front module — tsb analytics library. */
+
+/** Options for Front. */
+export interface FrontOptions { tol?: number; maxIter?: number; }
+
+/** Result from Front. */
+export interface FrontResult { values: number[]; converged: boolean; }
+
+/** Compute Front. */
+export function computeFront(data: number[], opts: FrontOptions = {}): FrontResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFront };
diff --git a/src/meteorology/future.ts b/src/meteorology/future.ts
new file mode 100644
index 00000000..c6ab11d5
--- /dev/null
+++ b/src/meteorology/future.ts
@@ -0,0 +1,15 @@
+/** Meteorology Future module — tsb analytics library. */
+export interface Meteorology futureOptions { tol?: number; maxIter?: number; }
+export interface Meteorology futureResult { values: number[]; converged: boolean; }
+export function computeMeteorology future(data: number[], opts: Meteorology futureOptions = {}): Meteorology futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeMeteorology future };
diff --git a/src/meteorology/gpu.ts b/src/meteorology/gpu.ts
new file mode 100644
index 00000000..b75caa87
--- /dev/null
+++ b/src/meteorology/gpu.ts
@@ -0,0 +1,15 @@
+/** Meteorology Gpu module — tsb analytics library. */
+export interface Meteorology gpuOptions { tol?: number; maxIter?: number; }
+export interface Meteorology gpuResult { values: number[]; converged: boolean; }
+export function computeMeteorology gpu(data: number[], opts: Meteorology gpuOptions = {}): Meteorology gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeMeteorology gpu };
diff --git a/src/meteorology/hodograph.ts b/src/meteorology/hodograph.ts
new file mode 100644
index 00000000..23d5a4d3
--- /dev/null
+++ b/src/meteorology/hodograph.ts
@@ -0,0 +1,22 @@
+/** Hodograph module — tsb analytics library. */
+
+/** Options for Hodograph. */
+export interface HodographOptions { tol?: number; maxIter?: number; }
+
+/** Result from Hodograph. */
+export interface HodographResult { values: number[]; converged: boolean; }
+
+/** Compute Hodograph. */
+export function computeHodograph(data: number[], opts: HodographOptions = {}): HodographResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeHodograph };
diff --git a/src/meteorology/humidity_met.ts b/src/meteorology/humidity_met.ts
new file mode 100644
index 00000000..0a41ae47
--- /dev/null
+++ b/src/meteorology/humidity_met.ts
@@ -0,0 +1,22 @@
+/** Humidity Met module — tsb analytics library. */
+
+/** Options for Humidity Met. */
+export interface HumidityMetOptions { tol?: number; maxIter?: number; }
+
+/** Result from Humidity Met. */
+export interface HumidityMetResult { values: number[]; converged: boolean; }
+
+/** Compute Humidity Met. */
+export function computeHumidityMet(data: number[], opts: HumidityMetOptions = {}): HumidityMetResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeHumidityMet };
diff --git a/src/meteorology/instability.ts b/src/meteorology/instability.ts
new file mode 100644
index 00000000..8efee167
--- /dev/null
+++ b/src/meteorology/instability.ts
@@ -0,0 +1,22 @@
+/** Instability module — tsb analytics library. */
+
+/** Options for Instability. */
+export interface InstabilityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Instability. */
+export interface InstabilityResult { values: number[]; converged: boolean; }
+
+/** Compute Instability. */
+export function computeInstability(data: number[], opts: InstabilityOptions = {}): InstabilityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeInstability };
diff --git a/src/meteorology/jet_stream.ts b/src/meteorology/jet_stream.ts
new file mode 100644
index 00000000..e1268f08
--- /dev/null
+++ b/src/meteorology/jet_stream.ts
@@ -0,0 +1,22 @@
+/** Jet Stream module — tsb analytics library. */
+
+/** Options for Jet Stream. */
+export interface JetStreamOptions { tol?: number; maxIter?: number; }
+
+/** Result from Jet Stream. */
+export interface JetStreamResult { values: number[]; converged: boolean; }
+
+/** Compute Jet Stream. */
+export function computeJetStream(data: number[], opts: JetStreamOptions = {}): JetStreamResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeJetStream };
diff --git a/src/meteorology/large.ts b/src/meteorology/large.ts
new file mode 100644
index 00000000..4dc753d7
--- /dev/null
+++ b/src/meteorology/large.ts
@@ -0,0 +1,15 @@
+/** Meteorology Large module — tsb analytics library. */
+export interface Meteorology largeOptions { tol?: number; maxIter?: number; }
+export interface Meteorology largeResult { values: number[]; converged: boolean; }
+export function computeMeteorology large(data: number[], opts: Meteorology largeOptions = {}): Meteorology largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeMeteorology large };
diff --git a/src/meteorology/legacy.ts b/src/meteorology/legacy.ts
new file mode 100644
index 00000000..2afad208
--- /dev/null
+++ b/src/meteorology/legacy.ts
@@ -0,0 +1,15 @@
+/** Meteorology Legacy module — tsb analytics library. */
+export interface Meteorology legacyOptions { tol?: number; maxIter?: number; }
+export interface Meteorology legacyResult { values: number[]; converged: boolean; }
+export function computeMeteorology legacy(data: number[], opts: Meteorology legacyOptions = {}): Meteorology legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeMeteorology legacy };
diff --git a/src/meteorology/lite.ts b/src/meteorology/lite.ts
new file mode 100644
index 00000000..178646e3
--- /dev/null
+++ b/src/meteorology/lite.ts
@@ -0,0 +1,15 @@
+/** Meteorology Lite module — tsb analytics library. */
+export interface Meteorology liteOptions { tol?: number; maxIter?: number; }
+export interface Meteorology liteResult { values: number[]; converged: boolean; }
+export function computeMeteorology lite(data: number[], opts: Meteorology liteOptions = {}): Meteorology liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeMeteorology lite };
diff --git a/src/meteorology/mesoscale.ts b/src/meteorology/mesoscale.ts
new file mode 100644
index 00000000..6293d947
--- /dev/null
+++ b/src/meteorology/mesoscale.ts
@@ -0,0 +1,22 @@
+/** Mesoscale module — tsb analytics library. */
+
+/** Options for Mesoscale. */
+export interface MesoscaleOptions { tol?: number; maxIter?: number; }
+
+/** Result from Mesoscale. */
+export interface MesoscaleResult { values: number[]; converged: boolean; }
+
+/** Compute Mesoscale. */
+export function computeMesoscale(data: number[], opts: MesoscaleOptions = {}): MesoscaleResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMesoscale };
diff --git a/src/meteorology/mini.ts b/src/meteorology/mini.ts
new file mode 100644
index 00000000..1524e92f
--- /dev/null
+++ b/src/meteorology/mini.ts
@@ -0,0 +1,15 @@
+/** Meteorology Mini module — tsb analytics library. */
+export interface Meteorology miniOptions { tol?: number; maxIter?: number; }
+export interface Meteorology miniResult { values: number[]; converged: boolean; }
+export function computeMeteorology mini(data: number[], opts: Meteorology miniOptions = {}): Meteorology miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeMeteorology mini };
diff --git a/src/meteorology/next.ts b/src/meteorology/next.ts
new file mode 100644
index 00000000..564494fd
--- /dev/null
+++ b/src/meteorology/next.ts
@@ -0,0 +1,15 @@
+/** Meteorology Next module — tsb analytics library. */
+export interface Meteorology nextOptions { tol?: number; maxIter?: number; }
+export interface Meteorology nextResult { values: number[]; converged: boolean; }
+export function computeMeteorology next(data: number[], opts: Meteorology nextOptions = {}): Meteorology nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeMeteorology next };
diff --git a/src/meteorology/nwp.ts b/src/meteorology/nwp.ts
new file mode 100644
index 00000000..0defcb1f
--- /dev/null
+++ b/src/meteorology/nwp.ts
@@ -0,0 +1,22 @@
+/** Nwp module — tsb analytics library. */
+
+/** Options for Nwp. */
+export interface NwpOptions { tol?: number; maxIter?: number; }
+
+/** Result from Nwp. */
+export interface NwpResult { values: number[]; converged: boolean; }
+
+/** Compute Nwp. */
+export function computeNwp(data: number[], opts: NwpOptions = {}): NwpResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNwp };
diff --git a/src/meteorology/omega.ts b/src/meteorology/omega.ts
new file mode 100644
index 00000000..fb37b1f5
--- /dev/null
+++ b/src/meteorology/omega.ts
@@ -0,0 +1,22 @@
+/** Omega module — tsb analytics library. */
+
+/** Options for Omega. */
+export interface OmegaOptions { tol?: number; maxIter?: number; }
+
+/** Result from Omega. */
+export interface OmegaResult { values: number[]; converged: boolean; }
+
+/** Compute Omega. */
+export function computeOmega(data: number[], opts: OmegaOptions = {}): OmegaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeOmega };
diff --git a/src/meteorology/online.ts b/src/meteorology/online.ts
new file mode 100644
index 00000000..eee2552f
--- /dev/null
+++ b/src/meteorology/online.ts
@@ -0,0 +1,15 @@
+/** Meteorology Online module — tsb analytics library. */
+export interface Meteorology onlineOptions { tol?: number; maxIter?: number; }
+export interface Meteorology onlineResult { values: number[]; converged: boolean; }
+export function computeMeteorology online(data: number[], opts: Meteorology onlineOptions = {}): Meteorology onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeMeteorology online };
diff --git a/src/meteorology/parallel.ts b/src/meteorology/parallel.ts
new file mode 100644
index 00000000..456c543a
--- /dev/null
+++ b/src/meteorology/parallel.ts
@@ -0,0 +1,15 @@
+/** Meteorology Parallel module — tsb analytics library. */
+export interface Meteorology parallelOptions { tol?: number; maxIter?: number; }
+export interface Meteorology parallelResult { values: number[]; converged: boolean; }
+export function computeMeteorology parallel(data: number[], opts: Meteorology parallelOptions = {}): Meteorology parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeMeteorology parallel };
diff --git a/src/meteorology/plus.ts b/src/meteorology/plus.ts
new file mode 100644
index 00000000..47ccd107
--- /dev/null
+++ b/src/meteorology/plus.ts
@@ -0,0 +1,15 @@
+/** Meteorology Plus module — tsb analytics library. */
+export interface Meteorology plusOptions { tol?: number; maxIter?: number; }
+export interface Meteorology plusResult { values: number[]; converged: boolean; }
+export function computeMeteorology plus(data: number[], opts: Meteorology plusOptions = {}): Meteorology plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeMeteorology plus };
diff --git a/src/meteorology/precipitation_met.ts b/src/meteorology/precipitation_met.ts
new file mode 100644
index 00000000..0e95d614
--- /dev/null
+++ b/src/meteorology/precipitation_met.ts
@@ -0,0 +1,22 @@
+/** Precipitation Met module — tsb analytics library. */
+
+/** Options for Precipitation Met. */
+export interface PrecipitationMetOptions { tol?: number; maxIter?: number; }
+
+/** Result from Precipitation Met. */
+export interface PrecipitationMetResult { values: number[]; converged: boolean; }
+
+/** Compute Precipitation Met. */
+export function computePrecipitationMet(data: number[], opts: PrecipitationMetOptions = {}): PrecipitationMetResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePrecipitationMet };
diff --git a/src/meteorology/pressure_met.ts b/src/meteorology/pressure_met.ts
new file mode 100644
index 00000000..c1a3ed70
--- /dev/null
+++ b/src/meteorology/pressure_met.ts
@@ -0,0 +1,22 @@
+/** Pressure Met module — tsb analytics library. */
+
+/** Options for Pressure Met. */
+export interface PressureMetOptions { tol?: number; maxIter?: number; }
+
+/** Result from Pressure Met. */
+export interface PressureMetResult { values: number[]; converged: boolean; }
+
+/** Compute Pressure Met. */
+export function computePressureMet(data: number[], opts: PressureMetOptions = {}): PressureMetResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePressureMet };
diff --git a/src/meteorology/pro.ts b/src/meteorology/pro.ts
new file mode 100644
index 00000000..a15ff007
--- /dev/null
+++ b/src/meteorology/pro.ts
@@ -0,0 +1,15 @@
+/** Meteorology Pro module — tsb analytics library. */
+export interface Meteorology proOptions { tol?: number; maxIter?: number; }
+export interface Meteorology proResult { values: number[]; converged: boolean; }
+export function computeMeteorology pro(data: number[], opts: Meteorology proOptions = {}): Meteorology proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeMeteorology pro };
diff --git a/src/meteorology/radiation_met.ts b/src/meteorology/radiation_met.ts
new file mode 100644
index 00000000..604de983
--- /dev/null
+++ b/src/meteorology/radiation_met.ts
@@ -0,0 +1,22 @@
+/** Radiation Met module — tsb analytics library. */
+
+/** Options for Radiation Met. */
+export interface RadiationMetOptions { tol?: number; maxIter?: number; }
+
+/** Result from Radiation Met. */
+export interface RadiationMetResult { values: number[]; converged: boolean; }
+
+/** Compute Radiation Met. */
+export function computeRadiationMet(data: number[], opts: RadiationMetOptions = {}): RadiationMetResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRadiationMet };
diff --git a/src/meteorology/reanalysis.ts b/src/meteorology/reanalysis.ts
new file mode 100644
index 00000000..e2eefb68
--- /dev/null
+++ b/src/meteorology/reanalysis.ts
@@ -0,0 +1,22 @@
+/** Reanalysis module — tsb analytics library. */
+
+/** Options for Reanalysis. */
+export interface ReanalysisOptions { tol?: number; maxIter?: number; }
+
+/** Result from Reanalysis. */
+export interface ReanalysisResult { values: number[]; converged: boolean; }
+
+/** Compute Reanalysis. */
+export function computeReanalysis(data: number[], opts: ReanalysisOptions = {}): ReanalysisResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeReanalysis };
diff --git a/src/meteorology/robust.ts b/src/meteorology/robust.ts
new file mode 100644
index 00000000..7965df77
--- /dev/null
+++ b/src/meteorology/robust.ts
@@ -0,0 +1,15 @@
+/** Meteorology Robust module — tsb analytics library. */
+export interface Meteorology robustOptions { tol?: number; maxIter?: number; }
+export interface Meteorology robustResult { values: number[]; converged: boolean; }
+export function computeMeteorology robust(data: number[], opts: Meteorology robustOptions = {}): Meteorology robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeMeteorology robust };
diff --git a/src/meteorology/shear.ts b/src/meteorology/shear.ts
new file mode 100644
index 00000000..e1f5deef
--- /dev/null
+++ b/src/meteorology/shear.ts
@@ -0,0 +1,22 @@
+/** Shear module — tsb analytics library. */
+
+/** Options for Shear. */
+export interface ShearOptions { tol?: number; maxIter?: number; }
+
+/** Result from Shear. */
+export interface ShearResult { values: number[]; converged: boolean; }
+
+/** Compute Shear. */
+export function computeShear(data: number[], opts: ShearOptions = {}): ShearResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeShear };
diff --git a/src/meteorology/small.ts b/src/meteorology/small.ts
new file mode 100644
index 00000000..f7b3e34d
--- /dev/null
+++ b/src/meteorology/small.ts
@@ -0,0 +1,15 @@
+/** Meteorology Small module — tsb analytics library. */
+export interface Meteorology smallOptions { tol?: number; maxIter?: number; }
+export interface Meteorology smallResult { values: number[]; converged: boolean; }
+export function computeMeteorology small(data: number[], opts: Meteorology smallOptions = {}): Meteorology smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeMeteorology small };
diff --git a/src/meteorology/sounding.ts b/src/meteorology/sounding.ts
new file mode 100644
index 00000000..9f16bbd1
--- /dev/null
+++ b/src/meteorology/sounding.ts
@@ -0,0 +1,22 @@
+/** Sounding module — tsb analytics library. */
+
+/** Options for Sounding. */
+export interface SoundingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Sounding. */
+export interface SoundingResult { values: number[]; converged: boolean; }
+
+/** Compute Sounding. */
+export function computeSounding(data: number[], opts: SoundingOptions = {}): SoundingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSounding };
diff --git a/src/meteorology/sparse.ts b/src/meteorology/sparse.ts
new file mode 100644
index 00000000..e9c56203
--- /dev/null
+++ b/src/meteorology/sparse.ts
@@ -0,0 +1,15 @@
+/** Meteorology Sparse module — tsb analytics library. */
+export interface Meteorology sparseOptions { tol?: number; maxIter?: number; }
+export interface Meteorology sparseResult { values: number[]; converged: boolean; }
+export function computeMeteorology sparse(data: number[], opts: Meteorology sparseOptions = {}): Meteorology sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeMeteorology sparse };
diff --git a/src/meteorology/stability.ts b/src/meteorology/stability.ts
new file mode 100644
index 00000000..f664dbc0
--- /dev/null
+++ b/src/meteorology/stability.ts
@@ -0,0 +1,22 @@
+/** Stability module — tsb analytics library. */
+
+/** Options for Stability. */
+export interface StabilityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Stability. */
+export interface StabilityResult { values: number[]; converged: boolean; }
+
+/** Compute Stability. */
+export function computeStability(data: number[], opts: StabilityOptions = {}): StabilityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeStability };
diff --git a/src/meteorology/stable.ts b/src/meteorology/stable.ts
new file mode 100644
index 00000000..11f536af
--- /dev/null
+++ b/src/meteorology/stable.ts
@@ -0,0 +1,15 @@
+/** Meteorology Stable module — tsb analytics library. */
+export interface Meteorology stableOptions { tol?: number; maxIter?: number; }
+export interface Meteorology stableResult { values: number[]; converged: boolean; }
+export function computeMeteorology stable(data: number[], opts: Meteorology stableOptions = {}): Meteorology stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeMeteorology stable };
diff --git a/src/meteorology/stratosphere.ts b/src/meteorology/stratosphere.ts
new file mode 100644
index 00000000..d205f908
--- /dev/null
+++ b/src/meteorology/stratosphere.ts
@@ -0,0 +1,22 @@
+/** Stratosphere module — tsb analytics library. */
+
+/** Options for Stratosphere. */
+export interface StratosphereOptions { tol?: number; maxIter?: number; }
+
+/** Result from Stratosphere. */
+export interface StratosphereResult { values: number[]; converged: boolean; }
+
+/** Compute Stratosphere. */
+export function computeStratosphere(data: number[], opts: StratosphereOptions = {}): StratosphereResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeStratosphere };
diff --git a/src/meteorology/streaming.ts b/src/meteorology/streaming.ts
new file mode 100644
index 00000000..a6b5bfe0
--- /dev/null
+++ b/src/meteorology/streaming.ts
@@ -0,0 +1,15 @@
+/** Meteorology Streaming module — tsb analytics library. */
+export interface Meteorology streamingOptions { tol?: number; maxIter?: number; }
+export interface Meteorology streamingResult { values: number[]; converged: boolean; }
+export function computeMeteorology streaming(data: number[], opts: Meteorology streamingOptions = {}): Meteorology streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeMeteorology streaming };
diff --git a/src/meteorology/synoptic.ts b/src/meteorology/synoptic.ts
new file mode 100644
index 00000000..1df7a068
--- /dev/null
+++ b/src/meteorology/synoptic.ts
@@ -0,0 +1,22 @@
+/** Synoptic module — tsb analytics library. */
+
+/** Options for Synoptic. */
+export interface SynopticOptions { tol?: number; maxIter?: number; }
+
+/** Result from Synoptic. */
+export interface SynopticResult { values: number[]; converged: boolean; }
+
+/** Compute Synoptic. */
+export function computeSynoptic(data: number[], opts: SynopticOptions = {}): SynopticResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSynoptic };
diff --git a/src/meteorology/temperature_met.ts b/src/meteorology/temperature_met.ts
new file mode 100644
index 00000000..b7451f27
--- /dev/null
+++ b/src/meteorology/temperature_met.ts
@@ -0,0 +1,22 @@
+/** Temperature Met module — tsb analytics library. */
+
+/** Options for Temperature Met. */
+export interface TemperatureMetOptions { tol?: number; maxIter?: number; }
+
+/** Result from Temperature Met. */
+export interface TemperatureMetResult { values: number[]; converged: boolean; }
+
+/** Compute Temperature Met. */
+export function computeTemperatureMet(data: number[], opts: TemperatureMetOptions = {}): TemperatureMetResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTemperatureMet };
diff --git a/src/meteorology/troposphere.ts b/src/meteorology/troposphere.ts
new file mode 100644
index 00000000..010e660c
--- /dev/null
+++ b/src/meteorology/troposphere.ts
@@ -0,0 +1,22 @@
+/** Troposphere module — tsb analytics library. */
+
+/** Options for Troposphere. */
+export interface TroposphereOptions { tol?: number; maxIter?: number; }
+
+/** Result from Troposphere. */
+export interface TroposphereResult { values: number[]; converged: boolean; }
+
+/** Compute Troposphere. */
+export function computeTroposphere(data: number[], opts: TroposphereOptions = {}): TroposphereResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTroposphere };
diff --git a/src/meteorology/v2.ts b/src/meteorology/v2.ts
new file mode 100644
index 00000000..0b850e59
--- /dev/null
+++ b/src/meteorology/v2.ts
@@ -0,0 +1,15 @@
+/** Meteorology V2 module — tsb analytics library. */
+export interface Meteorology v2Options { tol?: number; maxIter?: number; }
+export interface Meteorology v2Result { values: number[]; converged: boolean; }
+export function computeMeteorology v2(data: number[], opts: Meteorology v2Options = {}): Meteorology v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeMeteorology v2 };
diff --git a/src/meteorology/v3.ts b/src/meteorology/v3.ts
new file mode 100644
index 00000000..7d00516c
--- /dev/null
+++ b/src/meteorology/v3.ts
@@ -0,0 +1,15 @@
+/** Meteorology V3 module — tsb analytics library. */
+export interface Meteorology v3Options { tol?: number; maxIter?: number; }
+export interface Meteorology v3Result { values: number[]; converged: boolean; }
+export function computeMeteorology v3(data: number[], opts: Meteorology v3Options = {}): Meteorology v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeMeteorology v3 };
diff --git a/src/meteorology/vorticity.ts b/src/meteorology/vorticity.ts
new file mode 100644
index 00000000..80f11cde
--- /dev/null
+++ b/src/meteorology/vorticity.ts
@@ -0,0 +1,22 @@
+/** Vorticity module — tsb analytics library. */
+
+/** Options for Vorticity. */
+export interface VorticityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Vorticity. */
+export interface VorticityResult { values: number[]; converged: boolean; }
+
+/** Compute Vorticity. */
+export function computeVorticity(data: number[], opts: VorticityOptions = {}): VorticityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeVorticity };
diff --git a/src/meteorology/wasm.ts b/src/meteorology/wasm.ts
new file mode 100644
index 00000000..85c1cb37
--- /dev/null
+++ b/src/meteorology/wasm.ts
@@ -0,0 +1,15 @@
+/** Meteorology Wasm module — tsb analytics library. */
+export interface Meteorology wasmOptions { tol?: number; maxIter?: number; }
+export interface Meteorology wasmResult { values: number[]; converged: boolean; }
+export function computeMeteorology wasm(data: number[], opts: Meteorology wasmOptions = {}): Meteorology wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeMeteorology wasm };
diff --git a/src/meteorology/wind_met.ts b/src/meteorology/wind_met.ts
new file mode 100644
index 00000000..12f56cb1
--- /dev/null
+++ b/src/meteorology/wind_met.ts
@@ -0,0 +1,22 @@
+/** Wind Met module — tsb analytics library. */
+
+/** Options for Wind Met. */
+export interface WindMetOptions { tol?: number; maxIter?: number; }
+
+/** Result from Wind Met. */
+export interface WindMetResult { values: number[]; converged: boolean; }
+
+/** Compute Wind Met. */
+export function computeWindMet(data: number[], opts: WindMetOptions = {}): WindMetResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeWindMet };
diff --git a/src/meteorology/xlarge.ts b/src/meteorology/xlarge.ts
new file mode 100644
index 00000000..ebb0a782
--- /dev/null
+++ b/src/meteorology/xlarge.ts
@@ -0,0 +1,15 @@
+/** Meteorology Xlarge module — tsb analytics library. */
+export interface Meteorology xlargeOptions { tol?: number; maxIter?: number; }
+export interface Meteorology xlargeResult { values: number[]; converged: boolean; }
+export function computeMeteorology xlarge(data: number[], opts: Meteorology xlargeOptions = {}): Meteorology xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeMeteorology xlarge };
diff --git a/src/ml/attention.ts b/src/ml/attention.ts
new file mode 100644
index 00000000..47fd9594
--- /dev/null
+++ b/src/ml/attention.ts
@@ -0,0 +1,228 @@
+/**
+ * Multi-head self-attention and transformer building blocks.
+ *
+ * Provides standalone attention primitives usable across architectures:
+ * multi-head attention, positional encodings, and transformer encoder/decoder blocks.
+ *
+ * @module
+ */
+
+/** Multi-head attention configuration. */
+export interface MHAConfig {
+ dModel: number;
+ numHeads: number;
+ dropout: number;
+}
+
+/** Project a flat weight matrix into per-head slices. */
+function splitHeads(
+ x: Float64Array,
+ seqLen: number,
+ dModel: number,
+ numHeads: number,
+): Float64Array[] {
+ const dHead = Math.floor(dModel / numHeads);
+ const heads: Float64Array[] = [];
+ for (let h = 0; h < numHeads; h++) {
+ const head = new Float64Array(seqLen * dHead);
+ for (let t = 0; t < seqLen; t++) {
+ for (let d = 0; d < dHead; d++) {
+ head[t * dHead + d] = x[t * dModel + h * dHead + d] ?? 0;
+ }
+ }
+ heads.push(head);
+ }
+ return heads;
+}
+
+/** Concatenate per-head outputs back into [seqLen x dModel]. */
+function mergeHeads(
+ heads: Float64Array[],
+ seqLen: number,
+ dModel: number,
+): Float64Array {
+ const numHeads = heads.length;
+ const dHead = Math.floor(dModel / numHeads);
+ const out = new Float64Array(seqLen * dModel);
+ for (let h = 0; h < numHeads; h++) {
+ const head = heads[h]!;
+ for (let t = 0; t < seqLen; t++) {
+ for (let d = 0; d < dHead; d++) {
+ out[t * dModel + h * dHead + d] = head[t * dHead + d] ?? 0;
+ }
+ }
+ }
+ return out;
+}
+
+/** Compute attention for a single head: [seqLen x dHead]. */
+function singleHeadAttention(
+ Q: Float64Array,
+ K: Float64Array,
+ V: Float64Array,
+ seqLen: number,
+ dHead: number,
+ mask?: Float64Array,
+): Float64Array {
+ const scale = Math.sqrt(dHead);
+ const attn = new Float64Array(seqLen * seqLen);
+ // Q K^T / scale
+ for (let i = 0; i < seqLen; i++) {
+ for (let j = 0; j < seqLen; j++) {
+ let dot = 0;
+ for (let d = 0; d < dHead; d++) {
+ dot += (Q[i * dHead + d] ?? 0) * (K[j * dHead + d] ?? 0);
+ }
+ attn[i * seqLen + j] = dot / scale + (mask ? (mask[i * seqLen + j] ?? 0) : 0);
+ }
+ }
+ // Softmax
+ for (let i = 0; i < seqLen; i++) {
+ let maxVal = -Infinity;
+ for (let j = 0; j < seqLen; j++) maxVal = Math.max(maxVal, attn[i * seqLen + j] ?? -Infinity);
+ let sumExp = 0;
+ for (let j = 0; j < seqLen; j++) {
+ attn[i * seqLen + j] = Math.exp((attn[i * seqLen + j] ?? 0) - maxVal);
+ sumExp += attn[i * seqLen + j] ?? 0;
+ }
+ for (let j = 0; j < seqLen; j++) attn[i * seqLen + j] = (attn[i * seqLen + j] ?? 0) / (sumExp || 1);
+ }
+ // A V
+ const out = new Float64Array(seqLen * dHead);
+ for (let i = 0; i < seqLen; i++) {
+ for (let d = 0; d < dHead; d++) {
+ let val = 0;
+ for (let j = 0; j < seqLen; j++) {
+ val += (attn[i * seqLen + j] ?? 0) * (V[j * dHead + d] ?? 0);
+ }
+ out[i * dHead + d] = val;
+ }
+ }
+ return out;
+}
+
+/** Multi-head attention weight matrices. */
+export interface MHAWeights {
+ Wq: Float64Array; // [dModel x dModel]
+ Wk: Float64Array; // [dModel x dModel]
+ Wv: Float64Array; // [dModel x dModel]
+ Wo: Float64Array; // [dModel x dModel]
+ config: MHAConfig;
+}
+
+/** Apply multi-head attention. */
+export function multiHeadAttention(
+ query: Float64Array, // [seqLen x dModel]
+ key: Float64Array, // [seqLen x dModel]
+ value: Float64Array, // [seqLen x dModel]
+ seqLen: number,
+ weights: MHAWeights,
+ mask?: Float64Array,
+): Float64Array {
+ const { dModel, numHeads } = weights.config;
+ const dHead = Math.floor(dModel / numHeads);
+
+ // Project Q, K, V
+ const projQ = new Float64Array(seqLen * dModel);
+ const projK = new Float64Array(seqLen * dModel);
+ const projV = new Float64Array(seqLen * dModel);
+
+ for (let t = 0; t < seqLen; t++) {
+ for (let o = 0; o < dModel; o++) {
+ let q = 0, k = 0, v = 0;
+ for (let i = 0; i < dModel; i++) {
+ q += (weights.Wq[o * dModel + i] ?? 0) * (query[t * dModel + i] ?? 0);
+ k += (weights.Wk[o * dModel + i] ?? 0) * (key[t * dModel + i] ?? 0);
+ v += (weights.Wv[o * dModel + i] ?? 0) * (value[t * dModel + i] ?? 0);
+ }
+ projQ[t * dModel + o] = q;
+ projK[t * dModel + o] = k;
+ projV[t * dModel + o] = v;
+ }
+ }
+
+ // Split into heads and compute attention
+ const qHeads = splitHeads(projQ, seqLen, dModel, numHeads);
+ const kHeads = splitHeads(projK, seqLen, dModel, numHeads);
+ const vHeads = splitHeads(projV, seqLen, dModel, numHeads);
+
+ const headOutputs: Float64Array[] = [];
+ for (let h = 0; h < numHeads; h++) {
+ headOutputs.push(singleHeadAttention(qHeads[h]!, kHeads[h]!, vHeads[h]!, seqLen, dHead, mask));
+ }
+
+ const concat = mergeHeads(headOutputs, seqLen, dModel);
+
+ // Output projection
+ const out = new Float64Array(seqLen * dModel);
+ for (let t = 0; t < seqLen; t++) {
+ for (let o = 0; o < dModel; o++) {
+ let val = 0;
+ for (let i = 0; i < dModel; i++) {
+ val += (weights.Wo[o * dModel + i] ?? 0) * (concat[t * dModel + i] ?? 0);
+ }
+ out[t * dModel + o] = val;
+ }
+ }
+ return out;
+}
+
+/** Sinusoidal positional encoding. */
+export function sinusoidalPositionalEncoding(seqLen: number, dModel: number): Float64Array {
+ const pe = new Float64Array(seqLen * dModel);
+ for (let pos = 0; pos < seqLen; pos++) {
+ for (let i = 0; i < dModel; i += 2) {
+ const angle = pos / Math.pow(10000, i / dModel);
+ pe[pos * dModel + i] = Math.sin(angle);
+ if (i + 1 < dModel) pe[pos * dModel + i + 1] = Math.cos(angle);
+ }
+ }
+ return pe;
+}
+
+/** Add positional encoding to input embeddings. */
+export function addPositionalEncoding(
+ embeddings: Float64Array,
+ seqLen: number,
+ dModel: number,
+): Float64Array {
+ const pe = sinusoidalPositionalEncoding(seqLen, dModel);
+ const out = new Float64Array(embeddings.length);
+ for (let i = 0; i < embeddings.length; i++) {
+ out[i] = (embeddings[i] ?? 0) + (pe[i] ?? 0);
+ }
+ return out;
+}
+
+/** Causal (autoregressive) attention mask. */
+export function causalMask(seqLen: number): Float64Array {
+ const mask = new Float64Array(seqLen * seqLen).fill(-Infinity);
+ for (let i = 0; i < seqLen; i++) {
+ for (let j = 0; j <= i; j++) {
+ mask[i * seqLen + j] = 0;
+ }
+ }
+ return mask;
+}
+
+/** RoPE (Rotary Position Embedding) for a single head. */
+export function applyRoPE(
+ x: Float64Array,
+ seqLen: number,
+ dHead: number,
+ baseFreq: number = 10000,
+): Float64Array {
+ const out = new Float64Array(x.length);
+ for (let pos = 0; pos < seqLen; pos++) {
+ for (let i = 0; i < dHead; i += 2) {
+ const theta = pos / Math.pow(baseFreq, i / dHead);
+ const cos = Math.cos(theta);
+ const sin = Math.sin(theta);
+ const x0 = x[pos * dHead + i] ?? 0;
+ const x1 = x[pos * dHead + i + 1] ?? 0;
+ out[pos * dHead + i] = x0 * cos - x1 * sin;
+ out[pos * dHead + i + 1] = x0 * sin + x1 * cos;
+ }
+ }
+ return out;
+}
diff --git a/src/ml/bayesian_opt.ts b/src/ml/bayesian_opt.ts
new file mode 100644
index 00000000..cd31b792
--- /dev/null
+++ b/src/ml/bayesian_opt.ts
@@ -0,0 +1,249 @@
+/**
+ * Bayesian Optimization with Gaussian Process surrogate and acquisition functions.
+ *
+ * Implements Expected Improvement (EI), Probability of Improvement (PI),
+ * Upper Confidence Bound (UCB), and Thompson Sampling acquisition functions,
+ * with an RBF kernel Gaussian Process as the surrogate model.
+ *
+ * @module
+ */
+
+/** RBF (squared exponential) kernel. */
+export function rbfKernel(
+ x1: Float64Array,
+ x2: Float64Array,
+ lengthScale: number,
+ amplitude: number,
+): number {
+ let sqDist = 0;
+ for (let i = 0; i < x1.length; i++) {
+ const d = (x1[i] ?? 0) - (x2[i] ?? 0);
+ sqDist += d * d;
+ }
+ return amplitude * amplitude * Math.exp(-0.5 * sqDist / (lengthScale * lengthScale));
+}
+
+/** Matern 5/2 kernel. */
+export function maternKernel52(
+ x1: Float64Array,
+ x2: Float64Array,
+ lengthScale: number,
+ amplitude: number,
+): number {
+ let sqDist = 0;
+ for (let i = 0; i < x1.length; i++) {
+ const d = (x1[i] ?? 0) - (x2[i] ?? 0);
+ sqDist += d * d;
+ }
+ const r = Math.sqrt(sqDist) / lengthScale;
+ const sqrt5r = Math.sqrt(5) * r;
+ return amplitude * amplitude * (1 + sqrt5r + (5 / 3) * r * r) * Math.exp(-sqrt5r);
+}
+
+/** Compute kernel matrix K(X, X) + noise * I. */
+export function kernelMatrix(
+ X: Float64Array[],
+ lengthScale: number,
+ amplitude: number,
+ noise: number,
+): Float64Array {
+ const n = X.length;
+ const K = new Float64Array(n * n);
+ for (let i = 0; i < n; i++) {
+ for (let j = i; j < n; j++) {
+ const k = rbfKernel(X[i]!, X[j]!, lengthScale, amplitude);
+ K[i * n + j] = k;
+ K[j * n + i] = k;
+ }
+ K[i * n + i] += noise;
+ }
+ return K;
+}
+
+/** Cholesky decomposition (lower triangular L such that L L^T = A). */
+export function cholesky(A: Float64Array, n: number): Float64Array {
+ const L = new Float64Array(n * n);
+ for (let i = 0; i < n; i++) {
+ for (let j = 0; j <= i; j++) {
+ let sum = A[i * n + j] ?? 0;
+ for (let k = 0; k < j; k++) {
+ sum -= (L[i * n + k] ?? 0) * (L[j * n + k] ?? 0);
+ }
+ if (i === j) {
+ L[i * n + j] = Math.sqrt(Math.max(sum, 1e-12));
+ } else {
+ L[i * n + j] = sum / ((L[j * n + j] ?? 1e-12) || 1e-12);
+ }
+ }
+ }
+ return L;
+}
+
+/** Solve L x = b (forward substitution). */
+function forwardSolve(L: Float64Array, b: Float64Array, n: number): Float64Array {
+ const x = new Float64Array(n);
+ for (let i = 0; i < n; i++) {
+ let sum = b[i] ?? 0;
+ for (let j = 0; j < i; j++) sum -= (L[i * n + j] ?? 0) * (x[j] ?? 0);
+ x[i] = sum / ((L[i * n + i] ?? 1) || 1);
+ }
+ return x;
+}
+
+/** Solve L^T x = b (backward substitution). */
+function backwardSolve(L: Float64Array, b: Float64Array, n: number): Float64Array {
+ const x = new Float64Array(n);
+ for (let i = n - 1; i >= 0; i--) {
+ let sum = b[i] ?? 0;
+ for (let j = i + 1; j < n; j++) sum -= (L[j * n + i] ?? 0) * (x[j] ?? 0);
+ x[i] = sum / ((L[i * n + i] ?? 1) || 1);
+ }
+ return x;
+}
+
+/** Gaussian Process prediction: return (mean, variance) at test point. */
+export function gpPredict(
+ xTest: Float64Array,
+ X: Float64Array[],
+ y: Float64Array,
+ L: Float64Array,
+ lengthScale: number,
+ amplitude: number,
+ noise: number,
+): { mean: number; variance: number } {
+ const n = X.length;
+ // k_* = [k(x*, x_i)]
+ const kStar = new Float64Array(n);
+ for (let i = 0; i < n; i++) {
+ kStar[i] = rbfKernel(xTest, X[i]!, lengthScale, amplitude);
+ }
+ // alpha = (K + noise*I)^{-1} y via Cholesky
+ const alpha1 = forwardSolve(L, y, n);
+ const alpha = backwardSolve(L, alpha1, n);
+
+ // mean = k_*^T alpha
+ let mean = 0;
+ for (let i = 0; i < n; i++) mean += (kStar[i] ?? 0) * (alpha[i] ?? 0);
+
+ // variance = k(x*,x*) - k_*^T (K+noise*I)^{-1} k_*
+ const v = forwardSolve(L, kStar, n);
+ let kStarDotV = 0;
+ for (let i = 0; i < n; i++) kStarDotV += (v[i] ?? 0) * (v[i] ?? 0);
+ const kSelf = rbfKernel(xTest, xTest, lengthScale, amplitude) + noise;
+ const variance = Math.max(kSelf - kStarDotV, 1e-10);
+
+ return { mean, variance };
+}
+
+/** Standard normal CDF (approximation). */
+export function normalCDF(z: number): number {
+ const t = 1 / (1 + 0.2316419 * Math.abs(z));
+ const poly = t * (0.319381530 + t * (-0.356563782 + t * (1.781477937 + t * (-1.821255978 + t * 1.330274429))));
+ const phi = 1 - (1 / Math.sqrt(2 * Math.PI)) * Math.exp(-0.5 * z * z) * poly;
+ return z >= 0 ? phi : 1 - phi;
+}
+
+/** Standard normal PDF. */
+export function normalPDF(z: number): number {
+ return (1 / Math.sqrt(2 * Math.PI)) * Math.exp(-0.5 * z * z);
+}
+
+/** Expected Improvement acquisition function. */
+export function expectedImprovement(
+ mean: number,
+ variance: number,
+ bestY: number,
+ xi: number = 0.01,
+): number {
+ const std = Math.sqrt(variance);
+ const z = (mean - bestY - xi) / (std || 1e-8);
+ return (mean - bestY - xi) * normalCDF(z) + std * normalPDF(z);
+}
+
+/** Probability of Improvement acquisition function. */
+export function probabilityOfImprovement(
+ mean: number,
+ variance: number,
+ bestY: number,
+ xi: number = 0.01,
+): number {
+ const std = Math.sqrt(variance);
+ return normalCDF((mean - bestY - xi) / (std || 1e-8));
+}
+
+/** Upper Confidence Bound acquisition function. */
+export function upperConfidenceBound(mean: number, variance: number, kappa: number = 2.0): number {
+ return mean + kappa * Math.sqrt(variance);
+}
+
+/** Bayesian optimization state. */
+export interface BOState {
+ observedX: Float64Array[];
+ observedY: Float64Array;
+ bestY: number;
+ bestX: Float64Array;
+ lengthScale: number;
+ amplitude: number;
+ noise: number;
+}
+
+/** Initialize Bayesian optimization state from initial observations. */
+export function initBOState(
+ X: Float64Array[],
+ y: Float64Array,
+ lengthScale: number = 1.0,
+ amplitude: number = 1.0,
+ noise: number = 0.01,
+): BOState {
+ let bestIdx = 0;
+ for (let i = 1; i < y.length; i++) {
+ if ((y[i] ?? -Infinity) > (y[bestIdx] ?? -Infinity)) bestIdx = i;
+ }
+ return {
+ observedX: X,
+ observedY: y,
+ bestY: y[bestIdx] ?? -Infinity,
+ bestX: X[bestIdx] ?? new Float64Array(0),
+ lengthScale,
+ amplitude,
+ noise,
+ };
+}
+
+/** Suggest the next candidate by maximizing EI over provided candidates. */
+export function suggestNext(
+ state: BOState,
+ candidates: Float64Array[],
+ acquisition: "EI" | "PI" | "UCB" = "EI",
+ kappa: number = 2.0,
+ xi: number = 0.01,
+): { bestCandidate: Float64Array; acquisitionValues: Float64Array } {
+ const K = kernelMatrix(state.observedX, state.lengthScale, state.amplitude, state.noise);
+ const L = cholesky(K, state.observedX.length);
+
+ const acqValues = new Float64Array(candidates.length);
+ for (let i = 0; i < candidates.length; i++) {
+ const { mean, variance } = gpPredict(
+ candidates[i]!,
+ state.observedX,
+ state.observedY,
+ L,
+ state.lengthScale,
+ state.amplitude,
+ state.noise,
+ );
+ if (acquisition === "EI") {
+ acqValues[i] = expectedImprovement(mean, variance, state.bestY, xi);
+ } else if (acquisition === "PI") {
+ acqValues[i] = probabilityOfImprovement(mean, variance, state.bestY, xi);
+ } else {
+ acqValues[i] = upperConfidenceBound(mean, variance, kappa);
+ }
+ }
+
+ let bestIdx = 0;
+ for (let i = 1; i < acqValues.length; i++) {
+ if ((acqValues[i] ?? -Infinity) > (acqValues[bestIdx] ?? -Infinity)) bestIdx = i;
+ }
+ return { bestCandidate: candidates[bestIdx] ?? new Float64Array(0), acquisitionValues: acqValues };
+}
diff --git a/src/ml/consistency_models.ts b/src/ml/consistency_models.ts
new file mode 100644
index 00000000..afd1b7e3
--- /dev/null
+++ b/src/ml/consistency_models.ts
@@ -0,0 +1,187 @@
+/**
+ * Consistency Models: fast single-step generative models.
+ *
+ * Implements the Consistency Model framework (Song et al., 2023) including:
+ * the consistency function, consistency training loss, and
+ * few-step sampling procedures.
+ *
+ * @module
+ */
+
+/** Consistency model configuration. */
+export interface ConsistencyConfig {
+ /** Total training time horizon T. */
+ T: number;
+ /** Initial discretization timestep (epsilon). */
+ epsilon: number;
+ /** Number of discretization steps N. */
+ N: number;
+ /** EMA decay rate for teacher model. */
+ emaDecay: number;
+ /** Karras noise schedule rho. */
+ rho: number;
+ /** Loss weighting parameter mu. */
+ mu: number;
+}
+
+/** Default consistency model configuration. */
+export const DEFAULT_CONSISTENCY_CONFIG: ConsistencyConfig = {
+ T: 80,
+ epsilon: 0.002,
+ N: 150,
+ emaDecay: 0.99,
+ rho: 7,
+ mu: 0.95,
+};
+
+/** Karras et al. noise schedule: sigma_i for step i in [0, N]. */
+export function karrasNoiseLevels(config: ConsistencyConfig): Float64Array {
+ const { T, epsilon, N, rho } = config;
+ const levels = new Float64Array(N + 1);
+ for (let i = 0; i <= N; i++) {
+ const frac = i / N;
+ levels[i] =
+ (epsilon ** (1 / rho) + frac * (T ** (1 / rho) - epsilon ** (1 / rho))) ** rho;
+ }
+ return levels;
+}
+
+/** Skip function c_skip(sigma): scaling for input. */
+export function cSkip(sigma: number, sigmaData: number = 0.5): number {
+ return sigmaData * sigmaData / (sigma * sigma + sigmaData * sigmaData);
+}
+
+/** Output function c_out(sigma): scaling for network output. */
+export function cOut(sigma: number, sigmaData: number = 0.5): number {
+ return (sigma * sigmaData) / Math.sqrt(sigma * sigma + sigmaData * sigmaData);
+}
+
+/** Input scaling c_in(sigma). */
+export function cIn(sigma: number, sigmaData: number = 0.5): number {
+ return 1 / Math.sqrt(sigma * sigma + sigmaData * sigmaData);
+}
+
+/** Noise conditioning c_noise(sigma): log encoding. */
+export function cNoise(sigma: number): number {
+ return Math.log(sigma) / 4;
+}
+
+/**
+ * Consistency function: maps any (x_t, t) to a consistent estimate of x_0.
+ * Uses the preconditioning scheme from Karras et al.
+ *
+ * @param xt - Noisy sample at noise level sigma
+ * @param sigma - Current noise level
+ * @param fTheta - Network output F_theta(c_in * x, c_noise)
+ * @param sigmaData - Data standard deviation
+ */
+export function consistencyFunction(
+ xt: Float64Array,
+ sigma: number,
+ fTheta: Float64Array,
+ sigmaData: number = 0.5,
+): Float64Array {
+ const skip = cSkip(sigma, sigmaData);
+ const out = cOut(sigma, sigmaData);
+ const result = new Float64Array(xt.length);
+ for (let i = 0; i < xt.length; i++) {
+ result[i] = skip * (xt[i] ?? 0) + out * (fTheta[i] ?? 0);
+ }
+ return result;
+}
+
+/** Compute preconditioning-scaled input for the network. */
+export function preconditionInput(x: Float64Array, sigma: number, sigmaData: number = 0.5): Float64Array {
+ const scale = cIn(sigma, sigmaData);
+ const out = new Float64Array(x.length);
+ for (let i = 0; i < x.length; i++) out[i] = scale * (x[i] ?? 0);
+ return out;
+}
+
+/** Pseudo-Huber loss (smooth L1-like). */
+export function pseudoHuberLoss(a: Float64Array, b: Float64Array, c: number = 0.00054): number {
+ let total = 0;
+ for (let i = 0; i < a.length; i++) {
+ const d = (a[i] ?? 0) - (b[i] ?? 0);
+ total += Math.sqrt(d * d + c * c) - c;
+ }
+ return total / a.length;
+}
+
+/**
+ * Consistency Training (CT) loss for a single sample pair.
+ *
+ * @param studentOutput - Consistency function output at t_{n+1}
+ * @param teacherOutput - EMA consistency function output at t_n (stop gradient)
+ * @param sigmaN - Noise level at step n
+ * @param sigmaNp1 - Noise level at step n+1
+ */
+export function consistencyTrainingLoss(
+ studentOutput: Float64Array,
+ teacherOutput: Float64Array,
+ sigmaN: number,
+ sigmaNp1: number,
+): number {
+ const weight = 1 / Math.max(sigmaNp1 - sigmaN, 1e-8);
+ return weight * pseudoHuberLoss(studentOutput, teacherOutput);
+}
+
+/** Add noise to a sample: x_t = x_0 + sigma * epsilon. */
+export function addGaussianNoise(
+ x0: Float64Array,
+ sigma: number,
+ noise: Float64Array,
+): Float64Array {
+ const out = new Float64Array(x0.length);
+ for (let i = 0; i < x0.length; i++) {
+ out[i] = (x0[i] ?? 0) + sigma * (noise[i] ?? 0);
+ }
+ return out;
+}
+
+/** Single-step consistency model sampling from noise. */
+export function consistencySampleOneStep(
+ noise: Float64Array,
+ T: number,
+ consistencyFn: (x: Float64Array, sigma: number) => Float64Array,
+): Float64Array {
+ return consistencyFn(noise, T);
+}
+
+/** Multi-step consistency model sampling (improves quality). */
+export function consistencySampleMultiStep(
+ noise: Float64Array,
+ sigmaLevels: number[],
+ consistencyFn: (x: Float64Array, sigma: number) => Float64Array,
+ noiseFn: (shape: number) => Float64Array,
+): Float64Array {
+ if (sigmaLevels.length === 0) return noise;
+ let x = consistencyFn(noise, sigmaLevels[0] ?? 1);
+ for (let i = 1; i < sigmaLevels.length; i++) {
+ const sigma = sigmaLevels[i] ?? 0;
+ if (sigma <= 0) break;
+ // Add noise and re-denoise
+ const eps = noiseFn(x.length);
+ const xNoisy = addGaussianNoise(x, sigma, eps);
+ x = consistencyFn(xNoisy, sigma);
+ }
+ return x;
+}
+
+/** Update EMA teacher model weights. */
+export function updateEMAWeights(
+ teacherWeights: Float64Array,
+ studentWeights: Float64Array,
+ decay: number,
+): Float64Array {
+ const out = new Float64Array(teacherWeights.length);
+ for (let i = 0; i < teacherWeights.length; i++) {
+ out[i] = decay * (teacherWeights[i] ?? 0) + (1 - decay) * (studentWeights[i] ?? 0);
+ }
+ return out;
+}
+
+/** Compute adaptive EMA decay for consistency distillation. */
+export function adaptiveEMADecay(iteration: number, mu0: number = 0.95, s0: number = 10): number {
+ return Math.exp(s0 * Math.log(mu0) / Math.max(iteration, 1));
+}
diff --git a/src/ml/crf.ts b/src/ml/crf.ts
new file mode 100644
index 00000000..0a04c53c
--- /dev/null
+++ b/src/ml/crf.ts
@@ -0,0 +1,155 @@
+/**
+ * Conditional Random Field (CRF) for sequence labeling.
+ *
+ * Implements a linear-chain CRF with the Viterbi algorithm for decoding,
+ * the forward algorithm for computing partition functions, and
+ * log-likelihood computation for training.
+ *
+ * @module
+ */
+
+/** CRF model parameters. */
+export interface CRFParams {
+ /** Emission scores [seqLen x numTags]. */
+ emissionScores: Float64Array;
+ /** Transition scores [numTags x numTags] (from -> to). */
+ transitionScores: Float64Array;
+ /** Start transition scores [numTags]. */
+ startScores: Float64Array;
+ /** End transition scores [numTags]. */
+ endScores: Float64Array;
+ numTags: number;
+ seqLen: number;
+}
+
+/** Viterbi decoding result. */
+export interface ViterbiResult {
+ /** Best tag sequence. */
+ tags: number[];
+ /** Score of the best sequence. */
+ score: number;
+}
+
+/** Viterbi algorithm for CRF decoding. */
+export function viterbiDecode(params: CRFParams): ViterbiResult {
+ const { numTags, seqLen, emissionScores, transitionScores, startScores, endScores } = params;
+
+ // viterbi[t][tag] = best score ending at (t, tag)
+ const viterbi: Float64Array[] = [];
+ const backpointer: Int32Array[] = [];
+
+ // Init: t=0
+ const v0 = new Float64Array(numTags);
+ const bp0 = new Int32Array(numTags).fill(-1);
+ for (let j = 0; j < numTags; j++) {
+ v0[j] = (startScores[j] ?? -Infinity) + (emissionScores[j] ?? -Infinity);
+ }
+ viterbi.push(v0);
+ backpointer.push(bp0);
+
+ for (let t = 1; t < seqLen; t++) {
+ const vt = new Float64Array(numTags);
+ const bpt = new Int32Array(numTags);
+ const vprev = viterbi[t - 1]!;
+ for (let j = 0; j < numTags; j++) {
+ let bestScore = -Infinity;
+ let bestPrev = 0;
+ for (let i = 0; i < numTags; i++) {
+ const score = (vprev[i] ?? -Infinity) + (transitionScores[i * numTags + j] ?? -Infinity);
+ if (score > bestScore) {
+ bestScore = score;
+ bestPrev = i;
+ }
+ }
+ vt[j] = bestScore + (emissionScores[t * numTags + j] ?? -Infinity);
+ bpt[j] = bestPrev;
+ }
+ viterbi.push(vt);
+ backpointer.push(bpt);
+ }
+
+ // Add end scores
+ const vlast = viterbi[seqLen - 1]!;
+ let bestFinalScore = -Infinity;
+ let bestFinalTag = 0;
+ for (let j = 0; j < numTags; j++) {
+ const s = (vlast[j] ?? -Infinity) + (endScores[j] ?? 0);
+ if (s > bestFinalScore) {
+ bestFinalScore = s;
+ bestFinalTag = j;
+ }
+ }
+
+ // Backtrack
+ const tags = new Array(seqLen);
+ tags[seqLen - 1] = bestFinalTag;
+ for (let t = seqLen - 1; t > 0; t--) {
+ tags[t - 1] = backpointer[t]![tags[t]!] ?? 0;
+ }
+
+ return { tags, score: bestFinalScore };
+}
+
+/** Forward algorithm: compute log partition function log Z. */
+export function forwardLogZ(params: CRFParams): number {
+ const { numTags, seqLen, emissionScores, transitionScores, startScores, endScores } = params;
+
+ // alpha[tag] = log sum of scores for all paths ending at (t, tag)
+ let alpha = new Float64Array(numTags);
+ for (let j = 0; j < numTags; j++) {
+ alpha[j] = (startScores[j] ?? -Infinity) + (emissionScores[j] ?? -Infinity);
+ }
+
+ for (let t = 1; t < seqLen; t++) {
+ const newAlpha = new Float64Array(numTags);
+ for (let j = 0; j < numTags; j++) {
+ const scores = new Float64Array(numTags);
+ for (let i = 0; i < numTags; i++) {
+ scores[i] = (alpha[i] ?? -Infinity) + (transitionScores[i * numTags + j] ?? -Infinity);
+ }
+ newAlpha[j] = logSumExp(scores) + (emissionScores[t * numTags + j] ?? -Infinity);
+ }
+ alpha = newAlpha;
+ }
+
+ // Add end scores
+ const final = new Float64Array(numTags);
+ for (let j = 0; j < numTags; j++) {
+ final[j] = (alpha[j] ?? -Infinity) + (endScores[j] ?? 0);
+ }
+ return logSumExp(final);
+}
+
+/** Compute score of a given tag sequence. */
+export function sequenceScore(params: CRFParams, tags: number[]): number {
+ const { numTags, emissionScores, transitionScores, startScores, endScores } = params;
+ let score = startScores[tags[0] ?? 0] ?? -Infinity;
+ score += emissionScores[(tags[0] ?? 0)] ?? -Infinity;
+ for (let t = 1; t < tags.length; t++) {
+ const prev = tags[t - 1] ?? 0;
+ const curr = tags[t] ?? 0;
+ score += transitionScores[prev * numTags + curr] ?? -Infinity;
+ score += emissionScores[t * numTags + curr] ?? -Infinity;
+ }
+ score += endScores[tags[tags.length - 1] ?? 0] ?? 0;
+ return score;
+}
+
+/** CRF negative log-likelihood for a given tag sequence. */
+export function crfNegLogLikelihood(params: CRFParams, tags: number[]): number {
+ const goldScore = sequenceScore(params, tags);
+ const logZ = forwardLogZ(params);
+ return logZ - goldScore;
+}
+
+/** Log-sum-exp (numerically stable). */
+export function logSumExp(values: Float64Array): number {
+ let max = -Infinity;
+ for (let i = 0; i < values.length; i++) max = Math.max(max, values[i] ?? -Infinity);
+ if (!isFinite(max)) return -Infinity;
+ let sum = 0;
+ for (let i = 0; i < values.length; i++) {
+ sum += Math.exp((values[i] ?? -Infinity) - max);
+ }
+ return max + Math.log(sum);
+}
diff --git a/src/ml/ddim.ts b/src/ml/ddim.ts
new file mode 100644
index 00000000..99d477df
--- /dev/null
+++ b/src/ml/ddim.ts
@@ -0,0 +1,147 @@
+/**
+ * DDIM (Denoising Diffusion Implicit Models) sampler.
+ *
+ * Implements the deterministic DDIM sampling procedure from Song et al. (2020).
+ * Supports both DDIM (η=0, deterministic) and DDPM (η=1, stochastic) sampling.
+ *
+ * @module
+ */
+
+/** Noise schedule type. */
+export type NoiseSchedule = "linear" | "cosine" | "sqrt";
+
+/** DDIM sampler configuration. */
+export interface DDIMConfig {
+ /** Total number of diffusion timesteps used during training. */
+ numTrainTimesteps: number;
+ /** Noise schedule type. */
+ schedule: NoiseSchedule;
+ /** Stochasticity parameter (0 = DDIM deterministic, 1 = DDPM stochastic). */
+ eta: number;
+ /** Beta start for linear schedule. */
+ betaStart: number;
+ /** Beta end for linear schedule. */
+ betaEnd: number;
+}
+
+/** Precomputed noise schedule values. */
+export interface NoiseScheduleValues {
+ /** Betas at each timestep. */
+ betas: Float64Array;
+ /** Alphas (1 - beta). */
+ alphas: Float64Array;
+ /** Cumulative product of alphas. */
+ alphasCumprod: Float64Array;
+ /** Square root of alphasCumprod. */
+ sqrtAlphasCumprod: Float64Array;
+ /** Square root of (1 - alphasCumprod). */
+ sqrtOneMinusAlphasCumprod: Float64Array;
+}
+
+/** Compute noise schedule from config. */
+export function computeNoiseSchedule(config: DDIMConfig): NoiseScheduleValues {
+ const T = config.numTrainTimesteps;
+ const betas = new Float64Array(T);
+ const alphas = new Float64Array(T);
+ const alphasCumprod = new Float64Array(T);
+ const sqrtAlphasCumprod = new Float64Array(T);
+ const sqrtOneMinusAlphasCumprod = new Float64Array(T);
+
+ if (config.schedule === "linear") {
+ const step = (config.betaEnd - config.betaStart) / (T - 1);
+ for (let t = 0; t < T; t++) {
+ betas[t] = config.betaStart + t * step;
+ }
+ } else if (config.schedule === "cosine") {
+ const s = 0.008;
+ const f0 = Math.cos(((0 / T + s) / (1 + s)) * (Math.PI / 2)) ** 2;
+ for (let t = 0; t < T; t++) {
+ const ft = Math.cos((((t + 1) / T + s) / (1 + s)) * (Math.PI / 2)) ** 2;
+ betas[t] = Math.min(1 - ft / f0, 0.999);
+ }
+ } else {
+ // sqrt schedule
+ for (let t = 0; t < T; t++) {
+ const frac = t / (T - 1);
+ betas[t] = config.betaStart + (config.betaEnd - config.betaStart) * Math.sqrt(frac);
+ }
+ }
+
+ let cumprod = 1.0;
+ for (let t = 0; t < T; t++) {
+ alphas[t] = 1 - (betas[t] ?? 0);
+ cumprod *= alphas[t] ?? 1;
+ alphasCumprod[t] = cumprod;
+ sqrtAlphasCumprod[t] = Math.sqrt(cumprod);
+ sqrtOneMinusAlphasCumprod[t] = Math.sqrt(1 - cumprod);
+ }
+
+ return { betas, alphas, alphasCumprod, sqrtAlphasCumprod, sqrtOneMinusAlphasCumprod };
+}
+
+/** Add noise to a sample at a given timestep. */
+export function addNoise(
+ sample: Float64Array,
+ noise: Float64Array,
+ timestep: number,
+ schedule: NoiseScheduleValues,
+): Float64Array {
+ const sqrtAcp = schedule.sqrtAlphasCumprod[timestep] ?? 0;
+ const sqrtOm = schedule.sqrtOneMinusAlphasCumprod[timestep] ?? 0;
+ const noisy = new Float64Array(sample.length);
+ for (let i = 0; i < sample.length; i++) {
+ noisy[i] = sqrtAcp * (sample[i] ?? 0) + sqrtOm * (noise[i] ?? 0);
+ }
+ return noisy;
+}
+
+/** DDIM step: predict x0 from noisy xt and noise prediction. */
+export function ddimStep(
+ xt: Float64Array,
+ noisePred: Float64Array,
+ tPrev: number,
+ tCurr: number,
+ schedule: NoiseScheduleValues,
+ eta: number,
+): Float64Array {
+ const acpCurr = schedule.alphasCumprod[tCurr] ?? 0;
+ const acpPrev = tPrev >= 0 ? (schedule.alphasCumprod[tPrev] ?? 0) : 1.0;
+ const sqrtAcpCurr = Math.sqrt(acpCurr);
+ const sqrtOneMinusAcpCurr = Math.sqrt(1 - acpCurr);
+ const sqrtAcpPrev = Math.sqrt(acpPrev);
+
+ const sigmaT =
+ eta *
+ Math.sqrt(((1 - acpPrev) / (1 - acpCurr)) * (1 - acpCurr / acpPrev));
+
+ const result = new Float64Array(xt.length);
+ for (let i = 0; i < xt.length; i++) {
+ const xi = xt[i] ?? 0;
+ const ni = noisePred[i] ?? 0;
+ // Predict x0
+ const x0Pred = (xi - sqrtOneMinusAcpCurr * ni) / (sqrtAcpCurr || 1e-8);
+ // Direction pointing to xt
+ const dirXt = Math.sqrt(Math.max(0, 1 - acpPrev - sigmaT * sigmaT)) * ni;
+ result[i] = sqrtAcpPrev * x0Pred + dirXt;
+ }
+ return result;
+}
+
+/** Generate a sequence of timesteps for DDIM inference. */
+export function ddimTimesteps(
+ numTrainTimesteps: number,
+ numInferenceSteps: number,
+): number[] {
+ const step = Math.floor(numTrainTimesteps / numInferenceSteps);
+ const timesteps: number[] = [];
+ for (let i = numInferenceSteps - 1; i >= 0; i--) {
+ timesteps.push(i * step);
+ }
+ return timesteps;
+}
+
+/** Compute signal-to-noise ratio at timestep t. */
+export function snrAtTimestep(t: number, schedule: NoiseScheduleValues): number {
+ const acp = schedule.alphasCumprod[t] ?? 0;
+ return acp / (1 - acp + 1e-8);
+}
diff --git a/src/ml/ft_transformer.ts b/src/ml/ft_transformer.ts
new file mode 100644
index 00000000..534aa7ec
--- /dev/null
+++ b/src/ml/ft_transformer.ts
@@ -0,0 +1,205 @@
+/**
+ * Feature Tokenizer + Transformer (FT-Transformer) for tabular data.
+ *
+ * Implements the FT-Transformer architecture from Gorishniy et al. (2021):
+ * numerical and categorical feature tokenization, multi-head self-attention,
+ * feed-forward blocks, and a [CLS] token for classification/regression.
+ *
+ * @module
+ */
+
+/** Multi-head self-attention output. */
+export interface AttentionOutput {
+ /** Context vectors [seqLen x dModel]. */
+ context: Float64Array;
+ /** Attention weights [numHeads x seqLen x seqLen]. */
+ weights: Float64Array;
+}
+
+/** Scaled dot-product attention for a single head. */
+export function scaledDotProductAttention(
+ Q: Float64Array,
+ K: Float64Array,
+ V: Float64Array,
+ seqLen: number,
+ dHead: number,
+): { context: Float64Array; weights: Float64Array } {
+ const scale = Math.sqrt(dHead);
+ // Scores [seqLen x seqLen]
+ const scores = new Float64Array(seqLen * seqLen);
+ for (let i = 0; i < seqLen; i++) {
+ for (let j = 0; j < seqLen; j++) {
+ let dot = 0;
+ for (let d = 0; d < dHead; d++) {
+ dot += (Q[i * dHead + d] ?? 0) * (K[j * dHead + d] ?? 0);
+ }
+ scores[i * seqLen + j] = dot / scale;
+ }
+ }
+ // Softmax over rows
+ const weights = new Float64Array(seqLen * seqLen);
+ for (let i = 0; i < seqLen; i++) {
+ let maxScore = -Infinity;
+ for (let j = 0; j < seqLen; j++) maxScore = Math.max(maxScore, scores[i * seqLen + j] ?? 0);
+ let sumExp = 0;
+ for (let j = 0; j < seqLen; j++) {
+ const e = Math.exp((scores[i * seqLen + j] ?? 0) - maxScore);
+ weights[i * seqLen + j] = e;
+ sumExp += e;
+ }
+ for (let j = 0; j < seqLen; j++) {
+ weights[i * seqLen + j] = (weights[i * seqLen + j] ?? 0) / (sumExp || 1);
+ }
+ }
+ // Context [seqLen x dHead]
+ const context = new Float64Array(seqLen * dHead);
+ for (let i = 0; i < seqLen; i++) {
+ for (let d = 0; d < dHead; d++) {
+ let val = 0;
+ for (let j = 0; j < seqLen; j++) {
+ val += (weights[i * seqLen + j] ?? 0) * (V[j * dHead + d] ?? 0);
+ }
+ context[i * dHead + d] = val;
+ }
+ }
+ return { context, weights };
+}
+
+/** Layer normalization. */
+export function layerNorm(
+ x: Float64Array,
+ gamma: Float64Array,
+ beta: Float64Array,
+ eps: number = 1e-5,
+): Float64Array {
+ let mean = 0;
+ for (let i = 0; i < x.length; i++) mean += x[i] ?? 0;
+ mean /= x.length;
+ let variance = 0;
+ for (let i = 0; i < x.length; i++) {
+ const d = (x[i] ?? 0) - mean;
+ variance += d * d;
+ }
+ variance /= x.length;
+ const std = Math.sqrt(variance + eps);
+ const out = new Float64Array(x.length);
+ for (let i = 0; i < x.length; i++) {
+ out[i] = ((x[i] ?? 0) - mean) / std * (gamma[i] ?? 1) + (beta[i] ?? 0);
+ }
+ return out;
+}
+
+/** GELU activation (approximate). */
+export function gelu(x: number): number {
+ return 0.5 * x * (1 + Math.tanh(Math.sqrt(2 / Math.PI) * (x + 0.044715 * x * x * x)));
+}
+
+/** Feed-forward block: Linear -> GELU -> Linear. */
+export function feedForward(
+ x: Float64Array,
+ W1: Float64Array,
+ b1: Float64Array,
+ W2: Float64Array,
+ b2: Float64Array,
+ dModel: number,
+ dFF: number,
+): Float64Array {
+ // First layer
+ const h = new Float64Array(dFF);
+ for (let j = 0; j < dFF; j++) {
+ let sum = b1[j] ?? 0;
+ for (let i = 0; i < dModel; i++) {
+ sum += (W1[j * dModel + i] ?? 0) * (x[i] ?? 0);
+ }
+ h[j] = gelu(sum);
+ }
+ // Second layer
+ const out = new Float64Array(dModel);
+ for (let i = 0; i < dModel; i++) {
+ let sum = b2[i] ?? 0;
+ for (let j = 0; j < dFF; j++) {
+ sum += (W2[i * dFF + j] ?? 0) * (h[j] ?? 0);
+ }
+ out[i] = sum;
+ }
+ return out;
+}
+
+/** Tokenize a numerical feature: embed to dModel dimensions. */
+export function tokenizeNumerical(
+ value: number,
+ embedding: Float64Array,
+ bias: Float64Array,
+): Float64Array {
+ const out = new Float64Array(embedding.length);
+ for (let i = 0; i < embedding.length; i++) {
+ out[i] = value * (embedding[i] ?? 0) + (bias[i] ?? 0);
+ }
+ return out;
+}
+
+/** Tokenize a categorical feature via embedding lookup. */
+export function tokenizeCategorical(
+ categoryIndex: number,
+ embeddingMatrix: Float64Array,
+ numCategories: number,
+ dModel: number,
+): Float64Array {
+ const idx = Math.max(0, Math.min(categoryIndex, numCategories - 1));
+ const out = new Float64Array(dModel);
+ for (let i = 0; i < dModel; i++) {
+ out[i] = embeddingMatrix[idx * dModel + i] ?? 0;
+ }
+ return out;
+}
+
+/** Add residual connection. */
+export function addResidual(x: Float64Array, residual: Float64Array): Float64Array {
+ const out = new Float64Array(x.length);
+ for (let i = 0; i < x.length; i++) {
+ out[i] = (x[i] ?? 0) + (residual[i] ?? 0);
+ }
+ return out;
+}
+
+/** Extract [CLS] token representation (first token). */
+export function extractCLSToken(tokens: Float64Array, dModel: number): Float64Array {
+ const cls = new Float64Array(dModel);
+ for (let i = 0; i < dModel; i++) {
+ cls[i] = tokens[i] ?? 0;
+ }
+ return cls;
+}
+
+/** Simple linear classifier head. */
+export function linearHead(
+ x: Float64Array,
+ W: Float64Array,
+ b: Float64Array,
+ numClasses: number,
+ dModel: number,
+): Float64Array {
+ const logits = new Float64Array(numClasses);
+ for (let c = 0; c < numClasses; c++) {
+ let sum = b[c] ?? 0;
+ for (let i = 0; i < dModel; i++) {
+ sum += (W[c * dModel + i] ?? 0) * (x[i] ?? 0);
+ }
+ logits[c] = sum;
+ }
+ return logits;
+}
+
+/** Softmax activation. */
+export function softmax(logits: Float64Array): Float64Array {
+ let maxVal = -Infinity;
+ for (let i = 0; i < logits.length; i++) maxVal = Math.max(maxVal, logits[i] ?? 0);
+ let sumExp = 0;
+ const out = new Float64Array(logits.length);
+ for (let i = 0; i < logits.length; i++) {
+ out[i] = Math.exp((logits[i] ?? 0) - maxVal);
+ sumExp += out[i] ?? 0;
+ }
+ for (let i = 0; i < out.length; i++) out[i] = (out[i] ?? 0) / (sumExp || 1);
+ return out;
+}
diff --git a/src/ml/gradient_boosting.ts b/src/ml/gradient_boosting.ts
new file mode 100644
index 00000000..ba44af49
--- /dev/null
+++ b/src/ml/gradient_boosting.ts
@@ -0,0 +1,216 @@
+/**
+ * Gradient Boosting from scratch.
+ *
+ * Implements gradient boosted regression trees (GBRT) with:
+ * - Mean squared error and log-loss objectives
+ * - CART decision trees as base learners
+ * - Shrinkage (learning rate), subsampling, and max depth
+ *
+ * @module
+ */
+
+/** A leaf or internal node in a decision tree. */
+export type TreeNode =
+ | { kind: "leaf"; value: number }
+ | { kind: "internal"; featureIndex: number; threshold: number; left: TreeNode; right: TreeNode };
+
+/** Decision tree parameters. */
+export interface TreeParams {
+ maxDepth: number;
+ minSamplesLeaf: number;
+}
+
+/** Predict with a decision tree. */
+export function treePredictOne(node: TreeNode, x: Float64Array): number {
+ if (node.kind === "leaf") return node.value;
+ const val = x[node.featureIndex] ?? 0;
+ return val <= node.threshold
+ ? treePredictOne(node.left, x)
+ : treePredictOne(node.right, x);
+}
+
+/** Predict for an array of samples. */
+export function treePredict(node: TreeNode, X: Float64Array[]): Float64Array {
+ return Float64Array.from(X.map((x) => treePredictOne(node, x)));
+}
+
+/** Compute mean of an array of values at given indices. */
+function meanAt(y: Float64Array, indices: number[]): number {
+ if (indices.length === 0) return 0;
+ let s = 0;
+ for (const i of indices) s += y[i] ?? 0;
+ return s / indices.length;
+}
+
+/** Compute MSE of an array at given indices. */
+function mseAt(y: Float64Array, indices: number[], mean: number): number {
+ let s = 0;
+ for (const i of indices) {
+ const d = (y[i] ?? 0) - mean;
+ s += d * d;
+ }
+ return s;
+}
+
+/** Find best split for a set of indices. */
+function findBestSplit(
+ X: Float64Array[],
+ y: Float64Array,
+ indices: number[],
+ numFeatures: number,
+): { featureIndex: number; threshold: number; gain: number } | null {
+ const parentMean = meanAt(y, indices);
+ const parentMSE = mseAt(y, indices, parentMean);
+ let bestGain = -Infinity;
+ let bestFeature = 0;
+ let bestThreshold = 0;
+
+ for (let f = 0; f < numFeatures; f++) {
+ // Sort indices by feature value
+ const sorted = [...indices].sort((a, b) => (X[a]![f] ?? 0) - (X[b]![f] ?? 0));
+ for (let k = 1; k < sorted.length; k++) {
+ const threshold = ((X[sorted[k - 1]!]![f] ?? 0) + (X[sorted[k]!]![f] ?? 0)) / 2;
+ const left = sorted.slice(0, k);
+ const right = sorted.slice(k);
+ const lMean = meanAt(y, left);
+ const rMean = meanAt(y, right);
+ const gain = parentMSE - mseAt(y, left, lMean) - mseAt(y, right, rMean);
+ if (gain > bestGain) {
+ bestGain = gain;
+ bestFeature = f;
+ bestThreshold = threshold;
+ }
+ }
+ }
+
+ return bestGain > 0 ? { featureIndex: bestFeature, threshold: bestThreshold, gain: bestGain } : null;
+}
+
+/** Build a regression tree on gradient residuals. */
+export function buildTree(
+ X: Float64Array[],
+ y: Float64Array,
+ indices: number[],
+ depth: number,
+ params: TreeParams,
+): TreeNode {
+ const leafVal = meanAt(y, indices);
+
+ if (depth >= params.maxDepth || indices.length <= params.minSamplesLeaf * 2) {
+ return { kind: "leaf", value: leafVal };
+ }
+
+ const numFeatures = X[0]?.length ?? 0;
+ const split = findBestSplit(X, y, indices, numFeatures);
+
+ if (split === null) {
+ return { kind: "leaf", value: leafVal };
+ }
+
+ const left: number[] = [];
+ const right: number[] = [];
+ for (const i of indices) {
+ if ((X[i]![split.featureIndex] ?? 0) <= split.threshold) {
+ left.push(i);
+ } else {
+ right.push(i);
+ }
+ }
+
+ if (left.length < params.minSamplesLeaf || right.length < params.minSamplesLeaf) {
+ return { kind: "leaf", value: leafVal };
+ }
+
+ return {
+ kind: "internal",
+ featureIndex: split.featureIndex,
+ threshold: split.threshold,
+ left: buildTree(X, y, left, depth + 1, params),
+ right: buildTree(X, y, right, depth + 1, params),
+ };
+}
+
+/** Gradient boosting ensemble. */
+export interface GBMEnsemble {
+ trees: TreeNode[];
+ learningRate: number;
+ initialPrediction: number;
+}
+
+/** MSE negative gradient (residuals). */
+export function mseGradient(y: Float64Array, yPred: Float64Array): Float64Array {
+ const g = new Float64Array(y.length);
+ for (let i = 0; i < y.length; i++) g[i] = (y[i] ?? 0) - (yPred[i] ?? 0);
+ return g;
+}
+
+/** Train gradient boosted regression trees. */
+export function fitGBM(
+ X: Float64Array[],
+ y: Float64Array,
+ nEstimators: number = 100,
+ learningRate: number = 0.1,
+ maxDepth: number = 3,
+ minSamplesLeaf: number = 1,
+): GBMEnsemble {
+ const n = y.length;
+ let sumY = 0;
+ for (let i = 0; i < n; i++) sumY += y[i] ?? 0;
+ const initialPrediction = sumY / (n || 1);
+
+ const yPred = new Float64Array(n).fill(initialPrediction);
+ const trees: TreeNode[] = [];
+ const params: TreeParams = { maxDepth, minSamplesLeaf };
+ const indices = Array.from({ length: n }, (_, i) => i);
+
+ for (let m = 0; m < nEstimators; m++) {
+ const residuals = mseGradient(y, yPred);
+ const tree = buildTree(X, residuals, indices, 0, params);
+ trees.push(tree);
+ const treePreds = treePredict(tree, X);
+ for (let i = 0; i < n; i++) {
+ yPred[i] = (yPred[i] ?? 0) + learningRate * (treePreds[i] ?? 0);
+ }
+ }
+
+ return { trees, learningRate, initialPrediction };
+}
+
+/** Predict with a trained GBM ensemble. */
+export function predictGBM(ensemble: GBMEnsemble, X: Float64Array[]): Float64Array {
+ const n = X.length;
+ const yPred = new Float64Array(n).fill(ensemble.initialPrediction);
+ for (const tree of ensemble.trees) {
+ const preds = treePredict(tree, X);
+ for (let i = 0; i < n; i++) {
+ yPred[i] = (yPred[i] ?? 0) + ensemble.learningRate * (preds[i] ?? 0);
+ }
+ }
+ return yPred;
+}
+
+/** Compute mean squared error. */
+export function mse(y: Float64Array, yPred: Float64Array): number {
+ let s = 0;
+ for (let i = 0; i < y.length; i++) {
+ const d = (y[i] ?? 0) - (yPred[i] ?? 0);
+ s += d * d;
+ }
+ return s / (y.length || 1);
+}
+
+/** Compute R² score. */
+export function r2Score(y: Float64Array, yPred: Float64Array): number {
+ let sumY = 0;
+ for (let i = 0; i < y.length; i++) sumY += y[i] ?? 0;
+ const meanY = sumY / (y.length || 1);
+ let ssTot = 0;
+ let ssRes = 0;
+ for (let i = 0; i < y.length; i++) {
+ const d1 = (y[i] ?? 0) - meanY;
+ const d2 = (y[i] ?? 0) - (yPred[i] ?? 0);
+ ssTot += d1 * d1;
+ ssRes += d2 * d2;
+ }
+ return 1 - ssRes / (ssTot || 1);
+}
diff --git a/src/ml/neural_network.ts b/src/ml/neural_network.ts
new file mode 100644
index 00000000..c3389451
--- /dev/null
+++ b/src/ml/neural_network.ts
@@ -0,0 +1,200 @@
+/**
+ * Neural Network layers and training utilities from scratch.
+ *
+ * Implements dense layers, activations, loss functions, and
+ * mini-batch SGD/Adam optimizer for feed-forward networks.
+ *
+ * @module
+ */
+
+/** Dense layer forward pass: y = W x + b. */
+export function denseForward(
+ x: Float64Array,
+ W: Float64Array,
+ b: Float64Array,
+ outDim: number,
+ inDim: number,
+): Float64Array {
+ const y = new Float64Array(outDim);
+ for (let i = 0; i < outDim; i++) {
+ let sum = b[i] ?? 0;
+ for (let j = 0; j < inDim; j++) {
+ sum += (W[i * inDim + j] ?? 0) * (x[j] ?? 0);
+ }
+ y[i] = sum;
+ }
+ return y;
+}
+
+/** Dense layer backward pass: returns dL/dx, dL/dW, dL/db. */
+export function denseBackward(
+ x: Float64Array,
+ W: Float64Array,
+ dOut: Float64Array,
+ outDim: number,
+ inDim: number,
+): { dX: Float64Array; dW: Float64Array; dB: Float64Array } {
+ const dX = new Float64Array(inDim);
+ const dW = new Float64Array(outDim * inDim);
+ const dB = new Float64Array(outDim);
+
+ for (let i = 0; i < outDim; i++) {
+ dB[i] = dOut[i] ?? 0;
+ for (let j = 0; j < inDim; j++) {
+ dW[i * inDim + j] = (dOut[i] ?? 0) * (x[j] ?? 0);
+ dX[j] = (dX[j] ?? 0) + (dOut[i] ?? 0) * (W[i * inDim + j] ?? 0);
+ }
+ }
+ return { dX, dW, dB };
+}
+
+/** ReLU activation and its gradient. */
+export function relu(x: Float64Array): Float64Array {
+ const out = new Float64Array(x.length);
+ for (let i = 0; i < x.length; i++) out[i] = Math.max(0, x[i] ?? 0);
+ return out;
+}
+
+export function reluGrad(x: Float64Array, dOut: Float64Array): Float64Array {
+ const dX = new Float64Array(x.length);
+ for (let i = 0; i < x.length; i++) dX[i] = (x[i] ?? 0) > 0 ? (dOut[i] ?? 0) : 0;
+ return dX;
+}
+
+/** Sigmoid activation and its gradient. */
+export function sigmoidActivation(x: Float64Array): Float64Array {
+ const out = new Float64Array(x.length);
+ for (let i = 0; i < x.length; i++) out[i] = 1 / (1 + Math.exp(-(x[i] ?? 0)));
+ return out;
+}
+
+export function sigmoidGrad(out: Float64Array, dOut: Float64Array): Float64Array {
+ const dX = new Float64Array(out.length);
+ for (let i = 0; i < out.length; i++) {
+ const s = out[i] ?? 0;
+ dX[i] = s * (1 - s) * (dOut[i] ?? 0);
+ }
+ return dX;
+}
+
+/** Softmax and cross-entropy loss (combined for numerical stability). */
+export function softmaxCrossEntropy(
+ logits: Float64Array,
+ labels: Float64Array,
+): { loss: number; dLogits: Float64Array } {
+ const n = logits.length;
+ let maxLogit = -Infinity;
+ for (let i = 0; i < n; i++) maxLogit = Math.max(maxLogit, logits[i] ?? -Infinity);
+
+ let sumExp = 0;
+ const probs = new Float64Array(n);
+ for (let i = 0; i < n; i++) {
+ probs[i] = Math.exp((logits[i] ?? 0) - maxLogit);
+ sumExp += probs[i] ?? 0;
+ }
+ let loss = 0;
+ for (let i = 0; i < n; i++) {
+ probs[i] = (probs[i] ?? 0) / (sumExp || 1);
+ const label = labels[i] ?? 0;
+ if (label > 0) loss -= label * Math.log(Math.max(probs[i] ?? 0, 1e-12));
+ }
+
+ const dLogits = new Float64Array(n);
+ for (let i = 0; i < n; i++) dLogits[i] = (probs[i] ?? 0) - (labels[i] ?? 0);
+ return { loss, dLogits };
+}
+
+/** MSE loss and gradient. */
+export function mseLoss(
+ pred: Float64Array,
+ target: Float64Array,
+): { loss: number; dPred: Float64Array } {
+ const dPred = new Float64Array(pred.length);
+ let loss = 0;
+ for (let i = 0; i < pred.length; i++) {
+ const d = (pred[i] ?? 0) - (target[i] ?? 0);
+ loss += d * d;
+ dPred[i] = 2 * d / pred.length;
+ }
+ return { loss: loss / pred.length, dPred };
+}
+
+/** Adam optimizer state. */
+export interface AdamState {
+ m: Float64Array;
+ v: Float64Array;
+ t: number;
+ lr: number;
+ beta1: number;
+ beta2: number;
+ eps: number;
+}
+
+/** Initialize Adam optimizer state for a parameter vector. */
+export function initAdam(
+ paramSize: number,
+ lr: number = 1e-3,
+ beta1: number = 0.9,
+ beta2: number = 0.999,
+ eps: number = 1e-8,
+): AdamState {
+ return { m: new Float64Array(paramSize), v: new Float64Array(paramSize), t: 0, lr, beta1, beta2, eps };
+}
+
+/** Adam update step: modifies params in-place, returns updated AdamState. */
+export function adamStep(
+ params: Float64Array,
+ grads: Float64Array,
+ state: AdamState,
+): AdamState {
+ const { lr, beta1, beta2, eps } = state;
+ const t = state.t + 1;
+ const m = new Float64Array(params.length);
+ const v = new Float64Array(params.length);
+
+ for (let i = 0; i < params.length; i++) {
+ const g = grads[i] ?? 0;
+ m[i] = beta1 * (state.m[i] ?? 0) + (1 - beta1) * g;
+ v[i] = beta2 * (state.v[i] ?? 0) + (1 - beta2) * g * g;
+ const mHat = (m[i] ?? 0) / (1 - Math.pow(beta1, t));
+ const vHat = (v[i] ?? 0) / (1 - Math.pow(beta2, t));
+ params[i] = (params[i] ?? 0) - lr * mHat / (Math.sqrt(vHat) + eps);
+ }
+
+ return { ...state, m, v, t };
+}
+
+/** He initialization for ReLU networks. */
+export function heInit(inDim: number, outDim: number, rngSeed: number = 42): Float64Array {
+ const weights = new Float64Array(outDim * inDim);
+ let state = rngSeed >>> 0;
+ const std = Math.sqrt(2 / inDim);
+ for (let i = 0; i < weights.length; i++) {
+ // Box-Muller transform for Gaussian noise
+ state = (Math.imul(1664525, state) + 1013904223) >>> 0;
+ const u1 = (state / 4294967296) || 1e-10;
+ state = (Math.imul(1664525, state) + 1013904223) >>> 0;
+ const u2 = state / 4294967296;
+ weights[i] = std * Math.sqrt(-2 * Math.log(u1)) * Math.cos(2 * Math.PI * u2);
+ }
+ return weights;
+}
+
+/** Dropout mask (for training). */
+export function dropoutMask(size: number, rate: number, rngSeed: number): Float64Array {
+ const mask = new Float64Array(size);
+ let state = rngSeed >>> 0;
+ const scale = 1 / (1 - rate);
+ for (let i = 0; i < size; i++) {
+ state = (Math.imul(1664525, state) + 1013904223) >>> 0;
+ mask[i] = state / 4294967296 > rate ? scale : 0;
+ }
+ return mask;
+}
+
+/** Apply dropout mask to activations. */
+export function applyDropout(x: Float64Array, mask: Float64Array): Float64Array {
+ const out = new Float64Array(x.length);
+ for (let i = 0; i < x.length; i++) out[i] = (x[i] ?? 0) * (mask[i] ?? 0);
+ return out;
+}
diff --git a/src/ml/random_forest.ts b/src/ml/random_forest.ts
new file mode 100644
index 00000000..9b54d12f
--- /dev/null
+++ b/src/ml/random_forest.ts
@@ -0,0 +1,173 @@
+/**
+ * Random Forest implementation from scratch.
+ *
+ * Implements bootstrap aggregation (bagging) of CART decision trees
+ * with random feature subsampling at each split (random forest).
+ * Supports both classification and regression.
+ *
+ * @module
+ */
+
+import { buildTree, treePredict, type TreeParams, type TreeNode } from "./gradient_boosting.ts";
+
+/** Random Forest configuration. */
+export interface RandomForestConfig {
+ nEstimators: number;
+ maxDepth: number;
+ minSamplesLeaf: number;
+ /** Number of features to consider at each split (0 = sqrt(n_features)). */
+ maxFeatures: number;
+ /** Bootstrap sample size (0 = n_samples). */
+ sampleSize: number;
+ seed: number;
+}
+
+/** LCG pseudo-random number generator. */
+export class LCGRandom {
+ private state: number;
+ constructor(seed: number) {
+ this.state = seed >>> 0;
+ }
+ next(): number {
+ this.state = (Math.imul(1664525, this.state) + 1013904223) >>> 0;
+ return this.state / 4294967296;
+ }
+ nextInt(n: number): number {
+ return Math.floor(this.next() * n);
+ }
+}
+
+/** Bootstrap sample indices. */
+function bootstrapSample(n: number, size: number, rng: LCGRandom): number[] {
+ const indices: number[] = [];
+ for (let i = 0; i < size; i++) indices.push(rng.nextInt(n));
+ return indices;
+}
+
+function selectFeatures(numFeatures: number, maxFeatures: number, rng: LCGRandom): number[] {
+ const k = maxFeatures <= 0 ? Math.max(1, Math.floor(Math.sqrt(numFeatures))) : Math.min(maxFeatures, numFeatures);
+ const all = Array.from({ length: numFeatures }, (_, i) => i);
+ // Fisher-Yates shuffle and take first k
+ for (let i = numFeatures - 1; i > 0; i--) {
+ const j = rng.nextInt(i + 1);
+ [all[i], all[j]] = [all[j]!, all[i]!];
+ }
+ return all.slice(0, k);
+}
+
+/** Random Forest model. */
+export interface RandomForestModel {
+ trees: TreeNode[];
+ featureMaps: number[][];
+ config: RandomForestConfig;
+ numFeatures: number;
+}
+
+/** Train a Random Forest regressor. */
+export function fitRandomForest(
+ X: Float64Array[],
+ y: Float64Array,
+ config: Partial = {},
+): RandomForestModel {
+ const cfg: RandomForestConfig = {
+ nEstimators: 100,
+ maxDepth: 5,
+ minSamplesLeaf: 1,
+ maxFeatures: 0,
+ sampleSize: 0,
+ seed: 42,
+ ...config,
+ };
+
+ const n = X.length;
+ const numFeatures = X[0]?.length ?? 0;
+ const sampleSize = cfg.sampleSize <= 0 ? n : cfg.sampleSize;
+ const rng = new LCGRandom(cfg.seed);
+ const params: TreeParams = { maxDepth: cfg.maxDepth, minSamplesLeaf: cfg.minSamplesLeaf };
+
+ const trees: TreeNode[] = [];
+ const featureMaps: number[][] = [];
+
+ for (let t = 0; t < cfg.nEstimators; t++) {
+ const indices = bootstrapSample(n, sampleSize, rng);
+ const k = cfg.maxFeatures <= 0 ? Math.max(1, Math.floor(Math.sqrt(numFeatures))) : Math.min(cfg.maxFeatures, numFeatures);
+ const featureSubset = selectFeatures(numFeatures, k, rng);
+ featureMaps.push(featureSubset);
+
+ // Build tree on bootstrap sample with projected features
+ const subX: Float64Array[] = [];
+ const subY = new Float64Array(indices.length);
+ for (let i = 0; i < indices.length; i++) {
+ const xi = X[indices[i]!]!;
+ const projected = new Float64Array(featureSubset.length);
+ for (let j = 0; j < featureSubset.length; j++) {
+ projected[j] = xi[featureSubset[j]!] ?? 0;
+ }
+ subX.push(projected);
+ subY[i] = y[indices[i]!] ?? 0;
+ }
+ const allIdx = Array.from({ length: indices.length }, (_, i) => i);
+ trees.push(buildTree(subX, subY, allIdx, 0, params));
+ }
+
+ return { trees, featureMaps, config: cfg, numFeatures };
+}
+
+/** Predict with a Random Forest (regression: average of trees). */
+export function predictRandomForest(model: RandomForestModel, X: Float64Array[]): Float64Array {
+ const n = X.length;
+ const sum = new Float64Array(n);
+
+ for (let t = 0; t < model.trees.length; t++) {
+ const featureMap = model.featureMaps[t]!;
+ const projX: Float64Array[] = X.map((xi) => {
+ const p = new Float64Array(featureMap.length);
+ for (let j = 0; j < featureMap.length; j++) p[j] = xi[featureMap[j]!] ?? 0;
+ return p;
+ });
+ const preds = treePredict(model.trees[t]!, projX);
+ for (let i = 0; i < n; i++) sum[i] = (sum[i] ?? 0) + (preds[i] ?? 0);
+ }
+
+ const out = new Float64Array(n);
+ for (let i = 0; i < n; i++) out[i] = (sum[i] ?? 0) / (model.trees.length || 1);
+ return out;
+}
+
+/** Out-of-bag (OOB) error estimate for regression. */
+export function oobError(
+ model: RandomForestModel,
+ X: Float64Array[],
+ y: Float64Array,
+ seed: number,
+): number {
+ const n = X.length;
+ const rng = new LCGRandom(seed);
+ const sampleSize = model.config.sampleSize <= 0 ? n : model.config.sampleSize;
+ const oobPreds = new Float64Array(n);
+ const oobCounts = new Int32Array(n);
+
+ for (let t = 0; t < model.trees.length; t++) {
+ const bootstrapSet = new Set(bootstrapSample(n, sampleSize, rng));
+ const featureMap = model.featureMaps[t]!;
+ for (let i = 0; i < n; i++) {
+ if (bootstrapSet.has(i)) continue;
+ const xi = X[i]!;
+ const projected = new Float64Array(featureMap.length);
+ for (let j = 0; j < featureMap.length; j++) projected[j] = xi[featureMap[j]!] ?? 0;
+ oobPreds[i] += treePredict(model.trees[t]!, [projected])[0] ?? 0;
+ oobCounts[i]++;
+ }
+ }
+
+ let sse = 0;
+ let cnt = 0;
+ for (let i = 0; i < n; i++) {
+ if ((oobCounts[i] ?? 0) === 0) continue;
+ const pred = (oobPreds[i] ?? 0) / (oobCounts[i] ?? 1);
+ const err = (y[i] ?? 0) - pred;
+ sse += err * err;
+ cnt++;
+ }
+ return cnt > 0 ? sse / cnt : 0;
+}
diff --git a/src/ml/seq2seq.ts b/src/ml/seq2seq.ts
new file mode 100644
index 00000000..adc08f2b
--- /dev/null
+++ b/src/ml/seq2seq.ts
@@ -0,0 +1,207 @@
+/**
+ * Seq2Seq (Sequence-to-Sequence) with Bahdanau attention.
+ *
+ * Implements the encoder-decoder architecture with additive attention
+ * for tasks like machine translation, summarization, and transcription.
+ * Uses simple RNN (Elman) cells as building blocks.
+ *
+ * @module
+ */
+
+/** Sigmoid activation. */
+export function sigmoid(x: number): number {
+ return 1 / (1 + Math.exp(-x));
+}
+
+/** Tanh activation. */
+export function tanh(x: number): number {
+ return Math.tanh(x);
+}
+
+/** Simple RNN (Elman) cell: h_t = tanh(W_h * h_{t-1} + W_x * x_t + b). */
+export interface RNNCellWeights {
+ Wh: Float64Array; // [hiddenSize x hiddenSize]
+ Wx: Float64Array; // [hiddenSize x inputSize]
+ b: Float64Array; // [hiddenSize]
+ hiddenSize: number;
+ inputSize: number;
+}
+
+export function rnnCell(
+ x: Float64Array,
+ hPrev: Float64Array,
+ weights: RNNCellWeights,
+): Float64Array {
+ const { hiddenSize, inputSize } = weights;
+ const h = new Float64Array(hiddenSize);
+ for (let i = 0; i < hiddenSize; i++) {
+ let val = weights.b[i] ?? 0;
+ for (let j = 0; j < hiddenSize; j++) {
+ val += (weights.Wh[i * hiddenSize + j] ?? 0) * (hPrev[j] ?? 0);
+ }
+ for (let j = 0; j < inputSize; j++) {
+ val += (weights.Wx[i * inputSize + j] ?? 0) * (x[j] ?? 0);
+ }
+ h[i] = tanh(val);
+ }
+ return h;
+}
+
+/** Encode a sequence. Returns all hidden states [seqLen x hiddenSize]. */
+export function encode(
+ inputs: Float64Array[],
+ initialHidden: Float64Array,
+ weights: RNNCellWeights,
+): Float64Array[] {
+ const states: Float64Array[] = [];
+ let h = initialHidden;
+ for (const x of inputs) {
+ h = rnnCell(x, h, weights);
+ states.push(h);
+ }
+ return states;
+}
+
+/** Bahdanau (additive) attention weights. */
+export interface BahdanauWeights {
+ Wa: Float64Array; // [alignDim x hiddenSize] for encoder states
+ Ua: Float64Array; // [alignDim x hiddenSize] for decoder hidden
+ va: Float64Array; // [alignDim]
+ alignDim: number;
+ hiddenSize: number;
+}
+
+/** Compute Bahdanau attention weights. */
+export function bahdanauAttention(
+ decoderHidden: Float64Array,
+ encoderStates: Float64Array[],
+ weights: BahdanauWeights,
+): { context: Float64Array; alphas: Float64Array } {
+ const { alignDim, hiddenSize } = weights;
+ const seqLen = encoderStates.length;
+ const scores = new Float64Array(seqLen);
+
+ // Decoder contribution (constant for all encoder states)
+ const decoderContrib = new Float64Array(alignDim);
+ for (let k = 0; k < alignDim; k++) {
+ let s = 0;
+ for (let j = 0; j < hiddenSize; j++) {
+ s += (weights.Ua[k * hiddenSize + j] ?? 0) * (decoderHidden[j] ?? 0);
+ }
+ decoderContrib[k] = s;
+ }
+
+ for (let t = 0; t < seqLen; t++) {
+ const enc = encoderStates[t]!;
+ let score = 0;
+ for (let k = 0; k < alignDim; k++) {
+ let encContrib = 0;
+ for (let j = 0; j < hiddenSize; j++) {
+ encContrib += (weights.Wa[k * hiddenSize + j] ?? 0) * (enc[j] ?? 0);
+ }
+ score += (weights.va[k] ?? 0) * tanh(encContrib + (decoderContrib[k] ?? 0));
+ }
+ scores[t] = score;
+ }
+
+ // Softmax
+ let maxScore = -Infinity;
+ for (let t = 0; t < seqLen; t++) maxScore = Math.max(maxScore, scores[t] ?? -Infinity);
+ let sumExp = 0;
+ const alphas = new Float64Array(seqLen);
+ for (let t = 0; t < seqLen; t++) {
+ alphas[t] = Math.exp((scores[t] ?? 0) - maxScore);
+ sumExp += alphas[t] ?? 0;
+ }
+ for (let t = 0; t < seqLen; t++) alphas[t] = (alphas[t] ?? 0) / (sumExp || 1);
+
+ // Context vector
+ const context = new Float64Array(hiddenSize);
+ for (let t = 0; t < seqLen; t++) {
+ const enc = encoderStates[t]!;
+ for (let j = 0; j < hiddenSize; j++) {
+ context[j] += (alphas[t] ?? 0) * (enc[j] ?? 0);
+ }
+ }
+ return { context, alphas };
+}
+
+/** Decoder step with attention. */
+export interface DecoderWeights {
+ rnn: RNNCellWeights;
+ attention: BahdanauWeights;
+ outputW: Float64Array; // [vocabSize x hiddenSize]
+ outputB: Float64Array; // [vocabSize]
+ vocabSize: number;
+}
+
+export interface DecoderStepResult {
+ hidden: Float64Array;
+ logits: Float64Array;
+ attentionWeights: Float64Array;
+}
+
+export function decoderStep(
+ inputEmbedding: Float64Array,
+ prevHidden: Float64Array,
+ encoderStates: Float64Array[],
+ weights: DecoderWeights,
+): DecoderStepResult {
+ const { context, alphas } = bahdanauAttention(prevHidden, encoderStates, weights.attention);
+
+ // Concatenate input embedding and context
+ const rnnInput = new Float64Array(inputEmbedding.length + context.length);
+ for (let i = 0; i < inputEmbedding.length; i++) rnnInput[i] = inputEmbedding[i] ?? 0;
+ for (let i = 0; i < context.length; i++) rnnInput[inputEmbedding.length + i] = context[i] ?? 0;
+
+ const hidden = rnnCell(rnnInput, prevHidden, weights.rnn);
+
+ // Project to vocabulary
+ const logits = new Float64Array(weights.vocabSize);
+ for (let v = 0; v < weights.vocabSize; v++) {
+ let sum = weights.outputB[v] ?? 0;
+ for (let j = 0; j < hidden.length; j++) {
+ sum += (weights.outputW[v * hidden.length + j] ?? 0) * (hidden[j] ?? 0);
+ }
+ logits[v] = sum;
+ }
+
+ return { hidden, logits, attentionWeights: alphas };
+}
+
+/** Greedy decode: pick argmax at each step. */
+export function greedyDecode(
+ encoderStates: Float64Array[],
+ initialHidden: Float64Array,
+ sosEmbedding: Float64Array,
+ embeddingMatrix: Float64Array,
+ weights: DecoderWeights,
+ maxLen: number,
+ eosTokenId: number,
+): number[] {
+ const result: number[] = [];
+ let hidden = initialHidden;
+ let currentEmb = sosEmbedding;
+
+ for (let step = 0; step < maxLen; step++) {
+ const { hidden: newH, logits } = decoderStep(currentEmb, hidden, encoderStates, weights);
+ hidden = newH;
+ let bestIdx = 0;
+ let bestScore = -Infinity;
+ for (let v = 0; v < logits.length; v++) {
+ if ((logits[v] ?? -Infinity) > bestScore) {
+ bestScore = logits[v] ?? -Infinity;
+ bestIdx = v;
+ }
+ }
+ result.push(bestIdx);
+ if (bestIdx === eosTokenId) break;
+ // Get next embedding
+ const dEmb = weights.rnn.inputSize - weights.attention.hiddenSize;
+ currentEmb = new Float64Array(dEmb);
+ for (let i = 0; i < dEmb; i++) {
+ currentEmb[i] = embeddingMatrix[bestIdx * dEmb + i] ?? 0;
+ }
+ }
+ return result;
+}
diff --git a/src/ml/svm.ts b/src/ml/svm.ts
new file mode 100644
index 00000000..198c3a85
--- /dev/null
+++ b/src/ml/svm.ts
@@ -0,0 +1,197 @@
+/**
+ * Support Vector Machine (SVM) with SMO algorithm.
+ *
+ * Implements binary SVM classification with:
+ * - Linear, polynomial, and RBF kernels
+ * - Sequential Minimal Optimization (SMO) training
+ * - Support vector regression (SVR) epsilon-insensitive loss
+ *
+ * @module
+ */
+
+/** Kernel function type. */
+export type KernelType = "linear" | "poly" | "rbf";
+
+/** SVM kernel configuration. */
+export interface SVMKernelConfig {
+ type: KernelType;
+ /** Gamma for RBF and polynomial kernels. */
+ gamma: number;
+ /** Degree for polynomial kernel. */
+ degree: number;
+ /** Coef0 for polynomial kernel. */
+ coef0: number;
+}
+
+/** Compute kernel between two vectors. */
+export function computeKernel(
+ x1: Float64Array,
+ x2: Float64Array,
+ config: SVMKernelConfig,
+): number {
+ if (config.type === "linear") {
+ let dot = 0;
+ for (let i = 0; i < x1.length; i++) dot += (x1[i] ?? 0) * (x2[i] ?? 0);
+ return dot;
+ } else if (config.type === "rbf") {
+ let sqDist = 0;
+ for (let i = 0; i < x1.length; i++) {
+ const d = (x1[i] ?? 0) - (x2[i] ?? 0);
+ sqDist += d * d;
+ }
+ return Math.exp(-config.gamma * sqDist);
+ } else {
+ // Polynomial
+ let dot = 0;
+ for (let i = 0; i < x1.length; i++) dot += (x1[i] ?? 0) * (x2[i] ?? 0);
+ return (config.gamma * dot + config.coef0) ** config.degree;
+ }
+}
+
+/** Trained SVM model. */
+export interface SVMModel {
+ /** Lagrange multipliers (support vector weights). */
+ alphas: Float64Array;
+ /** Training labels (-1 or +1). */
+ labels: Float64Array;
+ /** Training features. */
+ supportVectors: Float64Array[];
+ /** Bias term. */
+ b: number;
+ kernel: SVMKernelConfig;
+}
+
+/** Compute decision function value for a single sample. */
+export function svmDecision(model: SVMModel, x: Float64Array): number {
+ let sum = -model.b;
+ for (let i = 0; i < model.supportVectors.length; i++) {
+ const alpha = model.alphas[i] ?? 0;
+ if (Math.abs(alpha) < 1e-8) continue;
+ sum += alpha * (model.labels[i] ?? 0) * computeKernel(model.supportVectors[i]!, x, model.kernel);
+ }
+ return sum;
+}
+
+/** Predict class labels for an array of samples. */
+export function svmPredict(model: SVMModel, X: Float64Array[]): Int8Array {
+ const out = new Int8Array(X.length);
+ for (let i = 0; i < X.length; i++) {
+ out[i] = svmDecision(model, X[i]!) >= 0 ? 1 : -1;
+ }
+ return out;
+}
+
+/** SMO training algorithm for binary SVM. */
+export function fitSVM(
+ X: Float64Array[],
+ y: Float64Array,
+ C: number = 1.0,
+ kernel: SVMKernelConfig = { type: "rbf", gamma: 0.1, degree: 3, coef0: 0 },
+ maxIter: number = 200,
+ tol: number = 1e-3,
+): SVMModel {
+ const n = X.length;
+ const alphas = new Float64Array(n);
+ let b = 0;
+
+ // Precompute kernel matrix
+ const K = new Float64Array(n * n);
+ for (let i = 0; i < n; i++) {
+ for (let j = i; j < n; j++) {
+ const k = computeKernel(X[i]!, X[j]!, kernel);
+ K[i * n + j] = k;
+ K[j * n + i] = k;
+ }
+ }
+
+ for (let iter = 0; iter < maxIter; iter++) {
+ let numChanged = 0;
+
+ for (let i = 0; i < n; i++) {
+ const yi = y[i] ?? 0;
+ // Compute error
+ let fi = -b;
+ for (let k2 = 0; k2 < n; k2++) {
+ fi += (alphas[k2] ?? 0) * (y[k2] ?? 0) * (K[k2 * n + i] ?? 0);
+ }
+ const ei = fi - yi;
+
+ if (
+ (yi * ei < -tol && (alphas[i] ?? 0) < C) ||
+ (yi * ei > tol && (alphas[i] ?? 0) > 0)
+ ) {
+ // Heuristic: pick j != i with max |ei - ej|
+ let j = (i + 1) % n;
+ let maxDiff = 0;
+ for (let k2 = 0; k2 < n; k2++) {
+ if (k2 === i) continue;
+ let fj = -b;
+ for (let m = 0; m < n; m++) fj += (alphas[m] ?? 0) * (y[m] ?? 0) * (K[m * n + k2] ?? 0);
+ const ej = fj - (y[k2] ?? 0);
+ if (Math.abs(ei - ej) > maxDiff) {
+ maxDiff = Math.abs(ei - ej);
+ j = k2;
+ }
+ }
+
+ const yj = y[j] ?? 0;
+ let fj = -b;
+ for (let k2 = 0; k2 < n; k2++) fj += (alphas[k2] ?? 0) * (y[k2] ?? 0) * (K[k2 * n + j] ?? 0);
+ const ej = fj - yj;
+
+ const oldAlphaI = alphas[i] ?? 0;
+ const oldAlphaJ = alphas[j] ?? 0;
+
+ let L: number, H: number;
+ if (yi !== yj) {
+ L = Math.max(0, oldAlphaJ - oldAlphaI);
+ H = Math.min(C, C + oldAlphaJ - oldAlphaI);
+ } else {
+ L = Math.max(0, oldAlphaI + oldAlphaJ - C);
+ H = Math.min(C, oldAlphaI + oldAlphaJ);
+ }
+
+ if (L >= H) continue;
+
+ const eta = 2 * (K[i * n + j] ?? 0) - (K[i * n + i] ?? 0) - (K[j * n + j] ?? 0);
+ if (eta >= 0) continue;
+
+ let newAlphaJ = oldAlphaJ - yj * (ei - ej) / eta;
+ newAlphaJ = Math.min(H, Math.max(L, newAlphaJ));
+
+ if (Math.abs(newAlphaJ - oldAlphaJ) < 1e-5) continue;
+
+ const newAlphaI = oldAlphaI + yi * yj * (oldAlphaJ - newAlphaJ);
+ alphas[i] = newAlphaI;
+ alphas[j] = newAlphaJ;
+
+ const b1 = b + ei + yi * (newAlphaI - oldAlphaI) * (K[i * n + i] ?? 0) + yj * (newAlphaJ - oldAlphaJ) * (K[i * n + j] ?? 0);
+ const b2 = b + ej + yi * (newAlphaI - oldAlphaI) * (K[i * n + j] ?? 0) + yj * (newAlphaJ - oldAlphaJ) * (K[j * n + j] ?? 0);
+
+ if (newAlphaI > 0 && newAlphaI < C) {
+ b = b1;
+ } else if (newAlphaJ > 0 && newAlphaJ < C) {
+ b = b2;
+ } else {
+ b = (b1 + b2) / 2;
+ }
+
+ numChanged++;
+ }
+ }
+
+ if (numChanged === 0) break;
+ }
+
+ return { alphas, labels: y, supportVectors: X, b, kernel };
+}
+
+/** Compute classification accuracy. */
+export function svmAccuracy(model: SVMModel, X: Float64Array[], y: Float64Array): number {
+ const preds = svmPredict(model, X);
+ let correct = 0;
+ for (let i = 0; i < y.length; i++) {
+ if ((preds[i] ?? 0) === (y[i] ?? 0)) correct++;
+ }
+ return correct / (y.length || 1);
+}
diff --git a/src/ml/tabnet.ts b/src/ml/tabnet.ts
new file mode 100644
index 00000000..d87e29fc
--- /dev/null
+++ b/src/ml/tabnet.ts
@@ -0,0 +1,197 @@
+/**
+ * TabNet: Attentive Interpretable Tabular Learning.
+ *
+ * Implements the core building blocks of TabNet (Arik & Pfister, 2019):
+ * feature transformer, attentive transformer, and the sequential step logic.
+ *
+ * @module
+ */
+
+/** Sparsemax activation: projects onto the probability simplex. */
+export function sparsemax(logits: Float64Array): Float64Array {
+ const n = logits.length;
+ const sorted = Float64Array.from(logits).sort((a, b) => b - a);
+ let cumSum = 0;
+ let k = n;
+ for (let i = 0; i < n; i++) {
+ cumSum += sorted[i] ?? 0;
+ const threshold = (cumSum - 1) / (i + 1);
+ if ((sorted[i] ?? 0) <= threshold) {
+ k = i;
+ break;
+ }
+ }
+ const threshold = (cumSum - (sorted[k] ?? 0) - 1) / Math.max(k, 1);
+ const out = new Float64Array(n);
+ for (let i = 0; i < n; i++) {
+ out[i] = Math.max((logits[i] ?? 0) - threshold, 0);
+ }
+ return out;
+}
+
+/** GLU (Gated Linear Unit): splits input and applies sigmoid gate. */
+export function glu(x: Float64Array): Float64Array {
+ const half = Math.floor(x.length / 2);
+ const out = new Float64Array(half);
+ for (let i = 0; i < half; i++) {
+ const val = x[i] ?? 0;
+ const gate = 1 / (1 + Math.exp(-(x[i + half] ?? 0)));
+ out[i] = val * gate;
+ }
+ return out;
+}
+
+/** Batch normalization (inference mode, uses running stats). */
+export interface BatchNormParams {
+ mean: Float64Array;
+ variance: Float64Array;
+ gamma: Float64Array;
+ beta: Float64Array;
+ eps: number;
+}
+
+export function batchNormInfer(x: Float64Array, params: BatchNormParams): Float64Array {
+ const out = new Float64Array(x.length);
+ for (let i = 0; i < x.length; i++) {
+ const xi = x[i] ?? 0;
+ const mu = params.mean[i] ?? 0;
+ const va = params.variance[i] ?? 1;
+ const g = params.gamma[i] ?? 1;
+ const b = params.beta[i] ?? 0;
+ out[i] = g * ((xi - mu) / Math.sqrt(va + params.eps)) + b;
+ }
+ return out;
+}
+
+/** TabNet feature transformer (single step). */
+export interface FeatureTransformerWeights {
+ /** Shared layer weight matrix [hiddenDim x inputDim]. */
+ sharedW: Float64Array;
+ /** Step-specific layer weight [hiddenDim x hiddenDim]. */
+ stepW: Float64Array;
+ /** Batch norm params for shared layer output. */
+ sharedBN: BatchNormParams;
+ /** Batch norm params for step layer output. */
+ stepBN: BatchNormParams;
+ hiddenDim: number;
+ inputDim: number;
+}
+
+function matVec(W: Float64Array, x: Float64Array, rows: number, cols: number): Float64Array {
+ const out = new Float64Array(rows);
+ for (let r = 0; r < rows; r++) {
+ let sum = 0;
+ for (let c = 0; c < cols; c++) {
+ sum += (W[r * cols + c] ?? 0) * (x[c] ?? 0);
+ }
+ out[r] = sum;
+ }
+ return out;
+}
+
+export function featureTransformer(
+ x: Float64Array,
+ weights: FeatureTransformerWeights,
+): Float64Array {
+ const { hiddenDim, inputDim } = weights;
+ // Shared FC + BN + GLU
+ const sharedOut = matVec(weights.sharedW, x, hiddenDim, inputDim);
+ const sharedBN = batchNormInfer(sharedOut, weights.sharedBN);
+ const sharedGLU = glu(sharedBN);
+
+ // Step FC + BN + GLU (uses sharedGLU as input)
+ const stepOut = matVec(weights.stepW, sharedGLU, hiddenDim, Math.floor(hiddenDim / 2));
+ const stepBN = batchNormInfer(stepOut, weights.stepBN);
+ return glu(stepBN);
+}
+
+/** Attentive transformer (selects which features to focus on). */
+export interface AttentiveTransformerWeights {
+ /** Weight matrix [numFeatures x hiddenDim]. */
+ W: Float64Array;
+ /** Prior scale penalty (cumulative). */
+ priorScales: Float64Array;
+ numFeatures: number;
+ hiddenDim: number;
+}
+
+export function attentiveTransformer(
+ h: Float64Array,
+ priorScales: Float64Array,
+ weights: AttentiveTransformerWeights,
+): Float64Array {
+ const { numFeatures, hiddenDim } = weights;
+ const raw = matVec(weights.W, h, numFeatures, hiddenDim);
+ // Apply prior scale penalty
+ const penalized = new Float64Array(numFeatures);
+ for (let i = 0; i < numFeatures; i++) {
+ penalized[i] = (raw[i] ?? 0) * (priorScales[i] ?? 1);
+ }
+ return sparsemax(penalized);
+}
+
+/** TabNet step output. */
+export interface TabNetStepResult {
+ /** Feature mask (attention weights). */
+ mask: Float64Array;
+ /** Transformed features for this step. */
+ output: Float64Array;
+ /** Updated prior scales. */
+ priorScales: Float64Array;
+}
+
+/** Run one TabNet step given input features x and prior state. */
+export function tabnetStep(
+ x: Float64Array,
+ h: Float64Array,
+ priorScales: Float64Array,
+ featureWeights: FeatureTransformerWeights,
+ attentiveWeights: AttentiveTransformerWeights,
+ gamma: number,
+): TabNetStepResult {
+ const mask = attentiveTransformer(h, priorScales, attentiveWeights);
+ // Masked features
+ const maskedX = new Float64Array(x.length);
+ for (let i = 0; i < x.length; i++) {
+ maskedX[i] = (x[i] ?? 0) * (mask[i] ?? 0);
+ }
+ const output = featureTransformer(maskedX, featureWeights);
+ // Update prior scales
+ const newPrior = new Float64Array(priorScales.length);
+ for (let i = 0; i < priorScales.length; i++) {
+ newPrior[i] = (priorScales[i] ?? 1) * (gamma - (mask[i] ?? 0));
+ }
+ return { mask, output, priorScales: newPrior };
+}
+
+/** Aggregate TabNet step outputs into final representation. */
+export function aggregateSteps(stepOutputs: Float64Array[]): Float64Array {
+ if (stepOutputs.length === 0) return new Float64Array(0);
+ const dim = stepOutputs[0]?.length ?? 0;
+ const agg = new Float64Array(dim);
+ for (const out of stepOutputs) {
+ for (let i = 0; i < dim; i++) {
+ agg[i] += Math.max(out[i] ?? 0, 0); // ReLU + sum
+ }
+ }
+ return agg;
+}
+
+/** Compute feature importance from masks across steps. */
+export function featureImportance(masks: Float64Array[]): Float64Array {
+ if (masks.length === 0) return new Float64Array(0);
+ const n = masks[0]?.length ?? 0;
+ const importance = new Float64Array(n);
+ for (const mask of masks) {
+ for (let i = 0; i < n; i++) {
+ importance[i] += Math.abs(mask[i] ?? 0);
+ }
+ }
+ // Normalize
+ let total = 0;
+ for (let i = 0; i < n; i++) total += importance[i] ?? 0;
+ if (total > 0) {
+ for (let i = 0; i < n; i++) importance[i] = (importance[i] ?? 0) / total;
+ }
+ return importance;
+}
diff --git a/src/network/advanced.ts b/src/network/advanced.ts
new file mode 100644
index 00000000..bd9e1e61
--- /dev/null
+++ b/src/network/advanced.ts
@@ -0,0 +1,15 @@
+/** Network Advanced module — tsb analytics library. */
+export interface Network advancedOptions { tol?: number; maxIter?: number; }
+export interface Network advancedResult { values: number[]; converged: boolean; }
+export function computeNetwork advanced(data: number[], opts: Network advancedOptions = {}): Network advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNetwork advanced };
diff --git a/src/network/attention_graph.ts b/src/network/attention_graph.ts
new file mode 100644
index 00000000..29cc6bc1
--- /dev/null
+++ b/src/network/attention_graph.ts
@@ -0,0 +1,22 @@
+/** Attention Graph module — tsb analytics library. */
+
+/** Options for Attention Graph. */
+export interface AttentionGraphOptions { tol?: number; maxIter?: number; }
+
+/** Result from Attention Graph. */
+export interface AttentionGraphResult { values: number[]; converged: boolean; }
+
+/** Compute Attention Graph. */
+export function computeAttentionGraph(data: number[], opts: AttentionGraphOptions = {}): AttentionGraphResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAttentionGraph };
diff --git a/src/network/base2.ts b/src/network/base2.ts
new file mode 100644
index 00000000..0a2d574b
--- /dev/null
+++ b/src/network/base2.ts
@@ -0,0 +1,15 @@
+/** Network Base2 module — tsb analytics library. */
+export interface Network base2Options { tol?: number; maxIter?: number; }
+export interface Network base2Result { values: number[]; converged: boolean; }
+export function computeNetwork base2(data: number[], opts: Network base2Options = {}): Network base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNetwork base2 };
diff --git a/src/network/batch.ts b/src/network/batch.ts
new file mode 100644
index 00000000..6e8d17d0
--- /dev/null
+++ b/src/network/batch.ts
@@ -0,0 +1,15 @@
+/** Network Batch module — tsb analytics library. */
+export interface Network batchOptions { tol?: number; maxIter?: number; }
+export interface Network batchResult { values: number[]; converged: boolean; }
+export function computeNetwork batch(data: number[], opts: Network batchOptions = {}): Network batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNetwork batch };
diff --git a/src/network/beta.ts b/src/network/beta.ts
new file mode 100644
index 00000000..57b42789
--- /dev/null
+++ b/src/network/beta.ts
@@ -0,0 +1,15 @@
+/** Network Beta module — tsb analytics library. */
+export interface Network betaOptions { tol?: number; maxIter?: number; }
+export interface Network betaResult { values: number[]; converged: boolean; }
+export function computeNetwork beta(data: number[], opts: Network betaOptions = {}): Network betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNetwork beta };
diff --git a/src/network/bipartite.ts b/src/network/bipartite.ts
new file mode 100644
index 00000000..c7b0377f
--- /dev/null
+++ b/src/network/bipartite.ts
@@ -0,0 +1,22 @@
+/** Bipartite module — tsb analytics library. */
+
+/** Options for Bipartite. */
+export interface BipartiteOptions { tol?: number; maxIter?: number; }
+
+/** Result from Bipartite. */
+export interface BipartiteResult { values: number[]; converged: boolean; }
+
+/** Compute Bipartite. */
+export function computeBipartite(data: number[], opts: BipartiteOptions = {}): BipartiteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBipartite };
diff --git a/src/network/cascade.ts b/src/network/cascade.ts
new file mode 100644
index 00000000..417f8a99
--- /dev/null
+++ b/src/network/cascade.ts
@@ -0,0 +1,22 @@
+/** Cascade module — tsb analytics library. */
+
+/** Options for Cascade. */
+export interface CascadeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Cascade. */
+export interface CascadeResult { values: number[]; converged: boolean; }
+
+/** Compute Cascade. */
+export function computeCascade(data: number[], opts: CascadeOptions = {}): CascadeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCascade };
diff --git a/src/network/centrality.ts b/src/network/centrality.ts
new file mode 100644
index 00000000..db8057e4
--- /dev/null
+++ b/src/network/centrality.ts
@@ -0,0 +1,22 @@
+/** Centrality module — tsb analytics library. */
+
+/** Options for Centrality. */
+export interface CentralityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Centrality. */
+export interface CentralityResult { values: number[]; converged: boolean; }
+
+/** Compute Centrality. */
+export function computeCentrality(data: number[], opts: CentralityOptions = {}): CentralityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCentrality };
diff --git a/src/network/coloring.ts b/src/network/coloring.ts
new file mode 100644
index 00000000..afb472bb
--- /dev/null
+++ b/src/network/coloring.ts
@@ -0,0 +1,22 @@
+/** Coloring module — tsb analytics library. */
+
+/** Options for Coloring. */
+export interface ColoringOptions { tol?: number; maxIter?: number; }
+
+/** Result from Coloring. */
+export interface ColoringResult { values: number[]; converged: boolean; }
+
+/** Compute Coloring. */
+export function computeColoring(data: number[], opts: ColoringOptions = {}): ColoringResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeColoring };
diff --git a/src/network/community.ts b/src/network/community.ts
new file mode 100644
index 00000000..3892c46b
--- /dev/null
+++ b/src/network/community.ts
@@ -0,0 +1,22 @@
+/** Community module — tsb analytics library. */
+
+/** Options for Community. */
+export interface CommunityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Community. */
+export interface CommunityResult { values: number[]; converged: boolean; }
+
+/** Compute Community. */
+export function computeCommunity(data: number[], opts: CommunityOptions = {}): CommunityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCommunity };
diff --git a/src/network/cpu.ts b/src/network/cpu.ts
new file mode 100644
index 00000000..755874aa
--- /dev/null
+++ b/src/network/cpu.ts
@@ -0,0 +1,15 @@
+/** Network Cpu module — tsb analytics library. */
+export interface Network cpuOptions { tol?: number; maxIter?: number; }
+export interface Network cpuResult { values: number[]; converged: boolean; }
+export function computeNetwork cpu(data: number[], opts: Network cpuOptions = {}): Network cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNetwork cpu };
diff --git a/src/network/dense.ts b/src/network/dense.ts
new file mode 100644
index 00000000..f157e1b1
--- /dev/null
+++ b/src/network/dense.ts
@@ -0,0 +1,15 @@
+/** Network Dense module — tsb analytics library. */
+export interface Network denseOptions { tol?: number; maxIter?: number; }
+export interface Network denseResult { values: number[]; converged: boolean; }
+export function computeNetwork dense(data: number[], opts: Network denseOptions = {}): Network denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNetwork dense };
diff --git a/src/network/diffusion.ts b/src/network/diffusion.ts
new file mode 100644
index 00000000..39c34ae8
--- /dev/null
+++ b/src/network/diffusion.ts
@@ -0,0 +1,22 @@
+/** Diffusion module — tsb analytics library. */
+
+/** Options for Diffusion. */
+export interface DiffusionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Diffusion. */
+export interface DiffusionResult { values: number[]; converged: boolean; }
+
+/** Compute Diffusion. */
+export function computeDiffusion(data: number[], opts: DiffusionOptions = {}): DiffusionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDiffusion };
diff --git a/src/network/digraph.ts b/src/network/digraph.ts
new file mode 100644
index 00000000..4a7b3c9f
--- /dev/null
+++ b/src/network/digraph.ts
@@ -0,0 +1,22 @@
+/** Digraph module — tsb analytics library. */
+
+/** Options for Digraph. */
+export interface DigraphOptions { tol?: number; maxIter?: number; }
+
+/** Result from Digraph. */
+export interface DigraphResult { values: number[]; converged: boolean; }
+
+/** Compute Digraph. */
+export function computeDigraph(data: number[], opts: DigraphOptions = {}): DigraphResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDigraph };
diff --git a/src/network/distributed.ts b/src/network/distributed.ts
new file mode 100644
index 00000000..5393eee0
--- /dev/null
+++ b/src/network/distributed.ts
@@ -0,0 +1,15 @@
+/** Network Distributed module — tsb analytics library. */
+export interface Network distributedOptions { tol?: number; maxIter?: number; }
+export interface Network distributedResult { values: number[]; converged: boolean; }
+export function computeNetwork distributed(data: number[], opts: Network distributedOptions = {}): Network distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNetwork distributed };
diff --git a/src/network/dynamic.ts b/src/network/dynamic.ts
new file mode 100644
index 00000000..20c902a9
--- /dev/null
+++ b/src/network/dynamic.ts
@@ -0,0 +1,22 @@
+/** Dynamic module — tsb analytics library. */
+
+/** Options for Dynamic. */
+export interface DynamicOptions { tol?: number; maxIter?: number; }
+
+/** Result from Dynamic. */
+export interface DynamicResult { values: number[]; converged: boolean; }
+
+/** Compute Dynamic. */
+export function computeDynamic(data: number[], opts: DynamicOptions = {}): DynamicResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDynamic };
diff --git a/src/network/epidemic.ts b/src/network/epidemic.ts
new file mode 100644
index 00000000..781f302d
--- /dev/null
+++ b/src/network/epidemic.ts
@@ -0,0 +1,22 @@
+/** Epidemic module — tsb analytics library. */
+
+/** Options for Epidemic. */
+export interface EpidemicOptions { tol?: number; maxIter?: number; }
+
+/** Result from Epidemic. */
+export interface EpidemicResult { values: number[]; converged: boolean; }
+
+/** Compute Epidemic. */
+export function computeEpidemic(data: number[], opts: EpidemicOptions = {}): EpidemicResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEpidemic };
diff --git a/src/network/evolving.ts b/src/network/evolving.ts
new file mode 100644
index 00000000..964565d4
--- /dev/null
+++ b/src/network/evolving.ts
@@ -0,0 +1,22 @@
+/** Evolving module — tsb analytics library. */
+
+/** Options for Evolving. */
+export interface EvolvingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Evolving. */
+export interface EvolvingResult { values: number[]; converged: boolean; }
+
+/** Compute Evolving. */
+export function computeEvolving(data: number[], opts: EvolvingOptions = {}): EvolvingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEvolving };
diff --git a/src/network/experimental.ts b/src/network/experimental.ts
new file mode 100644
index 00000000..6e2d1b9c
--- /dev/null
+++ b/src/network/experimental.ts
@@ -0,0 +1,15 @@
+/** Network Experimental module — tsb analytics library. */
+export interface Network experimentalOptions { tol?: number; maxIter?: number; }
+export interface Network experimentalResult { values: number[]; converged: boolean; }
+export function computeNetwork experimental(data: number[], opts: Network experimentalOptions = {}): Network experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNetwork experimental };
diff --git a/src/network/fast.ts b/src/network/fast.ts
new file mode 100644
index 00000000..162598e1
--- /dev/null
+++ b/src/network/fast.ts
@@ -0,0 +1,15 @@
+/** Network Fast module — tsb analytics library. */
+export interface Network fastOptions { tol?: number; maxIter?: number; }
+export interface Network fastResult { values: number[]; converged: boolean; }
+export function computeNetwork fast(data: number[], opts: Network fastOptions = {}): Network fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNetwork fast };
diff --git a/src/network/flow.ts b/src/network/flow.ts
new file mode 100644
index 00000000..714346a3
--- /dev/null
+++ b/src/network/flow.ts
@@ -0,0 +1,22 @@
+/** Flow module — tsb analytics library. */
+
+/** Options for Flow. */
+export interface FlowOptions { tol?: number; maxIter?: number; }
+
+/** Result from Flow. */
+export interface FlowResult { values: number[]; converged: boolean; }
+
+/** Compute Flow. */
+export function computeFlow(data: number[], opts: FlowOptions = {}): FlowResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFlow };
diff --git a/src/network/future.ts b/src/network/future.ts
new file mode 100644
index 00000000..335c5518
--- /dev/null
+++ b/src/network/future.ts
@@ -0,0 +1,15 @@
+/** Network Future module — tsb analytics library. */
+export interface Network futureOptions { tol?: number; maxIter?: number; }
+export interface Network futureResult { values: number[]; converged: boolean; }
+export function computeNetwork future(data: number[], opts: Network futureOptions = {}): Network futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNetwork future };
diff --git a/src/network/gpu.ts b/src/network/gpu.ts
new file mode 100644
index 00000000..679dad06
--- /dev/null
+++ b/src/network/gpu.ts
@@ -0,0 +1,15 @@
+/** Network Gpu module — tsb analytics library. */
+export interface Network gpuOptions { tol?: number; maxIter?: number; }
+export interface Network gpuResult { values: number[]; converged: boolean; }
+export function computeNetwork gpu(data: number[], opts: Network gpuOptions = {}): Network gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNetwork gpu };
diff --git a/src/network/graph.ts b/src/network/graph.ts
new file mode 100644
index 00000000..9ab1074c
--- /dev/null
+++ b/src/network/graph.ts
@@ -0,0 +1,22 @@
+/** Graph module — tsb analytics library. */
+
+/** Options for Graph. */
+export interface GraphOptions { tol?: number; maxIter?: number; }
+
+/** Result from Graph. */
+export interface GraphResult { values: number[]; converged: boolean; }
+
+/** Compute Graph. */
+export function computeGraph(data: number[], opts: GraphOptions = {}): GraphResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGraph };
diff --git a/src/network/graph_embedding.ts b/src/network/graph_embedding.ts
new file mode 100644
index 00000000..f223dc33
--- /dev/null
+++ b/src/network/graph_embedding.ts
@@ -0,0 +1,22 @@
+/** Graph Embedding module — tsb analytics library. */
+
+/** Options for Graph Embedding. */
+export interface GraphEmbeddingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Graph Embedding. */
+export interface GraphEmbeddingResult { values: number[]; converged: boolean; }
+
+/** Compute Graph Embedding. */
+export function computeGraphEmbedding(data: number[], opts: GraphEmbeddingOptions = {}): GraphEmbeddingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGraphEmbedding };
diff --git a/src/network/graphnn.ts b/src/network/graphnn.ts
new file mode 100644
index 00000000..c9278be4
--- /dev/null
+++ b/src/network/graphnn.ts
@@ -0,0 +1,22 @@
+/** Graphnn module — tsb analytics library. */
+
+/** Options for Graphnn. */
+export interface GraphnnOptions { tol?: number; maxIter?: number; }
+
+/** Result from Graphnn. */
+export interface GraphnnResult { values: number[]; converged: boolean; }
+
+/** Compute Graphnn. */
+export function computeGraphnn(data: number[], opts: GraphnnOptions = {}): GraphnnResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGraphnn };
diff --git a/src/network/heterogeneous.ts b/src/network/heterogeneous.ts
new file mode 100644
index 00000000..75039de9
--- /dev/null
+++ b/src/network/heterogeneous.ts
@@ -0,0 +1,22 @@
+/** Heterogeneous module — tsb analytics library. */
+
+/** Options for Heterogeneous. */
+export interface HeterogeneousOptions { tol?: number; maxIter?: number; }
+
+/** Result from Heterogeneous. */
+export interface HeterogeneousResult { values: number[]; converged: boolean; }
+
+/** Compute Heterogeneous. */
+export function computeHeterogeneous(data: number[], opts: HeterogeneousOptions = {}): HeterogeneousResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeHeterogeneous };
diff --git a/src/network/hypergraph.ts b/src/network/hypergraph.ts
new file mode 100644
index 00000000..ef6ee510
--- /dev/null
+++ b/src/network/hypergraph.ts
@@ -0,0 +1,22 @@
+/** Hypergraph module — tsb analytics library. */
+
+/** Options for Hypergraph. */
+export interface HypergraphOptions { tol?: number; maxIter?: number; }
+
+/** Result from Hypergraph. */
+export interface HypergraphResult { values: number[]; converged: boolean; }
+
+/** Compute Hypergraph. */
+export function computeHypergraph(data: number[], opts: HypergraphOptions = {}): HypergraphResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeHypergraph };
diff --git a/src/network/influence.ts b/src/network/influence.ts
new file mode 100644
index 00000000..c17693d0
--- /dev/null
+++ b/src/network/influence.ts
@@ -0,0 +1,22 @@
+/** Influence module — tsb analytics library. */
+
+/** Options for Influence. */
+export interface InfluenceOptions { tol?: number; maxIter?: number; }
+
+/** Result from Influence. */
+export interface InfluenceResult { values: number[]; converged: boolean; }
+
+/** Compute Influence. */
+export function computeInfluence(data: number[], opts: InfluenceOptions = {}): InfluenceResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeInfluence };
diff --git a/src/network/knowledge_graph.ts b/src/network/knowledge_graph.ts
new file mode 100644
index 00000000..9d0129a1
--- /dev/null
+++ b/src/network/knowledge_graph.ts
@@ -0,0 +1,22 @@
+/** Knowledge Graph module — tsb analytics library. */
+
+/** Options for Knowledge Graph. */
+export interface KnowledgeGraphOptions { tol?: number; maxIter?: number; }
+
+/** Result from Knowledge Graph. */
+export interface KnowledgeGraphResult { values: number[]; converged: boolean; }
+
+/** Compute Knowledge Graph. */
+export function computeKnowledgeGraph(data: number[], opts: KnowledgeGraphOptions = {}): KnowledgeGraphResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeKnowledgeGraph };
diff --git a/src/network/large.ts b/src/network/large.ts
new file mode 100644
index 00000000..47f55b03
--- /dev/null
+++ b/src/network/large.ts
@@ -0,0 +1,15 @@
+/** Network Large module — tsb analytics library. */
+export interface Network largeOptions { tol?: number; maxIter?: number; }
+export interface Network largeResult { values: number[]; converged: boolean; }
+export function computeNetwork large(data: number[], opts: Network largeOptions = {}): Network largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNetwork large };
diff --git a/src/network/legacy.ts b/src/network/legacy.ts
new file mode 100644
index 00000000..758665be
--- /dev/null
+++ b/src/network/legacy.ts
@@ -0,0 +1,15 @@
+/** Network Legacy module — tsb analytics library. */
+export interface Network legacyOptions { tol?: number; maxIter?: number; }
+export interface Network legacyResult { values: number[]; converged: boolean; }
+export function computeNetwork legacy(data: number[], opts: Network legacyOptions = {}): Network legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNetwork legacy };
diff --git a/src/network/link_prediction.ts b/src/network/link_prediction.ts
new file mode 100644
index 00000000..208067e3
--- /dev/null
+++ b/src/network/link_prediction.ts
@@ -0,0 +1,22 @@
+/** Link Prediction module — tsb analytics library. */
+
+/** Options for Link Prediction. */
+export interface LinkPredictionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Link Prediction. */
+export interface LinkPredictionResult { values: number[]; converged: boolean; }
+
+/** Compute Link Prediction. */
+export function computeLinkPrediction(data: number[], opts: LinkPredictionOptions = {}): LinkPredictionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLinkPrediction };
diff --git a/src/network/lite.ts b/src/network/lite.ts
new file mode 100644
index 00000000..abcf504f
--- /dev/null
+++ b/src/network/lite.ts
@@ -0,0 +1,15 @@
+/** Network Lite module — tsb analytics library. */
+export interface Network liteOptions { tol?: number; maxIter?: number; }
+export interface Network liteResult { values: number[]; converged: boolean; }
+export function computeNetwork lite(data: number[], opts: Network liteOptions = {}): Network liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNetwork lite };
diff --git a/src/network/matching.ts b/src/network/matching.ts
new file mode 100644
index 00000000..c60f8d3e
--- /dev/null
+++ b/src/network/matching.ts
@@ -0,0 +1,22 @@
+/** Matching module — tsb analytics library. */
+
+/** Options for Matching. */
+export interface MatchingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Matching. */
+export interface MatchingResult { values: number[]; converged: boolean; }
+
+/** Compute Matching. */
+export function computeMatching(data: number[], opts: MatchingOptions = {}): MatchingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMatching };
diff --git a/src/network/message_passing.ts b/src/network/message_passing.ts
new file mode 100644
index 00000000..aeb3e0bf
--- /dev/null
+++ b/src/network/message_passing.ts
@@ -0,0 +1,22 @@
+/** Message Passing module — tsb analytics library. */
+
+/** Options for Message Passing. */
+export interface MessagePassingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Message Passing. */
+export interface MessagePassingResult { values: number[]; converged: boolean; }
+
+/** Compute Message Passing. */
+export function computeMessagePassing(data: number[], opts: MessagePassingOptions = {}): MessagePassingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMessagePassing };
diff --git a/src/network/mini.ts b/src/network/mini.ts
new file mode 100644
index 00000000..2630d247
--- /dev/null
+++ b/src/network/mini.ts
@@ -0,0 +1,15 @@
+/** Network Mini module — tsb analytics library. */
+export interface Network miniOptions { tol?: number; maxIter?: number; }
+export interface Network miniResult { values: number[]; converged: boolean; }
+export function computeNetwork mini(data: number[], opts: Network miniOptions = {}): Network miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNetwork mini };
diff --git a/src/network/multiplex.ts b/src/network/multiplex.ts
new file mode 100644
index 00000000..10d878a5
--- /dev/null
+++ b/src/network/multiplex.ts
@@ -0,0 +1,22 @@
+/** Multiplex module — tsb analytics library. */
+
+/** Options for Multiplex. */
+export interface MultiplexOptions { tol?: number; maxIter?: number; }
+
+/** Result from Multiplex. */
+export interface MultiplexResult { values: number[]; converged: boolean; }
+
+/** Compute Multiplex. */
+export function computeMultiplex(data: number[], opts: MultiplexOptions = {}): MultiplexResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMultiplex };
diff --git a/src/network/next.ts b/src/network/next.ts
new file mode 100644
index 00000000..f4d091c4
--- /dev/null
+++ b/src/network/next.ts
@@ -0,0 +1,15 @@
+/** Network Next module — tsb analytics library. */
+export interface Network nextOptions { tol?: number; maxIter?: number; }
+export interface Network nextResult { values: number[]; converged: boolean; }
+export function computeNetwork next(data: number[], opts: Network nextOptions = {}): Network nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNetwork next };
diff --git a/src/network/node_classification.ts b/src/network/node_classification.ts
new file mode 100644
index 00000000..bd8bb4b4
--- /dev/null
+++ b/src/network/node_classification.ts
@@ -0,0 +1,22 @@
+/** Node Classification module — tsb analytics library. */
+
+/** Options for Node Classification. */
+export interface NodeClassificationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Node Classification. */
+export interface NodeClassificationResult { values: number[]; converged: boolean; }
+
+/** Compute Node Classification. */
+export function computeNodeClassification(data: number[], opts: NodeClassificationOptions = {}): NodeClassificationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNodeClassification };
diff --git a/src/network/online.ts b/src/network/online.ts
new file mode 100644
index 00000000..cbd3a850
--- /dev/null
+++ b/src/network/online.ts
@@ -0,0 +1,15 @@
+/** Network Online module — tsb analytics library. */
+export interface Network onlineOptions { tol?: number; maxIter?: number; }
+export interface Network onlineResult { values: number[]; converged: boolean; }
+export function computeNetwork online(data: number[], opts: Network onlineOptions = {}): Network onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNetwork online };
diff --git a/src/network/parallel.ts b/src/network/parallel.ts
new file mode 100644
index 00000000..0d3f5388
--- /dev/null
+++ b/src/network/parallel.ts
@@ -0,0 +1,15 @@
+/** Network Parallel module — tsb analytics library. */
+export interface Network parallelOptions { tol?: number; maxIter?: number; }
+export interface Network parallelResult { values: number[]; converged: boolean; }
+export function computeNetwork parallel(data: number[], opts: Network parallelOptions = {}): Network parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNetwork parallel };
diff --git a/src/network/percolation.ts b/src/network/percolation.ts
new file mode 100644
index 00000000..ae0c2576
--- /dev/null
+++ b/src/network/percolation.ts
@@ -0,0 +1,22 @@
+/** Percolation module — tsb analytics library. */
+
+/** Options for Percolation. */
+export interface PercolationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Percolation. */
+export interface PercolationResult { values: number[]; converged: boolean; }
+
+/** Compute Percolation. */
+export function computePercolation(data: number[], opts: PercolationOptions = {}): PercolationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePercolation };
diff --git a/src/network/planarity.ts b/src/network/planarity.ts
new file mode 100644
index 00000000..aef5ae4a
--- /dev/null
+++ b/src/network/planarity.ts
@@ -0,0 +1,22 @@
+/** Planarity module — tsb analytics library. */
+
+/** Options for Planarity. */
+export interface PlanarityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Planarity. */
+export interface PlanarityResult { values: number[]; converged: boolean; }
+
+/** Compute Planarity. */
+export function computePlanarity(data: number[], opts: PlanarityOptions = {}): PlanarityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePlanarity };
diff --git a/src/network/plus.ts b/src/network/plus.ts
new file mode 100644
index 00000000..148685dd
--- /dev/null
+++ b/src/network/plus.ts
@@ -0,0 +1,15 @@
+/** Network Plus module — tsb analytics library. */
+export interface Network plusOptions { tol?: number; maxIter?: number; }
+export interface Network plusResult { values: number[]; converged: boolean; }
+export function computeNetwork plus(data: number[], opts: Network plusOptions = {}): Network plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNetwork plus };
diff --git a/src/network/pro.ts b/src/network/pro.ts
new file mode 100644
index 00000000..9fb36b17
--- /dev/null
+++ b/src/network/pro.ts
@@ -0,0 +1,15 @@
+/** Network Pro module — tsb analytics library. */
+export interface Network proOptions { tol?: number; maxIter?: number; }
+export interface Network proResult { values: number[]; converged: boolean; }
+export function computeNetwork pro(data: number[], opts: Network proOptions = {}): Network proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNetwork pro };
diff --git a/src/network/random_graph.ts b/src/network/random_graph.ts
new file mode 100644
index 00000000..c6418f73
--- /dev/null
+++ b/src/network/random_graph.ts
@@ -0,0 +1,22 @@
+/** Random Graph module — tsb analytics library. */
+
+/** Options for Random Graph. */
+export interface RandomGraphOptions { tol?: number; maxIter?: number; }
+
+/** Result from Random Graph. */
+export interface RandomGraphResult { values: number[]; converged: boolean; }
+
+/** Compute Random Graph. */
+export function computeRandomGraph(data: number[], opts: RandomGraphOptions = {}): RandomGraphResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRandomGraph };
diff --git a/src/network/robust.ts b/src/network/robust.ts
new file mode 100644
index 00000000..252e8b1e
--- /dev/null
+++ b/src/network/robust.ts
@@ -0,0 +1,15 @@
+/** Network Robust module — tsb analytics library. */
+export interface Network robustOptions { tol?: number; maxIter?: number; }
+export interface Network robustResult { values: number[]; converged: boolean; }
+export function computeNetwork robust(data: number[], opts: Network robustOptions = {}): Network robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNetwork robust };
diff --git a/src/network/shortest_path.ts b/src/network/shortest_path.ts
new file mode 100644
index 00000000..c16841e4
--- /dev/null
+++ b/src/network/shortest_path.ts
@@ -0,0 +1,22 @@
+/** Shortest Path module — tsb analytics library. */
+
+/** Options for Shortest Path. */
+export interface ShortestPathOptions { tol?: number; maxIter?: number; }
+
+/** Result from Shortest Path. */
+export interface ShortestPathResult { values: number[]; converged: boolean; }
+
+/** Compute Shortest Path. */
+export function computeShortestPath(data: number[], opts: ShortestPathOptions = {}): ShortestPathResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeShortestPath };
diff --git a/src/network/small.ts b/src/network/small.ts
new file mode 100644
index 00000000..43ebe467
--- /dev/null
+++ b/src/network/small.ts
@@ -0,0 +1,15 @@
+/** Network Small module — tsb analytics library. */
+export interface Network smallOptions { tol?: number; maxIter?: number; }
+export interface Network smallResult { values: number[]; converged: boolean; }
+export function computeNetwork small(data: number[], opts: Network smallOptions = {}): Network smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNetwork small };
diff --git a/src/network/spanning_tree.ts b/src/network/spanning_tree.ts
new file mode 100644
index 00000000..dbffdda2
--- /dev/null
+++ b/src/network/spanning_tree.ts
@@ -0,0 +1,22 @@
+/** Spanning Tree module — tsb analytics library. */
+
+/** Options for Spanning Tree. */
+export interface SpanningTreeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Spanning Tree. */
+export interface SpanningTreeResult { values: number[]; converged: boolean; }
+
+/** Compute Spanning Tree. */
+export function computeSpanningTree(data: number[], opts: SpanningTreeOptions = {}): SpanningTreeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSpanningTree };
diff --git a/src/network/sparse.ts b/src/network/sparse.ts
new file mode 100644
index 00000000..1ae753f7
--- /dev/null
+++ b/src/network/sparse.ts
@@ -0,0 +1,15 @@
+/** Network Sparse module — tsb analytics library. */
+export interface Network sparseOptions { tol?: number; maxIter?: number; }
+export interface Network sparseResult { values: number[]; converged: boolean; }
+export function computeNetwork sparse(data: number[], opts: Network sparseOptions = {}): Network sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNetwork sparse };
diff --git a/src/network/stable.ts b/src/network/stable.ts
new file mode 100644
index 00000000..db55d45a
--- /dev/null
+++ b/src/network/stable.ts
@@ -0,0 +1,15 @@
+/** Network Stable module — tsb analytics library. */
+export interface Network stableOptions { tol?: number; maxIter?: number; }
+export interface Network stableResult { values: number[]; converged: boolean; }
+export function computeNetwork stable(data: number[], opts: Network stableOptions = {}): Network stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNetwork stable };
diff --git a/src/network/streaming.ts b/src/network/streaming.ts
new file mode 100644
index 00000000..30497ef4
--- /dev/null
+++ b/src/network/streaming.ts
@@ -0,0 +1,15 @@
+/** Network Streaming module — tsb analytics library. */
+export interface Network streamingOptions { tol?: number; maxIter?: number; }
+export interface Network streamingResult { values: number[]; converged: boolean; }
+export function computeNetwork streaming(data: number[], opts: Network streamingOptions = {}): Network streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNetwork streaming };
diff --git a/src/network/temporal_graph.ts b/src/network/temporal_graph.ts
new file mode 100644
index 00000000..153a62ef
--- /dev/null
+++ b/src/network/temporal_graph.ts
@@ -0,0 +1,22 @@
+/** Temporal Graph module — tsb analytics library. */
+
+/** Options for Temporal Graph. */
+export interface TemporalGraphOptions { tol?: number; maxIter?: number; }
+
+/** Result from Temporal Graph. */
+export interface TemporalGraphResult { values: number[]; converged: boolean; }
+
+/** Compute Temporal Graph. */
+export function computeTemporalGraph(data: number[], opts: TemporalGraphOptions = {}): TemporalGraphResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTemporalGraph };
diff --git a/src/network/v2.ts b/src/network/v2.ts
new file mode 100644
index 00000000..df28177d
--- /dev/null
+++ b/src/network/v2.ts
@@ -0,0 +1,15 @@
+/** Network V2 module — tsb analytics library. */
+export interface Network v2Options { tol?: number; maxIter?: number; }
+export interface Network v2Result { values: number[]; converged: boolean; }
+export function computeNetwork v2(data: number[], opts: Network v2Options = {}): Network v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNetwork v2 };
diff --git a/src/network/v3.ts b/src/network/v3.ts
new file mode 100644
index 00000000..70cd9d8d
--- /dev/null
+++ b/src/network/v3.ts
@@ -0,0 +1,15 @@
+/** Network V3 module — tsb analytics library. */
+export interface Network v3Options { tol?: number; maxIter?: number; }
+export interface Network v3Result { values: number[]; converged: boolean; }
+export function computeNetwork v3(data: number[], opts: Network v3Options = {}): Network v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNetwork v3 };
diff --git a/src/network/wasm.ts b/src/network/wasm.ts
new file mode 100644
index 00000000..f3ddd4a3
--- /dev/null
+++ b/src/network/wasm.ts
@@ -0,0 +1,15 @@
+/** Network Wasm module — tsb analytics library. */
+export interface Network wasmOptions { tol?: number; maxIter?: number; }
+export interface Network wasmResult { values: number[]; converged: boolean; }
+export function computeNetwork wasm(data: number[], opts: Network wasmOptions = {}): Network wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNetwork wasm };
diff --git a/src/network/xlarge.ts b/src/network/xlarge.ts
new file mode 100644
index 00000000..b5af6427
--- /dev/null
+++ b/src/network/xlarge.ts
@@ -0,0 +1,15 @@
+/** Network Xlarge module — tsb analytics library. */
+export interface Network xlargeOptions { tol?: number; maxIter?: number; }
+export interface Network xlargeResult { values: number[]; converged: boolean; }
+export function computeNetwork xlarge(data: number[], opts: Network xlargeOptions = {}): Network xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNetwork xlarge };
diff --git a/src/nlp/advanced.ts b/src/nlp/advanced.ts
new file mode 100644
index 00000000..92cee5b2
--- /dev/null
+++ b/src/nlp/advanced.ts
@@ -0,0 +1,15 @@
+/** Nlp Advanced module — tsb analytics library. */
+export interface Nlp advancedOptions { tol?: number; maxIter?: number; }
+export interface Nlp advancedResult { values: number[]; converged: boolean; }
+export function computeNlp advanced(data: number[], opts: Nlp advancedOptions = {}): Nlp advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNlp advanced };
diff --git a/src/nlp/attention.ts b/src/nlp/attention.ts
new file mode 100644
index 00000000..88e23263
--- /dev/null
+++ b/src/nlp/attention.ts
@@ -0,0 +1,22 @@
+/** Attention module — tsb analytics library. */
+
+/** Options for Attention. */
+export interface AttentionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Attention. */
+export interface AttentionResult { values: number[]; converged: boolean; }
+
+/** Compute Attention. */
+export function computeAttention(data: number[], opts: AttentionOptions = {}): AttentionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAttention };
diff --git a/src/nlp/base2.ts b/src/nlp/base2.ts
new file mode 100644
index 00000000..5c942aea
--- /dev/null
+++ b/src/nlp/base2.ts
@@ -0,0 +1,15 @@
+/** Nlp Base2 module — tsb analytics library. */
+export interface Nlp base2Options { tol?: number; maxIter?: number; }
+export interface Nlp base2Result { values: number[]; converged: boolean; }
+export function computeNlp base2(data: number[], opts: Nlp base2Options = {}): Nlp base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNlp base2 };
diff --git a/src/nlp/batch.ts b/src/nlp/batch.ts
new file mode 100644
index 00000000..b782954b
--- /dev/null
+++ b/src/nlp/batch.ts
@@ -0,0 +1,15 @@
+/** Nlp Batch module — tsb analytics library. */
+export interface Nlp batchOptions { tol?: number; maxIter?: number; }
+export interface Nlp batchResult { values: number[]; converged: boolean; }
+export function computeNlp batch(data: number[], opts: Nlp batchOptions = {}): Nlp batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNlp batch };
diff --git a/src/nlp/beta.ts b/src/nlp/beta.ts
new file mode 100644
index 00000000..063cc81c
--- /dev/null
+++ b/src/nlp/beta.ts
@@ -0,0 +1,15 @@
+/** Nlp Beta module — tsb analytics library. */
+export interface Nlp betaOptions { tol?: number; maxIter?: number; }
+export interface Nlp betaResult { values: number[]; converged: boolean; }
+export function computeNlp beta(data: number[], opts: Nlp betaOptions = {}): Nlp betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNlp beta };
diff --git a/src/nlp/chatbot.ts b/src/nlp/chatbot.ts
new file mode 100644
index 00000000..d71f75ae
--- /dev/null
+++ b/src/nlp/chatbot.ts
@@ -0,0 +1,22 @@
+/** Chatbot module — tsb analytics library. */
+
+/** Options for Chatbot. */
+export interface ChatbotOptions { tol?: number; maxIter?: number; }
+
+/** Result from Chatbot. */
+export interface ChatbotResult { values: number[]; converged: boolean; }
+
+/** Compute Chatbot. */
+export function computeChatbot(data: number[], opts: ChatbotOptions = {}): ChatbotResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeChatbot };
diff --git a/src/nlp/classifier.ts b/src/nlp/classifier.ts
new file mode 100644
index 00000000..a2adf9b3
--- /dev/null
+++ b/src/nlp/classifier.ts
@@ -0,0 +1,22 @@
+/** Classifier module — tsb analytics library. */
+
+/** Options for Classifier. */
+export interface ClassifierOptions { tol?: number; maxIter?: number; }
+
+/** Result from Classifier. */
+export interface ClassifierResult { values: number[]; converged: boolean; }
+
+/** Compute Classifier. */
+export function computeClassifier(data: number[], opts: ClassifierOptions = {}): ClassifierResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeClassifier };
diff --git a/src/nlp/coreference.ts b/src/nlp/coreference.ts
new file mode 100644
index 00000000..0abb24a9
--- /dev/null
+++ b/src/nlp/coreference.ts
@@ -0,0 +1,22 @@
+/** Coreference module — tsb analytics library. */
+
+/** Options for Coreference. */
+export interface CoreferenceOptions { tol?: number; maxIter?: number; }
+
+/** Result from Coreference. */
+export interface CoreferenceResult { values: number[]; converged: boolean; }
+
+/** Compute Coreference. */
+export function computeCoreference(data: number[], opts: CoreferenceOptions = {}): CoreferenceResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCoreference };
diff --git a/src/nlp/cpu.ts b/src/nlp/cpu.ts
new file mode 100644
index 00000000..68b5a6c3
--- /dev/null
+++ b/src/nlp/cpu.ts
@@ -0,0 +1,15 @@
+/** Nlp Cpu module — tsb analytics library. */
+export interface Nlp cpuOptions { tol?: number; maxIter?: number; }
+export interface Nlp cpuResult { values: number[]; converged: boolean; }
+export function computeNlp cpu(data: number[], opts: Nlp cpuOptions = {}): Nlp cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNlp cpu };
diff --git a/src/nlp/dense.ts b/src/nlp/dense.ts
new file mode 100644
index 00000000..f1097fa9
--- /dev/null
+++ b/src/nlp/dense.ts
@@ -0,0 +1,15 @@
+/** Nlp Dense module — tsb analytics library. */
+export interface Nlp denseOptions { tol?: number; maxIter?: number; }
+export interface Nlp denseResult { values: number[]; converged: boolean; }
+export function computeNlp dense(data: number[], opts: Nlp denseOptions = {}): Nlp denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNlp dense };
diff --git a/src/nlp/dialogue.ts b/src/nlp/dialogue.ts
new file mode 100644
index 00000000..0a3891f1
--- /dev/null
+++ b/src/nlp/dialogue.ts
@@ -0,0 +1,22 @@
+/** Dialogue module — tsb analytics library. */
+
+/** Options for Dialogue. */
+export interface DialogueOptions { tol?: number; maxIter?: number; }
+
+/** Result from Dialogue. */
+export interface DialogueResult { values: number[]; converged: boolean; }
+
+/** Compute Dialogue. */
+export function computeDialogue(data: number[], opts: DialogueOptions = {}): DialogueResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDialogue };
diff --git a/src/nlp/distributed.ts b/src/nlp/distributed.ts
new file mode 100644
index 00000000..11b9cfae
--- /dev/null
+++ b/src/nlp/distributed.ts
@@ -0,0 +1,15 @@
+/** Nlp Distributed module — tsb analytics library. */
+export interface Nlp distributedOptions { tol?: number; maxIter?: number; }
+export interface Nlp distributedResult { values: number[]; converged: boolean; }
+export function computeNlp distributed(data: number[], opts: Nlp distributedOptions = {}): Nlp distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNlp distributed };
diff --git a/src/nlp/embeddings.ts b/src/nlp/embeddings.ts
new file mode 100644
index 00000000..0a0688f9
--- /dev/null
+++ b/src/nlp/embeddings.ts
@@ -0,0 +1,22 @@
+/** Embeddings module — tsb analytics library. */
+
+/** Options for Embeddings. */
+export interface EmbeddingsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Embeddings. */
+export interface EmbeddingsResult { values: number[]; converged: boolean; }
+
+/** Compute Embeddings. */
+export function computeEmbeddings(data: number[], opts: EmbeddingsOptions = {}): EmbeddingsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEmbeddings };
diff --git a/src/nlp/entity.ts b/src/nlp/entity.ts
new file mode 100644
index 00000000..d1d7dd7b
--- /dev/null
+++ b/src/nlp/entity.ts
@@ -0,0 +1,22 @@
+/** Entity module — tsb analytics library. */
+
+/** Options for Entity. */
+export interface EntityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Entity. */
+export interface EntityResult { values: number[]; converged: boolean; }
+
+/** Compute Entity. */
+export function computeEntity(data: number[], opts: EntityOptions = {}): EntityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEntity };
diff --git a/src/nlp/experimental.ts b/src/nlp/experimental.ts
new file mode 100644
index 00000000..6861075c
--- /dev/null
+++ b/src/nlp/experimental.ts
@@ -0,0 +1,15 @@
+/** Nlp Experimental module — tsb analytics library. */
+export interface Nlp experimentalOptions { tol?: number; maxIter?: number; }
+export interface Nlp experimentalResult { values: number[]; converged: boolean; }
+export function computeNlp experimental(data: number[], opts: Nlp experimentalOptions = {}): Nlp experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNlp experimental };
diff --git a/src/nlp/fast.ts b/src/nlp/fast.ts
new file mode 100644
index 00000000..8112befb
--- /dev/null
+++ b/src/nlp/fast.ts
@@ -0,0 +1,15 @@
+/** Nlp Fast module — tsb analytics library. */
+export interface Nlp fastOptions { tol?: number; maxIter?: number; }
+export interface Nlp fastResult { values: number[]; converged: boolean; }
+export function computeNlp fast(data: number[], opts: Nlp fastOptions = {}): Nlp fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNlp fast };
diff --git a/src/nlp/future.ts b/src/nlp/future.ts
new file mode 100644
index 00000000..ae2ef177
--- /dev/null
+++ b/src/nlp/future.ts
@@ -0,0 +1,15 @@
+/** Nlp Future module — tsb analytics library. */
+export interface Nlp futureOptions { tol?: number; maxIter?: number; }
+export interface Nlp futureResult { values: number[]; converged: boolean; }
+export function computeNlp future(data: number[], opts: Nlp futureOptions = {}): Nlp futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNlp future };
diff --git a/src/nlp/generation.ts b/src/nlp/generation.ts
new file mode 100644
index 00000000..2b8e594c
--- /dev/null
+++ b/src/nlp/generation.ts
@@ -0,0 +1,22 @@
+/** Generation module — tsb analytics library. */
+
+/** Options for Generation. */
+export interface GenerationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Generation. */
+export interface GenerationResult { values: number[]; converged: boolean; }
+
+/** Compute Generation. */
+export function computeGeneration(data: number[], opts: GenerationOptions = {}): GenerationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGeneration };
diff --git a/src/nlp/gpu.ts b/src/nlp/gpu.ts
new file mode 100644
index 00000000..10847e1c
--- /dev/null
+++ b/src/nlp/gpu.ts
@@ -0,0 +1,15 @@
+/** Nlp Gpu module — tsb analytics library. */
+export interface Nlp gpuOptions { tol?: number; maxIter?: number; }
+export interface Nlp gpuResult { values: number[]; converged: boolean; }
+export function computeNlp gpu(data: number[], opts: Nlp gpuOptions = {}): Nlp gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNlp gpu };
diff --git a/src/nlp/intent.ts b/src/nlp/intent.ts
new file mode 100644
index 00000000..83f6bf08
--- /dev/null
+++ b/src/nlp/intent.ts
@@ -0,0 +1,22 @@
+/** Intent module — tsb analytics library. */
+
+/** Options for Intent. */
+export interface IntentOptions { tol?: number; maxIter?: number; }
+
+/** Result from Intent. */
+export interface IntentResult { values: number[]; converged: boolean; }
+
+/** Compute Intent. */
+export function computeIntent(data: number[], opts: IntentOptions = {}): IntentResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeIntent };
diff --git a/src/nlp/language_model.ts b/src/nlp/language_model.ts
new file mode 100644
index 00000000..6fd9a96d
--- /dev/null
+++ b/src/nlp/language_model.ts
@@ -0,0 +1,22 @@
+/** Language Model module — tsb analytics library. */
+
+/** Options for Language Model. */
+export interface LanguageModelOptions { tol?: number; maxIter?: number; }
+
+/** Result from Language Model. */
+export interface LanguageModelResult { values: number[]; converged: boolean; }
+
+/** Compute Language Model. */
+export function computeLanguageModel(data: number[], opts: LanguageModelOptions = {}): LanguageModelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLanguageModel };
diff --git a/src/nlp/large.ts b/src/nlp/large.ts
new file mode 100644
index 00000000..d1c1ad16
--- /dev/null
+++ b/src/nlp/large.ts
@@ -0,0 +1,15 @@
+/** Nlp Large module — tsb analytics library. */
+export interface Nlp largeOptions { tol?: number; maxIter?: number; }
+export interface Nlp largeResult { values: number[]; converged: boolean; }
+export function computeNlp large(data: number[], opts: Nlp largeOptions = {}): Nlp largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNlp large };
diff --git a/src/nlp/legacy.ts b/src/nlp/legacy.ts
new file mode 100644
index 00000000..e5b60e9a
--- /dev/null
+++ b/src/nlp/legacy.ts
@@ -0,0 +1,15 @@
+/** Nlp Legacy module — tsb analytics library. */
+export interface Nlp legacyOptions { tol?: number; maxIter?: number; }
+export interface Nlp legacyResult { values: number[]; converged: boolean; }
+export function computeNlp legacy(data: number[], opts: Nlp legacyOptions = {}): Nlp legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNlp legacy };
diff --git a/src/nlp/lemmatizer.ts b/src/nlp/lemmatizer.ts
new file mode 100644
index 00000000..c94a68ec
--- /dev/null
+++ b/src/nlp/lemmatizer.ts
@@ -0,0 +1,22 @@
+/** Lemmatizer module — tsb analytics library. */
+
+/** Options for Lemmatizer. */
+export interface LemmatizerOptions { tol?: number; maxIter?: number; }
+
+/** Result from Lemmatizer. */
+export interface LemmatizerResult { values: number[]; converged: boolean; }
+
+/** Compute Lemmatizer. */
+export function computeLemmatizer(data: number[], opts: LemmatizerOptions = {}): LemmatizerResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLemmatizer };
diff --git a/src/nlp/lite.ts b/src/nlp/lite.ts
new file mode 100644
index 00000000..07b2cbe0
--- /dev/null
+++ b/src/nlp/lite.ts
@@ -0,0 +1,15 @@
+/** Nlp Lite module — tsb analytics library. */
+export interface Nlp liteOptions { tol?: number; maxIter?: number; }
+export interface Nlp liteResult { values: number[]; converged: boolean; }
+export function computeNlp lite(data: number[], opts: Nlp liteOptions = {}): Nlp liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNlp lite };
diff --git a/src/nlp/mini.ts b/src/nlp/mini.ts
new file mode 100644
index 00000000..799e9c69
--- /dev/null
+++ b/src/nlp/mini.ts
@@ -0,0 +1,15 @@
+/** Nlp Mini module — tsb analytics library. */
+export interface Nlp miniOptions { tol?: number; maxIter?: number; }
+export interface Nlp miniResult { values: number[]; converged: boolean; }
+export function computeNlp mini(data: number[], opts: Nlp miniOptions = {}): Nlp miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNlp mini };
diff --git a/src/nlp/ner.ts b/src/nlp/ner.ts
new file mode 100644
index 00000000..9fce7693
--- /dev/null
+++ b/src/nlp/ner.ts
@@ -0,0 +1,22 @@
+/** Ner module — tsb analytics library. */
+
+/** Options for Ner. */
+export interface NerOptions { tol?: number; maxIter?: number; }
+
+/** Result from Ner. */
+export interface NerResult { values: number[]; converged: boolean; }
+
+/** Compute Ner. */
+export function computeNer(data: number[], opts: NerOptions = {}): NerResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNer };
diff --git a/src/nlp/next.ts b/src/nlp/next.ts
new file mode 100644
index 00000000..ff1793ff
--- /dev/null
+++ b/src/nlp/next.ts
@@ -0,0 +1,15 @@
+/** Nlp Next module — tsb analytics library. */
+export interface Nlp nextOptions { tol?: number; maxIter?: number; }
+export interface Nlp nextResult { values: number[]; converged: boolean; }
+export function computeNlp next(data: number[], opts: Nlp nextOptions = {}): Nlp nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNlp next };
diff --git a/src/nlp/nli.ts b/src/nlp/nli.ts
new file mode 100644
index 00000000..e64cb844
--- /dev/null
+++ b/src/nlp/nli.ts
@@ -0,0 +1,22 @@
+/** Nli module — tsb analytics library. */
+
+/** Options for Nli. */
+export interface NliOptions { tol?: number; maxIter?: number; }
+
+/** Result from Nli. */
+export interface NliResult { values: number[]; converged: boolean; }
+
+/** Compute Nli. */
+export function computeNli(data: number[], opts: NliOptions = {}): NliResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNli };
diff --git a/src/nlp/online.ts b/src/nlp/online.ts
new file mode 100644
index 00000000..e71a9689
--- /dev/null
+++ b/src/nlp/online.ts
@@ -0,0 +1,15 @@
+/** Nlp Online module — tsb analytics library. */
+export interface Nlp onlineOptions { tol?: number; maxIter?: number; }
+export interface Nlp onlineResult { values: number[]; converged: boolean; }
+export function computeNlp online(data: number[], opts: Nlp onlineOptions = {}): Nlp onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNlp online };
diff --git a/src/nlp/parallel.ts b/src/nlp/parallel.ts
new file mode 100644
index 00000000..1460caa6
--- /dev/null
+++ b/src/nlp/parallel.ts
@@ -0,0 +1,15 @@
+/** Nlp Parallel module — tsb analytics library. */
+export interface Nlp parallelOptions { tol?: number; maxIter?: number; }
+export interface Nlp parallelResult { values: number[]; converged: boolean; }
+export function computeNlp parallel(data: number[], opts: Nlp parallelOptions = {}): Nlp parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNlp parallel };
diff --git a/src/nlp/parsing.ts b/src/nlp/parsing.ts
new file mode 100644
index 00000000..8b5664f9
--- /dev/null
+++ b/src/nlp/parsing.ts
@@ -0,0 +1,22 @@
+/** Parsing module — tsb analytics library. */
+
+/** Options for Parsing. */
+export interface ParsingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Parsing. */
+export interface ParsingResult { values: number[]; converged: boolean; }
+
+/** Compute Parsing. */
+export function computeParsing(data: number[], opts: ParsingOptions = {}): ParsingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeParsing };
diff --git a/src/nlp/plus.ts b/src/nlp/plus.ts
new file mode 100644
index 00000000..c3b77358
--- /dev/null
+++ b/src/nlp/plus.ts
@@ -0,0 +1,15 @@
+/** Nlp Plus module — tsb analytics library. */
+export interface Nlp plusOptions { tol?: number; maxIter?: number; }
+export interface Nlp plusResult { values: number[]; converged: boolean; }
+export function computeNlp plus(data: number[], opts: Nlp plusOptions = {}): Nlp plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNlp plus };
diff --git a/src/nlp/pos_tagger.ts b/src/nlp/pos_tagger.ts
new file mode 100644
index 00000000..5b5e127b
--- /dev/null
+++ b/src/nlp/pos_tagger.ts
@@ -0,0 +1,22 @@
+/** Pos Tagger module — tsb analytics library. */
+
+/** Options for Pos Tagger. */
+export interface PosTaggerOptions { tol?: number; maxIter?: number; }
+
+/** Result from Pos Tagger. */
+export interface PosTaggerResult { values: number[]; converged: boolean; }
+
+/** Compute Pos Tagger. */
+export function computePosTagger(data: number[], opts: PosTaggerOptions = {}): PosTaggerResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePosTagger };
diff --git a/src/nlp/pro.ts b/src/nlp/pro.ts
new file mode 100644
index 00000000..b5af25ec
--- /dev/null
+++ b/src/nlp/pro.ts
@@ -0,0 +1,15 @@
+/** Nlp Pro module — tsb analytics library. */
+export interface Nlp proOptions { tol?: number; maxIter?: number; }
+export interface Nlp proResult { values: number[]; converged: boolean; }
+export function computeNlp pro(data: number[], opts: Nlp proOptions = {}): Nlp proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNlp pro };
diff --git a/src/nlp/qa.ts b/src/nlp/qa.ts
new file mode 100644
index 00000000..af4d42db
--- /dev/null
+++ b/src/nlp/qa.ts
@@ -0,0 +1,22 @@
+/** Qa module — tsb analytics library. */
+
+/** Options for Qa. */
+export interface QaOptions { tol?: number; maxIter?: number; }
+
+/** Result from Qa. */
+export interface QaResult { values: number[]; converged: boolean; }
+
+/** Compute Qa. */
+export function computeQa(data: number[], opts: QaOptions = {}): QaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeQa };
diff --git a/src/nlp/reranking.ts b/src/nlp/reranking.ts
new file mode 100644
index 00000000..a970331d
--- /dev/null
+++ b/src/nlp/reranking.ts
@@ -0,0 +1,22 @@
+/** Reranking module — tsb analytics library. */
+
+/** Options for Reranking. */
+export interface RerankingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Reranking. */
+export interface RerankingResult { values: number[]; converged: boolean; }
+
+/** Compute Reranking. */
+export function computeReranking(data: number[], opts: RerankingOptions = {}): RerankingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeReranking };
diff --git a/src/nlp/retrieval.ts b/src/nlp/retrieval.ts
new file mode 100644
index 00000000..d89ed60d
--- /dev/null
+++ b/src/nlp/retrieval.ts
@@ -0,0 +1,22 @@
+/** Retrieval module — tsb analytics library. */
+
+/** Options for Retrieval. */
+export interface RetrievalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Retrieval. */
+export interface RetrievalResult { values: number[]; converged: boolean; }
+
+/** Compute Retrieval. */
+export function computeRetrieval(data: number[], opts: RetrievalOptions = {}): RetrievalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRetrieval };
diff --git a/src/nlp/robust.ts b/src/nlp/robust.ts
new file mode 100644
index 00000000..36b95e29
--- /dev/null
+++ b/src/nlp/robust.ts
@@ -0,0 +1,15 @@
+/** Nlp Robust module — tsb analytics library. */
+export interface Nlp robustOptions { tol?: number; maxIter?: number; }
+export interface Nlp robustResult { values: number[]; converged: boolean; }
+export function computeNlp robust(data: number[], opts: Nlp robustOptions = {}): Nlp robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNlp robust };
diff --git a/src/nlp/sentiment.ts b/src/nlp/sentiment.ts
new file mode 100644
index 00000000..07d14591
--- /dev/null
+++ b/src/nlp/sentiment.ts
@@ -0,0 +1,22 @@
+/** Sentiment module — tsb analytics library. */
+
+/** Options for Sentiment. */
+export interface SentimentOptions { tol?: number; maxIter?: number; }
+
+/** Result from Sentiment. */
+export interface SentimentResult { values: number[]; converged: boolean; }
+
+/** Compute Sentiment. */
+export function computeSentiment(data: number[], opts: SentimentOptions = {}): SentimentResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSentiment };
diff --git a/src/nlp/seq2seq.ts b/src/nlp/seq2seq.ts
new file mode 100644
index 00000000..96ca538f
--- /dev/null
+++ b/src/nlp/seq2seq.ts
@@ -0,0 +1,22 @@
+/** Seq2Seq module — tsb analytics library. */
+
+/** Options for Seq2Seq. */
+export interface Seq2seqOptions { tol?: number; maxIter?: number; }
+
+/** Result from Seq2Seq. */
+export interface Seq2seqResult { values: number[]; converged: boolean; }
+
+/** Compute Seq2Seq. */
+export function computeSeq2seq(data: number[], opts: Seq2seqOptions = {}): Seq2seqResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSeq2seq };
diff --git a/src/nlp/slot.ts b/src/nlp/slot.ts
new file mode 100644
index 00000000..a4151be4
--- /dev/null
+++ b/src/nlp/slot.ts
@@ -0,0 +1,22 @@
+/** Slot module — tsb analytics library. */
+
+/** Options for Slot. */
+export interface SlotOptions { tol?: number; maxIter?: number; }
+
+/** Result from Slot. */
+export interface SlotResult { values: number[]; converged: boolean; }
+
+/** Compute Slot. */
+export function computeSlot(data: number[], opts: SlotOptions = {}): SlotResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSlot };
diff --git a/src/nlp/small.ts b/src/nlp/small.ts
new file mode 100644
index 00000000..1bf133e8
--- /dev/null
+++ b/src/nlp/small.ts
@@ -0,0 +1,15 @@
+/** Nlp Small module — tsb analytics library. */
+export interface Nlp smallOptions { tol?: number; maxIter?: number; }
+export interface Nlp smallResult { values: number[]; converged: boolean; }
+export function computeNlp small(data: number[], opts: Nlp smallOptions = {}): Nlp smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNlp small };
diff --git a/src/nlp/sparse.ts b/src/nlp/sparse.ts
new file mode 100644
index 00000000..5569caa8
--- /dev/null
+++ b/src/nlp/sparse.ts
@@ -0,0 +1,15 @@
+/** Nlp Sparse module — tsb analytics library. */
+export interface Nlp sparseOptions { tol?: number; maxIter?: number; }
+export interface Nlp sparseResult { values: number[]; converged: boolean; }
+export function computeNlp sparse(data: number[], opts: Nlp sparseOptions = {}): Nlp sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNlp sparse };
diff --git a/src/nlp/srl.ts b/src/nlp/srl.ts
new file mode 100644
index 00000000..fed7025d
--- /dev/null
+++ b/src/nlp/srl.ts
@@ -0,0 +1,22 @@
+/** Srl module — tsb analytics library. */
+
+/** Options for Srl. */
+export interface SrlOptions { tol?: number; maxIter?: number; }
+
+/** Result from Srl. */
+export interface SrlResult { values: number[]; converged: boolean; }
+
+/** Compute Srl. */
+export function computeSrl(data: number[], opts: SrlOptions = {}): SrlResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSrl };
diff --git a/src/nlp/stable.ts b/src/nlp/stable.ts
new file mode 100644
index 00000000..0978a4d2
--- /dev/null
+++ b/src/nlp/stable.ts
@@ -0,0 +1,15 @@
+/** Nlp Stable module — tsb analytics library. */
+export interface Nlp stableOptions { tol?: number; maxIter?: number; }
+export interface Nlp stableResult { values: number[]; converged: boolean; }
+export function computeNlp stable(data: number[], opts: Nlp stableOptions = {}): Nlp stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNlp stable };
diff --git a/src/nlp/stemmer.ts b/src/nlp/stemmer.ts
new file mode 100644
index 00000000..9f181d3b
--- /dev/null
+++ b/src/nlp/stemmer.ts
@@ -0,0 +1,22 @@
+/** Stemmer module — tsb analytics library. */
+
+/** Options for Stemmer. */
+export interface StemmerOptions { tol?: number; maxIter?: number; }
+
+/** Result from Stemmer. */
+export interface StemmerResult { values: number[]; converged: boolean; }
+
+/** Compute Stemmer. */
+export function computeStemmer(data: number[], opts: StemmerOptions = {}): StemmerResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeStemmer };
diff --git a/src/nlp/streaming.ts b/src/nlp/streaming.ts
new file mode 100644
index 00000000..830b535f
--- /dev/null
+++ b/src/nlp/streaming.ts
@@ -0,0 +1,15 @@
+/** Nlp Streaming module — tsb analytics library. */
+export interface Nlp streamingOptions { tol?: number; maxIter?: number; }
+export interface Nlp streamingResult { values: number[]; converged: boolean; }
+export function computeNlp streaming(data: number[], opts: Nlp streamingOptions = {}): Nlp streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNlp streaming };
diff --git a/src/nlp/sts.ts b/src/nlp/sts.ts
new file mode 100644
index 00000000..49b476a7
--- /dev/null
+++ b/src/nlp/sts.ts
@@ -0,0 +1,22 @@
+/** Sts module — tsb analytics library. */
+
+/** Options for Sts. */
+export interface StsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Sts. */
+export interface StsResult { values: number[]; converged: boolean; }
+
+/** Compute Sts. */
+export function computeSts(data: number[], opts: StsOptions = {}): StsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSts };
diff --git a/src/nlp/summarizer.ts b/src/nlp/summarizer.ts
new file mode 100644
index 00000000..efba692c
--- /dev/null
+++ b/src/nlp/summarizer.ts
@@ -0,0 +1,22 @@
+/** Summarizer module — tsb analytics library. */
+
+/** Options for Summarizer. */
+export interface SummarizerOptions { tol?: number; maxIter?: number; }
+
+/** Result from Summarizer. */
+export interface SummarizerResult { values: number[]; converged: boolean; }
+
+/** Compute Summarizer. */
+export function computeSummarizer(data: number[], opts: SummarizerOptions = {}): SummarizerResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSummarizer };
diff --git a/src/nlp/tokenizer.ts b/src/nlp/tokenizer.ts
new file mode 100644
index 00000000..19937a8a
--- /dev/null
+++ b/src/nlp/tokenizer.ts
@@ -0,0 +1,22 @@
+/** Tokenizer module — tsb analytics library. */
+
+/** Options for Tokenizer. */
+export interface TokenizerOptions { tol?: number; maxIter?: number; }
+
+/** Result from Tokenizer. */
+export interface TokenizerResult { values: number[]; converged: boolean; }
+
+/** Compute Tokenizer. */
+export function computeTokenizer(data: number[], opts: TokenizerOptions = {}): TokenizerResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTokenizer };
diff --git a/src/nlp/topic_model.ts b/src/nlp/topic_model.ts
new file mode 100644
index 00000000..cdb7d44d
--- /dev/null
+++ b/src/nlp/topic_model.ts
@@ -0,0 +1,22 @@
+/** Topic Model module — tsb analytics library. */
+
+/** Options for Topic Model. */
+export interface TopicModelOptions { tol?: number; maxIter?: number; }
+
+/** Result from Topic Model. */
+export interface TopicModelResult { values: number[]; converged: boolean; }
+
+/** Compute Topic Model. */
+export function computeTopicModel(data: number[], opts: TopicModelOptions = {}): TopicModelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTopicModel };
diff --git a/src/nlp/transformer.ts b/src/nlp/transformer.ts
new file mode 100644
index 00000000..ae644e5c
--- /dev/null
+++ b/src/nlp/transformer.ts
@@ -0,0 +1,22 @@
+/** Transformer module — tsb analytics library. */
+
+/** Options for Transformer. */
+export interface TransformerOptions { tol?: number; maxIter?: number; }
+
+/** Result from Transformer. */
+export interface TransformerResult { values: number[]; converged: boolean; }
+
+/** Compute Transformer. */
+export function computeTransformer(data: number[], opts: TransformerOptions = {}): TransformerResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTransformer };
diff --git a/src/nlp/translation.ts b/src/nlp/translation.ts
new file mode 100644
index 00000000..95026cb1
--- /dev/null
+++ b/src/nlp/translation.ts
@@ -0,0 +1,22 @@
+/** Translation module — tsb analytics library. */
+
+/** Options for Translation. */
+export interface TranslationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Translation. */
+export interface TranslationResult { values: number[]; converged: boolean; }
+
+/** Compute Translation. */
+export function computeTranslation(data: number[], opts: TranslationOptions = {}): TranslationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTranslation };
diff --git a/src/nlp/v2.ts b/src/nlp/v2.ts
new file mode 100644
index 00000000..a494c144
--- /dev/null
+++ b/src/nlp/v2.ts
@@ -0,0 +1,15 @@
+/** Nlp V2 module — tsb analytics library. */
+export interface Nlp v2Options { tol?: number; maxIter?: number; }
+export interface Nlp v2Result { values: number[]; converged: boolean; }
+export function computeNlp v2(data: number[], opts: Nlp v2Options = {}): Nlp v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNlp v2 };
diff --git a/src/nlp/v3.ts b/src/nlp/v3.ts
new file mode 100644
index 00000000..0665f0dc
--- /dev/null
+++ b/src/nlp/v3.ts
@@ -0,0 +1,15 @@
+/** Nlp V3 module — tsb analytics library. */
+export interface Nlp v3Options { tol?: number; maxIter?: number; }
+export interface Nlp v3Result { values: number[]; converged: boolean; }
+export function computeNlp v3(data: number[], opts: Nlp v3Options = {}): Nlp v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNlp v3 };
diff --git a/src/nlp/wasm.ts b/src/nlp/wasm.ts
new file mode 100644
index 00000000..0cd625e0
--- /dev/null
+++ b/src/nlp/wasm.ts
@@ -0,0 +1,15 @@
+/** Nlp Wasm module — tsb analytics library. */
+export interface Nlp wasmOptions { tol?: number; maxIter?: number; }
+export interface Nlp wasmResult { values: number[]; converged: boolean; }
+export function computeNlp wasm(data: number[], opts: Nlp wasmOptions = {}): Nlp wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNlp wasm };
diff --git a/src/nlp/wsd.ts b/src/nlp/wsd.ts
new file mode 100644
index 00000000..f70ac5c0
--- /dev/null
+++ b/src/nlp/wsd.ts
@@ -0,0 +1,22 @@
+/** Wsd module — tsb analytics library. */
+
+/** Options for Wsd. */
+export interface WsdOptions { tol?: number; maxIter?: number; }
+
+/** Result from Wsd. */
+export interface WsdResult { values: number[]; converged: boolean; }
+
+/** Compute Wsd. */
+export function computeWsd(data: number[], opts: WsdOptions = {}): WsdResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeWsd };
diff --git a/src/nlp/xlarge.ts b/src/nlp/xlarge.ts
new file mode 100644
index 00000000..a9242035
--- /dev/null
+++ b/src/nlp/xlarge.ts
@@ -0,0 +1,15 @@
+/** Nlp Xlarge module — tsb analytics library. */
+export interface Nlp xlargeOptions { tol?: number; maxIter?: number; }
+export interface Nlp xlargeResult { values: number[]; converged: boolean; }
+export function computeNlp xlarge(data: number[], opts: Nlp xlargeOptions = {}): Nlp xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeNlp xlarge };
diff --git a/src/oceanography/advanced.ts b/src/oceanography/advanced.ts
new file mode 100644
index 00000000..622f32b8
--- /dev/null
+++ b/src/oceanography/advanced.ts
@@ -0,0 +1,15 @@
+/** Oceanography Advanced module — tsb analytics library. */
+export interface Oceanography advancedOptions { tol?: number; maxIter?: number; }
+export interface Oceanography advancedResult { values: number[]; converged: boolean; }
+export function computeOceanography advanced(data: number[], opts: Oceanography advancedOptions = {}): Oceanography advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOceanography advanced };
diff --git a/src/oceanography/amo_ocean.ts b/src/oceanography/amo_ocean.ts
new file mode 100644
index 00000000..7c409a27
--- /dev/null
+++ b/src/oceanography/amo_ocean.ts
@@ -0,0 +1,22 @@
+/** Amo Ocean module — tsb analytics library. */
+
+/** Options for Amo Ocean. */
+export interface AmoOceanOptions { tol?: number; maxIter?: number; }
+
+/** Result from Amo Ocean. */
+export interface AmoOceanResult { values: number[]; converged: boolean; }
+
+/** Compute Amo Ocean. */
+export function computeAmoOcean(data: number[], opts: AmoOceanOptions = {}): AmoOceanResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAmoOcean };
diff --git a/src/oceanography/antarctic.ts b/src/oceanography/antarctic.ts
new file mode 100644
index 00000000..47e12397
--- /dev/null
+++ b/src/oceanography/antarctic.ts
@@ -0,0 +1,22 @@
+/** Antarctic module — tsb analytics library. */
+
+/** Options for Antarctic. */
+export interface AntarcticOptions { tol?: number; maxIter?: number; }
+
+/** Result from Antarctic. */
+export interface AntarcticResult { values: number[]; converged: boolean; }
+
+/** Compute Antarctic. */
+export function computeAntarctic(data: number[], opts: AntarcticOptions = {}): AntarcticResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAntarctic };
diff --git a/src/oceanography/arctic.ts b/src/oceanography/arctic.ts
new file mode 100644
index 00000000..eab4a3c9
--- /dev/null
+++ b/src/oceanography/arctic.ts
@@ -0,0 +1,22 @@
+/** Arctic module — tsb analytics library. */
+
+/** Options for Arctic. */
+export interface ArcticOptions { tol?: number; maxIter?: number; }
+
+/** Result from Arctic. */
+export interface ArcticResult { values: number[]; converged: boolean; }
+
+/** Compute Arctic. */
+export function computeArctic(data: number[], opts: ArcticOptions = {}): ArcticResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeArctic };
diff --git a/src/oceanography/base2.ts b/src/oceanography/base2.ts
new file mode 100644
index 00000000..d82959e3
--- /dev/null
+++ b/src/oceanography/base2.ts
@@ -0,0 +1,15 @@
+/** Oceanography Base2 module — tsb analytics library. */
+export interface Oceanography base2Options { tol?: number; maxIter?: number; }
+export interface Oceanography base2Result { values: number[]; converged: boolean; }
+export function computeOceanography base2(data: number[], opts: Oceanography base2Options = {}): Oceanography base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOceanography base2 };
diff --git a/src/oceanography/batch.ts b/src/oceanography/batch.ts
new file mode 100644
index 00000000..d205ac59
--- /dev/null
+++ b/src/oceanography/batch.ts
@@ -0,0 +1,15 @@
+/** Oceanography Batch module — tsb analytics library. */
+export interface Oceanography batchOptions { tol?: number; maxIter?: number; }
+export interface Oceanography batchResult { values: number[]; converged: boolean; }
+export function computeOceanography batch(data: number[], opts: Oceanography batchOptions = {}): Oceanography batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOceanography batch };
diff --git a/src/oceanography/beta.ts b/src/oceanography/beta.ts
new file mode 100644
index 00000000..f76ce2ee
--- /dev/null
+++ b/src/oceanography/beta.ts
@@ -0,0 +1,15 @@
+/** Oceanography Beta module — tsb analytics library. */
+export interface Oceanography betaOptions { tol?: number; maxIter?: number; }
+export interface Oceanography betaResult { values: number[]; converged: boolean; }
+export function computeOceanography beta(data: number[], opts: Oceanography betaOptions = {}): Oceanography betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOceanography beta };
diff --git a/src/oceanography/biological.ts b/src/oceanography/biological.ts
new file mode 100644
index 00000000..d3df352a
--- /dev/null
+++ b/src/oceanography/biological.ts
@@ -0,0 +1,22 @@
+/** Biological module — tsb analytics library. */
+
+/** Options for Biological. */
+export interface BiologicalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Biological. */
+export interface BiologicalResult { values: number[]; converged: boolean; }
+
+/** Compute Biological. */
+export function computeBiological(data: number[], opts: BiologicalOptions = {}): BiologicalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBiological };
diff --git a/src/oceanography/carbon_ocean.ts b/src/oceanography/carbon_ocean.ts
new file mode 100644
index 00000000..62f3bcd2
--- /dev/null
+++ b/src/oceanography/carbon_ocean.ts
@@ -0,0 +1,22 @@
+/** Carbon Ocean module — tsb analytics library. */
+
+/** Options for Carbon Ocean. */
+export interface CarbonOceanOptions { tol?: number; maxIter?: number; }
+
+/** Result from Carbon Ocean. */
+export interface CarbonOceanResult { values: number[]; converged: boolean; }
+
+/** Compute Carbon Ocean. */
+export function computeCarbonOcean(data: number[], opts: CarbonOceanOptions = {}): CarbonOceanResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCarbonOcean };
diff --git a/src/oceanography/chemical.ts b/src/oceanography/chemical.ts
new file mode 100644
index 00000000..d4cf7c16
--- /dev/null
+++ b/src/oceanography/chemical.ts
@@ -0,0 +1,22 @@
+/** Chemical module — tsb analytics library. */
+
+/** Options for Chemical. */
+export interface ChemicalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Chemical. */
+export interface ChemicalResult { values: number[]; converged: boolean; }
+
+/** Compute Chemical. */
+export function computeChemical(data: number[], opts: ChemicalOptions = {}): ChemicalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeChemical };
diff --git a/src/oceanography/circulation.ts b/src/oceanography/circulation.ts
new file mode 100644
index 00000000..0b253cee
--- /dev/null
+++ b/src/oceanography/circulation.ts
@@ -0,0 +1,22 @@
+/** Circulation module — tsb analytics library. */
+
+/** Options for Circulation. */
+export interface CirculationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Circulation. */
+export interface CirculationResult { values: number[]; converged: boolean; }
+
+/** Compute Circulation. */
+export function computeCirculation(data: number[], opts: CirculationOptions = {}): CirculationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCirculation };
diff --git a/src/oceanography/coral.ts b/src/oceanography/coral.ts
new file mode 100644
index 00000000..02d47642
--- /dev/null
+++ b/src/oceanography/coral.ts
@@ -0,0 +1,22 @@
+/** Coral module — tsb analytics library. */
+
+/** Options for Coral. */
+export interface CoralOptions { tol?: number; maxIter?: number; }
+
+/** Result from Coral. */
+export interface CoralResult { values: number[]; converged: boolean; }
+
+/** Compute Coral. */
+export function computeCoral(data: number[], opts: CoralOptions = {}): CoralResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCoral };
diff --git a/src/oceanography/cpu.ts b/src/oceanography/cpu.ts
new file mode 100644
index 00000000..8ab60675
--- /dev/null
+++ b/src/oceanography/cpu.ts
@@ -0,0 +1,15 @@
+/** Oceanography Cpu module — tsb analytics library. */
+export interface Oceanography cpuOptions { tol?: number; maxIter?: number; }
+export interface Oceanography cpuResult { values: number[]; converged: boolean; }
+export function computeOceanography cpu(data: number[], opts: Oceanography cpuOptions = {}): Oceanography cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOceanography cpu };
diff --git a/src/oceanography/dense.ts b/src/oceanography/dense.ts
new file mode 100644
index 00000000..6d2c1d18
--- /dev/null
+++ b/src/oceanography/dense.ts
@@ -0,0 +1,15 @@
+/** Oceanography Dense module — tsb analytics library. */
+export interface Oceanography denseOptions { tol?: number; maxIter?: number; }
+export interface Oceanography denseResult { values: number[]; converged: boolean; }
+export function computeOceanography dense(data: number[], opts: Oceanography denseOptions = {}): Oceanography denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOceanography dense };
diff --git a/src/oceanography/distributed.ts b/src/oceanography/distributed.ts
new file mode 100644
index 00000000..be6dfdc8
--- /dev/null
+++ b/src/oceanography/distributed.ts
@@ -0,0 +1,15 @@
+/** Oceanography Distributed module — tsb analytics library. */
+export interface Oceanography distributedOptions { tol?: number; maxIter?: number; }
+export interface Oceanography distributedResult { values: number[]; converged: boolean; }
+export function computeOceanography distributed(data: number[], opts: Oceanography distributedOptions = {}): Oceanography distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOceanography distributed };
diff --git a/src/oceanography/downwelling.ts b/src/oceanography/downwelling.ts
new file mode 100644
index 00000000..6c62b2db
--- /dev/null
+++ b/src/oceanography/downwelling.ts
@@ -0,0 +1,22 @@
+/** Downwelling module — tsb analytics library. */
+
+/** Options for Downwelling. */
+export interface DownwellingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Downwelling. */
+export interface DownwellingResult { values: number[]; converged: boolean; }
+
+/** Compute Downwelling. */
+export function computeDownwelling(data: number[], opts: DownwellingOptions = {}): DownwellingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDownwelling };
diff --git a/src/oceanography/el_nino.ts b/src/oceanography/el_nino.ts
new file mode 100644
index 00000000..e4c9dde8
--- /dev/null
+++ b/src/oceanography/el_nino.ts
@@ -0,0 +1,22 @@
+/** El Nino module — tsb analytics library. */
+
+/** Options for El Nino. */
+export interface ElNinoOptions { tol?: number; maxIter?: number; }
+
+/** Result from El Nino. */
+export interface ElNinoResult { values: number[]; converged: boolean; }
+
+/** Compute El Nino. */
+export function computeElNino(data: number[], opts: ElNinoOptions = {}): ElNinoResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeElNino };
diff --git a/src/oceanography/experimental.ts b/src/oceanography/experimental.ts
new file mode 100644
index 00000000..a1071baa
--- /dev/null
+++ b/src/oceanography/experimental.ts
@@ -0,0 +1,15 @@
+/** Oceanography Experimental module — tsb analytics library. */
+export interface Oceanography experimentalOptions { tol?: number; maxIter?: number; }
+export interface Oceanography experimentalResult { values: number[]; converged: boolean; }
+export function computeOceanography experimental(data: number[], opts: Oceanography experimentalOptions = {}): Oceanography experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOceanography experimental };
diff --git a/src/oceanography/fast.ts b/src/oceanography/fast.ts
new file mode 100644
index 00000000..c66404ac
--- /dev/null
+++ b/src/oceanography/fast.ts
@@ -0,0 +1,15 @@
+/** Oceanography Fast module — tsb analytics library. */
+export interface Oceanography fastOptions { tol?: number; maxIter?: number; }
+export interface Oceanography fastResult { values: number[]; converged: boolean; }
+export function computeOceanography fast(data: number[], opts: Oceanography fastOptions = {}): Oceanography fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOceanography fast };
diff --git a/src/oceanography/future.ts b/src/oceanography/future.ts
new file mode 100644
index 00000000..4bd5fb28
--- /dev/null
+++ b/src/oceanography/future.ts
@@ -0,0 +1,15 @@
+/** Oceanography Future module — tsb analytics library. */
+export interface Oceanography futureOptions { tol?: number; maxIter?: number; }
+export interface Oceanography futureResult { values: number[]; converged: boolean; }
+export function computeOceanography future(data: number[], opts: Oceanography futureOptions = {}): Oceanography futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOceanography future };
diff --git a/src/oceanography/geological.ts b/src/oceanography/geological.ts
new file mode 100644
index 00000000..ca329b2f
--- /dev/null
+++ b/src/oceanography/geological.ts
@@ -0,0 +1,22 @@
+/** Geological module — tsb analytics library. */
+
+/** Options for Geological. */
+export interface GeologicalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Geological. */
+export interface GeologicalResult { values: number[]; converged: boolean; }
+
+/** Compute Geological. */
+export function computeGeological(data: number[], opts: GeologicalOptions = {}): GeologicalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGeological };
diff --git a/src/oceanography/gpu.ts b/src/oceanography/gpu.ts
new file mode 100644
index 00000000..87cbd54d
--- /dev/null
+++ b/src/oceanography/gpu.ts
@@ -0,0 +1,15 @@
+/** Oceanography Gpu module — tsb analytics library. */
+export interface Oceanography gpuOptions { tol?: number; maxIter?: number; }
+export interface Oceanography gpuResult { values: number[]; converged: boolean; }
+export function computeOceanography gpu(data: number[], opts: Oceanography gpuOptions = {}): Oceanography gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOceanography gpu };
diff --git a/src/oceanography/gyre.ts b/src/oceanography/gyre.ts
new file mode 100644
index 00000000..47233fe7
--- /dev/null
+++ b/src/oceanography/gyre.ts
@@ -0,0 +1,22 @@
+/** Gyre module — tsb analytics library. */
+
+/** Options for Gyre. */
+export interface GyreOptions { tol?: number; maxIter?: number; }
+
+/** Result from Gyre. */
+export interface GyreResult { values: number[]; converged: boolean; }
+
+/** Compute Gyre. */
+export function computeGyre(data: number[], opts: GyreOptions = {}): GyreResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGyre };
diff --git a/src/oceanography/la_nina.ts b/src/oceanography/la_nina.ts
new file mode 100644
index 00000000..d444c109
--- /dev/null
+++ b/src/oceanography/la_nina.ts
@@ -0,0 +1,22 @@
+/** La Nina module — tsb analytics library. */
+
+/** Options for La Nina. */
+export interface LaNinaOptions { tol?: number; maxIter?: number; }
+
+/** Result from La Nina. */
+export interface LaNinaResult { values: number[]; converged: boolean; }
+
+/** Compute La Nina. */
+export function computeLaNina(data: number[], opts: LaNinaOptions = {}): LaNinaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLaNina };
diff --git a/src/oceanography/large.ts b/src/oceanography/large.ts
new file mode 100644
index 00000000..ca954e15
--- /dev/null
+++ b/src/oceanography/large.ts
@@ -0,0 +1,15 @@
+/** Oceanography Large module — tsb analytics library. */
+export interface Oceanography largeOptions { tol?: number; maxIter?: number; }
+export interface Oceanography largeResult { values: number[]; converged: boolean; }
+export function computeOceanography large(data: number[], opts: Oceanography largeOptions = {}): Oceanography largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOceanography large };
diff --git a/src/oceanography/legacy.ts b/src/oceanography/legacy.ts
new file mode 100644
index 00000000..e9963623
--- /dev/null
+++ b/src/oceanography/legacy.ts
@@ -0,0 +1,15 @@
+/** Oceanography Legacy module — tsb analytics library. */
+export interface Oceanography legacyOptions { tol?: number; maxIter?: number; }
+export interface Oceanography legacyResult { values: number[]; converged: boolean; }
+export function computeOceanography legacy(data: number[], opts: Oceanography legacyOptions = {}): Oceanography legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOceanography legacy };
diff --git a/src/oceanography/lite.ts b/src/oceanography/lite.ts
new file mode 100644
index 00000000..3ebc3eca
--- /dev/null
+++ b/src/oceanography/lite.ts
@@ -0,0 +1,15 @@
+/** Oceanography Lite module — tsb analytics library. */
+export interface Oceanography liteOptions { tol?: number; maxIter?: number; }
+export interface Oceanography liteResult { values: number[]; converged: boolean; }
+export function computeOceanography lite(data: number[], opts: Oceanography liteOptions = {}): Oceanography liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOceanography lite };
diff --git a/src/oceanography/mangrove.ts b/src/oceanography/mangrove.ts
new file mode 100644
index 00000000..b5b92a4e
--- /dev/null
+++ b/src/oceanography/mangrove.ts
@@ -0,0 +1,22 @@
+/** Mangrove module — tsb analytics library. */
+
+/** Options for Mangrove. */
+export interface MangroveOptions { tol?: number; maxIter?: number; }
+
+/** Result from Mangrove. */
+export interface MangroveResult { values: number[]; converged: boolean; }
+
+/** Compute Mangrove. */
+export function computeMangrove(data: number[], opts: MangroveOptions = {}): MangroveResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMangrove };
diff --git a/src/oceanography/mini.ts b/src/oceanography/mini.ts
new file mode 100644
index 00000000..c32b8d98
--- /dev/null
+++ b/src/oceanography/mini.ts
@@ -0,0 +1,15 @@
+/** Oceanography Mini module — tsb analytics library. */
+export interface Oceanography miniOptions { tol?: number; maxIter?: number; }
+export interface Oceanography miniResult { values: number[]; converged: boolean; }
+export function computeOceanography mini(data: number[], opts: Oceanography miniOptions = {}): Oceanography miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOceanography mini };
diff --git a/src/oceanography/mixing.ts b/src/oceanography/mixing.ts
new file mode 100644
index 00000000..ca69588b
--- /dev/null
+++ b/src/oceanography/mixing.ts
@@ -0,0 +1,22 @@
+/** Mixing module — tsb analytics library. */
+
+/** Options for Mixing. */
+export interface MixingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Mixing. */
+export interface MixingResult { values: number[]; converged: boolean; }
+
+/** Compute Mixing. */
+export function computeMixing(data: number[], opts: MixingOptions = {}): MixingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMixing };
diff --git a/src/oceanography/next.ts b/src/oceanography/next.ts
new file mode 100644
index 00000000..897a0a29
--- /dev/null
+++ b/src/oceanography/next.ts
@@ -0,0 +1,15 @@
+/** Oceanography Next module — tsb analytics library. */
+export interface Oceanography nextOptions { tol?: number; maxIter?: number; }
+export interface Oceanography nextResult { values: number[]; converged: boolean; }
+export function computeOceanography next(data: number[], opts: Oceanography nextOptions = {}): Oceanography nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOceanography next };
diff --git a/src/oceanography/nutrients.ts b/src/oceanography/nutrients.ts
new file mode 100644
index 00000000..a475cfa1
--- /dev/null
+++ b/src/oceanography/nutrients.ts
@@ -0,0 +1,22 @@
+/** Nutrients module — tsb analytics library. */
+
+/** Options for Nutrients. */
+export interface NutrientsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Nutrients. */
+export interface NutrientsResult { values: number[]; converged: boolean; }
+
+/** Compute Nutrients. */
+export function computeNutrients(data: number[], opts: NutrientsOptions = {}): NutrientsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNutrients };
diff --git a/src/oceanography/online.ts b/src/oceanography/online.ts
new file mode 100644
index 00000000..b4b0560b
--- /dev/null
+++ b/src/oceanography/online.ts
@@ -0,0 +1,15 @@
+/** Oceanography Online module — tsb analytics library. */
+export interface Oceanography onlineOptions { tol?: number; maxIter?: number; }
+export interface Oceanography onlineResult { values: number[]; converged: boolean; }
+export function computeOceanography online(data: number[], opts: Oceanography onlineOptions = {}): Oceanography onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOceanography online };
diff --git a/src/oceanography/oxygen.ts b/src/oceanography/oxygen.ts
new file mode 100644
index 00000000..7fa18906
--- /dev/null
+++ b/src/oceanography/oxygen.ts
@@ -0,0 +1,22 @@
+/** Oxygen module — tsb analytics library. */
+
+/** Options for Oxygen. */
+export interface OxygenOptions { tol?: number; maxIter?: number; }
+
+/** Result from Oxygen. */
+export interface OxygenResult { values: number[]; converged: boolean; }
+
+/** Compute Oxygen. */
+export function computeOxygen(data: number[], opts: OxygenOptions = {}): OxygenResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeOxygen };
diff --git a/src/oceanography/parallel.ts b/src/oceanography/parallel.ts
new file mode 100644
index 00000000..e2ecb9f5
--- /dev/null
+++ b/src/oceanography/parallel.ts
@@ -0,0 +1,15 @@
+/** Oceanography Parallel module — tsb analytics library. */
+export interface Oceanography parallelOptions { tol?: number; maxIter?: number; }
+export interface Oceanography parallelResult { values: number[]; converged: boolean; }
+export function computeOceanography parallel(data: number[], opts: Oceanography parallelOptions = {}): Oceanography parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOceanography parallel };
diff --git a/src/oceanography/pdo_ocean.ts b/src/oceanography/pdo_ocean.ts
new file mode 100644
index 00000000..23b5238b
--- /dev/null
+++ b/src/oceanography/pdo_ocean.ts
@@ -0,0 +1,22 @@
+/** Pdo Ocean module — tsb analytics library. */
+
+/** Options for Pdo Ocean. */
+export interface PdoOceanOptions { tol?: number; maxIter?: number; }
+
+/** Result from Pdo Ocean. */
+export interface PdoOceanResult { values: number[]; converged: boolean; }
+
+/** Compute Pdo Ocean. */
+export function computePdoOcean(data: number[], opts: PdoOceanOptions = {}): PdoOceanResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePdoOcean };
diff --git a/src/oceanography/physical.ts b/src/oceanography/physical.ts
new file mode 100644
index 00000000..21199935
--- /dev/null
+++ b/src/oceanography/physical.ts
@@ -0,0 +1,22 @@
+/** Physical module — tsb analytics library. */
+
+/** Options for Physical. */
+export interface PhysicalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Physical. */
+export interface PhysicalResult { values: number[]; converged: boolean; }
+
+/** Compute Physical. */
+export function computePhysical(data: number[], opts: PhysicalOptions = {}): PhysicalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePhysical };
diff --git a/src/oceanography/plankton.ts b/src/oceanography/plankton.ts
new file mode 100644
index 00000000..53b289de
--- /dev/null
+++ b/src/oceanography/plankton.ts
@@ -0,0 +1,22 @@
+/** Plankton module — tsb analytics library. */
+
+/** Options for Plankton. */
+export interface PlanktonOptions { tol?: number; maxIter?: number; }
+
+/** Result from Plankton. */
+export interface PlanktonResult { values: number[]; converged: boolean; }
+
+/** Compute Plankton. */
+export function computePlankton(data: number[], opts: PlanktonOptions = {}): PlanktonResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePlankton };
diff --git a/src/oceanography/plus.ts b/src/oceanography/plus.ts
new file mode 100644
index 00000000..9232f664
--- /dev/null
+++ b/src/oceanography/plus.ts
@@ -0,0 +1,15 @@
+/** Oceanography Plus module — tsb analytics library. */
+export interface Oceanography plusOptions { tol?: number; maxIter?: number; }
+export interface Oceanography plusResult { values: number[]; converged: boolean; }
+export function computeOceanography plus(data: number[], opts: Oceanography plusOptions = {}): Oceanography plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOceanography plus };
diff --git a/src/oceanography/primary_production.ts b/src/oceanography/primary_production.ts
new file mode 100644
index 00000000..d6ef07e7
--- /dev/null
+++ b/src/oceanography/primary_production.ts
@@ -0,0 +1,22 @@
+/** Primary Production module — tsb analytics library. */
+
+/** Options for Primary Production. */
+export interface PrimaryProductionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Primary Production. */
+export interface PrimaryProductionResult { values: number[]; converged: boolean; }
+
+/** Compute Primary Production. */
+export function computePrimaryProduction(data: number[], opts: PrimaryProductionOptions = {}): PrimaryProductionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePrimaryProduction };
diff --git a/src/oceanography/pro.ts b/src/oceanography/pro.ts
new file mode 100644
index 00000000..56b9b468
--- /dev/null
+++ b/src/oceanography/pro.ts
@@ -0,0 +1,15 @@
+/** Oceanography Pro module — tsb analytics library. */
+export interface Oceanography proOptions { tol?: number; maxIter?: number; }
+export interface Oceanography proResult { values: number[]; converged: boolean; }
+export function computeOceanography pro(data: number[], opts: Oceanography proOptions = {}): Oceanography proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOceanography pro };
diff --git a/src/oceanography/robust.ts b/src/oceanography/robust.ts
new file mode 100644
index 00000000..4b080c1d
--- /dev/null
+++ b/src/oceanography/robust.ts
@@ -0,0 +1,15 @@
+/** Oceanography Robust module — tsb analytics library. */
+export interface Oceanography robustOptions { tol?: number; maxIter?: number; }
+export interface Oceanography robustResult { values: number[]; converged: boolean; }
+export function computeOceanography robust(data: number[], opts: Oceanography robustOptions = {}): Oceanography robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOceanography robust };
diff --git a/src/oceanography/sea_ice.ts b/src/oceanography/sea_ice.ts
new file mode 100644
index 00000000..fd274183
--- /dev/null
+++ b/src/oceanography/sea_ice.ts
@@ -0,0 +1,22 @@
+/** Sea Ice module — tsb analytics library. */
+
+/** Options for Sea Ice. */
+export interface SeaIceOptions { tol?: number; maxIter?: number; }
+
+/** Result from Sea Ice. */
+export interface SeaIceResult { values: number[]; converged: boolean; }
+
+/** Compute Sea Ice. */
+export function computeSeaIce(data: number[], opts: SeaIceOptions = {}): SeaIceResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSeaIce };
diff --git a/src/oceanography/seagrass.ts b/src/oceanography/seagrass.ts
new file mode 100644
index 00000000..868bb402
--- /dev/null
+++ b/src/oceanography/seagrass.ts
@@ -0,0 +1,22 @@
+/** Seagrass module — tsb analytics library. */
+
+/** Options for Seagrass. */
+export interface SeagrassOptions { tol?: number; maxIter?: number; }
+
+/** Result from Seagrass. */
+export interface SeagrassResult { values: number[]; converged: boolean; }
+
+/** Compute Seagrass. */
+export function computeSeagrass(data: number[], opts: SeagrassOptions = {}): SeagrassResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSeagrass };
diff --git a/src/oceanography/small.ts b/src/oceanography/small.ts
new file mode 100644
index 00000000..2361762d
--- /dev/null
+++ b/src/oceanography/small.ts
@@ -0,0 +1,15 @@
+/** Oceanography Small module — tsb analytics library. */
+export interface Oceanography smallOptions { tol?: number; maxIter?: number; }
+export interface Oceanography smallResult { values: number[]; converged: boolean; }
+export function computeOceanography small(data: number[], opts: Oceanography smallOptions = {}): Oceanography smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOceanography small };
diff --git a/src/oceanography/sparse.ts b/src/oceanography/sparse.ts
new file mode 100644
index 00000000..f2a1dc25
--- /dev/null
+++ b/src/oceanography/sparse.ts
@@ -0,0 +1,15 @@
+/** Oceanography Sparse module — tsb analytics library. */
+export interface Oceanography sparseOptions { tol?: number; maxIter?: number; }
+export interface Oceanography sparseResult { values: number[]; converged: boolean; }
+export function computeOceanography sparse(data: number[], opts: Oceanography sparseOptions = {}): Oceanography sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOceanography sparse };
diff --git a/src/oceanography/stable.ts b/src/oceanography/stable.ts
new file mode 100644
index 00000000..4112bbf4
--- /dev/null
+++ b/src/oceanography/stable.ts
@@ -0,0 +1,15 @@
+/** Oceanography Stable module — tsb analytics library. */
+export interface Oceanography stableOptions { tol?: number; maxIter?: number; }
+export interface Oceanography stableResult { values: number[]; converged: boolean; }
+export function computeOceanography stable(data: number[], opts: Oceanography stableOptions = {}): Oceanography stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOceanography stable };
diff --git a/src/oceanography/streaming.ts b/src/oceanography/streaming.ts
new file mode 100644
index 00000000..5816fc9f
--- /dev/null
+++ b/src/oceanography/streaming.ts
@@ -0,0 +1,15 @@
+/** Oceanography Streaming module — tsb analytics library. */
+export interface Oceanography streamingOptions { tol?: number; maxIter?: number; }
+export interface Oceanography streamingResult { values: number[]; converged: boolean; }
+export function computeOceanography streaming(data: number[], opts: Oceanography streamingOptions = {}): Oceanography streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOceanography streaming };
diff --git a/src/oceanography/surge.ts b/src/oceanography/surge.ts
new file mode 100644
index 00000000..ecf88dd2
--- /dev/null
+++ b/src/oceanography/surge.ts
@@ -0,0 +1,22 @@
+/** Surge module — tsb analytics library. */
+
+/** Options for Surge. */
+export interface SurgeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Surge. */
+export interface SurgeResult { values: number[]; converged: boolean; }
+
+/** Compute Surge. */
+export function computeSurge(data: number[], opts: SurgeOptions = {}): SurgeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSurge };
diff --git a/src/oceanography/thermohaline.ts b/src/oceanography/thermohaline.ts
new file mode 100644
index 00000000..0ab4d9c3
--- /dev/null
+++ b/src/oceanography/thermohaline.ts
@@ -0,0 +1,22 @@
+/** Thermohaline module — tsb analytics library. */
+
+/** Options for Thermohaline. */
+export interface ThermohalineOptions { tol?: number; maxIter?: number; }
+
+/** Result from Thermohaline. */
+export interface ThermohalineResult { values: number[]; converged: boolean; }
+
+/** Compute Thermohaline. */
+export function computeThermohaline(data: number[], opts: ThermohalineOptions = {}): ThermohalineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeThermohaline };
diff --git a/src/oceanography/tide.ts b/src/oceanography/tide.ts
new file mode 100644
index 00000000..f1bd1a49
--- /dev/null
+++ b/src/oceanography/tide.ts
@@ -0,0 +1,22 @@
+/** Tide module — tsb analytics library. */
+
+/** Options for Tide. */
+export interface TideOptions { tol?: number; maxIter?: number; }
+
+/** Result from Tide. */
+export interface TideResult { values: number[]; converged: boolean; }
+
+/** Compute Tide. */
+export function computeTide(data: number[], opts: TideOptions = {}): TideResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTide };
diff --git a/src/oceanography/tsunami.ts b/src/oceanography/tsunami.ts
new file mode 100644
index 00000000..150cf738
--- /dev/null
+++ b/src/oceanography/tsunami.ts
@@ -0,0 +1,22 @@
+/** Tsunami module — tsb analytics library. */
+
+/** Options for Tsunami. */
+export interface TsunamiOptions { tol?: number; maxIter?: number; }
+
+/** Result from Tsunami. */
+export interface TsunamiResult { values: number[]; converged: boolean; }
+
+/** Compute Tsunami. */
+export function computeTsunami(data: number[], opts: TsunamiOptions = {}): TsunamiResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTsunami };
diff --git a/src/oceanography/turbulence.ts b/src/oceanography/turbulence.ts
new file mode 100644
index 00000000..6e452049
--- /dev/null
+++ b/src/oceanography/turbulence.ts
@@ -0,0 +1,22 @@
+/** Turbulence module — tsb analytics library. */
+
+/** Options for Turbulence. */
+export interface TurbulenceOptions { tol?: number; maxIter?: number; }
+
+/** Result from Turbulence. */
+export interface TurbulenceResult { values: number[]; converged: boolean; }
+
+/** Compute Turbulence. */
+export function computeTurbulence(data: number[], opts: TurbulenceOptions = {}): TurbulenceResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTurbulence };
diff --git a/src/oceanography/upwelling.ts b/src/oceanography/upwelling.ts
new file mode 100644
index 00000000..b63b44f5
--- /dev/null
+++ b/src/oceanography/upwelling.ts
@@ -0,0 +1,22 @@
+/** Upwelling module — tsb analytics library. */
+
+/** Options for Upwelling. */
+export interface UpwellingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Upwelling. */
+export interface UpwellingResult { values: number[]; converged: boolean; }
+
+/** Compute Upwelling. */
+export function computeUpwelling(data: number[], opts: UpwellingOptions = {}): UpwellingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeUpwelling };
diff --git a/src/oceanography/v2.ts b/src/oceanography/v2.ts
new file mode 100644
index 00000000..4c2479bd
--- /dev/null
+++ b/src/oceanography/v2.ts
@@ -0,0 +1,15 @@
+/** Oceanography V2 module — tsb analytics library. */
+export interface Oceanography v2Options { tol?: number; maxIter?: number; }
+export interface Oceanography v2Result { values: number[]; converged: boolean; }
+export function computeOceanography v2(data: number[], opts: Oceanography v2Options = {}): Oceanography v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOceanography v2 };
diff --git a/src/oceanography/v3.ts b/src/oceanography/v3.ts
new file mode 100644
index 00000000..0c912138
--- /dev/null
+++ b/src/oceanography/v3.ts
@@ -0,0 +1,15 @@
+/** Oceanography V3 module — tsb analytics library. */
+export interface Oceanography v3Options { tol?: number; maxIter?: number; }
+export interface Oceanography v3Result { values: number[]; converged: boolean; }
+export function computeOceanography v3(data: number[], opts: Oceanography v3Options = {}): Oceanography v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOceanography v3 };
diff --git a/src/oceanography/wasm.ts b/src/oceanography/wasm.ts
new file mode 100644
index 00000000..7a8381fb
--- /dev/null
+++ b/src/oceanography/wasm.ts
@@ -0,0 +1,15 @@
+/** Oceanography Wasm module — tsb analytics library. */
+export interface Oceanography wasmOptions { tol?: number; maxIter?: number; }
+export interface Oceanography wasmResult { values: number[]; converged: boolean; }
+export function computeOceanography wasm(data: number[], opts: Oceanography wasmOptions = {}): Oceanography wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOceanography wasm };
diff --git a/src/oceanography/wave_ocean.ts b/src/oceanography/wave_ocean.ts
new file mode 100644
index 00000000..a988ccd2
--- /dev/null
+++ b/src/oceanography/wave_ocean.ts
@@ -0,0 +1,22 @@
+/** Wave Ocean module — tsb analytics library. */
+
+/** Options for Wave Ocean. */
+export interface WaveOceanOptions { tol?: number; maxIter?: number; }
+
+/** Result from Wave Ocean. */
+export interface WaveOceanResult { values: number[]; converged: boolean; }
+
+/** Compute Wave Ocean. */
+export function computeWaveOcean(data: number[], opts: WaveOceanOptions = {}): WaveOceanResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeWaveOcean };
diff --git a/src/oceanography/xlarge.ts b/src/oceanography/xlarge.ts
new file mode 100644
index 00000000..14550fc2
--- /dev/null
+++ b/src/oceanography/xlarge.ts
@@ -0,0 +1,15 @@
+/** Oceanography Xlarge module — tsb analytics library. */
+export interface Oceanography xlargeOptions { tol?: number; maxIter?: number; }
+export interface Oceanography xlargeResult { values: number[]; converged: boolean; }
+export function computeOceanography xlarge(data: number[], opts: Oceanography xlargeOptions = {}): Oceanography xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOceanography xlarge };
diff --git a/src/operations/acceptance_sampling.ts b/src/operations/acceptance_sampling.ts
new file mode 100644
index 00000000..bfb2deca
--- /dev/null
+++ b/src/operations/acceptance_sampling.ts
@@ -0,0 +1,22 @@
+/** Acceptance Sampling module — tsb analytics library. */
+
+/** Options for Acceptance Sampling. */
+export interface AcceptanceSamplingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Acceptance Sampling. */
+export interface AcceptanceSamplingResult { values: number[]; converged: boolean; }
+
+/** Compute Acceptance Sampling. */
+export function computeAcceptanceSampling(data: number[], opts: AcceptanceSamplingOptions = {}): AcceptanceSamplingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAcceptanceSampling };
diff --git a/src/operations/advanced.ts b/src/operations/advanced.ts
new file mode 100644
index 00000000..6f3dac7b
--- /dev/null
+++ b/src/operations/advanced.ts
@@ -0,0 +1,15 @@
+/** Operations Advanced module — tsb analytics library. */
+export interface Operations advancedOptions { tol?: number; maxIter?: number; }
+export interface Operations advancedResult { values: number[]; converged: boolean; }
+export function computeOperations advanced(data: number[], opts: Operations advancedOptions = {}): Operations advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOperations advanced };
diff --git a/src/operations/base2.ts b/src/operations/base2.ts
new file mode 100644
index 00000000..8bf02253
--- /dev/null
+++ b/src/operations/base2.ts
@@ -0,0 +1,15 @@
+/** Operations Base2 module — tsb analytics library. */
+export interface Operations base2Options { tol?: number; maxIter?: number; }
+export interface Operations base2Result { values: number[]; converged: boolean; }
+export function computeOperations base2(data: number[], opts: Operations base2Options = {}): Operations base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOperations base2 };
diff --git a/src/operations/batch.ts b/src/operations/batch.ts
new file mode 100644
index 00000000..f89911c4
--- /dev/null
+++ b/src/operations/batch.ts
@@ -0,0 +1,15 @@
+/** Operations Batch module — tsb analytics library. */
+export interface Operations batchOptions { tol?: number; maxIter?: number; }
+export interface Operations batchResult { values: number[]; converged: boolean; }
+export function computeOperations batch(data: number[], opts: Operations batchOptions = {}): Operations batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOperations batch };
diff --git a/src/operations/beta.ts b/src/operations/beta.ts
new file mode 100644
index 00000000..11e78ec3
--- /dev/null
+++ b/src/operations/beta.ts
@@ -0,0 +1,15 @@
+/** Operations Beta module — tsb analytics library. */
+export interface Operations betaOptions { tol?: number; maxIter?: number; }
+export interface Operations betaResult { values: number[]; converged: boolean; }
+export function computeOperations beta(data: number[], opts: Operations betaOptions = {}): Operations betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOperations beta };
diff --git a/src/operations/bottleneck.ts b/src/operations/bottleneck.ts
new file mode 100644
index 00000000..8bd1322d
--- /dev/null
+++ b/src/operations/bottleneck.ts
@@ -0,0 +1,22 @@
+/** Bottleneck module — tsb analytics library. */
+
+/** Options for Bottleneck. */
+export interface BottleneckOptions { tol?: number; maxIter?: number; }
+
+/** Result from Bottleneck. */
+export interface BottleneckResult { values: number[]; converged: boolean; }
+
+/** Compute Bottleneck. */
+export function computeBottleneck(data: number[], opts: BottleneckOptions = {}): BottleneckResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBottleneck };
diff --git a/src/operations/capacity_ops.ts b/src/operations/capacity_ops.ts
new file mode 100644
index 00000000..332e4f8a
--- /dev/null
+++ b/src/operations/capacity_ops.ts
@@ -0,0 +1,22 @@
+/** Capacity Ops module — tsb analytics library. */
+
+/** Options for Capacity Ops. */
+export interface CapacityOpsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Capacity Ops. */
+export interface CapacityOpsResult { values: number[]; converged: boolean; }
+
+/** Compute Capacity Ops. */
+export function computeCapacityOps(data: number[], opts: CapacityOpsOptions = {}): CapacityOpsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCapacityOps };
diff --git a/src/operations/constraint_ops.ts b/src/operations/constraint_ops.ts
new file mode 100644
index 00000000..7478ad90
--- /dev/null
+++ b/src/operations/constraint_ops.ts
@@ -0,0 +1,22 @@
+/** Constraint Ops module — tsb analytics library. */
+
+/** Options for Constraint Ops. */
+export interface ConstraintOpsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Constraint Ops. */
+export interface ConstraintOpsResult { values: number[]; converged: boolean; }
+
+/** Compute Constraint Ops. */
+export function computeConstraintOps(data: number[], opts: ConstraintOpsOptions = {}): ConstraintOpsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeConstraintOps };
diff --git a/src/operations/control_chart.ts b/src/operations/control_chart.ts
new file mode 100644
index 00000000..646d4492
--- /dev/null
+++ b/src/operations/control_chart.ts
@@ -0,0 +1,22 @@
+/** Control Chart module — tsb analytics library. */
+
+/** Options for Control Chart. */
+export interface ControlChartOptions { tol?: number; maxIter?: number; }
+
+/** Result from Control Chart. */
+export interface ControlChartResult { values: number[]; converged: boolean; }
+
+/** Compute Control Chart. */
+export function computeControlChart(data: number[], opts: ControlChartOptions = {}): ControlChartResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeControlChart };
diff --git a/src/operations/cpu.ts b/src/operations/cpu.ts
new file mode 100644
index 00000000..12e460c1
--- /dev/null
+++ b/src/operations/cpu.ts
@@ -0,0 +1,15 @@
+/** Operations Cpu module — tsb analytics library. */
+export interface Operations cpuOptions { tol?: number; maxIter?: number; }
+export interface Operations cpuResult { values: number[]; converged: boolean; }
+export function computeOperations cpu(data: number[], opts: Operations cpuOptions = {}): Operations cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOperations cpu };
diff --git a/src/operations/critical_path.ts b/src/operations/critical_path.ts
new file mode 100644
index 00000000..6175a8ad
--- /dev/null
+++ b/src/operations/critical_path.ts
@@ -0,0 +1,22 @@
+/** Critical Path module — tsb analytics library. */
+
+/** Options for Critical Path. */
+export interface CriticalPathOptions { tol?: number; maxIter?: number; }
+
+/** Result from Critical Path. */
+export interface CriticalPathResult { values: number[]; converged: boolean; }
+
+/** Compute Critical Path. */
+export function computeCriticalPath(data: number[], opts: CriticalPathOptions = {}): CriticalPathResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCriticalPath };
diff --git a/src/operations/cycle_time.ts b/src/operations/cycle_time.ts
new file mode 100644
index 00000000..8edd5fb6
--- /dev/null
+++ b/src/operations/cycle_time.ts
@@ -0,0 +1,22 @@
+/** Cycle Time module — tsb analytics library. */
+
+/** Options for Cycle Time. */
+export interface CycleTimeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Cycle Time. */
+export interface CycleTimeResult { values: number[]; converged: boolean; }
+
+/** Compute Cycle Time. */
+export function computeCycleTime(data: number[], opts: CycleTimeOptions = {}): CycleTimeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCycleTime };
diff --git a/src/operations/dense.ts b/src/operations/dense.ts
new file mode 100644
index 00000000..dfd151dd
--- /dev/null
+++ b/src/operations/dense.ts
@@ -0,0 +1,15 @@
+/** Operations Dense module — tsb analytics library. */
+export interface Operations denseOptions { tol?: number; maxIter?: number; }
+export interface Operations denseResult { values: number[]; converged: boolean; }
+export function computeOperations dense(data: number[], opts: Operations denseOptions = {}): Operations denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOperations dense };
diff --git a/src/operations/design_of_experiments.ts b/src/operations/design_of_experiments.ts
new file mode 100644
index 00000000..e5aaa072
--- /dev/null
+++ b/src/operations/design_of_experiments.ts
@@ -0,0 +1,22 @@
+/** Design Of Experiments module — tsb analytics library. */
+
+/** Options for Design Of Experiments. */
+export interface DesignOfExperimentsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Design Of Experiments. */
+export interface DesignOfExperimentsResult { values: number[]; converged: boolean; }
+
+/** Compute Design Of Experiments. */
+export function computeDesignOfExperiments(data: number[], opts: DesignOfExperimentsOptions = {}): DesignOfExperimentsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDesignOfExperiments };
diff --git a/src/operations/distributed.ts b/src/operations/distributed.ts
new file mode 100644
index 00000000..0674f7c7
--- /dev/null
+++ b/src/operations/distributed.ts
@@ -0,0 +1,15 @@
+/** Operations Distributed module — tsb analytics library. */
+export interface Operations distributedOptions { tol?: number; maxIter?: number; }
+export interface Operations distributedResult { values: number[]; converged: boolean; }
+export function computeOperations distributed(data: number[], opts: Operations distributedOptions = {}): Operations distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOperations distributed };
diff --git a/src/operations/experimental.ts b/src/operations/experimental.ts
new file mode 100644
index 00000000..49b0fad8
--- /dev/null
+++ b/src/operations/experimental.ts
@@ -0,0 +1,15 @@
+/** Operations Experimental module — tsb analytics library. */
+export interface Operations experimentalOptions { tol?: number; maxIter?: number; }
+export interface Operations experimentalResult { values: number[]; converged: boolean; }
+export function computeOperations experimental(data: number[], opts: Operations experimentalOptions = {}): Operations experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOperations experimental };
diff --git a/src/operations/failure_mode.ts b/src/operations/failure_mode.ts
new file mode 100644
index 00000000..69c23763
--- /dev/null
+++ b/src/operations/failure_mode.ts
@@ -0,0 +1,22 @@
+/** Failure Mode module — tsb analytics library. */
+
+/** Options for Failure Mode. */
+export interface FailureModeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Failure Mode. */
+export interface FailureModeResult { values: number[]; converged: boolean; }
+
+/** Compute Failure Mode. */
+export function computeFailureMode(data: number[], opts: FailureModeOptions = {}): FailureModeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFailureMode };
diff --git a/src/operations/fast.ts b/src/operations/fast.ts
new file mode 100644
index 00000000..02a646bb
--- /dev/null
+++ b/src/operations/fast.ts
@@ -0,0 +1,15 @@
+/** Operations Fast module — tsb analytics library. */
+export interface Operations fastOptions { tol?: number; maxIter?: number; }
+export interface Operations fastResult { values: number[]; converged: boolean; }
+export function computeOperations fast(data: number[], opts: Operations fastOptions = {}): Operations fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOperations fast };
diff --git a/src/operations/future.ts b/src/operations/future.ts
new file mode 100644
index 00000000..19adb0f9
--- /dev/null
+++ b/src/operations/future.ts
@@ -0,0 +1,15 @@
+/** Operations Future module — tsb analytics library. */
+export interface Operations futureOptions { tol?: number; maxIter?: number; }
+export interface Operations futureResult { values: number[]; converged: boolean; }
+export function computeOperations future(data: number[], opts: Operations futureOptions = {}): Operations futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOperations future };
diff --git a/src/operations/gantt.ts b/src/operations/gantt.ts
new file mode 100644
index 00000000..b5c4cf66
--- /dev/null
+++ b/src/operations/gantt.ts
@@ -0,0 +1,22 @@
+/** Gantt module — tsb analytics library. */
+
+/** Options for Gantt. */
+export interface GanttOptions { tol?: number; maxIter?: number; }
+
+/** Result from Gantt. */
+export interface GanttResult { values: number[]; converged: boolean; }
+
+/** Compute Gantt. */
+export function computeGantt(data: number[], opts: GanttOptions = {}): GanttResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGantt };
diff --git a/src/operations/gpu.ts b/src/operations/gpu.ts
new file mode 100644
index 00000000..00436237
--- /dev/null
+++ b/src/operations/gpu.ts
@@ -0,0 +1,15 @@
+/** Operations Gpu module — tsb analytics library. */
+export interface Operations gpuOptions { tol?: number; maxIter?: number; }
+export interface Operations gpuResult { values: number[]; converged: boolean; }
+export function computeOperations gpu(data: number[], opts: Operations gpuOptions = {}): Operations gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOperations gpu };
diff --git a/src/operations/kanban.ts b/src/operations/kanban.ts
new file mode 100644
index 00000000..47e6e352
--- /dev/null
+++ b/src/operations/kanban.ts
@@ -0,0 +1,22 @@
+/** Kanban module — tsb analytics library. */
+
+/** Options for Kanban. */
+export interface KanbanOptions { tol?: number; maxIter?: number; }
+
+/** Result from Kanban. */
+export interface KanbanResult { values: number[]; converged: boolean; }
+
+/** Compute Kanban. */
+export function computeKanban(data: number[], opts: KanbanOptions = {}): KanbanResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeKanban };
diff --git a/src/operations/large.ts b/src/operations/large.ts
new file mode 100644
index 00000000..b357d0d7
--- /dev/null
+++ b/src/operations/large.ts
@@ -0,0 +1,15 @@
+/** Operations Large module — tsb analytics library. */
+export interface Operations largeOptions { tol?: number; maxIter?: number; }
+export interface Operations largeResult { values: number[]; converged: boolean; }
+export function computeOperations large(data: number[], opts: Operations largeOptions = {}): Operations largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOperations large };
diff --git a/src/operations/lead_time.ts b/src/operations/lead_time.ts
new file mode 100644
index 00000000..a6214b56
--- /dev/null
+++ b/src/operations/lead_time.ts
@@ -0,0 +1,22 @@
+/** Lead Time module — tsb analytics library. */
+
+/** Options for Lead Time. */
+export interface LeadTimeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Lead Time. */
+export interface LeadTimeResult { values: number[]; converged: boolean; }
+
+/** Compute Lead Time. */
+export function computeLeadTime(data: number[], opts: LeadTimeOptions = {}): LeadTimeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLeadTime };
diff --git a/src/operations/lean.ts b/src/operations/lean.ts
new file mode 100644
index 00000000..106c5d00
--- /dev/null
+++ b/src/operations/lean.ts
@@ -0,0 +1,22 @@
+/** Lean module — tsb analytics library. */
+
+/** Options for Lean. */
+export interface LeanOptions { tol?: number; maxIter?: number; }
+
+/** Result from Lean. */
+export interface LeanResult { values: number[]; converged: boolean; }
+
+/** Compute Lean. */
+export function computeLean(data: number[], opts: LeanOptions = {}): LeanResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLean };
diff --git a/src/operations/legacy.ts b/src/operations/legacy.ts
new file mode 100644
index 00000000..61940371
--- /dev/null
+++ b/src/operations/legacy.ts
@@ -0,0 +1,15 @@
+/** Operations Legacy module — tsb analytics library. */
+export interface Operations legacyOptions { tol?: number; maxIter?: number; }
+export interface Operations legacyResult { values: number[]; converged: boolean; }
+export function computeOperations legacy(data: number[], opts: Operations legacyOptions = {}): Operations legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOperations legacy };
diff --git a/src/operations/lite.ts b/src/operations/lite.ts
new file mode 100644
index 00000000..8dda8c9e
--- /dev/null
+++ b/src/operations/lite.ts
@@ -0,0 +1,15 @@
+/** Operations Lite module — tsb analytics library. */
+export interface Operations liteOptions { tol?: number; maxIter?: number; }
+export interface Operations liteResult { values: number[]; converged: boolean; }
+export function computeOperations lite(data: number[], opts: Operations liteOptions = {}): Operations liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOperations lite };
diff --git a/src/operations/maintenance.ts b/src/operations/maintenance.ts
new file mode 100644
index 00000000..59c7c3a1
--- /dev/null
+++ b/src/operations/maintenance.ts
@@ -0,0 +1,22 @@
+/** Maintenance module — tsb analytics library. */
+
+/** Options for Maintenance. */
+export interface MaintenanceOptions { tol?: number; maxIter?: number; }
+
+/** Result from Maintenance. */
+export interface MaintenanceResult { values: number[]; converged: boolean; }
+
+/** Compute Maintenance. */
+export function computeMaintenance(data: number[], opts: MaintenanceOptions = {}): MaintenanceResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMaintenance };
diff --git a/src/operations/mini.ts b/src/operations/mini.ts
new file mode 100644
index 00000000..700d0362
--- /dev/null
+++ b/src/operations/mini.ts
@@ -0,0 +1,15 @@
+/** Operations Mini module — tsb analytics library. */
+export interface Operations miniOptions { tol?: number; maxIter?: number; }
+export interface Operations miniResult { values: number[]; converged: boolean; }
+export function computeOperations mini(data: number[], opts: Operations miniOptions = {}): Operations miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOperations mini };
diff --git a/src/operations/next.ts b/src/operations/next.ts
new file mode 100644
index 00000000..6456d47e
--- /dev/null
+++ b/src/operations/next.ts
@@ -0,0 +1,15 @@
+/** Operations Next module — tsb analytics library. */
+export interface Operations nextOptions { tol?: number; maxIter?: number; }
+export interface Operations nextResult { values: number[]; converged: boolean; }
+export function computeOperations next(data: number[], opts: Operations nextOptions = {}): Operations nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOperations next };
diff --git a/src/operations/online.ts b/src/operations/online.ts
new file mode 100644
index 00000000..428e773b
--- /dev/null
+++ b/src/operations/online.ts
@@ -0,0 +1,15 @@
+/** Operations Online module — tsb analytics library. */
+export interface Operations onlineOptions { tol?: number; maxIter?: number; }
+export interface Operations onlineResult { values: number[]; converged: boolean; }
+export function computeOperations online(data: number[], opts: Operations onlineOptions = {}): Operations onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOperations online };
diff --git a/src/operations/parallel.ts b/src/operations/parallel.ts
new file mode 100644
index 00000000..ca32c4e9
--- /dev/null
+++ b/src/operations/parallel.ts
@@ -0,0 +1,15 @@
+/** Operations Parallel module — tsb analytics library. */
+export interface Operations parallelOptions { tol?: number; maxIter?: number; }
+export interface Operations parallelResult { values: number[]; converged: boolean; }
+export function computeOperations parallel(data: number[], opts: Operations parallelOptions = {}): Operations parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOperations parallel };
diff --git a/src/operations/pert.ts b/src/operations/pert.ts
new file mode 100644
index 00000000..a8afb597
--- /dev/null
+++ b/src/operations/pert.ts
@@ -0,0 +1,22 @@
+/** Pert module — tsb analytics library. */
+
+/** Options for Pert. */
+export interface PertOptions { tol?: number; maxIter?: number; }
+
+/** Result from Pert. */
+export interface PertResult { values: number[]; converged: boolean; }
+
+/** Compute Pert. */
+export function computePert(data: number[], opts: PertOptions = {}): PertResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePert };
diff --git a/src/operations/plus.ts b/src/operations/plus.ts
new file mode 100644
index 00000000..b38dd4d4
--- /dev/null
+++ b/src/operations/plus.ts
@@ -0,0 +1,15 @@
+/** Operations Plus module — tsb analytics library. */
+export interface Operations plusOptions { tol?: number; maxIter?: number; }
+export interface Operations plusResult { values: number[]; converged: boolean; }
+export function computeOperations plus(data: number[], opts: Operations plusOptions = {}): Operations plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOperations plus };
diff --git a/src/operations/pro.ts b/src/operations/pro.ts
new file mode 100644
index 00000000..46c742cf
--- /dev/null
+++ b/src/operations/pro.ts
@@ -0,0 +1,15 @@
+/** Operations Pro module — tsb analytics library. */
+export interface Operations proOptions { tol?: number; maxIter?: number; }
+export interface Operations proResult { values: number[]; converged: boolean; }
+export function computeOperations pro(data: number[], opts: Operations proOptions = {}): Operations proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOperations pro };
diff --git a/src/operations/project.ts b/src/operations/project.ts
new file mode 100644
index 00000000..dffd6f74
--- /dev/null
+++ b/src/operations/project.ts
@@ -0,0 +1,22 @@
+/** Project module — tsb analytics library. */
+
+/** Options for Project. */
+export interface ProjectOptions { tol?: number; maxIter?: number; }
+
+/** Result from Project. */
+export interface ProjectResult { values: number[]; converged: boolean; }
+
+/** Compute Project. */
+export function computeProject(data: number[], opts: ProjectOptions = {}): ProjectResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeProject };
diff --git a/src/operations/quality_function.ts b/src/operations/quality_function.ts
new file mode 100644
index 00000000..5888b8d9
--- /dev/null
+++ b/src/operations/quality_function.ts
@@ -0,0 +1,22 @@
+/** Quality Function module — tsb analytics library. */
+
+/** Options for Quality Function. */
+export interface QualityFunctionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Quality Function. */
+export interface QualityFunctionResult { values: number[]; converged: boolean; }
+
+/** Compute Quality Function. */
+export function computeQualityFunction(data: number[], opts: QualityFunctionOptions = {}): QualityFunctionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeQualityFunction };
diff --git a/src/operations/queuing.ts b/src/operations/queuing.ts
new file mode 100644
index 00000000..870f0c63
--- /dev/null
+++ b/src/operations/queuing.ts
@@ -0,0 +1,22 @@
+/** Queuing module — tsb analytics library. */
+
+/** Options for Queuing. */
+export interface QueuingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Queuing. */
+export interface QueuingResult { values: number[]; converged: boolean; }
+
+/** Compute Queuing. */
+export function computeQueuing(data: number[], opts: QueuingOptions = {}): QueuingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeQueuing };
diff --git a/src/operations/reliability_ops.ts b/src/operations/reliability_ops.ts
new file mode 100644
index 00000000..dc1241e8
--- /dev/null
+++ b/src/operations/reliability_ops.ts
@@ -0,0 +1,22 @@
+/** Reliability Ops module — tsb analytics library. */
+
+/** Options for Reliability Ops. */
+export interface ReliabilityOpsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Reliability Ops. */
+export interface ReliabilityOpsResult { values: number[]; converged: boolean; }
+
+/** Compute Reliability Ops. */
+export function computeReliabilityOps(data: number[], opts: ReliabilityOpsOptions = {}): ReliabilityOpsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeReliabilityOps };
diff --git a/src/operations/resource.ts b/src/operations/resource.ts
new file mode 100644
index 00000000..8a495f76
--- /dev/null
+++ b/src/operations/resource.ts
@@ -0,0 +1,22 @@
+/** Resource module — tsb analytics library. */
+
+/** Options for Resource. */
+export interface ResourceOptions { tol?: number; maxIter?: number; }
+
+/** Result from Resource. */
+export interface ResourceResult { values: number[]; converged: boolean; }
+
+/** Compute Resource. */
+export function computeResource(data: number[], opts: ResourceOptions = {}): ResourceResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeResource };
diff --git a/src/operations/response_surface.ts b/src/operations/response_surface.ts
new file mode 100644
index 00000000..f6f779f3
--- /dev/null
+++ b/src/operations/response_surface.ts
@@ -0,0 +1,22 @@
+/** Response Surface module — tsb analytics library. */
+
+/** Options for Response Surface. */
+export interface ResponseSurfaceOptions { tol?: number; maxIter?: number; }
+
+/** Result from Response Surface. */
+export interface ResponseSurfaceResult { values: number[]; converged: boolean; }
+
+/** Compute Response Surface. */
+export function computeResponseSurface(data: number[], opts: ResponseSurfaceOptions = {}): ResponseSurfaceResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeResponseSurface };
diff --git a/src/operations/robust.ts b/src/operations/robust.ts
new file mode 100644
index 00000000..00d96fc2
--- /dev/null
+++ b/src/operations/robust.ts
@@ -0,0 +1,15 @@
+/** Operations Robust module — tsb analytics library. */
+export interface Operations robustOptions { tol?: number; maxIter?: number; }
+export interface Operations robustResult { values: number[]; converged: boolean; }
+export function computeOperations robust(data: number[], opts: Operations robustOptions = {}): Operations robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOperations robust };
diff --git a/src/operations/robust_design.ts b/src/operations/robust_design.ts
new file mode 100644
index 00000000..5f1114f0
--- /dev/null
+++ b/src/operations/robust_design.ts
@@ -0,0 +1,22 @@
+/** Robust Design module — tsb analytics library. */
+
+/** Options for Robust Design. */
+export interface RobustDesignOptions { tol?: number; maxIter?: number; }
+
+/** Result from Robust Design. */
+export interface RobustDesignResult { values: number[]; converged: boolean; }
+
+/** Compute Robust Design. */
+export function computeRobustDesign(data: number[], opts: RobustDesignOptions = {}): RobustDesignResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRobustDesign };
diff --git a/src/operations/root_cause.ts b/src/operations/root_cause.ts
new file mode 100644
index 00000000..8b33a8d3
--- /dev/null
+++ b/src/operations/root_cause.ts
@@ -0,0 +1,22 @@
+/** Root Cause module — tsb analytics library. */
+
+/** Options for Root Cause. */
+export interface RootCauseOptions { tol?: number; maxIter?: number; }
+
+/** Result from Root Cause. */
+export interface RootCauseResult { values: number[]; converged: boolean; }
+
+/** Compute Root Cause. */
+export function computeRootCause(data: number[], opts: RootCauseOptions = {}): RootCauseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRootCause };
diff --git a/src/operations/scheduling_ops.ts b/src/operations/scheduling_ops.ts
new file mode 100644
index 00000000..fd798916
--- /dev/null
+++ b/src/operations/scheduling_ops.ts
@@ -0,0 +1,22 @@
+/** Scheduling Ops module — tsb analytics library. */
+
+/** Options for Scheduling Ops. */
+export interface SchedulingOpsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Scheduling Ops. */
+export interface SchedulingOpsResult { values: number[]; converged: boolean; }
+
+/** Compute Scheduling Ops. */
+export function computeSchedulingOps(data: number[], opts: SchedulingOpsOptions = {}): SchedulingOpsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSchedulingOps };
diff --git a/src/operations/simulation_ops.ts b/src/operations/simulation_ops.ts
new file mode 100644
index 00000000..5223ed71
--- /dev/null
+++ b/src/operations/simulation_ops.ts
@@ -0,0 +1,22 @@
+/** Simulation Ops module — tsb analytics library. */
+
+/** Options for Simulation Ops. */
+export interface SimulationOpsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Simulation Ops. */
+export interface SimulationOpsResult { values: number[]; converged: boolean; }
+
+/** Compute Simulation Ops. */
+export function computeSimulationOps(data: number[], opts: SimulationOpsOptions = {}): SimulationOpsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSimulationOps };
diff --git a/src/operations/six_sigma.ts b/src/operations/six_sigma.ts
new file mode 100644
index 00000000..b7190dcf
--- /dev/null
+++ b/src/operations/six_sigma.ts
@@ -0,0 +1,22 @@
+/** Six Sigma module — tsb analytics library. */
+
+/** Options for Six Sigma. */
+export interface SixSigmaOptions { tol?: number; maxIter?: number; }
+
+/** Result from Six Sigma. */
+export interface SixSigmaResult { values: number[]; converged: boolean; }
+
+/** Compute Six Sigma. */
+export function computeSixSigma(data: number[], opts: SixSigmaOptions = {}): SixSigmaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSixSigma };
diff --git a/src/operations/small.ts b/src/operations/small.ts
new file mode 100644
index 00000000..6a99607c
--- /dev/null
+++ b/src/operations/small.ts
@@ -0,0 +1,15 @@
+/** Operations Small module — tsb analytics library. */
+export interface Operations smallOptions { tol?: number; maxIter?: number; }
+export interface Operations smallResult { values: number[]; converged: boolean; }
+export function computeOperations small(data: number[], opts: Operations smallOptions = {}): Operations smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOperations small };
diff --git a/src/operations/sparse.ts b/src/operations/sparse.ts
new file mode 100644
index 00000000..7cd145eb
--- /dev/null
+++ b/src/operations/sparse.ts
@@ -0,0 +1,15 @@
+/** Operations Sparse module — tsb analytics library. */
+export interface Operations sparseOptions { tol?: number; maxIter?: number; }
+export interface Operations sparseResult { values: number[]; converged: boolean; }
+export function computeOperations sparse(data: number[], opts: Operations sparseOptions = {}): Operations sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOperations sparse };
diff --git a/src/operations/stable.ts b/src/operations/stable.ts
new file mode 100644
index 00000000..1ed7a7e0
--- /dev/null
+++ b/src/operations/stable.ts
@@ -0,0 +1,15 @@
+/** Operations Stable module — tsb analytics library. */
+export interface Operations stableOptions { tol?: number; maxIter?: number; }
+export interface Operations stableResult { values: number[]; converged: boolean; }
+export function computeOperations stable(data: number[], opts: Operations stableOptions = {}): Operations stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOperations stable };
diff --git a/src/operations/statistical_process.ts b/src/operations/statistical_process.ts
new file mode 100644
index 00000000..1f972942
--- /dev/null
+++ b/src/operations/statistical_process.ts
@@ -0,0 +1,22 @@
+/** Statistical Process module — tsb analytics library. */
+
+/** Options for Statistical Process. */
+export interface StatisticalProcessOptions { tol?: number; maxIter?: number; }
+
+/** Result from Statistical Process. */
+export interface StatisticalProcessResult { values: number[]; converged: boolean; }
+
+/** Compute Statistical Process. */
+export function computeStatisticalProcess(data: number[], opts: StatisticalProcessOptions = {}): StatisticalProcessResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeStatisticalProcess };
diff --git a/src/operations/streaming.ts b/src/operations/streaming.ts
new file mode 100644
index 00000000..98c54821
--- /dev/null
+++ b/src/operations/streaming.ts
@@ -0,0 +1,15 @@
+/** Operations Streaming module — tsb analytics library. */
+export interface Operations streamingOptions { tol?: number; maxIter?: number; }
+export interface Operations streamingResult { values: number[]; converged: boolean; }
+export function computeOperations streaming(data: number[], opts: Operations streamingOptions = {}): Operations streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOperations streaming };
diff --git a/src/operations/throughput.ts b/src/operations/throughput.ts
new file mode 100644
index 00000000..84287616
--- /dev/null
+++ b/src/operations/throughput.ts
@@ -0,0 +1,22 @@
+/** Throughput module — tsb analytics library. */
+
+/** Options for Throughput. */
+export interface ThroughputOptions { tol?: number; maxIter?: number; }
+
+/** Result from Throughput. */
+export interface ThroughputResult { values: number[]; converged: boolean; }
+
+/** Compute Throughput. */
+export function computeThroughput(data: number[], opts: ThroughputOptions = {}): ThroughputResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeThroughput };
diff --git a/src/operations/toc.ts b/src/operations/toc.ts
new file mode 100644
index 00000000..06a96022
--- /dev/null
+++ b/src/operations/toc.ts
@@ -0,0 +1,22 @@
+/** Toc module — tsb analytics library. */
+
+/** Options for Toc. */
+export interface TocOptions { tol?: number; maxIter?: number; }
+
+/** Result from Toc. */
+export interface TocResult { values: number[]; converged: boolean; }
+
+/** Compute Toc. */
+export function computeToc(data: number[], opts: TocOptions = {}): TocResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeToc };
diff --git a/src/operations/v2.ts b/src/operations/v2.ts
new file mode 100644
index 00000000..0038efc8
--- /dev/null
+++ b/src/operations/v2.ts
@@ -0,0 +1,15 @@
+/** Operations V2 module — tsb analytics library. */
+export interface Operations v2Options { tol?: number; maxIter?: number; }
+export interface Operations v2Result { values: number[]; converged: boolean; }
+export function computeOperations v2(data: number[], opts: Operations v2Options = {}): Operations v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOperations v2 };
diff --git a/src/operations/v3.ts b/src/operations/v3.ts
new file mode 100644
index 00000000..0fdeaa8c
--- /dev/null
+++ b/src/operations/v3.ts
@@ -0,0 +1,15 @@
+/** Operations V3 module — tsb analytics library. */
+export interface Operations v3Options { tol?: number; maxIter?: number; }
+export interface Operations v3Result { values: number[]; converged: boolean; }
+export function computeOperations v3(data: number[], opts: Operations v3Options = {}): Operations v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOperations v3 };
diff --git a/src/operations/wasm.ts b/src/operations/wasm.ts
new file mode 100644
index 00000000..985d45a8
--- /dev/null
+++ b/src/operations/wasm.ts
@@ -0,0 +1,15 @@
+/** Operations Wasm module — tsb analytics library. */
+export interface Operations wasmOptions { tol?: number; maxIter?: number; }
+export interface Operations wasmResult { values: number[]; converged: boolean; }
+export function computeOperations wasm(data: number[], opts: Operations wasmOptions = {}): Operations wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOperations wasm };
diff --git a/src/operations/work_in_progress.ts b/src/operations/work_in_progress.ts
new file mode 100644
index 00000000..deae339f
--- /dev/null
+++ b/src/operations/work_in_progress.ts
@@ -0,0 +1,22 @@
+/** Work In Progress module — tsb analytics library. */
+
+/** Options for Work In Progress. */
+export interface WorkInProgressOptions { tol?: number; maxIter?: number; }
+
+/** Result from Work In Progress. */
+export interface WorkInProgressResult { values: number[]; converged: boolean; }
+
+/** Compute Work In Progress. */
+export function computeWorkInProgress(data: number[], opts: WorkInProgressOptions = {}): WorkInProgressResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeWorkInProgress };
diff --git a/src/operations/xlarge.ts b/src/operations/xlarge.ts
new file mode 100644
index 00000000..3fba5577
--- /dev/null
+++ b/src/operations/xlarge.ts
@@ -0,0 +1,15 @@
+/** Operations Xlarge module — tsb analytics library. */
+export interface Operations xlargeOptions { tol?: number; maxIter?: number; }
+export interface Operations xlargeResult { values: number[]; converged: boolean; }
+export function computeOperations xlarge(data: number[], opts: Operations xlargeOptions = {}): Operations xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOperations xlarge };
diff --git a/src/optimization/active_set.ts b/src/optimization/active_set.ts
new file mode 100644
index 00000000..d06a7484
--- /dev/null
+++ b/src/optimization/active_set.ts
@@ -0,0 +1,22 @@
+/** Active Set module — tsb analytics library. */
+
+/** Options for Active Set. */
+export interface ActiveSetOptions { tol?: number; maxIter?: number; }
+
+/** Result from Active Set. */
+export interface ActiveSetResult { values: number[]; converged: boolean; }
+
+/** Compute Active Set. */
+export function computeActiveSet(data: number[], opts: ActiveSetOptions = {}): ActiveSetResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeActiveSet };
diff --git a/src/optimization/adam.ts b/src/optimization/adam.ts
new file mode 100644
index 00000000..b00c9b0e
--- /dev/null
+++ b/src/optimization/adam.ts
@@ -0,0 +1,22 @@
+/** Adam module — tsb analytics library. */
+
+/** Options for Adam. */
+export interface AdamOptions { tol?: number; maxIter?: number; }
+
+/** Result from Adam. */
+export interface AdamResult { values: number[]; converged: boolean; }
+
+/** Compute Adam. */
+export function computeAdam(data: number[], opts: AdamOptions = {}): AdamResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAdam };
diff --git a/src/optimization/admm.ts b/src/optimization/admm.ts
new file mode 100644
index 00000000..1d135da1
--- /dev/null
+++ b/src/optimization/admm.ts
@@ -0,0 +1,22 @@
+/** Admm module — tsb analytics library. */
+
+/** Options for Admm. */
+export interface AdmmOptions { tol?: number; maxIter?: number; }
+
+/** Result from Admm. */
+export interface AdmmResult { values: number[]; converged: boolean; }
+
+/** Compute Admm. */
+export function computeAdmm(data: number[], opts: AdmmOptions = {}): AdmmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAdmm };
diff --git a/src/optimization/advanced.ts b/src/optimization/advanced.ts
new file mode 100644
index 00000000..8cc7f5b5
--- /dev/null
+++ b/src/optimization/advanced.ts
@@ -0,0 +1,15 @@
+/** Optimization Advanced module — tsb analytics library. */
+export interface Optimization advancedOptions { tol?: number; maxIter?: number; }
+export interface Optimization advancedResult { values: number[]; converged: boolean; }
+export function computeOptimization advanced(data: number[], opts: Optimization advancedOptions = {}): Optimization advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOptimization advanced };
diff --git a/src/optimization/base2.ts b/src/optimization/base2.ts
new file mode 100644
index 00000000..59b518a6
--- /dev/null
+++ b/src/optimization/base2.ts
@@ -0,0 +1,15 @@
+/** Optimization Base2 module — tsb analytics library. */
+export interface Optimization base2Options { tol?: number; maxIter?: number; }
+export interface Optimization base2Result { values: number[]; converged: boolean; }
+export function computeOptimization base2(data: number[], opts: Optimization base2Options = {}): Optimization base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOptimization base2 };
diff --git a/src/optimization/batch.ts b/src/optimization/batch.ts
new file mode 100644
index 00000000..80b48633
--- /dev/null
+++ b/src/optimization/batch.ts
@@ -0,0 +1,15 @@
+/** Optimization Batch module — tsb analytics library. */
+export interface Optimization batchOptions { tol?: number; maxIter?: number; }
+export interface Optimization batchResult { values: number[]; converged: boolean; }
+export function computeOptimization batch(data: number[], opts: Optimization batchOptions = {}): Optimization batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOptimization batch };
diff --git a/src/optimization/bellman.ts b/src/optimization/bellman.ts
new file mode 100644
index 00000000..ac74ee66
--- /dev/null
+++ b/src/optimization/bellman.ts
@@ -0,0 +1,22 @@
+/** Bellman module — tsb analytics library. */
+
+/** Options for Bellman. */
+export interface BellmanOptions { tol?: number; maxIter?: number; }
+
+/** Result from Bellman. */
+export interface BellmanResult { values: number[]; converged: boolean; }
+
+/** Compute Bellman. */
+export function computeBellman(data: number[], opts: BellmanOptions = {}): BellmanResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBellman };
diff --git a/src/optimization/beta.ts b/src/optimization/beta.ts
new file mode 100644
index 00000000..d26dca79
--- /dev/null
+++ b/src/optimization/beta.ts
@@ -0,0 +1,15 @@
+/** Optimization Beta module — tsb analytics library. */
+export interface Optimization betaOptions { tol?: number; maxIter?: number; }
+export interface Optimization betaResult { values: number[]; converged: boolean; }
+export function computeOptimization beta(data: number[], opts: Optimization betaOptions = {}): Optimization betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOptimization beta };
diff --git a/src/optimization/bfgs.ts b/src/optimization/bfgs.ts
new file mode 100644
index 00000000..ab5c727c
--- /dev/null
+++ b/src/optimization/bfgs.ts
@@ -0,0 +1,22 @@
+/** Bfgs module — tsb analytics library. */
+
+/** Options for Bfgs. */
+export interface BfgsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Bfgs. */
+export interface BfgsResult { values: number[]; converged: boolean; }
+
+/** Compute Bfgs. */
+export function computeBfgs(data: number[], opts: BfgsOptions = {}): BfgsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBfgs };
diff --git a/src/optimization/branch_bound.ts b/src/optimization/branch_bound.ts
new file mode 100644
index 00000000..fea70263
--- /dev/null
+++ b/src/optimization/branch_bound.ts
@@ -0,0 +1,22 @@
+/** Branch Bound module — tsb analytics library. */
+
+/** Options for Branch Bound. */
+export interface BranchBoundOptions { tol?: number; maxIter?: number; }
+
+/** Result from Branch Bound. */
+export interface BranchBoundResult { values: number[]; converged: boolean; }
+
+/** Compute Branch Bound. */
+export function computeBranchBound(data: number[], opts: BranchBoundOptions = {}): BranchBoundResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBranchBound };
diff --git a/src/optimization/cg.ts b/src/optimization/cg.ts
new file mode 100644
index 00000000..2be02ca0
--- /dev/null
+++ b/src/optimization/cg.ts
@@ -0,0 +1,22 @@
+/** Cg module — tsb analytics library. */
+
+/** Options for Cg. */
+export interface CgOptions { tol?: number; maxIter?: number; }
+
+/** Result from Cg. */
+export interface CgResult { values: number[]; converged: boolean; }
+
+/** Compute Cg. */
+export function computeCg(data: number[], opts: CgOptions = {}): CgResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCg };
diff --git a/src/optimization/coordinate_descent.ts b/src/optimization/coordinate_descent.ts
new file mode 100644
index 00000000..6425b76c
--- /dev/null
+++ b/src/optimization/coordinate_descent.ts
@@ -0,0 +1,22 @@
+/** Coordinate Descent module — tsb analytics library. */
+
+/** Options for Coordinate Descent. */
+export interface CoordinateDescentOptions { tol?: number; maxIter?: number; }
+
+/** Result from Coordinate Descent. */
+export interface CoordinateDescentResult { values: number[]; converged: boolean; }
+
+/** Compute Coordinate Descent. */
+export function computeCoordinateDescent(data: number[], opts: CoordinateDescentOptions = {}): CoordinateDescentResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCoordinateDescent };
diff --git a/src/optimization/cpu.ts b/src/optimization/cpu.ts
new file mode 100644
index 00000000..61d7e40e
--- /dev/null
+++ b/src/optimization/cpu.ts
@@ -0,0 +1,15 @@
+/** Optimization Cpu module — tsb analytics library. */
+export interface Optimization cpuOptions { tol?: number; maxIter?: number; }
+export interface Optimization cpuResult { values: number[]; converged: boolean; }
+export function computeOptimization cpu(data: number[], opts: Optimization cpuOptions = {}): Optimization cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOptimization cpu };
diff --git a/src/optimization/cutting_plane.ts b/src/optimization/cutting_plane.ts
new file mode 100644
index 00000000..02731d3a
--- /dev/null
+++ b/src/optimization/cutting_plane.ts
@@ -0,0 +1,22 @@
+/** Cutting Plane module — tsb analytics library. */
+
+/** Options for Cutting Plane. */
+export interface CuttingPlaneOptions { tol?: number; maxIter?: number; }
+
+/** Result from Cutting Plane. */
+export interface CuttingPlaneResult { values: number[]; converged: boolean; }
+
+/** Compute Cutting Plane. */
+export function computeCuttingPlane(data: number[], opts: CuttingPlaneOptions = {}): CuttingPlaneResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCuttingPlane };
diff --git a/src/optimization/dense.ts b/src/optimization/dense.ts
new file mode 100644
index 00000000..7daa05b1
--- /dev/null
+++ b/src/optimization/dense.ts
@@ -0,0 +1,15 @@
+/** Optimization Dense module — tsb analytics library. */
+export interface Optimization denseOptions { tol?: number; maxIter?: number; }
+export interface Optimization denseResult { values: number[]; converged: boolean; }
+export function computeOptimization dense(data: number[], opts: Optimization denseOptions = {}): Optimization denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOptimization dense };
diff --git a/src/optimization/distributed.ts b/src/optimization/distributed.ts
new file mode 100644
index 00000000..c05197f4
--- /dev/null
+++ b/src/optimization/distributed.ts
@@ -0,0 +1,15 @@
+/** Optimization Distributed module — tsb analytics library. */
+export interface Optimization distributedOptions { tol?: number; maxIter?: number; }
+export interface Optimization distributedResult { values: number[]; converged: boolean; }
+export function computeOptimization distributed(data: number[], opts: Optimization distributedOptions = {}): Optimization distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOptimization distributed };
diff --git a/src/optimization/dual.ts b/src/optimization/dual.ts
new file mode 100644
index 00000000..9b37f323
--- /dev/null
+++ b/src/optimization/dual.ts
@@ -0,0 +1,22 @@
+/** Dual module — tsb analytics library. */
+
+/** Options for Dual. */
+export interface DualOptions { tol?: number; maxIter?: number; }
+
+/** Result from Dual. */
+export interface DualResult { values: number[]; converged: boolean; }
+
+/** Compute Dual. */
+export function computeDual(data: number[], opts: DualOptions = {}): DualResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDual };
diff --git a/src/optimization/dynamic_prog.ts b/src/optimization/dynamic_prog.ts
new file mode 100644
index 00000000..7d2c58fd
--- /dev/null
+++ b/src/optimization/dynamic_prog.ts
@@ -0,0 +1,22 @@
+/** Dynamic Prog module — tsb analytics library. */
+
+/** Options for Dynamic Prog. */
+export interface DynamicProgOptions { tol?: number; maxIter?: number; }
+
+/** Result from Dynamic Prog. */
+export interface DynamicProgResult { values: number[]; converged: boolean; }
+
+/** Compute Dynamic Prog. */
+export function computeDynamicProg(data: number[], opts: DynamicProgOptions = {}): DynamicProgResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDynamicProg };
diff --git a/src/optimization/experimental.ts b/src/optimization/experimental.ts
new file mode 100644
index 00000000..8e82eac1
--- /dev/null
+++ b/src/optimization/experimental.ts
@@ -0,0 +1,15 @@
+/** Optimization Experimental module — tsb analytics library. */
+export interface Optimization experimentalOptions { tol?: number; maxIter?: number; }
+export interface Optimization experimentalResult { values: number[]; converged: boolean; }
+export function computeOptimization experimental(data: number[], opts: Optimization experimentalOptions = {}): Optimization experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOptimization experimental };
diff --git a/src/optimization/fast.ts b/src/optimization/fast.ts
new file mode 100644
index 00000000..f7e04374
--- /dev/null
+++ b/src/optimization/fast.ts
@@ -0,0 +1,15 @@
+/** Optimization Fast module — tsb analytics library. */
+export interface Optimization fastOptions { tol?: number; maxIter?: number; }
+export interface Optimization fastResult { values: number[]; converged: boolean; }
+export function computeOptimization fast(data: number[], opts: Optimization fastOptions = {}): Optimization fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOptimization fast };
diff --git a/src/optimization/frank_wolfe.ts b/src/optimization/frank_wolfe.ts
new file mode 100644
index 00000000..ab46cd5a
--- /dev/null
+++ b/src/optimization/frank_wolfe.ts
@@ -0,0 +1,22 @@
+/** Frank Wolfe module — tsb analytics library. */
+
+/** Options for Frank Wolfe. */
+export interface FrankWolfeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Frank Wolfe. */
+export interface FrankWolfeResult { values: number[]; converged: boolean; }
+
+/** Compute Frank Wolfe. */
+export function computeFrankWolfe(data: number[], opts: FrankWolfeOptions = {}): FrankWolfeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFrankWolfe };
diff --git a/src/optimization/future.ts b/src/optimization/future.ts
new file mode 100644
index 00000000..d6ef2378
--- /dev/null
+++ b/src/optimization/future.ts
@@ -0,0 +1,15 @@
+/** Optimization Future module — tsb analytics library. */
+export interface Optimization futureOptions { tol?: number; maxIter?: number; }
+export interface Optimization futureResult { values: number[]; converged: boolean; }
+export function computeOptimization future(data: number[], opts: Optimization futureOptions = {}): Optimization futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOptimization future };
diff --git a/src/optimization/genetic.ts b/src/optimization/genetic.ts
new file mode 100644
index 00000000..e9151d70
--- /dev/null
+++ b/src/optimization/genetic.ts
@@ -0,0 +1,22 @@
+/** Genetic module — tsb analytics library. */
+
+/** Options for Genetic. */
+export interface GeneticOptions { tol?: number; maxIter?: number; }
+
+/** Result from Genetic. */
+export interface GeneticResult { values: number[]; converged: boolean; }
+
+/** Compute Genetic. */
+export function computeGenetic(data: number[], opts: GeneticOptions = {}): GeneticResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGenetic };
diff --git a/src/optimization/gpu.ts b/src/optimization/gpu.ts
new file mode 100644
index 00000000..0a515435
--- /dev/null
+++ b/src/optimization/gpu.ts
@@ -0,0 +1,15 @@
+/** Optimization Gpu module — tsb analytics library. */
+export interface Optimization gpuOptions { tol?: number; maxIter?: number; }
+export interface Optimization gpuResult { values: number[]; converged: boolean; }
+export function computeOptimization gpu(data: number[], opts: Optimization gpuOptions = {}): Optimization gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOptimization gpu };
diff --git a/src/optimization/gradient_descent.ts b/src/optimization/gradient_descent.ts
new file mode 100644
index 00000000..1e32ce26
--- /dev/null
+++ b/src/optimization/gradient_descent.ts
@@ -0,0 +1,22 @@
+/** Gradient Descent module — tsb analytics library. */
+
+/** Options for Gradient Descent. */
+export interface GradientDescentOptions { tol?: number; maxIter?: number; }
+
+/** Result from Gradient Descent. */
+export interface GradientDescentResult { values: number[]; converged: boolean; }
+
+/** Compute Gradient Descent. */
+export function computeGradientDescent(data: number[], opts: GradientDescentOptions = {}): GradientDescentResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGradientDescent };
diff --git a/src/optimization/integer_prog.ts b/src/optimization/integer_prog.ts
new file mode 100644
index 00000000..08dad85e
--- /dev/null
+++ b/src/optimization/integer_prog.ts
@@ -0,0 +1,22 @@
+/** Integer Prog module — tsb analytics library. */
+
+/** Options for Integer Prog. */
+export interface IntegerProgOptions { tol?: number; maxIter?: number; }
+
+/** Result from Integer Prog. */
+export interface IntegerProgResult { values: number[]; converged: boolean; }
+
+/** Compute Integer Prog. */
+export function computeIntegerProg(data: number[], opts: IntegerProgOptions = {}): IntegerProgResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeIntegerProg };
diff --git a/src/optimization/interior_point.ts b/src/optimization/interior_point.ts
new file mode 100644
index 00000000..96e015f0
--- /dev/null
+++ b/src/optimization/interior_point.ts
@@ -0,0 +1,22 @@
+/** Interior Point module — tsb analytics library. */
+
+/** Options for Interior Point. */
+export interface InteriorPointOptions { tol?: number; maxIter?: number; }
+
+/** Result from Interior Point. */
+export interface InteriorPointResult { values: number[]; converged: boolean; }
+
+/** Compute Interior Point. */
+export function computeInteriorPoint(data: number[], opts: InteriorPointOptions = {}): InteriorPointResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeInteriorPoint };
diff --git a/src/optimization/kkt.ts b/src/optimization/kkt.ts
new file mode 100644
index 00000000..7bf66b7b
--- /dev/null
+++ b/src/optimization/kkt.ts
@@ -0,0 +1,22 @@
+/** Kkt module — tsb analytics library. */
+
+/** Options for Kkt. */
+export interface KktOptions { tol?: number; maxIter?: number; }
+
+/** Result from Kkt. */
+export interface KktResult { values: number[]; converged: boolean; }
+
+/** Compute Kkt. */
+export function computeKkt(data: number[], opts: KktOptions = {}): KktResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeKkt };
diff --git a/src/optimization/lagrangian.ts b/src/optimization/lagrangian.ts
new file mode 100644
index 00000000..7d54b14a
--- /dev/null
+++ b/src/optimization/lagrangian.ts
@@ -0,0 +1,22 @@
+/** Lagrangian module — tsb analytics library. */
+
+/** Options for Lagrangian. */
+export interface LagrangianOptions { tol?: number; maxIter?: number; }
+
+/** Result from Lagrangian. */
+export interface LagrangianResult { values: number[]; converged: boolean; }
+
+/** Compute Lagrangian. */
+export function computeLagrangian(data: number[], opts: LagrangianOptions = {}): LagrangianResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLagrangian };
diff --git a/src/optimization/large.ts b/src/optimization/large.ts
new file mode 100644
index 00000000..1a223c1e
--- /dev/null
+++ b/src/optimization/large.ts
@@ -0,0 +1,15 @@
+/** Optimization Large module — tsb analytics library. */
+export interface Optimization largeOptions { tol?: number; maxIter?: number; }
+export interface Optimization largeResult { values: number[]; converged: boolean; }
+export function computeOptimization large(data: number[], opts: Optimization largeOptions = {}): Optimization largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOptimization large };
diff --git a/src/optimization/lbfgs.ts b/src/optimization/lbfgs.ts
new file mode 100644
index 00000000..9eb488ea
--- /dev/null
+++ b/src/optimization/lbfgs.ts
@@ -0,0 +1,22 @@
+/** Lbfgs module — tsb analytics library. */
+
+/** Options for Lbfgs. */
+export interface LbfgsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Lbfgs. */
+export interface LbfgsResult { values: number[]; converged: boolean; }
+
+/** Compute Lbfgs. */
+export function computeLbfgs(data: number[], opts: LbfgsOptions = {}): LbfgsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLbfgs };
diff --git a/src/optimization/legacy.ts b/src/optimization/legacy.ts
new file mode 100644
index 00000000..5e4952db
--- /dev/null
+++ b/src/optimization/legacy.ts
@@ -0,0 +1,15 @@
+/** Optimization Legacy module — tsb analytics library. */
+export interface Optimization legacyOptions { tol?: number; maxIter?: number; }
+export interface Optimization legacyResult { values: number[]; converged: boolean; }
+export function computeOptimization legacy(data: number[], opts: Optimization legacyOptions = {}): Optimization legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOptimization legacy };
diff --git a/src/optimization/linear_prog.ts b/src/optimization/linear_prog.ts
new file mode 100644
index 00000000..d0aedf6e
--- /dev/null
+++ b/src/optimization/linear_prog.ts
@@ -0,0 +1,22 @@
+/** Linear Prog module — tsb analytics library. */
+
+/** Options for Linear Prog. */
+export interface LinearProgOptions { tol?: number; maxIter?: number; }
+
+/** Result from Linear Prog. */
+export interface LinearProgResult { values: number[]; converged: boolean; }
+
+/** Compute Linear Prog. */
+export function computeLinearProg(data: number[], opts: LinearProgOptions = {}): LinearProgResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLinearProg };
diff --git a/src/optimization/lite.ts b/src/optimization/lite.ts
new file mode 100644
index 00000000..4b0d939c
--- /dev/null
+++ b/src/optimization/lite.ts
@@ -0,0 +1,15 @@
+/** Optimization Lite module — tsb analytics library. */
+export interface Optimization liteOptions { tol?: number; maxIter?: number; }
+export interface Optimization liteResult { values: number[]; converged: boolean; }
+export function computeOptimization lite(data: number[], opts: Optimization liteOptions = {}): Optimization liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOptimization lite };
diff --git a/src/optimization/mini.ts b/src/optimization/mini.ts
new file mode 100644
index 00000000..dad862b0
--- /dev/null
+++ b/src/optimization/mini.ts
@@ -0,0 +1,15 @@
+/** Optimization Mini module — tsb analytics library. */
+export interface Optimization miniOptions { tol?: number; maxIter?: number; }
+export interface Optimization miniResult { values: number[]; converged: boolean; }
+export function computeOptimization mini(data: number[], opts: Optimization miniOptions = {}): Optimization miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOptimization mini };
diff --git a/src/optimization/mirror_descent.ts b/src/optimization/mirror_descent.ts
new file mode 100644
index 00000000..a65617aa
--- /dev/null
+++ b/src/optimization/mirror_descent.ts
@@ -0,0 +1,22 @@
+/** Mirror Descent module — tsb analytics library. */
+
+/** Options for Mirror Descent. */
+export interface MirrorDescentOptions { tol?: number; maxIter?: number; }
+
+/** Result from Mirror Descent. */
+export interface MirrorDescentResult { values: number[]; converged: boolean; }
+
+/** Compute Mirror Descent. */
+export function computeMirrorDescent(data: number[], opts: MirrorDescentOptions = {}): MirrorDescentResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMirrorDescent };
diff --git a/src/optimization/nelder_mead.ts b/src/optimization/nelder_mead.ts
new file mode 100644
index 00000000..6340fa8f
--- /dev/null
+++ b/src/optimization/nelder_mead.ts
@@ -0,0 +1,22 @@
+/** Nelder Mead module — tsb analytics library. */
+
+/** Options for Nelder Mead. */
+export interface NelderMeadOptions { tol?: number; maxIter?: number; }
+
+/** Result from Nelder Mead. */
+export interface NelderMeadResult { values: number[]; converged: boolean; }
+
+/** Compute Nelder Mead. */
+export function computeNelderMead(data: number[], opts: NelderMeadOptions = {}): NelderMeadResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNelderMead };
diff --git a/src/optimization/next.ts b/src/optimization/next.ts
new file mode 100644
index 00000000..c228b1a9
--- /dev/null
+++ b/src/optimization/next.ts
@@ -0,0 +1,15 @@
+/** Optimization Next module — tsb analytics library. */
+export interface Optimization nextOptions { tol?: number; maxIter?: number; }
+export interface Optimization nextResult { values: number[]; converged: boolean; }
+export function computeOptimization next(data: number[], opts: Optimization nextOptions = {}): Optimization nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOptimization next };
diff --git a/src/optimization/nonlinear_prog.ts b/src/optimization/nonlinear_prog.ts
new file mode 100644
index 00000000..dfe81665
--- /dev/null
+++ b/src/optimization/nonlinear_prog.ts
@@ -0,0 +1,22 @@
+/** Nonlinear Prog module — tsb analytics library. */
+
+/** Options for Nonlinear Prog. */
+export interface NonlinearProgOptions { tol?: number; maxIter?: number; }
+
+/** Result from Nonlinear Prog. */
+export interface NonlinearProgResult { values: number[]; converged: boolean; }
+
+/** Compute Nonlinear Prog. */
+export function computeNonlinearProg(data: number[], opts: NonlinearProgOptions = {}): NonlinearProgResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNonlinearProg };
diff --git a/src/optimization/online.ts b/src/optimization/online.ts
new file mode 100644
index 00000000..2e4a402c
--- /dev/null
+++ b/src/optimization/online.ts
@@ -0,0 +1,15 @@
+/** Optimization Online module — tsb analytics library. */
+export interface Optimization onlineOptions { tol?: number; maxIter?: number; }
+export interface Optimization onlineResult { values: number[]; converged: boolean; }
+export function computeOptimization online(data: number[], opts: Optimization onlineOptions = {}): Optimization onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOptimization online };
diff --git a/src/optimization/parallel.ts b/src/optimization/parallel.ts
new file mode 100644
index 00000000..36443d92
--- /dev/null
+++ b/src/optimization/parallel.ts
@@ -0,0 +1,15 @@
+/** Optimization Parallel module — tsb analytics library. */
+export interface Optimization parallelOptions { tol?: number; maxIter?: number; }
+export interface Optimization parallelResult { values: number[]; converged: boolean; }
+export function computeOptimization parallel(data: number[], opts: Optimization parallelOptions = {}): Optimization parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOptimization parallel };
diff --git a/src/optimization/plus.ts b/src/optimization/plus.ts
new file mode 100644
index 00000000..690f2721
--- /dev/null
+++ b/src/optimization/plus.ts
@@ -0,0 +1,15 @@
+/** Optimization Plus module — tsb analytics library. */
+export interface Optimization plusOptions { tol?: number; maxIter?: number; }
+export interface Optimization plusResult { values: number[]; converged: boolean; }
+export function computeOptimization plus(data: number[], opts: Optimization plusOptions = {}): Optimization plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOptimization plus };
diff --git a/src/optimization/policy_gradient.ts b/src/optimization/policy_gradient.ts
new file mode 100644
index 00000000..a8ce6410
--- /dev/null
+++ b/src/optimization/policy_gradient.ts
@@ -0,0 +1,22 @@
+/** Policy Gradient module — tsb analytics library. */
+
+/** Options for Policy Gradient. */
+export interface PolicyGradientOptions { tol?: number; maxIter?: number; }
+
+/** Result from Policy Gradient. */
+export interface PolicyGradientResult { values: number[]; converged: boolean; }
+
+/** Compute Policy Gradient. */
+export function computePolicyGradient(data: number[], opts: PolicyGradientOptions = {}): PolicyGradientResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePolicyGradient };
diff --git a/src/optimization/pro.ts b/src/optimization/pro.ts
new file mode 100644
index 00000000..ac4adae6
--- /dev/null
+++ b/src/optimization/pro.ts
@@ -0,0 +1,15 @@
+/** Optimization Pro module — tsb analytics library. */
+export interface Optimization proOptions { tol?: number; maxIter?: number; }
+export interface Optimization proResult { values: number[]; converged: boolean; }
+export function computeOptimization pro(data: number[], opts: Optimization proOptions = {}): Optimization proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOptimization pro };
diff --git a/src/optimization/proximal.ts b/src/optimization/proximal.ts
new file mode 100644
index 00000000..57e92ce1
--- /dev/null
+++ b/src/optimization/proximal.ts
@@ -0,0 +1,22 @@
+/** Proximal module — tsb analytics library. */
+
+/** Options for Proximal. */
+export interface ProximalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Proximal. */
+export interface ProximalResult { values: number[]; converged: boolean; }
+
+/** Compute Proximal. */
+export function computeProximal(data: number[], opts: ProximalOptions = {}): ProximalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeProximal };
diff --git a/src/optimization/pso.ts b/src/optimization/pso.ts
new file mode 100644
index 00000000..7515d236
--- /dev/null
+++ b/src/optimization/pso.ts
@@ -0,0 +1,22 @@
+/** Pso module — tsb analytics library. */
+
+/** Options for Pso. */
+export interface PsoOptions { tol?: number; maxIter?: number; }
+
+/** Result from Pso. */
+export interface PsoResult { values: number[]; converged: boolean; }
+
+/** Compute Pso. */
+export function computePso(data: number[], opts: PsoOptions = {}): PsoResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePso };
diff --git a/src/optimization/quadratic_prog.ts b/src/optimization/quadratic_prog.ts
new file mode 100644
index 00000000..f9b4040e
--- /dev/null
+++ b/src/optimization/quadratic_prog.ts
@@ -0,0 +1,22 @@
+/** Quadratic Prog module — tsb analytics library. */
+
+/** Options for Quadratic Prog. */
+export interface QuadraticProgOptions { tol?: number; maxIter?: number; }
+
+/** Result from Quadratic Prog. */
+export interface QuadraticProgResult { values: number[]; converged: boolean; }
+
+/** Compute Quadratic Prog. */
+export function computeQuadraticProg(data: number[], opts: QuadraticProgOptions = {}): QuadraticProgResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeQuadraticProg };
diff --git a/src/optimization/robust.ts b/src/optimization/robust.ts
new file mode 100644
index 00000000..ece40585
--- /dev/null
+++ b/src/optimization/robust.ts
@@ -0,0 +1,15 @@
+/** Optimization Robust module — tsb analytics library. */
+export interface Optimization robustOptions { tol?: number; maxIter?: number; }
+export interface Optimization robustResult { values: number[]; converged: boolean; }
+export function computeOptimization robust(data: number[], opts: Optimization robustOptions = {}): Optimization robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOptimization robust };
diff --git a/src/optimization/sa.ts b/src/optimization/sa.ts
new file mode 100644
index 00000000..08fbdf2b
--- /dev/null
+++ b/src/optimization/sa.ts
@@ -0,0 +1,22 @@
+/** Sa module — tsb analytics library. */
+
+/** Options for Sa. */
+export interface SaOptions { tol?: number; maxIter?: number; }
+
+/** Result from Sa. */
+export interface SaResult { values: number[]; converged: boolean; }
+
+/** Compute Sa. */
+export function computeSa(data: number[], opts: SaOptions = {}): SaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSa };
diff --git a/src/optimization/small.ts b/src/optimization/small.ts
new file mode 100644
index 00000000..879dd7c6
--- /dev/null
+++ b/src/optimization/small.ts
@@ -0,0 +1,15 @@
+/** Optimization Small module — tsb analytics library. */
+export interface Optimization smallOptions { tol?: number; maxIter?: number; }
+export interface Optimization smallResult { values: number[]; converged: boolean; }
+export function computeOptimization small(data: number[], opts: Optimization smallOptions = {}): Optimization smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOptimization small };
diff --git a/src/optimization/sparse.ts b/src/optimization/sparse.ts
new file mode 100644
index 00000000..77026798
--- /dev/null
+++ b/src/optimization/sparse.ts
@@ -0,0 +1,15 @@
+/** Optimization Sparse module — tsb analytics library. */
+export interface Optimization sparseOptions { tol?: number; maxIter?: number; }
+export interface Optimization sparseResult { values: number[]; converged: boolean; }
+export function computeOptimization sparse(data: number[], opts: Optimization sparseOptions = {}): Optimization sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOptimization sparse };
diff --git a/src/optimization/stable.ts b/src/optimization/stable.ts
new file mode 100644
index 00000000..9f22d2ac
--- /dev/null
+++ b/src/optimization/stable.ts
@@ -0,0 +1,15 @@
+/** Optimization Stable module — tsb analytics library. */
+export interface Optimization stableOptions { tol?: number; maxIter?: number; }
+export interface Optimization stableResult { values: number[]; converged: boolean; }
+export function computeOptimization stable(data: number[], opts: Optimization stableOptions = {}): Optimization stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOptimization stable };
diff --git a/src/optimization/stochastic_opt.ts b/src/optimization/stochastic_opt.ts
new file mode 100644
index 00000000..43b9b97e
--- /dev/null
+++ b/src/optimization/stochastic_opt.ts
@@ -0,0 +1,22 @@
+/** Stochastic Opt module — tsb analytics library. */
+
+/** Options for Stochastic Opt. */
+export interface StochasticOptOptions { tol?: number; maxIter?: number; }
+
+/** Result from Stochastic Opt. */
+export interface StochasticOptResult { values: number[]; converged: boolean; }
+
+/** Compute Stochastic Opt. */
+export function computeStochasticOpt(data: number[], opts: StochasticOptOptions = {}): StochasticOptResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeStochasticOpt };
diff --git a/src/optimization/streaming.ts b/src/optimization/streaming.ts
new file mode 100644
index 00000000..281dc22b
--- /dev/null
+++ b/src/optimization/streaming.ts
@@ -0,0 +1,15 @@
+/** Optimization Streaming module — tsb analytics library. */
+export interface Optimization streamingOptions { tol?: number; maxIter?: number; }
+export interface Optimization streamingResult { values: number[]; converged: boolean; }
+export function computeOptimization streaming(data: number[], opts: Optimization streamingOptions = {}): Optimization streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOptimization streaming };
diff --git a/src/optimization/trust_region.ts b/src/optimization/trust_region.ts
new file mode 100644
index 00000000..5268ed94
--- /dev/null
+++ b/src/optimization/trust_region.ts
@@ -0,0 +1,22 @@
+/** Trust Region module — tsb analytics library. */
+
+/** Options for Trust Region. */
+export interface TrustRegionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Trust Region. */
+export interface TrustRegionResult { values: number[]; converged: boolean; }
+
+/** Compute Trust Region. */
+export function computeTrustRegion(data: number[], opts: TrustRegionOptions = {}): TrustRegionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTrustRegion };
diff --git a/src/optimization/v2.ts b/src/optimization/v2.ts
new file mode 100644
index 00000000..d5731766
--- /dev/null
+++ b/src/optimization/v2.ts
@@ -0,0 +1,15 @@
+/** Optimization V2 module — tsb analytics library. */
+export interface Optimization v2Options { tol?: number; maxIter?: number; }
+export interface Optimization v2Result { values: number[]; converged: boolean; }
+export function computeOptimization v2(data: number[], opts: Optimization v2Options = {}): Optimization v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOptimization v2 };
diff --git a/src/optimization/v3.ts b/src/optimization/v3.ts
new file mode 100644
index 00000000..02feb62e
--- /dev/null
+++ b/src/optimization/v3.ts
@@ -0,0 +1,15 @@
+/** Optimization V3 module — tsb analytics library. */
+export interface Optimization v3Options { tol?: number; maxIter?: number; }
+export interface Optimization v3Result { values: number[]; converged: boolean; }
+export function computeOptimization v3(data: number[], opts: Optimization v3Options = {}): Optimization v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOptimization v3 };
diff --git a/src/optimization/wasm.ts b/src/optimization/wasm.ts
new file mode 100644
index 00000000..d1feca8b
--- /dev/null
+++ b/src/optimization/wasm.ts
@@ -0,0 +1,15 @@
+/** Optimization Wasm module — tsb analytics library. */
+export interface Optimization wasmOptions { tol?: number; maxIter?: number; }
+export interface Optimization wasmResult { values: number[]; converged: boolean; }
+export function computeOptimization wasm(data: number[], opts: Optimization wasmOptions = {}): Optimization wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOptimization wasm };
diff --git a/src/optimization/xlarge.ts b/src/optimization/xlarge.ts
new file mode 100644
index 00000000..2fba96da
--- /dev/null
+++ b/src/optimization/xlarge.ts
@@ -0,0 +1,15 @@
+/** Optimization Xlarge module — tsb analytics library. */
+export interface Optimization xlargeOptions { tol?: number; maxIter?: number; }
+export interface Optimization xlargeResult { values: number[]; converged: boolean; }
+export function computeOptimization xlarge(data: number[], opts: Optimization xlargeOptions = {}): Optimization xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeOptimization xlarge };
diff --git a/src/physics/acoustics.ts b/src/physics/acoustics.ts
new file mode 100644
index 00000000..a2bbe51c
--- /dev/null
+++ b/src/physics/acoustics.ts
@@ -0,0 +1,22 @@
+/** Acoustics module — tsb analytics library. */
+
+/** Options for Acoustics. */
+export interface AcousticsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Acoustics. */
+export interface AcousticsResult { values: number[]; converged: boolean; }
+
+/** Compute Acoustics. */
+export function computeAcoustics(data: number[], opts: AcousticsOptions = {}): AcousticsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAcoustics };
diff --git a/src/physics/advanced.ts b/src/physics/advanced.ts
new file mode 100644
index 00000000..2a876915
--- /dev/null
+++ b/src/physics/advanced.ts
@@ -0,0 +1,15 @@
+/** Physics Advanced module — tsb analytics library. */
+export interface Physics advancedOptions { tol?: number; maxIter?: number; }
+export interface Physics advancedResult { values: number[]; converged: boolean; }
+export function computePhysics advanced(data: number[], opts: Physics advancedOptions = {}): Physics advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePhysics advanced };
diff --git a/src/physics/base2.ts b/src/physics/base2.ts
new file mode 100644
index 00000000..2d6414e4
--- /dev/null
+++ b/src/physics/base2.ts
@@ -0,0 +1,15 @@
+/** Physics Base2 module — tsb analytics library. */
+export interface Physics base2Options { tol?: number; maxIter?: number; }
+export interface Physics base2Result { values: number[]; converged: boolean; }
+export function computePhysics base2(data: number[], opts: Physics base2Options = {}): Physics base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePhysics base2 };
diff --git a/src/physics/batch.ts b/src/physics/batch.ts
new file mode 100644
index 00000000..73d38223
--- /dev/null
+++ b/src/physics/batch.ts
@@ -0,0 +1,15 @@
+/** Physics Batch module — tsb analytics library. */
+export interface Physics batchOptions { tol?: number; maxIter?: number; }
+export interface Physics batchResult { values: number[]; converged: boolean; }
+export function computePhysics batch(data: number[], opts: Physics batchOptions = {}): Physics batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePhysics batch };
diff --git a/src/physics/beta.ts b/src/physics/beta.ts
new file mode 100644
index 00000000..de221eb1
--- /dev/null
+++ b/src/physics/beta.ts
@@ -0,0 +1,15 @@
+/** Physics Beta module — tsb analytics library. */
+export interface Physics betaOptions { tol?: number; maxIter?: number; }
+export interface Physics betaResult { values: number[]; converged: boolean; }
+export function computePhysics beta(data: number[], opts: Physics betaOptions = {}): Physics betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePhysics beta };
diff --git a/src/physics/condensed_matter.ts b/src/physics/condensed_matter.ts
new file mode 100644
index 00000000..052b2abc
--- /dev/null
+++ b/src/physics/condensed_matter.ts
@@ -0,0 +1,22 @@
+/** Condensed Matter module — tsb analytics library. */
+
+/** Options for Condensed Matter. */
+export interface CondensedMatterOptions { tol?: number; maxIter?: number; }
+
+/** Result from Condensed Matter. */
+export interface CondensedMatterResult { values: number[]; converged: boolean; }
+
+/** Compute Condensed Matter. */
+export function computeCondensedMatter(data: number[], opts: CondensedMatterOptions = {}): CondensedMatterResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCondensedMatter };
diff --git a/src/physics/cosmology.ts b/src/physics/cosmology.ts
new file mode 100644
index 00000000..5d00153b
--- /dev/null
+++ b/src/physics/cosmology.ts
@@ -0,0 +1,22 @@
+/** Cosmology module — tsb analytics library. */
+
+/** Options for Cosmology. */
+export interface CosmologyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Cosmology. */
+export interface CosmologyResult { values: number[]; converged: boolean; }
+
+/** Compute Cosmology. */
+export function computeCosmology(data: number[], opts: CosmologyOptions = {}): CosmologyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCosmology };
diff --git a/src/physics/cpu.ts b/src/physics/cpu.ts
new file mode 100644
index 00000000..e40d7a83
--- /dev/null
+++ b/src/physics/cpu.ts
@@ -0,0 +1,15 @@
+/** Physics Cpu module — tsb analytics library. */
+export interface Physics cpuOptions { tol?: number; maxIter?: number; }
+export interface Physics cpuResult { values: number[]; converged: boolean; }
+export function computePhysics cpu(data: number[], opts: Physics cpuOptions = {}): Physics cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePhysics cpu };
diff --git a/src/physics/critical.ts b/src/physics/critical.ts
new file mode 100644
index 00000000..9564854b
--- /dev/null
+++ b/src/physics/critical.ts
@@ -0,0 +1,22 @@
+/** Critical module — tsb analytics library. */
+
+/** Options for Critical. */
+export interface CriticalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Critical. */
+export interface CriticalResult { values: number[]; converged: boolean; }
+
+/** Compute Critical. */
+export function computeCritical(data: number[], opts: CriticalOptions = {}): CriticalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCritical };
diff --git a/src/physics/dense.ts b/src/physics/dense.ts
new file mode 100644
index 00000000..03b2fdf0
--- /dev/null
+++ b/src/physics/dense.ts
@@ -0,0 +1,15 @@
+/** Physics Dense module — tsb analytics library. */
+export interface Physics denseOptions { tol?: number; maxIter?: number; }
+export interface Physics denseResult { values: number[]; converged: boolean; }
+export function computePhysics dense(data: number[], opts: Physics denseOptions = {}): Physics denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePhysics dense };
diff --git a/src/physics/density_functional.ts b/src/physics/density_functional.ts
new file mode 100644
index 00000000..60171f3c
--- /dev/null
+++ b/src/physics/density_functional.ts
@@ -0,0 +1,22 @@
+/** Density Functional module — tsb analytics library. */
+
+/** Options for Density Functional. */
+export interface DensityFunctionalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Density Functional. */
+export interface DensityFunctionalResult { values: number[]; converged: boolean; }
+
+/** Compute Density Functional. */
+export function computeDensityFunctional(data: number[], opts: DensityFunctionalOptions = {}): DensityFunctionalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDensityFunctional };
diff --git a/src/physics/distributed.ts b/src/physics/distributed.ts
new file mode 100644
index 00000000..e5d1a53b
--- /dev/null
+++ b/src/physics/distributed.ts
@@ -0,0 +1,15 @@
+/** Physics Distributed module — tsb analytics library. */
+export interface Physics distributedOptions { tol?: number; maxIter?: number; }
+export interface Physics distributedResult { values: number[]; converged: boolean; }
+export function computePhysics distributed(data: number[], opts: Physics distributedOptions = {}): Physics distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePhysics distributed };
diff --git a/src/physics/electrodynamics.ts b/src/physics/electrodynamics.ts
new file mode 100644
index 00000000..e181754c
--- /dev/null
+++ b/src/physics/electrodynamics.ts
@@ -0,0 +1,22 @@
+/** Electrodynamics module — tsb analytics library. */
+
+/** Options for Electrodynamics. */
+export interface ElectrodynamicsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Electrodynamics. */
+export interface ElectrodynamicsResult { values: number[]; converged: boolean; }
+
+/** Compute Electrodynamics. */
+export function computeElectrodynamics(data: number[], opts: ElectrodynamicsOptions = {}): ElectrodynamicsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeElectrodynamics };
diff --git a/src/physics/experimental.ts b/src/physics/experimental.ts
new file mode 100644
index 00000000..eb8bd45e
--- /dev/null
+++ b/src/physics/experimental.ts
@@ -0,0 +1,15 @@
+/** Physics Experimental module — tsb analytics library. */
+export interface Physics experimentalOptions { tol?: number; maxIter?: number; }
+export interface Physics experimentalResult { values: number[]; converged: boolean; }
+export function computePhysics experimental(data: number[], opts: Physics experimentalOptions = {}): Physics experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePhysics experimental };
diff --git a/src/physics/fast.ts b/src/physics/fast.ts
new file mode 100644
index 00000000..414812c5
--- /dev/null
+++ b/src/physics/fast.ts
@@ -0,0 +1,15 @@
+/** Physics Fast module — tsb analytics library. */
+export interface Physics fastOptions { tol?: number; maxIter?: number; }
+export interface Physics fastResult { values: number[]; converged: boolean; }
+export function computePhysics fast(data: number[], opts: Physics fastOptions = {}): Physics fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePhysics fast };
diff --git a/src/physics/field_theory.ts b/src/physics/field_theory.ts
new file mode 100644
index 00000000..da594bb2
--- /dev/null
+++ b/src/physics/field_theory.ts
@@ -0,0 +1,22 @@
+/** Field Theory module — tsb analytics library. */
+
+/** Options for Field Theory. */
+export interface FieldTheoryOptions { tol?: number; maxIter?: number; }
+
+/** Result from Field Theory. */
+export interface FieldTheoryResult { values: number[]; converged: boolean; }
+
+/** Compute Field Theory. */
+export function computeFieldTheory(data: number[], opts: FieldTheoryOptions = {}): FieldTheoryResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFieldTheory };
diff --git a/src/physics/fluid_dynamics.ts b/src/physics/fluid_dynamics.ts
new file mode 100644
index 00000000..b97aa147
--- /dev/null
+++ b/src/physics/fluid_dynamics.ts
@@ -0,0 +1,22 @@
+/** Fluid Dynamics module — tsb analytics library. */
+
+/** Options for Fluid Dynamics. */
+export interface FluidDynamicsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Fluid Dynamics. */
+export interface FluidDynamicsResult { values: number[]; converged: boolean; }
+
+/** Compute Fluid Dynamics. */
+export function computeFluidDynamics(data: number[], opts: FluidDynamicsOptions = {}): FluidDynamicsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFluidDynamics };
diff --git a/src/physics/future.ts b/src/physics/future.ts
new file mode 100644
index 00000000..76f090f6
--- /dev/null
+++ b/src/physics/future.ts
@@ -0,0 +1,15 @@
+/** Physics Future module — tsb analytics library. */
+export interface Physics futureOptions { tol?: number; maxIter?: number; }
+export interface Physics futureResult { values: number[]; converged: boolean; }
+export function computePhysics future(data: number[], opts: Physics futureOptions = {}): Physics futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePhysics future };
diff --git a/src/physics/gpu.ts b/src/physics/gpu.ts
new file mode 100644
index 00000000..6589ad78
--- /dev/null
+++ b/src/physics/gpu.ts
@@ -0,0 +1,15 @@
+/** Physics Gpu module — tsb analytics library. */
+export interface Physics gpuOptions { tol?: number; maxIter?: number; }
+export interface Physics gpuResult { values: number[]; converged: boolean; }
+export function computePhysics gpu(data: number[], opts: Physics gpuOptions = {}): Physics gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePhysics gpu };
diff --git a/src/physics/large.ts b/src/physics/large.ts
new file mode 100644
index 00000000..6d8ee025
--- /dev/null
+++ b/src/physics/large.ts
@@ -0,0 +1,15 @@
+/** Physics Large module — tsb analytics library. */
+export interface Physics largeOptions { tol?: number; maxIter?: number; }
+export interface Physics largeResult { values: number[]; converged: boolean; }
+export function computePhysics large(data: number[], opts: Physics largeOptions = {}): Physics largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePhysics large };
diff --git a/src/physics/lattice.ts b/src/physics/lattice.ts
new file mode 100644
index 00000000..64afadcb
--- /dev/null
+++ b/src/physics/lattice.ts
@@ -0,0 +1,22 @@
+/** Lattice module — tsb analytics library. */
+
+/** Options for Lattice. */
+export interface LatticeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Lattice. */
+export interface LatticeResult { values: number[]; converged: boolean; }
+
+/** Compute Lattice. */
+export function computeLattice(data: number[], opts: LatticeOptions = {}): LatticeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLattice };
diff --git a/src/physics/legacy.ts b/src/physics/legacy.ts
new file mode 100644
index 00000000..068b8a0a
--- /dev/null
+++ b/src/physics/legacy.ts
@@ -0,0 +1,15 @@
+/** Physics Legacy module — tsb analytics library. */
+export interface Physics legacyOptions { tol?: number; maxIter?: number; }
+export interface Physics legacyResult { values: number[]; converged: boolean; }
+export function computePhysics legacy(data: number[], opts: Physics legacyOptions = {}): Physics legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePhysics legacy };
diff --git a/src/physics/lite.ts b/src/physics/lite.ts
new file mode 100644
index 00000000..40ed0398
--- /dev/null
+++ b/src/physics/lite.ts
@@ -0,0 +1,15 @@
+/** Physics Lite module — tsb analytics library. */
+export interface Physics liteOptions { tol?: number; maxIter?: number; }
+export interface Physics liteResult { values: number[]; converged: boolean; }
+export function computePhysics lite(data: number[], opts: Physics liteOptions = {}): Physics liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePhysics lite };
diff --git a/src/physics/mechanics.ts b/src/physics/mechanics.ts
new file mode 100644
index 00000000..734934fb
--- /dev/null
+++ b/src/physics/mechanics.ts
@@ -0,0 +1,22 @@
+/** Mechanics module — tsb analytics library. */
+
+/** Options for Mechanics. */
+export interface MechanicsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Mechanics. */
+export interface MechanicsResult { values: number[]; converged: boolean; }
+
+/** Compute Mechanics. */
+export function computeMechanics(data: number[], opts: MechanicsOptions = {}): MechanicsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMechanics };
diff --git a/src/physics/mini.ts b/src/physics/mini.ts
new file mode 100644
index 00000000..1582cfe9
--- /dev/null
+++ b/src/physics/mini.ts
@@ -0,0 +1,15 @@
+/** Physics Mini module — tsb analytics library. */
+export interface Physics miniOptions { tol?: number; maxIter?: number; }
+export interface Physics miniResult { values: number[]; converged: boolean; }
+export function computePhysics mini(data: number[], opts: Physics miniOptions = {}): Physics miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePhysics mini };
diff --git a/src/physics/molecular_dynamics.ts b/src/physics/molecular_dynamics.ts
new file mode 100644
index 00000000..e11db597
--- /dev/null
+++ b/src/physics/molecular_dynamics.ts
@@ -0,0 +1,22 @@
+/** Molecular Dynamics module — tsb analytics library. */
+
+/** Options for Molecular Dynamics. */
+export interface MolecularDynamicsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Molecular Dynamics. */
+export interface MolecularDynamicsResult { values: number[]; converged: boolean; }
+
+/** Compute Molecular Dynamics. */
+export function computeMolecularDynamics(data: number[], opts: MolecularDynamicsOptions = {}): MolecularDynamicsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMolecularDynamics };
diff --git a/src/physics/monte_carlo.ts b/src/physics/monte_carlo.ts
new file mode 100644
index 00000000..80ebf4a3
--- /dev/null
+++ b/src/physics/monte_carlo.ts
@@ -0,0 +1,22 @@
+/** Monte Carlo module — tsb analytics library. */
+
+/** Options for Monte Carlo. */
+export interface MonteCarloOptions { tol?: number; maxIter?: number; }
+
+/** Result from Monte Carlo. */
+export interface MonteCarloResult { values: number[]; converged: boolean; }
+
+/** Compute Monte Carlo. */
+export function computeMonteCarlo(data: number[], opts: MonteCarloOptions = {}): MonteCarloResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMonteCarlo };
diff --git a/src/physics/next.ts b/src/physics/next.ts
new file mode 100644
index 00000000..51771ea5
--- /dev/null
+++ b/src/physics/next.ts
@@ -0,0 +1,15 @@
+/** Physics Next module — tsb analytics library. */
+export interface Physics nextOptions { tol?: number; maxIter?: number; }
+export interface Physics nextResult { values: number[]; converged: boolean; }
+export function computePhysics next(data: number[], opts: Physics nextOptions = {}): Physics nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePhysics next };
diff --git a/src/physics/nonequilibrium.ts b/src/physics/nonequilibrium.ts
new file mode 100644
index 00000000..29291a40
--- /dev/null
+++ b/src/physics/nonequilibrium.ts
@@ -0,0 +1,22 @@
+/** Nonequilibrium module — tsb analytics library. */
+
+/** Options for Nonequilibrium. */
+export interface NonequilibriumOptions { tol?: number; maxIter?: number; }
+
+/** Result from Nonequilibrium. */
+export interface NonequilibriumResult { values: number[]; converged: boolean; }
+
+/** Compute Nonequilibrium. */
+export function computeNonequilibrium(data: number[], opts: NonequilibriumOptions = {}): NonequilibriumResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNonequilibrium };
diff --git a/src/physics/nuclear.ts b/src/physics/nuclear.ts
new file mode 100644
index 00000000..1576cc3a
--- /dev/null
+++ b/src/physics/nuclear.ts
@@ -0,0 +1,22 @@
+/** Nuclear module — tsb analytics library. */
+
+/** Options for Nuclear. */
+export interface NuclearOptions { tol?: number; maxIter?: number; }
+
+/** Result from Nuclear. */
+export interface NuclearResult { values: number[]; converged: boolean; }
+
+/** Compute Nuclear. */
+export function computeNuclear(data: number[], opts: NuclearOptions = {}): NuclearResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNuclear };
diff --git a/src/physics/online.ts b/src/physics/online.ts
new file mode 100644
index 00000000..586c017d
--- /dev/null
+++ b/src/physics/online.ts
@@ -0,0 +1,15 @@
+/** Physics Online module — tsb analytics library. */
+export interface Physics onlineOptions { tol?: number; maxIter?: number; }
+export interface Physics onlineResult { values: number[]; converged: boolean; }
+export function computePhysics online(data: number[], opts: Physics onlineOptions = {}): Physics onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePhysics online };
diff --git a/src/physics/optics.ts b/src/physics/optics.ts
new file mode 100644
index 00000000..90c3b115
--- /dev/null
+++ b/src/physics/optics.ts
@@ -0,0 +1,22 @@
+/** Optics module — tsb analytics library. */
+
+/** Options for Optics. */
+export interface OpticsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Optics. */
+export interface OpticsResult { values: number[]; converged: boolean; }
+
+/** Compute Optics. */
+export function computeOptics(data: number[], opts: OpticsOptions = {}): OpticsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeOptics };
diff --git a/src/physics/parallel.ts b/src/physics/parallel.ts
new file mode 100644
index 00000000..adcae150
--- /dev/null
+++ b/src/physics/parallel.ts
@@ -0,0 +1,15 @@
+/** Physics Parallel module — tsb analytics library. */
+export interface Physics parallelOptions { tol?: number; maxIter?: number; }
+export interface Physics parallelResult { values: number[]; converged: boolean; }
+export function computePhysics parallel(data: number[], opts: Physics parallelOptions = {}): Physics parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePhysics parallel };
diff --git a/src/physics/particles.ts b/src/physics/particles.ts
new file mode 100644
index 00000000..b8e7a053
--- /dev/null
+++ b/src/physics/particles.ts
@@ -0,0 +1,22 @@
+/** Particles module — tsb analytics library. */
+
+/** Options for Particles. */
+export interface ParticlesOptions { tol?: number; maxIter?: number; }
+
+/** Result from Particles. */
+export interface ParticlesResult { values: number[]; converged: boolean; }
+
+/** Compute Particles. */
+export function computeParticles(data: number[], opts: ParticlesOptions = {}): ParticlesResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeParticles };
diff --git a/src/physics/path_integral.ts b/src/physics/path_integral.ts
new file mode 100644
index 00000000..333fac5e
--- /dev/null
+++ b/src/physics/path_integral.ts
@@ -0,0 +1,22 @@
+/** Path Integral module — tsb analytics library. */
+
+/** Options for Path Integral. */
+export interface PathIntegralOptions { tol?: number; maxIter?: number; }
+
+/** Result from Path Integral. */
+export interface PathIntegralResult { values: number[]; converged: boolean; }
+
+/** Compute Path Integral. */
+export function computePathIntegral(data: number[], opts: PathIntegralOptions = {}): PathIntegralResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePathIntegral };
diff --git a/src/physics/perturbation.ts b/src/physics/perturbation.ts
new file mode 100644
index 00000000..f7eca34e
--- /dev/null
+++ b/src/physics/perturbation.ts
@@ -0,0 +1,22 @@
+/** Perturbation module — tsb analytics library. */
+
+/** Options for Perturbation. */
+export interface PerturbationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Perturbation. */
+export interface PerturbationResult { values: number[]; converged: boolean; }
+
+/** Compute Perturbation. */
+export function computePerturbation(data: number[], opts: PerturbationOptions = {}): PerturbationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePerturbation };
diff --git a/src/physics/phase_transition.ts b/src/physics/phase_transition.ts
new file mode 100644
index 00000000..1d40ac29
--- /dev/null
+++ b/src/physics/phase_transition.ts
@@ -0,0 +1,22 @@
+/** Phase Transition module — tsb analytics library. */
+
+/** Options for Phase Transition. */
+export interface PhaseTransitionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Phase Transition. */
+export interface PhaseTransitionResult { values: number[]; converged: boolean; }
+
+/** Compute Phase Transition. */
+export function computePhaseTransition(data: number[], opts: PhaseTransitionOptions = {}): PhaseTransitionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePhaseTransition };
diff --git a/src/physics/plasma.ts b/src/physics/plasma.ts
new file mode 100644
index 00000000..59998f5d
--- /dev/null
+++ b/src/physics/plasma.ts
@@ -0,0 +1,22 @@
+/** Plasma module — tsb analytics library. */
+
+/** Options for Plasma. */
+export interface PlasmaOptions { tol?: number; maxIter?: number; }
+
+/** Result from Plasma. */
+export interface PlasmaResult { values: number[]; converged: boolean; }
+
+/** Compute Plasma. */
+export function computePlasma(data: number[], opts: PlasmaOptions = {}): PlasmaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePlasma };
diff --git a/src/physics/plus.ts b/src/physics/plus.ts
new file mode 100644
index 00000000..0e972f9d
--- /dev/null
+++ b/src/physics/plus.ts
@@ -0,0 +1,15 @@
+/** Physics Plus module — tsb analytics library. */
+export interface Physics plusOptions { tol?: number; maxIter?: number; }
+export interface Physics plusResult { values: number[]; converged: boolean; }
+export function computePhysics plus(data: number[], opts: Physics plusOptions = {}): Physics plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePhysics plus };
diff --git a/src/physics/pro.ts b/src/physics/pro.ts
new file mode 100644
index 00000000..d6479dc6
--- /dev/null
+++ b/src/physics/pro.ts
@@ -0,0 +1,15 @@
+/** Physics Pro module — tsb analytics library. */
+export interface Physics proOptions { tol?: number; maxIter?: number; }
+export interface Physics proResult { values: number[]; converged: boolean; }
+export function computePhysics pro(data: number[], opts: Physics proOptions = {}): Physics proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePhysics pro };
diff --git a/src/physics/quantum.ts b/src/physics/quantum.ts
new file mode 100644
index 00000000..8a796469
--- /dev/null
+++ b/src/physics/quantum.ts
@@ -0,0 +1,22 @@
+/** Quantum module — tsb analytics library. */
+
+/** Options for Quantum. */
+export interface QuantumOptions { tol?: number; maxIter?: number; }
+
+/** Result from Quantum. */
+export interface QuantumResult { values: number[]; converged: boolean; }
+
+/** Compute Quantum. */
+export function computeQuantum(data: number[], opts: QuantumOptions = {}): QuantumResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeQuantum };
diff --git a/src/physics/relativity.ts b/src/physics/relativity.ts
new file mode 100644
index 00000000..72cc2e37
--- /dev/null
+++ b/src/physics/relativity.ts
@@ -0,0 +1,22 @@
+/** Relativity module — tsb analytics library. */
+
+/** Options for Relativity. */
+export interface RelativityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Relativity. */
+export interface RelativityResult { values: number[]; converged: boolean; }
+
+/** Compute Relativity. */
+export function computeRelativity(data: number[], opts: RelativityOptions = {}): RelativityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRelativity };
diff --git a/src/physics/renormalization.ts b/src/physics/renormalization.ts
new file mode 100644
index 00000000..c7781070
--- /dev/null
+++ b/src/physics/renormalization.ts
@@ -0,0 +1,22 @@
+/** Renormalization module — tsb analytics library. */
+
+/** Options for Renormalization. */
+export interface RenormalizationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Renormalization. */
+export interface RenormalizationResult { values: number[]; converged: boolean; }
+
+/** Compute Renormalization. */
+export function computeRenormalization(data: number[], opts: RenormalizationOptions = {}): RenormalizationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRenormalization };
diff --git a/src/physics/robust.ts b/src/physics/robust.ts
new file mode 100644
index 00000000..bb906e6c
--- /dev/null
+++ b/src/physics/robust.ts
@@ -0,0 +1,15 @@
+/** Physics Robust module — tsb analytics library. */
+export interface Physics robustOptions { tol?: number; maxIter?: number; }
+export interface Physics robustResult { values: number[]; converged: boolean; }
+export function computePhysics robust(data: number[], opts: Physics robustOptions = {}): Physics robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePhysics robust };
diff --git a/src/physics/small.ts b/src/physics/small.ts
new file mode 100644
index 00000000..f8c88fbf
--- /dev/null
+++ b/src/physics/small.ts
@@ -0,0 +1,15 @@
+/** Physics Small module — tsb analytics library. */
+export interface Physics smallOptions { tol?: number; maxIter?: number; }
+export interface Physics smallResult { values: number[]; converged: boolean; }
+export function computePhysics small(data: number[], opts: Physics smallOptions = {}): Physics smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePhysics small };
diff --git a/src/physics/sparse.ts b/src/physics/sparse.ts
new file mode 100644
index 00000000..fb30743d
--- /dev/null
+++ b/src/physics/sparse.ts
@@ -0,0 +1,15 @@
+/** Physics Sparse module — tsb analytics library. */
+export interface Physics sparseOptions { tol?: number; maxIter?: number; }
+export interface Physics sparseResult { values: number[]; converged: boolean; }
+export function computePhysics sparse(data: number[], opts: Physics sparseOptions = {}): Physics sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePhysics sparse };
diff --git a/src/physics/stable.ts b/src/physics/stable.ts
new file mode 100644
index 00000000..d6174327
--- /dev/null
+++ b/src/physics/stable.ts
@@ -0,0 +1,15 @@
+/** Physics Stable module — tsb analytics library. */
+export interface Physics stableOptions { tol?: number; maxIter?: number; }
+export interface Physics stableResult { values: number[]; converged: boolean; }
+export function computePhysics stable(data: number[], opts: Physics stableOptions = {}): Physics stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePhysics stable };
diff --git a/src/physics/statistical_physics.ts b/src/physics/statistical_physics.ts
new file mode 100644
index 00000000..9f5e2dfe
--- /dev/null
+++ b/src/physics/statistical_physics.ts
@@ -0,0 +1,22 @@
+/** Statistical Physics module — tsb analytics library. */
+
+/** Options for Statistical Physics. */
+export interface StatisticalPhysicsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Statistical Physics. */
+export interface StatisticalPhysicsResult { values: number[]; converged: boolean; }
+
+/** Compute Statistical Physics. */
+export function computeStatisticalPhysics(data: number[], opts: StatisticalPhysicsOptions = {}): StatisticalPhysicsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeStatisticalPhysics };
diff --git a/src/physics/streaming.ts b/src/physics/streaming.ts
new file mode 100644
index 00000000..0d5da6c2
--- /dev/null
+++ b/src/physics/streaming.ts
@@ -0,0 +1,15 @@
+/** Physics Streaming module — tsb analytics library. */
+export interface Physics streamingOptions { tol?: number; maxIter?: number; }
+export interface Physics streamingResult { values: number[]; converged: boolean; }
+export function computePhysics streaming(data: number[], opts: Physics streamingOptions = {}): Physics streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePhysics streaming };
diff --git a/src/physics/thermodynamics.ts b/src/physics/thermodynamics.ts
new file mode 100644
index 00000000..23fc6c0f
--- /dev/null
+++ b/src/physics/thermodynamics.ts
@@ -0,0 +1,22 @@
+/** Thermodynamics module — tsb analytics library. */
+
+/** Options for Thermodynamics. */
+export interface ThermodynamicsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Thermodynamics. */
+export interface ThermodynamicsResult { values: number[]; converged: boolean; }
+
+/** Compute Thermodynamics. */
+export function computeThermodynamics(data: number[], opts: ThermodynamicsOptions = {}): ThermodynamicsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeThermodynamics };
diff --git a/src/physics/tight_binding.ts b/src/physics/tight_binding.ts
new file mode 100644
index 00000000..78057b65
--- /dev/null
+++ b/src/physics/tight_binding.ts
@@ -0,0 +1,22 @@
+/** Tight Binding module — tsb analytics library. */
+
+/** Options for Tight Binding. */
+export interface TightBindingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Tight Binding. */
+export interface TightBindingResult { values: number[]; converged: boolean; }
+
+/** Compute Tight Binding. */
+export function computeTightBinding(data: number[], opts: TightBindingOptions = {}): TightBindingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTightBinding };
diff --git a/src/physics/transport.ts b/src/physics/transport.ts
new file mode 100644
index 00000000..fc969aa6
--- /dev/null
+++ b/src/physics/transport.ts
@@ -0,0 +1,22 @@
+/** Transport module — tsb analytics library. */
+
+/** Options for Transport. */
+export interface TransportOptions { tol?: number; maxIter?: number; }
+
+/** Result from Transport. */
+export interface TransportResult { values: number[]; converged: boolean; }
+
+/** Compute Transport. */
+export function computeTransport(data: number[], opts: TransportOptions = {}): TransportResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTransport };
diff --git a/src/physics/v2.ts b/src/physics/v2.ts
new file mode 100644
index 00000000..32c3c3ce
--- /dev/null
+++ b/src/physics/v2.ts
@@ -0,0 +1,15 @@
+/** Physics V2 module — tsb analytics library. */
+export interface Physics v2Options { tol?: number; maxIter?: number; }
+export interface Physics v2Result { values: number[]; converged: boolean; }
+export function computePhysics v2(data: number[], opts: Physics v2Options = {}): Physics v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePhysics v2 };
diff --git a/src/physics/v3.ts b/src/physics/v3.ts
new file mode 100644
index 00000000..c910fbea
--- /dev/null
+++ b/src/physics/v3.ts
@@ -0,0 +1,15 @@
+/** Physics V3 module — tsb analytics library. */
+export interface Physics v3Options { tol?: number; maxIter?: number; }
+export interface Physics v3Result { values: number[]; converged: boolean; }
+export function computePhysics v3(data: number[], opts: Physics v3Options = {}): Physics v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePhysics v3 };
diff --git a/src/physics/variational.ts b/src/physics/variational.ts
new file mode 100644
index 00000000..b48b6602
--- /dev/null
+++ b/src/physics/variational.ts
@@ -0,0 +1,22 @@
+/** Variational module — tsb analytics library. */
+
+/** Options for Variational. */
+export interface VariationalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Variational. */
+export interface VariationalResult { values: number[]; converged: boolean; }
+
+/** Compute Variational. */
+export function computeVariational(data: number[], opts: VariationalOptions = {}): VariationalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeVariational };
diff --git a/src/physics/wasm.ts b/src/physics/wasm.ts
new file mode 100644
index 00000000..28c427aa
--- /dev/null
+++ b/src/physics/wasm.ts
@@ -0,0 +1,15 @@
+/** Physics Wasm module — tsb analytics library. */
+export interface Physics wasmOptions { tol?: number; maxIter?: number; }
+export interface Physics wasmResult { values: number[]; converged: boolean; }
+export function computePhysics wasm(data: number[], opts: Physics wasmOptions = {}): Physics wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePhysics wasm };
diff --git a/src/physics/wave.ts b/src/physics/wave.ts
new file mode 100644
index 00000000..d43c9528
--- /dev/null
+++ b/src/physics/wave.ts
@@ -0,0 +1,22 @@
+/** Wave module — tsb analytics library. */
+
+/** Options for Wave. */
+export interface WaveOptions { tol?: number; maxIter?: number; }
+
+/** Result from Wave. */
+export interface WaveResult { values: number[]; converged: boolean; }
+
+/** Compute Wave. */
+export function computeWave(data: number[], opts: WaveOptions = {}): WaveResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeWave };
diff --git a/src/physics/xlarge.ts b/src/physics/xlarge.ts
new file mode 100644
index 00000000..67a4d31e
--- /dev/null
+++ b/src/physics/xlarge.ts
@@ -0,0 +1,15 @@
+/** Physics Xlarge module — tsb analytics library. */
+export interface Physics xlargeOptions { tol?: number; maxIter?: number; }
+export interface Physics xlargeResult { values: number[]; converged: boolean; }
+export function computePhysics xlarge(data: number[], opts: Physics xlargeOptions = {}): Physics xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePhysics xlarge };
diff --git a/src/political_science/advanced.ts b/src/political_science/advanced.ts
new file mode 100644
index 00000000..a13e081f
--- /dev/null
+++ b/src/political_science/advanced.ts
@@ -0,0 +1,15 @@
+/** Political Science Advanced module — tsb analytics library. */
+export interface PoliticalScience advancedOptions { tol?: number; maxIter?: number; }
+export interface PoliticalScience advancedResult { values: number[]; converged: boolean; }
+export function computePoliticalScience advanced(data: number[], opts: PoliticalScience advancedOptions = {}): PoliticalScience advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePoliticalScience advanced };
diff --git a/src/political_science/authoritarianism.ts b/src/political_science/authoritarianism.ts
new file mode 100644
index 00000000..587eadff
--- /dev/null
+++ b/src/political_science/authoritarianism.ts
@@ -0,0 +1,22 @@
+/** Authoritarianism module — tsb analytics library. */
+
+/** Options for Authoritarianism. */
+export interface AuthoritarianismOptions { tol?: number; maxIter?: number; }
+
+/** Result from Authoritarianism. */
+export interface AuthoritarianismResult { values: number[]; converged: boolean; }
+
+/** Compute Authoritarianism. */
+export function computeAuthoritarianism(data: number[], opts: AuthoritarianismOptions = {}): AuthoritarianismResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAuthoritarianism };
diff --git a/src/political_science/base2.ts b/src/political_science/base2.ts
new file mode 100644
index 00000000..ee631195
--- /dev/null
+++ b/src/political_science/base2.ts
@@ -0,0 +1,15 @@
+/** Political Science Base2 module — tsb analytics library. */
+export interface PoliticalScience base2Options { tol?: number; maxIter?: number; }
+export interface PoliticalScience base2Result { values: number[]; converged: boolean; }
+export function computePoliticalScience base2(data: number[], opts: PoliticalScience base2Options = {}): PoliticalScience base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePoliticalScience base2 };
diff --git a/src/political_science/batch.ts b/src/political_science/batch.ts
new file mode 100644
index 00000000..e8844f26
--- /dev/null
+++ b/src/political_science/batch.ts
@@ -0,0 +1,15 @@
+/** Political Science Batch module — tsb analytics library. */
+export interface PoliticalScience batchOptions { tol?: number; maxIter?: number; }
+export interface PoliticalScience batchResult { values: number[]; converged: boolean; }
+export function computePoliticalScience batch(data: number[], opts: PoliticalScience batchOptions = {}): PoliticalScience batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePoliticalScience batch };
diff --git a/src/political_science/beta.ts b/src/political_science/beta.ts
new file mode 100644
index 00000000..47dc0ce0
--- /dev/null
+++ b/src/political_science/beta.ts
@@ -0,0 +1,15 @@
+/** Political Science Beta module — tsb analytics library. */
+export interface PoliticalScience betaOptions { tol?: number; maxIter?: number; }
+export interface PoliticalScience betaResult { values: number[]; converged: boolean; }
+export function computePoliticalScience beta(data: number[], opts: PoliticalScience betaOptions = {}): PoliticalScience betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePoliticalScience beta };
diff --git a/src/political_science/coalitions.ts b/src/political_science/coalitions.ts
new file mode 100644
index 00000000..ab865fcd
--- /dev/null
+++ b/src/political_science/coalitions.ts
@@ -0,0 +1,22 @@
+/** Coalitions module — tsb analytics library. */
+
+/** Options for Coalitions. */
+export interface CoalitionsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Coalitions. */
+export interface CoalitionsResult { values: number[]; converged: boolean; }
+
+/** Compute Coalitions. */
+export function computeCoalitions(data: number[], opts: CoalitionsOptions = {}): CoalitionsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCoalitions };
diff --git a/src/political_science/comparative.ts b/src/political_science/comparative.ts
new file mode 100644
index 00000000..8a4490aa
--- /dev/null
+++ b/src/political_science/comparative.ts
@@ -0,0 +1,22 @@
+/** Comparative module — tsb analytics library. */
+
+/** Options for Comparative. */
+export interface ComparativeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Comparative. */
+export interface ComparativeResult { values: number[]; converged: boolean; }
+
+/** Compute Comparative. */
+export function computeComparative(data: number[], opts: ComparativeOptions = {}): ComparativeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeComparative };
diff --git a/src/political_science/conflict.ts b/src/political_science/conflict.ts
new file mode 100644
index 00000000..f20c0474
--- /dev/null
+++ b/src/political_science/conflict.ts
@@ -0,0 +1,22 @@
+/** Conflict module — tsb analytics library. */
+
+/** Options for Conflict. */
+export interface ConflictOptions { tol?: number; maxIter?: number; }
+
+/** Result from Conflict. */
+export interface ConflictResult { values: number[]; converged: boolean; }
+
+/** Compute Conflict. */
+export function computeConflict(data: number[], opts: ConflictOptions = {}): ConflictResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeConflict };
diff --git a/src/political_science/cpu.ts b/src/political_science/cpu.ts
new file mode 100644
index 00000000..d3aec23f
--- /dev/null
+++ b/src/political_science/cpu.ts
@@ -0,0 +1,15 @@
+/** Political Science Cpu module — tsb analytics library. */
+export interface PoliticalScience cpuOptions { tol?: number; maxIter?: number; }
+export interface PoliticalScience cpuResult { values: number[]; converged: boolean; }
+export function computePoliticalScience cpu(data: number[], opts: PoliticalScience cpuOptions = {}): PoliticalScience cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePoliticalScience cpu };
diff --git a/src/political_science/democracy.ts b/src/political_science/democracy.ts
new file mode 100644
index 00000000..d9c2dad8
--- /dev/null
+++ b/src/political_science/democracy.ts
@@ -0,0 +1,22 @@
+/** Democracy module — tsb analytics library. */
+
+/** Options for Democracy. */
+export interface DemocracyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Democracy. */
+export interface DemocracyResult { values: number[]; converged: boolean; }
+
+/** Compute Democracy. */
+export function computeDemocracy(data: number[], opts: DemocracyOptions = {}): DemocracyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDemocracy };
diff --git a/src/political_science/dense.ts b/src/political_science/dense.ts
new file mode 100644
index 00000000..0449fdec
--- /dev/null
+++ b/src/political_science/dense.ts
@@ -0,0 +1,15 @@
+/** Political Science Dense module — tsb analytics library. */
+export interface PoliticalScience denseOptions { tol?: number; maxIter?: number; }
+export interface PoliticalScience denseResult { values: number[]; converged: boolean; }
+export function computePoliticalScience dense(data: number[], opts: PoliticalScience denseOptions = {}): PoliticalScience denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePoliticalScience dense };
diff --git a/src/political_science/development.ts b/src/political_science/development.ts
new file mode 100644
index 00000000..5e72e96d
--- /dev/null
+++ b/src/political_science/development.ts
@@ -0,0 +1,22 @@
+/** Development module — tsb analytics library. */
+
+/** Options for Development. */
+export interface DevelopmentOptions { tol?: number; maxIter?: number; }
+
+/** Result from Development. */
+export interface DevelopmentResult { values: number[]; converged: boolean; }
+
+/** Compute Development. */
+export function computeDevelopment(data: number[], opts: DevelopmentOptions = {}): DevelopmentResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDevelopment };
diff --git a/src/political_science/distributed.ts b/src/political_science/distributed.ts
new file mode 100644
index 00000000..362b5826
--- /dev/null
+++ b/src/political_science/distributed.ts
@@ -0,0 +1,15 @@
+/** Political Science Distributed module — tsb analytics library. */
+export interface PoliticalScience distributedOptions { tol?: number; maxIter?: number; }
+export interface PoliticalScience distributedResult { values: number[]; converged: boolean; }
+export function computePoliticalScience distributed(data: number[], opts: PoliticalScience distributedOptions = {}): PoliticalScience distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePoliticalScience distributed };
diff --git a/src/political_science/elections.ts b/src/political_science/elections.ts
new file mode 100644
index 00000000..7c343e29
--- /dev/null
+++ b/src/political_science/elections.ts
@@ -0,0 +1,22 @@
+/** Elections module — tsb analytics library. */
+
+/** Options for Elections. */
+export interface ElectionsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Elections. */
+export interface ElectionsResult { values: number[]; converged: boolean; }
+
+/** Compute Elections. */
+export function computeElections(data: number[], opts: ElectionsOptions = {}): ElectionsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeElections };
diff --git a/src/political_science/executive.ts b/src/political_science/executive.ts
new file mode 100644
index 00000000..af0c901d
--- /dev/null
+++ b/src/political_science/executive.ts
@@ -0,0 +1,22 @@
+/** Executive module — tsb analytics library. */
+
+/** Options for Executive. */
+export interface ExecutiveOptions { tol?: number; maxIter?: number; }
+
+/** Result from Executive. */
+export interface ExecutiveResult { values: number[]; converged: boolean; }
+
+/** Compute Executive. */
+export function computeExecutive(data: number[], opts: ExecutiveOptions = {}): ExecutiveResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeExecutive };
diff --git a/src/political_science/experimental.ts b/src/political_science/experimental.ts
new file mode 100644
index 00000000..161750ac
--- /dev/null
+++ b/src/political_science/experimental.ts
@@ -0,0 +1,15 @@
+/** Political Science Experimental module — tsb analytics library. */
+export interface PoliticalScience experimentalOptions { tol?: number; maxIter?: number; }
+export interface PoliticalScience experimentalResult { values: number[]; converged: boolean; }
+export function computePoliticalScience experimental(data: number[], opts: PoliticalScience experimentalOptions = {}): PoliticalScience experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePoliticalScience experimental };
diff --git a/src/political_science/fast.ts b/src/political_science/fast.ts
new file mode 100644
index 00000000..7c6b7dea
--- /dev/null
+++ b/src/political_science/fast.ts
@@ -0,0 +1,15 @@
+/** Political Science Fast module — tsb analytics library. */
+export interface PoliticalScience fastOptions { tol?: number; maxIter?: number; }
+export interface PoliticalScience fastResult { values: number[]; converged: boolean; }
+export function computePoliticalScience fast(data: number[], opts: PoliticalScience fastOptions = {}): PoliticalScience fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePoliticalScience fast };
diff --git a/src/political_science/federalism.ts b/src/political_science/federalism.ts
new file mode 100644
index 00000000..46d0bcab
--- /dev/null
+++ b/src/political_science/federalism.ts
@@ -0,0 +1,22 @@
+/** Federalism module — tsb analytics library. */
+
+/** Options for Federalism. */
+export interface FederalismOptions { tol?: number; maxIter?: number; }
+
+/** Result from Federalism. */
+export interface FederalismResult { values: number[]; converged: boolean; }
+
+/** Compute Federalism. */
+export function computeFederalism(data: number[], opts: FederalismOptions = {}): FederalismResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFederalism };
diff --git a/src/political_science/future.ts b/src/political_science/future.ts
new file mode 100644
index 00000000..2ef2a269
--- /dev/null
+++ b/src/political_science/future.ts
@@ -0,0 +1,15 @@
+/** Political Science Future module — tsb analytics library. */
+export interface PoliticalScience futureOptions { tol?: number; maxIter?: number; }
+export interface PoliticalScience futureResult { values: number[]; converged: boolean; }
+export function computePoliticalScience future(data: number[], opts: PoliticalScience futureOptions = {}): PoliticalScience futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePoliticalScience future };
diff --git a/src/political_science/government.ts b/src/political_science/government.ts
new file mode 100644
index 00000000..1a5eb270
--- /dev/null
+++ b/src/political_science/government.ts
@@ -0,0 +1,22 @@
+/** Government module — tsb analytics library. */
+
+/** Options for Government. */
+export interface GovernmentOptions { tol?: number; maxIter?: number; }
+
+/** Result from Government. */
+export interface GovernmentResult { values: number[]; converged: boolean; }
+
+/** Compute Government. */
+export function computeGovernment(data: number[], opts: GovernmentOptions = {}): GovernmentResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGovernment };
diff --git a/src/political_science/gpu.ts b/src/political_science/gpu.ts
new file mode 100644
index 00000000..d2ae7e3e
--- /dev/null
+++ b/src/political_science/gpu.ts
@@ -0,0 +1,15 @@
+/** Political Science Gpu module — tsb analytics library. */
+export interface PoliticalScience gpuOptions { tol?: number; maxIter?: number; }
+export interface PoliticalScience gpuResult { values: number[]; converged: boolean; }
+export function computePoliticalScience gpu(data: number[], opts: PoliticalScience gpuOptions = {}): PoliticalScience gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePoliticalScience gpu };
diff --git a/src/political_science/hybrid.ts b/src/political_science/hybrid.ts
new file mode 100644
index 00000000..3314ae42
--- /dev/null
+++ b/src/political_science/hybrid.ts
@@ -0,0 +1,22 @@
+/** Hybrid module — tsb analytics library. */
+
+/** Options for Hybrid. */
+export interface HybridOptions { tol?: number; maxIter?: number; }
+
+/** Result from Hybrid. */
+export interface HybridResult { values: number[]; converged: boolean; }
+
+/** Compute Hybrid. */
+export function computeHybrid(data: number[], opts: HybridOptions = {}): HybridResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeHybrid };
diff --git a/src/political_science/inequality_pol.ts b/src/political_science/inequality_pol.ts
new file mode 100644
index 00000000..3605e6c2
--- /dev/null
+++ b/src/political_science/inequality_pol.ts
@@ -0,0 +1,22 @@
+/** Inequality Pol module — tsb analytics library. */
+
+/** Options for Inequality Pol. */
+export interface InequalityPolOptions { tol?: number; maxIter?: number; }
+
+/** Result from Inequality Pol. */
+export interface InequalityPolResult { values: number[]; converged: boolean; }
+
+/** Compute Inequality Pol. */
+export function computeInequalityPol(data: number[], opts: InequalityPolOptions = {}): InequalityPolResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeInequalityPol };
diff --git a/src/political_science/institutions.ts b/src/political_science/institutions.ts
new file mode 100644
index 00000000..bcc4c071
--- /dev/null
+++ b/src/political_science/institutions.ts
@@ -0,0 +1,22 @@
+/** Institutions module — tsb analytics library. */
+
+/** Options for Institutions. */
+export interface InstitutionsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Institutions. */
+export interface InstitutionsResult { values: number[]; converged: boolean; }
+
+/** Compute Institutions. */
+export function computeInstitutions(data: number[], opts: InstitutionsOptions = {}): InstitutionsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeInstitutions };
diff --git a/src/political_science/international.ts b/src/political_science/international.ts
new file mode 100644
index 00000000..3d36bd8d
--- /dev/null
+++ b/src/political_science/international.ts
@@ -0,0 +1,22 @@
+/** International module — tsb analytics library. */
+
+/** Options for International. */
+export interface InternationalOptions { tol?: number; maxIter?: number; }
+
+/** Result from International. */
+export interface InternationalResult { values: number[]; converged: boolean; }
+
+/** Compute International. */
+export function computeInternational(data: number[], opts: InternationalOptions = {}): InternationalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeInternational };
diff --git a/src/political_science/international_law.ts b/src/political_science/international_law.ts
new file mode 100644
index 00000000..c1f3a0f1
--- /dev/null
+++ b/src/political_science/international_law.ts
@@ -0,0 +1,22 @@
+/** International Law module — tsb analytics library. */
+
+/** Options for International Law. */
+export interface InternationalLawOptions { tol?: number; maxIter?: number; }
+
+/** Result from International Law. */
+export interface InternationalLawResult { values: number[]; converged: boolean; }
+
+/** Compute International Law. */
+export function computeInternationalLaw(data: number[], opts: InternationalLawOptions = {}): InternationalLawResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeInternationalLaw };
diff --git a/src/political_science/international_organization.ts b/src/political_science/international_organization.ts
new file mode 100644
index 00000000..a21941d5
--- /dev/null
+++ b/src/political_science/international_organization.ts
@@ -0,0 +1,22 @@
+/** International Organization module — tsb analytics library. */
+
+/** Options for International Organization. */
+export interface InternationalOrganizationOptions { tol?: number; maxIter?: number; }
+
+/** Result from International Organization. */
+export interface InternationalOrganizationResult { values: number[]; converged: boolean; }
+
+/** Compute International Organization. */
+export function computeInternationalOrganization(data: number[], opts: InternationalOrganizationOptions = {}): InternationalOrganizationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeInternationalOrganization };
diff --git a/src/political_science/judiciary.ts b/src/political_science/judiciary.ts
new file mode 100644
index 00000000..0d836bc3
--- /dev/null
+++ b/src/political_science/judiciary.ts
@@ -0,0 +1,22 @@
+/** Judiciary module — tsb analytics library. */
+
+/** Options for Judiciary. */
+export interface JudiciaryOptions { tol?: number; maxIter?: number; }
+
+/** Result from Judiciary. */
+export interface JudiciaryResult { values: number[]; converged: boolean; }
+
+/** Compute Judiciary. */
+export function computeJudiciary(data: number[], opts: JudiciaryOptions = {}): JudiciaryResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeJudiciary };
diff --git a/src/political_science/large.ts b/src/political_science/large.ts
new file mode 100644
index 00000000..fb1efa84
--- /dev/null
+++ b/src/political_science/large.ts
@@ -0,0 +1,15 @@
+/** Political Science Large module — tsb analytics library. */
+export interface PoliticalScience largeOptions { tol?: number; maxIter?: number; }
+export interface PoliticalScience largeResult { values: number[]; converged: boolean; }
+export function computePoliticalScience large(data: number[], opts: PoliticalScience largeOptions = {}): PoliticalScience largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePoliticalScience large };
diff --git a/src/political_science/legacy.ts b/src/political_science/legacy.ts
new file mode 100644
index 00000000..4522a375
--- /dev/null
+++ b/src/political_science/legacy.ts
@@ -0,0 +1,15 @@
+/** Political Science Legacy module — tsb analytics library. */
+export interface PoliticalScience legacyOptions { tol?: number; maxIter?: number; }
+export interface PoliticalScience legacyResult { values: number[]; converged: boolean; }
+export function computePoliticalScience legacy(data: number[], opts: PoliticalScience legacyOptions = {}): PoliticalScience legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePoliticalScience legacy };
diff --git a/src/political_science/legislature.ts b/src/political_science/legislature.ts
new file mode 100644
index 00000000..06cebc2a
--- /dev/null
+++ b/src/political_science/legislature.ts
@@ -0,0 +1,22 @@
+/** Legislature module — tsb analytics library. */
+
+/** Options for Legislature. */
+export interface LegislatureOptions { tol?: number; maxIter?: number; }
+
+/** Result from Legislature. */
+export interface LegislatureResult { values: number[]; converged: boolean; }
+
+/** Compute Legislature. */
+export function computeLegislature(data: number[], opts: LegislatureOptions = {}): LegislatureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLegislature };
diff --git a/src/political_science/lite.ts b/src/political_science/lite.ts
new file mode 100644
index 00000000..774621c1
--- /dev/null
+++ b/src/political_science/lite.ts
@@ -0,0 +1,15 @@
+/** Political Science Lite module — tsb analytics library. */
+export interface PoliticalScience liteOptions { tol?: number; maxIter?: number; }
+export interface PoliticalScience liteResult { values: number[]; converged: boolean; }
+export function computePoliticalScience lite(data: number[], opts: PoliticalScience liteOptions = {}): PoliticalScience liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePoliticalScience lite };
diff --git a/src/political_science/media_pol.ts b/src/political_science/media_pol.ts
new file mode 100644
index 00000000..d39470e6
--- /dev/null
+++ b/src/political_science/media_pol.ts
@@ -0,0 +1,22 @@
+/** Media Pol module — tsb analytics library. */
+
+/** Options for Media Pol. */
+export interface MediaPolOptions { tol?: number; maxIter?: number; }
+
+/** Result from Media Pol. */
+export interface MediaPolResult { values: number[]; converged: boolean; }
+
+/** Compute Media Pol. */
+export function computeMediaPol(data: number[], opts: MediaPolOptions = {}): MediaPolResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMediaPol };
diff --git a/src/political_science/mini.ts b/src/political_science/mini.ts
new file mode 100644
index 00000000..712ed98e
--- /dev/null
+++ b/src/political_science/mini.ts
@@ -0,0 +1,15 @@
+/** Political Science Mini module — tsb analytics library. */
+export interface PoliticalScience miniOptions { tol?: number; maxIter?: number; }
+export interface PoliticalScience miniResult { values: number[]; converged: boolean; }
+export function computePoliticalScience mini(data: number[], opts: PoliticalScience miniOptions = {}): PoliticalScience miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePoliticalScience mini };
diff --git a/src/political_science/negotiation.ts b/src/political_science/negotiation.ts
new file mode 100644
index 00000000..74d18f03
--- /dev/null
+++ b/src/political_science/negotiation.ts
@@ -0,0 +1,22 @@
+/** Negotiation module — tsb analytics library. */
+
+/** Options for Negotiation. */
+export interface NegotiationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Negotiation. */
+export interface NegotiationResult { values: number[]; converged: boolean; }
+
+/** Compute Negotiation. */
+export function computeNegotiation(data: number[], opts: NegotiationOptions = {}): NegotiationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNegotiation };
diff --git a/src/political_science/next.ts b/src/political_science/next.ts
new file mode 100644
index 00000000..dfac7d68
--- /dev/null
+++ b/src/political_science/next.ts
@@ -0,0 +1,15 @@
+/** Political Science Next module — tsb analytics library. */
+export interface PoliticalScience nextOptions { tol?: number; maxIter?: number; }
+export interface PoliticalScience nextResult { values: number[]; converged: boolean; }
+export function computePoliticalScience next(data: number[], opts: PoliticalScience nextOptions = {}): PoliticalScience nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePoliticalScience next };
diff --git a/src/political_science/online.ts b/src/political_science/online.ts
new file mode 100644
index 00000000..2f9613ed
--- /dev/null
+++ b/src/political_science/online.ts
@@ -0,0 +1,15 @@
+/** Political Science Online module — tsb analytics library. */
+export interface PoliticalScience onlineOptions { tol?: number; maxIter?: number; }
+export interface PoliticalScience onlineResult { values: number[]; converged: boolean; }
+export function computePoliticalScience online(data: number[], opts: PoliticalScience onlineOptions = {}): PoliticalScience onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePoliticalScience online };
diff --git a/src/political_science/parallel.ts b/src/political_science/parallel.ts
new file mode 100644
index 00000000..0c5bd829
--- /dev/null
+++ b/src/political_science/parallel.ts
@@ -0,0 +1,15 @@
+/** Political Science Parallel module — tsb analytics library. */
+export interface PoliticalScience parallelOptions { tol?: number; maxIter?: number; }
+export interface PoliticalScience parallelResult { values: number[]; converged: boolean; }
+export function computePoliticalScience parallel(data: number[], opts: PoliticalScience parallelOptions = {}): PoliticalScience parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePoliticalScience parallel };
diff --git a/src/political_science/parties.ts b/src/political_science/parties.ts
new file mode 100644
index 00000000..e53eb45b
--- /dev/null
+++ b/src/political_science/parties.ts
@@ -0,0 +1,22 @@
+/** Parties module — tsb analytics library. */
+
+/** Options for Parties. */
+export interface PartiesOptions { tol?: number; maxIter?: number; }
+
+/** Result from Parties. */
+export interface PartiesResult { values: number[]; converged: boolean; }
+
+/** Compute Parties. */
+export function computeParties(data: number[], opts: PartiesOptions = {}): PartiesResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeParties };
diff --git a/src/political_science/peace.ts b/src/political_science/peace.ts
new file mode 100644
index 00000000..ed170d3d
--- /dev/null
+++ b/src/political_science/peace.ts
@@ -0,0 +1,22 @@
+/** Peace module — tsb analytics library. */
+
+/** Options for Peace. */
+export interface PeaceOptions { tol?: number; maxIter?: number; }
+
+/** Result from Peace. */
+export interface PeaceResult { values: number[]; converged: boolean; }
+
+/** Compute Peace. */
+export function computePeace(data: number[], opts: PeaceOptions = {}): PeaceResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePeace };
diff --git a/src/political_science/plus.ts b/src/political_science/plus.ts
new file mode 100644
index 00000000..507f1c6a
--- /dev/null
+++ b/src/political_science/plus.ts
@@ -0,0 +1,15 @@
+/** Political Science Plus module — tsb analytics library. */
+export interface PoliticalScience plusOptions { tol?: number; maxIter?: number; }
+export interface PoliticalScience plusResult { values: number[]; converged: boolean; }
+export function computePoliticalScience plus(data: number[], opts: PoliticalScience plusOptions = {}): PoliticalScience plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePoliticalScience plus };
diff --git a/src/political_science/political_economy.ts b/src/political_science/political_economy.ts
new file mode 100644
index 00000000..4bd04a4c
--- /dev/null
+++ b/src/political_science/political_economy.ts
@@ -0,0 +1,22 @@
+/** Political Economy module — tsb analytics library. */
+
+/** Options for Political Economy. */
+export interface PoliticalEconomyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Political Economy. */
+export interface PoliticalEconomyResult { values: number[]; converged: boolean; }
+
+/** Compute Political Economy. */
+export function computePoliticalEconomy(data: number[], opts: PoliticalEconomyOptions = {}): PoliticalEconomyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePoliticalEconomy };
diff --git a/src/political_science/pro.ts b/src/political_science/pro.ts
new file mode 100644
index 00000000..e17cc588
--- /dev/null
+++ b/src/political_science/pro.ts
@@ -0,0 +1,15 @@
+/** Political Science Pro module — tsb analytics library. */
+export interface PoliticalScience proOptions { tol?: number; maxIter?: number; }
+export interface PoliticalScience proResult { values: number[]; converged: boolean; }
+export function computePoliticalScience pro(data: number[], opts: PoliticalScience proOptions = {}): PoliticalScience proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePoliticalScience pro };
diff --git a/src/political_science/public_opinion.ts b/src/political_science/public_opinion.ts
new file mode 100644
index 00000000..6927484e
--- /dev/null
+++ b/src/political_science/public_opinion.ts
@@ -0,0 +1,22 @@
+/** Public Opinion module — tsb analytics library. */
+
+/** Options for Public Opinion. */
+export interface PublicOpinionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Public Opinion. */
+export interface PublicOpinionResult { values: number[]; converged: boolean; }
+
+/** Compute Public Opinion. */
+export function computePublicOpinion(data: number[], opts: PublicOpinionOptions = {}): PublicOpinionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePublicOpinion };
diff --git a/src/political_science/redistribution.ts b/src/political_science/redistribution.ts
new file mode 100644
index 00000000..18158075
--- /dev/null
+++ b/src/political_science/redistribution.ts
@@ -0,0 +1,22 @@
+/** Redistribution module — tsb analytics library. */
+
+/** Options for Redistribution. */
+export interface RedistributionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Redistribution. */
+export interface RedistributionResult { values: number[]; converged: boolean; }
+
+/** Compute Redistribution. */
+export function computeRedistribution(data: number[], opts: RedistributionOptions = {}): RedistributionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRedistribution };
diff --git a/src/political_science/robust.ts b/src/political_science/robust.ts
new file mode 100644
index 00000000..34072b23
--- /dev/null
+++ b/src/political_science/robust.ts
@@ -0,0 +1,15 @@
+/** Political Science Robust module — tsb analytics library. */
+export interface PoliticalScience robustOptions { tol?: number; maxIter?: number; }
+export interface PoliticalScience robustResult { values: number[]; converged: boolean; }
+export function computePoliticalScience robust(data: number[], opts: PoliticalScience robustOptions = {}): PoliticalScience robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePoliticalScience robust };
diff --git a/src/political_science/security.ts b/src/political_science/security.ts
new file mode 100644
index 00000000..17ab8512
--- /dev/null
+++ b/src/political_science/security.ts
@@ -0,0 +1,22 @@
+/** Security module — tsb analytics library. */
+
+/** Options for Security. */
+export interface SecurityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Security. */
+export interface SecurityResult { values: number[]; converged: boolean; }
+
+/** Compute Security. */
+export function computeSecurity(data: number[], opts: SecurityOptions = {}): SecurityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSecurity };
diff --git a/src/political_science/small.ts b/src/political_science/small.ts
new file mode 100644
index 00000000..678af518
--- /dev/null
+++ b/src/political_science/small.ts
@@ -0,0 +1,15 @@
+/** Political Science Small module — tsb analytics library. */
+export interface PoliticalScience smallOptions { tol?: number; maxIter?: number; }
+export interface PoliticalScience smallResult { values: number[]; converged: boolean; }
+export function computePoliticalScience small(data: number[], opts: PoliticalScience smallOptions = {}): PoliticalScience smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePoliticalScience small };
diff --git a/src/political_science/sparse.ts b/src/political_science/sparse.ts
new file mode 100644
index 00000000..9183105a
--- /dev/null
+++ b/src/political_science/sparse.ts
@@ -0,0 +1,15 @@
+/** Political Science Sparse module — tsb analytics library. */
+export interface PoliticalScience sparseOptions { tol?: number; maxIter?: number; }
+export interface PoliticalScience sparseResult { values: number[]; converged: boolean; }
+export function computePoliticalScience sparse(data: number[], opts: PoliticalScience sparseOptions = {}): PoliticalScience sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePoliticalScience sparse };
diff --git a/src/political_science/stable.ts b/src/political_science/stable.ts
new file mode 100644
index 00000000..f4ce6c64
--- /dev/null
+++ b/src/political_science/stable.ts
@@ -0,0 +1,15 @@
+/** Political Science Stable module — tsb analytics library. */
+export interface PoliticalScience stableOptions { tol?: number; maxIter?: number; }
+export interface PoliticalScience stableResult { values: number[]; converged: boolean; }
+export function computePoliticalScience stable(data: number[], opts: PoliticalScience stableOptions = {}): PoliticalScience stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePoliticalScience stable };
diff --git a/src/political_science/streaming.ts b/src/political_science/streaming.ts
new file mode 100644
index 00000000..e626d2c2
--- /dev/null
+++ b/src/political_science/streaming.ts
@@ -0,0 +1,15 @@
+/** Political Science Streaming module — tsb analytics library. */
+export interface PoliticalScience streamingOptions { tol?: number; maxIter?: number; }
+export interface PoliticalScience streamingResult { values: number[]; converged: boolean; }
+export function computePoliticalScience streaming(data: number[], opts: PoliticalScience streamingOptions = {}): PoliticalScience streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePoliticalScience streaming };
diff --git a/src/political_science/taxation.ts b/src/political_science/taxation.ts
new file mode 100644
index 00000000..08e8e325
--- /dev/null
+++ b/src/political_science/taxation.ts
@@ -0,0 +1,22 @@
+/** Taxation module — tsb analytics library. */
+
+/** Options for Taxation. */
+export interface TaxationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Taxation. */
+export interface TaxationResult { values: number[]; converged: boolean; }
+
+/** Compute Taxation. */
+export function computeTaxation(data: number[], opts: TaxationOptions = {}): TaxationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTaxation };
diff --git a/src/political_science/v2.ts b/src/political_science/v2.ts
new file mode 100644
index 00000000..2c40e393
--- /dev/null
+++ b/src/political_science/v2.ts
@@ -0,0 +1,15 @@
+/** Political Science V2 module — tsb analytics library. */
+export interface PoliticalScience v2Options { tol?: number; maxIter?: number; }
+export interface PoliticalScience v2Result { values: number[]; converged: boolean; }
+export function computePoliticalScience v2(data: number[], opts: PoliticalScience v2Options = {}): PoliticalScience v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePoliticalScience v2 };
diff --git a/src/political_science/v3.ts b/src/political_science/v3.ts
new file mode 100644
index 00000000..d7c9de4f
--- /dev/null
+++ b/src/political_science/v3.ts
@@ -0,0 +1,15 @@
+/** Political Science V3 module — tsb analytics library. */
+export interface PoliticalScience v3Options { tol?: number; maxIter?: number; }
+export interface PoliticalScience v3Result { values: number[]; converged: boolean; }
+export function computePoliticalScience v3(data: number[], opts: PoliticalScience v3Options = {}): PoliticalScience v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePoliticalScience v3 };
diff --git a/src/political_science/voting.ts b/src/political_science/voting.ts
new file mode 100644
index 00000000..deab1da7
--- /dev/null
+++ b/src/political_science/voting.ts
@@ -0,0 +1,22 @@
+/** Voting module — tsb analytics library. */
+
+/** Options for Voting. */
+export interface VotingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Voting. */
+export interface VotingResult { values: number[]; converged: boolean; }
+
+/** Compute Voting. */
+export function computeVoting(data: number[], opts: VotingOptions = {}): VotingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeVoting };
diff --git a/src/political_science/war.ts b/src/political_science/war.ts
new file mode 100644
index 00000000..f0afd038
--- /dev/null
+++ b/src/political_science/war.ts
@@ -0,0 +1,22 @@
+/** War module — tsb analytics library. */
+
+/** Options for War. */
+export interface WarOptions { tol?: number; maxIter?: number; }
+
+/** Result from War. */
+export interface WarResult { values: number[]; converged: boolean; }
+
+/** Compute War. */
+export function computeWar(data: number[], opts: WarOptions = {}): WarResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeWar };
diff --git a/src/political_science/wasm.ts b/src/political_science/wasm.ts
new file mode 100644
index 00000000..798d9f1b
--- /dev/null
+++ b/src/political_science/wasm.ts
@@ -0,0 +1,15 @@
+/** Political Science Wasm module — tsb analytics library. */
+export interface PoliticalScience wasmOptions { tol?: number; maxIter?: number; }
+export interface PoliticalScience wasmResult { values: number[]; converged: boolean; }
+export function computePoliticalScience wasm(data: number[], opts: PoliticalScience wasmOptions = {}): PoliticalScience wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePoliticalScience wasm };
diff --git a/src/political_science/welfare.ts b/src/political_science/welfare.ts
new file mode 100644
index 00000000..ed9de08a
--- /dev/null
+++ b/src/political_science/welfare.ts
@@ -0,0 +1,22 @@
+/** Welfare module — tsb analytics library. */
+
+/** Options for Welfare. */
+export interface WelfareOptions { tol?: number; maxIter?: number; }
+
+/** Result from Welfare. */
+export interface WelfareResult { values: number[]; converged: boolean; }
+
+/** Compute Welfare. */
+export function computeWelfare(data: number[], opts: WelfareOptions = {}): WelfareResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeWelfare };
diff --git a/src/political_science/xlarge.ts b/src/political_science/xlarge.ts
new file mode 100644
index 00000000..0df3dbcd
--- /dev/null
+++ b/src/political_science/xlarge.ts
@@ -0,0 +1,15 @@
+/** Political Science Xlarge module — tsb analytics library. */
+export interface PoliticalScience xlargeOptions { tol?: number; maxIter?: number; }
+export interface PoliticalScience xlargeResult { values: number[]; converged: boolean; }
+export function computePoliticalScience xlarge(data: number[], opts: PoliticalScience xlargeOptions = {}): PoliticalScience xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePoliticalScience xlarge };
diff --git a/src/psychology/advanced.ts b/src/psychology/advanced.ts
new file mode 100644
index 00000000..88edc94f
--- /dev/null
+++ b/src/psychology/advanced.ts
@@ -0,0 +1,15 @@
+/** Psychology Advanced module — tsb analytics library. */
+export interface Psychology advancedOptions { tol?: number; maxIter?: number; }
+export interface Psychology advancedResult { values: number[]; converged: boolean; }
+export function computePsychology advanced(data: number[], opts: Psychology advancedOptions = {}): Psychology advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePsychology advanced };
diff --git a/src/psychology/attention_psych.ts b/src/psychology/attention_psych.ts
new file mode 100644
index 00000000..e16988b0
--- /dev/null
+++ b/src/psychology/attention_psych.ts
@@ -0,0 +1,22 @@
+/** Attention Psych module — tsb analytics library. */
+
+/** Options for Attention Psych. */
+export interface AttentionPsychOptions { tol?: number; maxIter?: number; }
+
+/** Result from Attention Psych. */
+export interface AttentionPsychResult { values: number[]; converged: boolean; }
+
+/** Compute Attention Psych. */
+export function computeAttentionPsych(data: number[], opts: AttentionPsychOptions = {}): AttentionPsychResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAttentionPsych };
diff --git a/src/psychology/attitude.ts b/src/psychology/attitude.ts
new file mode 100644
index 00000000..c8f80be0
--- /dev/null
+++ b/src/psychology/attitude.ts
@@ -0,0 +1,22 @@
+/** Attitude module — tsb analytics library. */
+
+/** Options for Attitude. */
+export interface AttitudeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Attitude. */
+export interface AttitudeResult { values: number[]; converged: boolean; }
+
+/** Compute Attitude. */
+export function computeAttitude(data: number[], opts: AttitudeOptions = {}): AttitudeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAttitude };
diff --git a/src/psychology/base2.ts b/src/psychology/base2.ts
new file mode 100644
index 00000000..5f233fb3
--- /dev/null
+++ b/src/psychology/base2.ts
@@ -0,0 +1,15 @@
+/** Psychology Base2 module — tsb analytics library. */
+export interface Psychology base2Options { tol?: number; maxIter?: number; }
+export interface Psychology base2Result { values: number[]; converged: boolean; }
+export function computePsychology base2(data: number[], opts: Psychology base2Options = {}): Psychology base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePsychology base2 };
diff --git a/src/psychology/batch.ts b/src/psychology/batch.ts
new file mode 100644
index 00000000..b2e12499
--- /dev/null
+++ b/src/psychology/batch.ts
@@ -0,0 +1,15 @@
+/** Psychology Batch module — tsb analytics library. */
+export interface Psychology batchOptions { tol?: number; maxIter?: number; }
+export interface Psychology batchResult { values: number[]; converged: boolean; }
+export function computePsychology batch(data: number[], opts: Psychology batchOptions = {}): Psychology batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePsychology batch };
diff --git a/src/psychology/behavioral.ts b/src/psychology/behavioral.ts
new file mode 100644
index 00000000..2a3f6e1a
--- /dev/null
+++ b/src/psychology/behavioral.ts
@@ -0,0 +1,22 @@
+/** Behavioral module — tsb analytics library. */
+
+/** Options for Behavioral. */
+export interface BehavioralOptions { tol?: number; maxIter?: number; }
+
+/** Result from Behavioral. */
+export interface BehavioralResult { values: number[]; converged: boolean; }
+
+/** Compute Behavioral. */
+export function computeBehavioral(data: number[], opts: BehavioralOptions = {}): BehavioralResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBehavioral };
diff --git a/src/psychology/beta.ts b/src/psychology/beta.ts
new file mode 100644
index 00000000..57e489ef
--- /dev/null
+++ b/src/psychology/beta.ts
@@ -0,0 +1,15 @@
+/** Psychology Beta module — tsb analytics library. */
+export interface Psychology betaOptions { tol?: number; maxIter?: number; }
+export interface Psychology betaResult { values: number[]; converged: boolean; }
+export function computePsychology beta(data: number[], opts: Psychology betaOptions = {}): Psychology betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePsychology beta };
diff --git a/src/psychology/clinical.ts b/src/psychology/clinical.ts
new file mode 100644
index 00000000..da261672
--- /dev/null
+++ b/src/psychology/clinical.ts
@@ -0,0 +1,22 @@
+/** Clinical module — tsb analytics library. */
+
+/** Options for Clinical. */
+export interface ClinicalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Clinical. */
+export interface ClinicalResult { values: number[]; converged: boolean; }
+
+/** Compute Clinical. */
+export function computeClinical(data: number[], opts: ClinicalOptions = {}): ClinicalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeClinical };
diff --git a/src/psychology/cognitive.ts b/src/psychology/cognitive.ts
new file mode 100644
index 00000000..ab547fa2
--- /dev/null
+++ b/src/psychology/cognitive.ts
@@ -0,0 +1,22 @@
+/** Cognitive module — tsb analytics library. */
+
+/** Options for Cognitive. */
+export interface CognitiveOptions { tol?: number; maxIter?: number; }
+
+/** Result from Cognitive. */
+export interface CognitiveResult { values: number[]; converged: boolean; }
+
+/** Compute Cognitive. */
+export function computeCognitive(data: number[], opts: CognitiveOptions = {}): CognitiveResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCognitive };
diff --git a/src/psychology/conformity.ts b/src/psychology/conformity.ts
new file mode 100644
index 00000000..93de132b
--- /dev/null
+++ b/src/psychology/conformity.ts
@@ -0,0 +1,22 @@
+/** Conformity module — tsb analytics library. */
+
+/** Options for Conformity. */
+export interface ConformityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Conformity. */
+export interface ConformityResult { values: number[]; converged: boolean; }
+
+/** Compute Conformity. */
+export function computeConformity(data: number[], opts: ConformityOptions = {}): ConformityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeConformity };
diff --git a/src/psychology/cpu.ts b/src/psychology/cpu.ts
new file mode 100644
index 00000000..6f99e322
--- /dev/null
+++ b/src/psychology/cpu.ts
@@ -0,0 +1,15 @@
+/** Psychology Cpu module — tsb analytics library. */
+export interface Psychology cpuOptions { tol?: number; maxIter?: number; }
+export interface Psychology cpuResult { values: number[]; converged: boolean; }
+export function computePsychology cpu(data: number[], opts: Psychology cpuOptions = {}): Psychology cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePsychology cpu };
diff --git a/src/psychology/cross_cultural.ts b/src/psychology/cross_cultural.ts
new file mode 100644
index 00000000..680bdd13
--- /dev/null
+++ b/src/psychology/cross_cultural.ts
@@ -0,0 +1,22 @@
+/** Cross Cultural module — tsb analytics library. */
+
+/** Options for Cross Cultural. */
+export interface CrossCulturalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Cross Cultural. */
+export interface CrossCulturalResult { values: number[]; converged: boolean; }
+
+/** Compute Cross Cultural. */
+export function computeCrossCultural(data: number[], opts: CrossCulturalOptions = {}): CrossCulturalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCrossCultural };
diff --git a/src/psychology/decision.ts b/src/psychology/decision.ts
new file mode 100644
index 00000000..d53c8d4b
--- /dev/null
+++ b/src/psychology/decision.ts
@@ -0,0 +1,22 @@
+/** Decision module — tsb analytics library. */
+
+/** Options for Decision. */
+export interface DecisionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Decision. */
+export interface DecisionResult { values: number[]; converged: boolean; }
+
+/** Compute Decision. */
+export function computeDecision(data: number[], opts: DecisionOptions = {}): DecisionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDecision };
diff --git a/src/psychology/dense.ts b/src/psychology/dense.ts
new file mode 100644
index 00000000..f6f03793
--- /dev/null
+++ b/src/psychology/dense.ts
@@ -0,0 +1,15 @@
+/** Psychology Dense module — tsb analytics library. */
+export interface Psychology denseOptions { tol?: number; maxIter?: number; }
+export interface Psychology denseResult { values: number[]; converged: boolean; }
+export function computePsychology dense(data: number[], opts: Psychology denseOptions = {}): Psychology denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePsychology dense };
diff --git a/src/psychology/developmental.ts b/src/psychology/developmental.ts
new file mode 100644
index 00000000..66e3377e
--- /dev/null
+++ b/src/psychology/developmental.ts
@@ -0,0 +1,22 @@
+/** Developmental module — tsb analytics library. */
+
+/** Options for Developmental. */
+export interface DevelopmentalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Developmental. */
+export interface DevelopmentalResult { values: number[]; converged: boolean; }
+
+/** Compute Developmental. */
+export function computeDevelopmental(data: number[], opts: DevelopmentalOptions = {}): DevelopmentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDevelopmental };
diff --git a/src/psychology/distributed.ts b/src/psychology/distributed.ts
new file mode 100644
index 00000000..d552a050
--- /dev/null
+++ b/src/psychology/distributed.ts
@@ -0,0 +1,15 @@
+/** Psychology Distributed module — tsb analytics library. */
+export interface Psychology distributedOptions { tol?: number; maxIter?: number; }
+export interface Psychology distributedResult { values: number[]; converged: boolean; }
+export function computePsychology distributed(data: number[], opts: Psychology distributedOptions = {}): Psychology distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePsychology distributed };
diff --git a/src/psychology/educational_psych.ts b/src/psychology/educational_psych.ts
new file mode 100644
index 00000000..739d410f
--- /dev/null
+++ b/src/psychology/educational_psych.ts
@@ -0,0 +1,22 @@
+/** Educational Psych module — tsb analytics library. */
+
+/** Options for Educational Psych. */
+export interface EducationalPsychOptions { tol?: number; maxIter?: number; }
+
+/** Result from Educational Psych. */
+export interface EducationalPsychResult { values: number[]; converged: boolean; }
+
+/** Compute Educational Psych. */
+export function computeEducationalPsych(data: number[], opts: EducationalPsychOptions = {}): EducationalPsychResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEducationalPsych };
diff --git a/src/psychology/emotion.ts b/src/psychology/emotion.ts
new file mode 100644
index 00000000..922279b9
--- /dev/null
+++ b/src/psychology/emotion.ts
@@ -0,0 +1,22 @@
+/** Emotion module — tsb analytics library. */
+
+/** Options for Emotion. */
+export interface EmotionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Emotion. */
+export interface EmotionResult { values: number[]; converged: boolean; }
+
+/** Compute Emotion. */
+export function computeEmotion(data: number[], opts: EmotionOptions = {}): EmotionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEmotion };
diff --git a/src/psychology/environmental_psych.ts b/src/psychology/environmental_psych.ts
new file mode 100644
index 00000000..d434db45
--- /dev/null
+++ b/src/psychology/environmental_psych.ts
@@ -0,0 +1,22 @@
+/** Environmental Psych module — tsb analytics library. */
+
+/** Options for Environmental Psych. */
+export interface EnvironmentalPsychOptions { tol?: number; maxIter?: number; }
+
+/** Result from Environmental Psych. */
+export interface EnvironmentalPsychResult { values: number[]; converged: boolean; }
+
+/** Compute Environmental Psych. */
+export function computeEnvironmentalPsych(data: number[], opts: EnvironmentalPsychOptions = {}): EnvironmentalPsychResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEnvironmentalPsych };
diff --git a/src/psychology/evolutionary.ts b/src/psychology/evolutionary.ts
new file mode 100644
index 00000000..4fddc3bb
--- /dev/null
+++ b/src/psychology/evolutionary.ts
@@ -0,0 +1,22 @@
+/** Evolutionary module — tsb analytics library. */
+
+/** Options for Evolutionary. */
+export interface EvolutionaryOptions { tol?: number; maxIter?: number; }
+
+/** Result from Evolutionary. */
+export interface EvolutionaryResult { values: number[]; converged: boolean; }
+
+/** Compute Evolutionary. */
+export function computeEvolutionary(data: number[], opts: EvolutionaryOptions = {}): EvolutionaryResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEvolutionary };
diff --git a/src/psychology/experimental.ts b/src/psychology/experimental.ts
new file mode 100644
index 00000000..76d4d64b
--- /dev/null
+++ b/src/psychology/experimental.ts
@@ -0,0 +1,15 @@
+/** Psychology Experimental module — tsb analytics library. */
+export interface Psychology experimentalOptions { tol?: number; maxIter?: number; }
+export interface Psychology experimentalResult { values: number[]; converged: boolean; }
+export function computePsychology experimental(data: number[], opts: Psychology experimentalOptions = {}): Psychology experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePsychology experimental };
diff --git a/src/psychology/fast.ts b/src/psychology/fast.ts
new file mode 100644
index 00000000..ea001f01
--- /dev/null
+++ b/src/psychology/fast.ts
@@ -0,0 +1,15 @@
+/** Psychology Fast module — tsb analytics library. */
+export interface Psychology fastOptions { tol?: number; maxIter?: number; }
+export interface Psychology fastResult { values: number[]; converged: boolean; }
+export function computePsychology fast(data: number[], opts: Psychology fastOptions = {}): Psychology fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePsychology fast };
diff --git a/src/psychology/forensic.ts b/src/psychology/forensic.ts
new file mode 100644
index 00000000..5ce904d8
--- /dev/null
+++ b/src/psychology/forensic.ts
@@ -0,0 +1,22 @@
+/** Forensic module — tsb analytics library. */
+
+/** Options for Forensic. */
+export interface ForensicOptions { tol?: number; maxIter?: number; }
+
+/** Result from Forensic. */
+export interface ForensicResult { values: number[]; converged: boolean; }
+
+/** Compute Forensic. */
+export function computeForensic(data: number[], opts: ForensicOptions = {}): ForensicResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeForensic };
diff --git a/src/psychology/future.ts b/src/psychology/future.ts
new file mode 100644
index 00000000..513c06a2
--- /dev/null
+++ b/src/psychology/future.ts
@@ -0,0 +1,15 @@
+/** Psychology Future module — tsb analytics library. */
+export interface Psychology futureOptions { tol?: number; maxIter?: number; }
+export interface Psychology futureResult { values: number[]; converged: boolean; }
+export function computePsychology future(data: number[], opts: Psychology futureOptions = {}): Psychology futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePsychology future };
diff --git a/src/psychology/gpu.ts b/src/psychology/gpu.ts
new file mode 100644
index 00000000..b04ffc22
--- /dev/null
+++ b/src/psychology/gpu.ts
@@ -0,0 +1,15 @@
+/** Psychology Gpu module — tsb analytics library. */
+export interface Psychology gpuOptions { tol?: number; maxIter?: number; }
+export interface Psychology gpuResult { values: number[]; converged: boolean; }
+export function computePsychology gpu(data: number[], opts: Psychology gpuOptions = {}): Psychology gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePsychology gpu };
diff --git a/src/psychology/health_psych.ts b/src/psychology/health_psych.ts
new file mode 100644
index 00000000..6bcd615e
--- /dev/null
+++ b/src/psychology/health_psych.ts
@@ -0,0 +1,22 @@
+/** Health Psych module — tsb analytics library. */
+
+/** Options for Health Psych. */
+export interface HealthPsychOptions { tol?: number; maxIter?: number; }
+
+/** Result from Health Psych. */
+export interface HealthPsychResult { values: number[]; converged: boolean; }
+
+/** Compute Health Psych. */
+export function computeHealthPsych(data: number[], opts: HealthPsychOptions = {}): HealthPsychResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeHealthPsych };
diff --git a/src/psychology/judgment.ts b/src/psychology/judgment.ts
new file mode 100644
index 00000000..fc649353
--- /dev/null
+++ b/src/psychology/judgment.ts
@@ -0,0 +1,22 @@
+/** Judgment module — tsb analytics library. */
+
+/** Options for Judgment. */
+export interface JudgmentOptions { tol?: number; maxIter?: number; }
+
+/** Result from Judgment. */
+export interface JudgmentResult { values: number[]; converged: boolean; }
+
+/** Compute Judgment. */
+export function computeJudgment(data: number[], opts: JudgmentOptions = {}): JudgmentResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeJudgment };
diff --git a/src/psychology/language_psych.ts b/src/psychology/language_psych.ts
new file mode 100644
index 00000000..1f48be3e
--- /dev/null
+++ b/src/psychology/language_psych.ts
@@ -0,0 +1,22 @@
+/** Language Psych module — tsb analytics library. */
+
+/** Options for Language Psych. */
+export interface LanguagePsychOptions { tol?: number; maxIter?: number; }
+
+/** Result from Language Psych. */
+export interface LanguagePsychResult { values: number[]; converged: boolean; }
+
+/** Compute Language Psych. */
+export function computeLanguagePsych(data: number[], opts: LanguagePsychOptions = {}): LanguagePsychResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLanguagePsych };
diff --git a/src/psychology/large.ts b/src/psychology/large.ts
new file mode 100644
index 00000000..c58e2c9c
--- /dev/null
+++ b/src/psychology/large.ts
@@ -0,0 +1,15 @@
+/** Psychology Large module — tsb analytics library. */
+export interface Psychology largeOptions { tol?: number; maxIter?: number; }
+export interface Psychology largeResult { values: number[]; converged: boolean; }
+export function computePsychology large(data: number[], opts: Psychology largeOptions = {}): Psychology largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePsychology large };
diff --git a/src/psychology/legacy.ts b/src/psychology/legacy.ts
new file mode 100644
index 00000000..fce96b0e
--- /dev/null
+++ b/src/psychology/legacy.ts
@@ -0,0 +1,15 @@
+/** Psychology Legacy module — tsb analytics library. */
+export interface Psychology legacyOptions { tol?: number; maxIter?: number; }
+export interface Psychology legacyResult { values: number[]; converged: boolean; }
+export function computePsychology legacy(data: number[], opts: Psychology legacyOptions = {}): Psychology legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePsychology legacy };
diff --git a/src/psychology/lifespan.ts b/src/psychology/lifespan.ts
new file mode 100644
index 00000000..41939b65
--- /dev/null
+++ b/src/psychology/lifespan.ts
@@ -0,0 +1,22 @@
+/** Lifespan module — tsb analytics library. */
+
+/** Options for Lifespan. */
+export interface LifespanOptions { tol?: number; maxIter?: number; }
+
+/** Result from Lifespan. */
+export interface LifespanResult { values: number[]; converged: boolean; }
+
+/** Compute Lifespan. */
+export function computeLifespan(data: number[], opts: LifespanOptions = {}): LifespanResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLifespan };
diff --git a/src/psychology/lite.ts b/src/psychology/lite.ts
new file mode 100644
index 00000000..4e06688b
--- /dev/null
+++ b/src/psychology/lite.ts
@@ -0,0 +1,15 @@
+/** Psychology Lite module — tsb analytics library. */
+export interface Psychology liteOptions { tol?: number; maxIter?: number; }
+export interface Psychology liteResult { values: number[]; converged: boolean; }
+export function computePsychology lite(data: number[], opts: Psychology liteOptions = {}): Psychology liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePsychology lite };
diff --git a/src/psychology/memory.ts b/src/psychology/memory.ts
new file mode 100644
index 00000000..b8374e4f
--- /dev/null
+++ b/src/psychology/memory.ts
@@ -0,0 +1,22 @@
+/** Memory module — tsb analytics library. */
+
+/** Options for Memory. */
+export interface MemoryOptions { tol?: number; maxIter?: number; }
+
+/** Result from Memory. */
+export interface MemoryResult { values: number[]; converged: boolean; }
+
+/** Compute Memory. */
+export function computeMemory(data: number[], opts: MemoryOptions = {}): MemoryResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMemory };
diff --git a/src/psychology/mini.ts b/src/psychology/mini.ts
new file mode 100644
index 00000000..df6cc44b
--- /dev/null
+++ b/src/psychology/mini.ts
@@ -0,0 +1,15 @@
+/** Psychology Mini module — tsb analytics library. */
+export interface Psychology miniOptions { tol?: number; maxIter?: number; }
+export interface Psychology miniResult { values: number[]; converged: boolean; }
+export function computePsychology mini(data: number[], opts: Psychology miniOptions = {}): Psychology miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePsychology mini };
diff --git a/src/psychology/motivation.ts b/src/psychology/motivation.ts
new file mode 100644
index 00000000..5d716b08
--- /dev/null
+++ b/src/psychology/motivation.ts
@@ -0,0 +1,22 @@
+/** Motivation module — tsb analytics library. */
+
+/** Options for Motivation. */
+export interface MotivationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Motivation. */
+export interface MotivationResult { values: number[]; converged: boolean; }
+
+/** Compute Motivation. */
+export function computeMotivation(data: number[], opts: MotivationOptions = {}): MotivationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMotivation };
diff --git a/src/psychology/neuropsychology.ts b/src/psychology/neuropsychology.ts
new file mode 100644
index 00000000..eb43fabe
--- /dev/null
+++ b/src/psychology/neuropsychology.ts
@@ -0,0 +1,22 @@
+/** Neuropsychology module — tsb analytics library. */
+
+/** Options for Neuropsychology. */
+export interface NeuropsychologyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Neuropsychology. */
+export interface NeuropsychologyResult { values: number[]; converged: boolean; }
+
+/** Compute Neuropsychology. */
+export function computeNeuropsychology(data: number[], opts: NeuropsychologyOptions = {}): NeuropsychologyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNeuropsychology };
diff --git a/src/psychology/next.ts b/src/psychology/next.ts
new file mode 100644
index 00000000..5db0838c
--- /dev/null
+++ b/src/psychology/next.ts
@@ -0,0 +1,15 @@
+/** Psychology Next module — tsb analytics library. */
+export interface Psychology nextOptions { tol?: number; maxIter?: number; }
+export interface Psychology nextResult { values: number[]; converged: boolean; }
+export function computePsychology next(data: number[], opts: Psychology nextOptions = {}): Psychology nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePsychology next };
diff --git a/src/psychology/obedience.ts b/src/psychology/obedience.ts
new file mode 100644
index 00000000..fcec5136
--- /dev/null
+++ b/src/psychology/obedience.ts
@@ -0,0 +1,22 @@
+/** Obedience module — tsb analytics library. */
+
+/** Options for Obedience. */
+export interface ObedienceOptions { tol?: number; maxIter?: number; }
+
+/** Result from Obedience. */
+export interface ObedienceResult { values: number[]; converged: boolean; }
+
+/** Compute Obedience. */
+export function computeObedience(data: number[], opts: ObedienceOptions = {}): ObedienceResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeObedience };
diff --git a/src/psychology/online.ts b/src/psychology/online.ts
new file mode 100644
index 00000000..716b5953
--- /dev/null
+++ b/src/psychology/online.ts
@@ -0,0 +1,15 @@
+/** Psychology Online module — tsb analytics library. */
+export interface Psychology onlineOptions { tol?: number; maxIter?: number; }
+export interface Psychology onlineResult { values: number[]; converged: boolean; }
+export function computePsychology online(data: number[], opts: Psychology onlineOptions = {}): Psychology onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePsychology online };
diff --git a/src/psychology/organizational.ts b/src/psychology/organizational.ts
new file mode 100644
index 00000000..daf551f5
--- /dev/null
+++ b/src/psychology/organizational.ts
@@ -0,0 +1,22 @@
+/** Organizational module — tsb analytics library. */
+
+/** Options for Organizational. */
+export interface OrganizationalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Organizational. */
+export interface OrganizationalResult { values: number[]; converged: boolean; }
+
+/** Compute Organizational. */
+export function computeOrganizational(data: number[], opts: OrganizationalOptions = {}): OrganizationalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeOrganizational };
diff --git a/src/psychology/parallel.ts b/src/psychology/parallel.ts
new file mode 100644
index 00000000..7cc2fade
--- /dev/null
+++ b/src/psychology/parallel.ts
@@ -0,0 +1,15 @@
+/** Psychology Parallel module — tsb analytics library. */
+export interface Psychology parallelOptions { tol?: number; maxIter?: number; }
+export interface Psychology parallelResult { values: number[]; converged: boolean; }
+export function computePsychology parallel(data: number[], opts: Psychology parallelOptions = {}): Psychology parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePsychology parallel };
diff --git a/src/psychology/perception.ts b/src/psychology/perception.ts
new file mode 100644
index 00000000..e2e04ea8
--- /dev/null
+++ b/src/psychology/perception.ts
@@ -0,0 +1,22 @@
+/** Perception module — tsb analytics library. */
+
+/** Options for Perception. */
+export interface PerceptionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Perception. */
+export interface PerceptionResult { values: number[]; converged: boolean; }
+
+/** Compute Perception. */
+export function computePerception(data: number[], opts: PerceptionOptions = {}): PerceptionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePerception };
diff --git a/src/psychology/personality.ts b/src/psychology/personality.ts
new file mode 100644
index 00000000..4018756b
--- /dev/null
+++ b/src/psychology/personality.ts
@@ -0,0 +1,22 @@
+/** Personality module — tsb analytics library. */
+
+/** Options for Personality. */
+export interface PersonalityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Personality. */
+export interface PersonalityResult { values: number[]; converged: boolean; }
+
+/** Compute Personality. */
+export function computePersonality(data: number[], opts: PersonalityOptions = {}): PersonalityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePersonality };
diff --git a/src/psychology/persuasion.ts b/src/psychology/persuasion.ts
new file mode 100644
index 00000000..e446ae41
--- /dev/null
+++ b/src/psychology/persuasion.ts
@@ -0,0 +1,22 @@
+/** Persuasion module — tsb analytics library. */
+
+/** Options for Persuasion. */
+export interface PersuasionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Persuasion. */
+export interface PersuasionResult { values: number[]; converged: boolean; }
+
+/** Compute Persuasion. */
+export function computePersuasion(data: number[], opts: PersuasionOptions = {}): PersuasionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePersuasion };
diff --git a/src/psychology/plus.ts b/src/psychology/plus.ts
new file mode 100644
index 00000000..79745d16
--- /dev/null
+++ b/src/psychology/plus.ts
@@ -0,0 +1,15 @@
+/** Psychology Plus module — tsb analytics library. */
+export interface Psychology plusOptions { tol?: number; maxIter?: number; }
+export interface Psychology plusResult { values: number[]; converged: boolean; }
+export function computePsychology plus(data: number[], opts: Psychology plusOptions = {}): Psychology plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePsychology plus };
diff --git a/src/psychology/positive.ts b/src/psychology/positive.ts
new file mode 100644
index 00000000..c7c1d76a
--- /dev/null
+++ b/src/psychology/positive.ts
@@ -0,0 +1,22 @@
+/** Positive module — tsb analytics library. */
+
+/** Options for Positive. */
+export interface PositiveOptions { tol?: number; maxIter?: number; }
+
+/** Result from Positive. */
+export interface PositiveResult { values: number[]; converged: boolean; }
+
+/** Compute Positive. */
+export function computePositive(data: number[], opts: PositiveOptions = {}): PositiveResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePositive };
diff --git a/src/psychology/pro.ts b/src/psychology/pro.ts
new file mode 100644
index 00000000..c076cfd2
--- /dev/null
+++ b/src/psychology/pro.ts
@@ -0,0 +1,15 @@
+/** Psychology Pro module — tsb analytics library. */
+export interface Psychology proOptions { tol?: number; maxIter?: number; }
+export interface Psychology proResult { values: number[]; converged: boolean; }
+export function computePsychology pro(data: number[], opts: Psychology proOptions = {}): Psychology proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePsychology pro };
diff --git a/src/psychology/psychophysiology.ts b/src/psychology/psychophysiology.ts
new file mode 100644
index 00000000..c6544425
--- /dev/null
+++ b/src/psychology/psychophysiology.ts
@@ -0,0 +1,22 @@
+/** Psychophysiology module — tsb analytics library. */
+
+/** Options for Psychophysiology. */
+export interface PsychophysiologyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Psychophysiology. */
+export interface PsychophysiologyResult { values: number[]; converged: boolean; }
+
+/** Compute Psychophysiology. */
+export function computePsychophysiology(data: number[], opts: PsychophysiologyOptions = {}): PsychophysiologyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePsychophysiology };
diff --git a/src/psychology/robust.ts b/src/psychology/robust.ts
new file mode 100644
index 00000000..0e66402b
--- /dev/null
+++ b/src/psychology/robust.ts
@@ -0,0 +1,15 @@
+/** Psychology Robust module — tsb analytics library. */
+export interface Psychology robustOptions { tol?: number; maxIter?: number; }
+export interface Psychology robustResult { values: number[]; converged: boolean; }
+export function computePsychology robust(data: number[], opts: Psychology robustOptions = {}): Psychology robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePsychology robust };
diff --git a/src/psychology/small.ts b/src/psychology/small.ts
new file mode 100644
index 00000000..c3e31a1e
--- /dev/null
+++ b/src/psychology/small.ts
@@ -0,0 +1,15 @@
+/** Psychology Small module — tsb analytics library. */
+export interface Psychology smallOptions { tol?: number; maxIter?: number; }
+export interface Psychology smallResult { values: number[]; converged: boolean; }
+export function computePsychology small(data: number[], opts: Psychology smallOptions = {}): Psychology smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePsychology small };
diff --git a/src/psychology/social.ts b/src/psychology/social.ts
new file mode 100644
index 00000000..6804cb4a
--- /dev/null
+++ b/src/psychology/social.ts
@@ -0,0 +1,22 @@
+/** Social module — tsb analytics library. */
+
+/** Options for Social. */
+export interface SocialOptions { tol?: number; maxIter?: number; }
+
+/** Result from Social. */
+export interface SocialResult { values: number[]; converged: boolean; }
+
+/** Compute Social. */
+export function computeSocial(data: number[], opts: SocialOptions = {}): SocialResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSocial };
diff --git a/src/psychology/sparse.ts b/src/psychology/sparse.ts
new file mode 100644
index 00000000..a85f87f0
--- /dev/null
+++ b/src/psychology/sparse.ts
@@ -0,0 +1,15 @@
+/** Psychology Sparse module — tsb analytics library. */
+export interface Psychology sparseOptions { tol?: number; maxIter?: number; }
+export interface Psychology sparseResult { values: number[]; converged: boolean; }
+export function computePsychology sparse(data: number[], opts: Psychology sparseOptions = {}): Psychology sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePsychology sparse };
diff --git a/src/psychology/sport.ts b/src/psychology/sport.ts
new file mode 100644
index 00000000..d354760c
--- /dev/null
+++ b/src/psychology/sport.ts
@@ -0,0 +1,22 @@
+/** Sport module — tsb analytics library. */
+
+/** Options for Sport. */
+export interface SportOptions { tol?: number; maxIter?: number; }
+
+/** Result from Sport. */
+export interface SportResult { values: number[]; converged: boolean; }
+
+/** Compute Sport. */
+export function computeSport(data: number[], opts: SportOptions = {}): SportResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSport };
diff --git a/src/psychology/stable.ts b/src/psychology/stable.ts
new file mode 100644
index 00000000..f94f5906
--- /dev/null
+++ b/src/psychology/stable.ts
@@ -0,0 +1,15 @@
+/** Psychology Stable module — tsb analytics library. */
+export interface Psychology stableOptions { tol?: number; maxIter?: number; }
+export interface Psychology stableResult { values: number[]; converged: boolean; }
+export function computePsychology stable(data: number[], opts: Psychology stableOptions = {}): Psychology stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePsychology stable };
diff --git a/src/psychology/streaming.ts b/src/psychology/streaming.ts
new file mode 100644
index 00000000..963a813c
--- /dev/null
+++ b/src/psychology/streaming.ts
@@ -0,0 +1,15 @@
+/** Psychology Streaming module — tsb analytics library. */
+export interface Psychology streamingOptions { tol?: number; maxIter?: number; }
+export interface Psychology streamingResult { values: number[]; converged: boolean; }
+export function computePsychology streaming(data: number[], opts: Psychology streamingOptions = {}): Psychology streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePsychology streaming };
diff --git a/src/psychology/v2.ts b/src/psychology/v2.ts
new file mode 100644
index 00000000..6263f6ab
--- /dev/null
+++ b/src/psychology/v2.ts
@@ -0,0 +1,15 @@
+/** Psychology V2 module — tsb analytics library. */
+export interface Psychology v2Options { tol?: number; maxIter?: number; }
+export interface Psychology v2Result { values: number[]; converged: boolean; }
+export function computePsychology v2(data: number[], opts: Psychology v2Options = {}): Psychology v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePsychology v2 };
diff --git a/src/psychology/v3.ts b/src/psychology/v3.ts
new file mode 100644
index 00000000..21bb6033
--- /dev/null
+++ b/src/psychology/v3.ts
@@ -0,0 +1,15 @@
+/** Psychology V3 module — tsb analytics library. */
+export interface Psychology v3Options { tol?: number; maxIter?: number; }
+export interface Psychology v3Result { values: number[]; converged: boolean; }
+export function computePsychology v3(data: number[], opts: Psychology v3Options = {}): Psychology v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePsychology v3 };
diff --git a/src/psychology/wasm.ts b/src/psychology/wasm.ts
new file mode 100644
index 00000000..d72f34e1
--- /dev/null
+++ b/src/psychology/wasm.ts
@@ -0,0 +1,15 @@
+/** Psychology Wasm module — tsb analytics library. */
+export interface Psychology wasmOptions { tol?: number; maxIter?: number; }
+export interface Psychology wasmResult { values: number[]; converged: boolean; }
+export function computePsychology wasm(data: number[], opts: Psychology wasmOptions = {}): Psychology wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePsychology wasm };
diff --git a/src/psychology/xlarge.ts b/src/psychology/xlarge.ts
new file mode 100644
index 00000000..2f8fb896
--- /dev/null
+++ b/src/psychology/xlarge.ts
@@ -0,0 +1,15 @@
+/** Psychology Xlarge module — tsb analytics library. */
+export interface Psychology xlargeOptions { tol?: number; maxIter?: number; }
+export interface Psychology xlargeResult { values: number[]; converged: boolean; }
+export function computePsychology xlarge(data: number[], opts: Psychology xlargeOptions = {}): Psychology xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computePsychology xlarge };
diff --git a/src/quant/accruals.ts b/src/quant/accruals.ts
new file mode 100644
index 00000000..6e24ed02
--- /dev/null
+++ b/src/quant/accruals.ts
@@ -0,0 +1,22 @@
+/** Accruals module — tsb analytics library. */
+
+/** Options for Accruals. */
+export interface AccrualsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Accruals. */
+export interface AccrualsResult { values: number[]; converged: boolean; }
+
+/** Compute Accruals. */
+export function computeAccruals(data: number[], opts: AccrualsOptions = {}): AccrualsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAccruals };
diff --git a/src/quant/advanced.ts b/src/quant/advanced.ts
new file mode 100644
index 00000000..b79f2fc5
--- /dev/null
+++ b/src/quant/advanced.ts
@@ -0,0 +1,15 @@
+/** Quant Advanced module — tsb analytics library. */
+export interface Quant advancedOptions { tol?: number; maxIter?: number; }
+export interface Quant advancedResult { values: number[]; converged: boolean; }
+export function computeQuant advanced(data: number[], opts: Quant advancedOptions = {}): Quant advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeQuant advanced };
diff --git a/src/quant/alpha_model.ts b/src/quant/alpha_model.ts
new file mode 100644
index 00000000..0c88297f
--- /dev/null
+++ b/src/quant/alpha_model.ts
@@ -0,0 +1,22 @@
+/** Alpha Model module — tsb analytics library. */
+
+/** Options for Alpha Model. */
+export interface AlphaModelOptions { tol?: number; maxIter?: number; }
+
+/** Result from Alpha Model. */
+export interface AlphaModelResult { values: number[]; converged: boolean; }
+
+/** Compute Alpha Model. */
+export function computeAlphaModel(data: number[], opts: AlphaModelOptions = {}): AlphaModelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAlphaModel };
diff --git a/src/quant/analyst.ts b/src/quant/analyst.ts
new file mode 100644
index 00000000..7d4b0dd8
--- /dev/null
+++ b/src/quant/analyst.ts
@@ -0,0 +1,22 @@
+/** Analyst module — tsb analytics library. */
+
+/** Options for Analyst. */
+export interface AnalystOptions { tol?: number; maxIter?: number; }
+
+/** Result from Analyst. */
+export interface AnalystResult { values: number[]; converged: boolean; }
+
+/** Compute Analyst. */
+export function computeAnalyst(data: number[], opts: AnalystOptions = {}): AnalystResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAnalyst };
diff --git a/src/quant/base2.ts b/src/quant/base2.ts
new file mode 100644
index 00000000..2fddcd9b
--- /dev/null
+++ b/src/quant/base2.ts
@@ -0,0 +1,15 @@
+/** Quant Base2 module — tsb analytics library. */
+export interface Quant base2Options { tol?: number; maxIter?: number; }
+export interface Quant base2Result { values: number[]; converged: boolean; }
+export function computeQuant base2(data: number[], opts: Quant base2Options = {}): Quant base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeQuant base2 };
diff --git a/src/quant/batch.ts b/src/quant/batch.ts
new file mode 100644
index 00000000..a73ce5c2
--- /dev/null
+++ b/src/quant/batch.ts
@@ -0,0 +1,15 @@
+/** Quant Batch module — tsb analytics library. */
+export interface Quant batchOptions { tol?: number; maxIter?: number; }
+export interface Quant batchResult { values: number[]; converged: boolean; }
+export function computeQuant batch(data: number[], opts: Quant batchOptions = {}): Quant batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeQuant batch };
diff --git a/src/quant/beta.ts b/src/quant/beta.ts
new file mode 100644
index 00000000..b7378f56
--- /dev/null
+++ b/src/quant/beta.ts
@@ -0,0 +1,15 @@
+/** Quant Beta module — tsb analytics library. */
+export interface Quant betaOptions { tol?: number; maxIter?: number; }
+export interface Quant betaResult { values: number[]; converged: boolean; }
+export function computeQuant beta(data: number[], opts: Quant betaOptions = {}): Quant betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeQuant beta };
diff --git a/src/quant/book_to_market.ts b/src/quant/book_to_market.ts
new file mode 100644
index 00000000..2ce2cbd5
--- /dev/null
+++ b/src/quant/book_to_market.ts
@@ -0,0 +1,22 @@
+/** Book To Market module — tsb analytics library. */
+
+/** Options for Book To Market. */
+export interface BookToMarketOptions { tol?: number; maxIter?: number; }
+
+/** Result from Book To Market. */
+export interface BookToMarketResult { values: number[]; converged: boolean; }
+
+/** Compute Book To Market. */
+export function computeBookToMarket(data: number[], opts: BookToMarketOptions = {}): BookToMarketResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBookToMarket };
diff --git a/src/quant/carry.ts b/src/quant/carry.ts
new file mode 100644
index 00000000..2722a38e
--- /dev/null
+++ b/src/quant/carry.ts
@@ -0,0 +1,22 @@
+/** Carry module — tsb analytics library. */
+
+/** Options for Carry. */
+export interface CarryOptions { tol?: number; maxIter?: number; }
+
+/** Result from Carry. */
+export interface CarryResult { values: number[]; converged: boolean; }
+
+/** Compute Carry. */
+export function computeCarry(data: number[], opts: CarryOptions = {}): CarryResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCarry };
diff --git a/src/quant/cpu.ts b/src/quant/cpu.ts
new file mode 100644
index 00000000..dbc445b5
--- /dev/null
+++ b/src/quant/cpu.ts
@@ -0,0 +1,15 @@
+/** Quant Cpu module — tsb analytics library. */
+export interface Quant cpuOptions { tol?: number; maxIter?: number; }
+export interface Quant cpuResult { values: number[]; converged: boolean; }
+export function computeQuant cpu(data: number[], opts: Quant cpuOptions = {}): Quant cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeQuant cpu };
diff --git a/src/quant/dense.ts b/src/quant/dense.ts
new file mode 100644
index 00000000..d37d8c48
--- /dev/null
+++ b/src/quant/dense.ts
@@ -0,0 +1,15 @@
+/** Quant Dense module — tsb analytics library. */
+export interface Quant denseOptions { tol?: number; maxIter?: number; }
+export interface Quant denseResult { values: number[]; converged: boolean; }
+export function computeQuant dense(data: number[], opts: Quant denseOptions = {}): Quant denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeQuant dense };
diff --git a/src/quant/distributed.ts b/src/quant/distributed.ts
new file mode 100644
index 00000000..380bee6d
--- /dev/null
+++ b/src/quant/distributed.ts
@@ -0,0 +1,15 @@
+/** Quant Distributed module — tsb analytics library. */
+export interface Quant distributedOptions { tol?: number; maxIter?: number; }
+export interface Quant distributedResult { values: number[]; converged: boolean; }
+export function computeQuant distributed(data: number[], opts: Quant distributedOptions = {}): Quant distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeQuant distributed };
diff --git a/src/quant/earnings.ts b/src/quant/earnings.ts
new file mode 100644
index 00000000..f3716482
--- /dev/null
+++ b/src/quant/earnings.ts
@@ -0,0 +1,22 @@
+/** Earnings module — tsb analytics library. */
+
+/** Options for Earnings. */
+export interface EarningsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Earnings. */
+export interface EarningsResult { values: number[]; converged: boolean; }
+
+/** Compute Earnings. */
+export function computeEarnings(data: number[], opts: EarningsOptions = {}): EarningsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEarnings };
diff --git a/src/quant/execution_model.ts b/src/quant/execution_model.ts
new file mode 100644
index 00000000..3b3e8fde
--- /dev/null
+++ b/src/quant/execution_model.ts
@@ -0,0 +1,22 @@
+/** Execution Model module — tsb analytics library. */
+
+/** Options for Execution Model. */
+export interface ExecutionModelOptions { tol?: number; maxIter?: number; }
+
+/** Result from Execution Model. */
+export interface ExecutionModelResult { values: number[]; converged: boolean; }
+
+/** Compute Execution Model. */
+export function computeExecutionModel(data: number[], opts: ExecutionModelOptions = {}): ExecutionModelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeExecutionModel };
diff --git a/src/quant/experimental.ts b/src/quant/experimental.ts
new file mode 100644
index 00000000..ccd574b7
--- /dev/null
+++ b/src/quant/experimental.ts
@@ -0,0 +1,15 @@
+/** Quant Experimental module — tsb analytics library. */
+export interface Quant experimentalOptions { tol?: number; maxIter?: number; }
+export interface Quant experimentalResult { values: number[]; converged: boolean; }
+export function computeQuant experimental(data: number[], opts: Quant experimentalOptions = {}): Quant experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeQuant experimental };
diff --git a/src/quant/factor_model.ts b/src/quant/factor_model.ts
new file mode 100644
index 00000000..b82fad82
--- /dev/null
+++ b/src/quant/factor_model.ts
@@ -0,0 +1,22 @@
+/** Factor Model module — tsb analytics library. */
+
+/** Options for Factor Model. */
+export interface FactorModelOptions { tol?: number; maxIter?: number; }
+
+/** Result from Factor Model. */
+export interface FactorModelResult { values: number[]; converged: boolean; }
+
+/** Compute Factor Model. */
+export function computeFactorModel(data: number[], opts: FactorModelOptions = {}): FactorModelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFactorModel };
diff --git a/src/quant/fast.ts b/src/quant/fast.ts
new file mode 100644
index 00000000..f63c0c85
--- /dev/null
+++ b/src/quant/fast.ts
@@ -0,0 +1,15 @@
+/** Quant Fast module — tsb analytics library. */
+export interface Quant fastOptions { tol?: number; maxIter?: number; }
+export interface Quant fastResult { values: number[]; converged: boolean; }
+export function computeQuant fast(data: number[], opts: Quant fastOptions = {}): Quant fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeQuant fast };
diff --git a/src/quant/fill_rate.ts b/src/quant/fill_rate.ts
new file mode 100644
index 00000000..699b0f60
--- /dev/null
+++ b/src/quant/fill_rate.ts
@@ -0,0 +1,22 @@
+/** Fill Rate module — tsb analytics library. */
+
+/** Options for Fill Rate. */
+export interface FillRateOptions { tol?: number; maxIter?: number; }
+
+/** Result from Fill Rate. */
+export interface FillRateResult { values: number[]; converged: boolean; }
+
+/** Compute Fill Rate. */
+export function computeFillRate(data: number[], opts: FillRateOptions = {}): FillRateResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFillRate };
diff --git a/src/quant/future.ts b/src/quant/future.ts
new file mode 100644
index 00000000..380af0d2
--- /dev/null
+++ b/src/quant/future.ts
@@ -0,0 +1,15 @@
+/** Quant Future module — tsb analytics library. */
+export interface Quant futureOptions { tol?: number; maxIter?: number; }
+export interface Quant futureResult { values: number[]; converged: boolean; }
+export function computeQuant future(data: number[], opts: Quant futureOptions = {}): Quant futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeQuant future };
diff --git a/src/quant/gpu.ts b/src/quant/gpu.ts
new file mode 100644
index 00000000..5d3b6951
--- /dev/null
+++ b/src/quant/gpu.ts
@@ -0,0 +1,15 @@
+/** Quant Gpu module — tsb analytics library. */
+export interface Quant gpuOptions { tol?: number; maxIter?: number; }
+export interface Quant gpuResult { values: number[]; converged: boolean; }
+export function computeQuant gpu(data: number[], opts: Quant gpuOptions = {}): Quant gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeQuant gpu };
diff --git a/src/quant/intraday.ts b/src/quant/intraday.ts
new file mode 100644
index 00000000..69e10f85
--- /dev/null
+++ b/src/quant/intraday.ts
@@ -0,0 +1,22 @@
+/** Intraday module — tsb analytics library. */
+
+/** Options for Intraday. */
+export interface IntradayOptions { tol?: number; maxIter?: number; }
+
+/** Result from Intraday. */
+export interface IntradayResult { values: number[]; converged: boolean; }
+
+/** Compute Intraday. */
+export function computeIntraday(data: number[], opts: IntradayOptions = {}): IntradayResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeIntraday };
diff --git a/src/quant/investment.ts b/src/quant/investment.ts
new file mode 100644
index 00000000..bc0d0cbd
--- /dev/null
+++ b/src/quant/investment.ts
@@ -0,0 +1,22 @@
+/** Investment module — tsb analytics library. */
+
+/** Options for Investment. */
+export interface InvestmentOptions { tol?: number; maxIter?: number; }
+
+/** Result from Investment. */
+export interface InvestmentResult { values: number[]; converged: boolean; }
+
+/** Compute Investment. */
+export function computeInvestment(data: number[], opts: InvestmentOptions = {}): InvestmentResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeInvestment };
diff --git a/src/quant/large.ts b/src/quant/large.ts
new file mode 100644
index 00000000..8fd46502
--- /dev/null
+++ b/src/quant/large.ts
@@ -0,0 +1,15 @@
+/** Quant Large module — tsb analytics library. */
+export interface Quant largeOptions { tol?: number; maxIter?: number; }
+export interface Quant largeResult { values: number[]; converged: boolean; }
+export function computeQuant large(data: number[], opts: Quant largeOptions = {}): Quant largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeQuant large };
diff --git a/src/quant/legacy.ts b/src/quant/legacy.ts
new file mode 100644
index 00000000..30feae82
--- /dev/null
+++ b/src/quant/legacy.ts
@@ -0,0 +1,15 @@
+/** Quant Legacy module — tsb analytics library. */
+export interface Quant legacyOptions { tol?: number; maxIter?: number; }
+export interface Quant legacyResult { values: number[]; converged: boolean; }
+export function computeQuant legacy(data: number[], opts: Quant legacyOptions = {}): Quant legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeQuant legacy };
diff --git a/src/quant/lite.ts b/src/quant/lite.ts
new file mode 100644
index 00000000..851dd243
--- /dev/null
+++ b/src/quant/lite.ts
@@ -0,0 +1,15 @@
+/** Quant Lite module — tsb analytics library. */
+export interface Quant liteOptions { tol?: number; maxIter?: number; }
+export interface Quant liteResult { values: number[]; converged: boolean; }
+export function computeQuant lite(data: number[], opts: Quant liteOptions = {}): Quant liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeQuant lite };
diff --git a/src/quant/low_vol.ts b/src/quant/low_vol.ts
new file mode 100644
index 00000000..6ccabe92
--- /dev/null
+++ b/src/quant/low_vol.ts
@@ -0,0 +1,22 @@
+/** Low Vol module — tsb analytics library. */
+
+/** Options for Low Vol. */
+export interface LowVolOptions { tol?: number; maxIter?: number; }
+
+/** Result from Low Vol. */
+export interface LowVolResult { values: number[]; converged: boolean; }
+
+/** Compute Low Vol. */
+export function computeLowVol(data: number[], opts: LowVolOptions = {}): LowVolResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLowVol };
diff --git a/src/quant/market_impact.ts b/src/quant/market_impact.ts
new file mode 100644
index 00000000..dee0eb91
--- /dev/null
+++ b/src/quant/market_impact.ts
@@ -0,0 +1,22 @@
+/** Market Impact module — tsb analytics library. */
+
+/** Options for Market Impact. */
+export interface MarketImpactOptions { tol?: number; maxIter?: number; }
+
+/** Result from Market Impact. */
+export interface MarketImpactResult { values: number[]; converged: boolean; }
+
+/** Compute Market Impact. */
+export function computeMarketImpact(data: number[], opts: MarketImpactOptions = {}): MarketImpactResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMarketImpact };
diff --git a/src/quant/mini.ts b/src/quant/mini.ts
new file mode 100644
index 00000000..cb396f59
--- /dev/null
+++ b/src/quant/mini.ts
@@ -0,0 +1,15 @@
+/** Quant Mini module — tsb analytics library. */
+export interface Quant miniOptions { tol?: number; maxIter?: number; }
+export interface Quant miniResult { values: number[]; converged: boolean; }
+export function computeQuant mini(data: number[], opts: Quant miniOptions = {}): Quant miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeQuant mini };
diff --git a/src/quant/momentum_quant.ts b/src/quant/momentum_quant.ts
new file mode 100644
index 00000000..52880b35
--- /dev/null
+++ b/src/quant/momentum_quant.ts
@@ -0,0 +1,22 @@
+/** Momentum Quant module — tsb analytics library. */
+
+/** Options for Momentum Quant. */
+export interface MomentumQuantOptions { tol?: number; maxIter?: number; }
+
+/** Result from Momentum Quant. */
+export interface MomentumQuantResult { values: number[]; converged: boolean; }
+
+/** Compute Momentum Quant. */
+export function computeMomentumQuant(data: number[], opts: MomentumQuantOptions = {}): MomentumQuantResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMomentumQuant };
diff --git a/src/quant/next.ts b/src/quant/next.ts
new file mode 100644
index 00000000..17497430
--- /dev/null
+++ b/src/quant/next.ts
@@ -0,0 +1,15 @@
+/** Quant Next module — tsb analytics library. */
+export interface Quant nextOptions { tol?: number; maxIter?: number; }
+export interface Quant nextResult { values: number[]; converged: boolean; }
+export function computeQuant next(data: number[], opts: Quant nextOptions = {}): Quant nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeQuant next };
diff --git a/src/quant/online.ts b/src/quant/online.ts
new file mode 100644
index 00000000..df037753
--- /dev/null
+++ b/src/quant/online.ts
@@ -0,0 +1,15 @@
+/** Quant Online module — tsb analytics library. */
+export interface Quant onlineOptions { tol?: number; maxIter?: number; }
+export interface Quant onlineResult { values: number[]; converged: boolean; }
+export function computeQuant online(data: number[], opts: Quant onlineOptions = {}): Quant onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeQuant online };
diff --git a/src/quant/overnight.ts b/src/quant/overnight.ts
new file mode 100644
index 00000000..2098594a
--- /dev/null
+++ b/src/quant/overnight.ts
@@ -0,0 +1,22 @@
+/** Overnight module — tsb analytics library. */
+
+/** Options for Overnight. */
+export interface OvernightOptions { tol?: number; maxIter?: number; }
+
+/** Result from Overnight. */
+export interface OvernightResult { values: number[]; converged: boolean; }
+
+/** Compute Overnight. */
+export function computeOvernight(data: number[], opts: OvernightOptions = {}): OvernightResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeOvernight };
diff --git a/src/quant/parallel.ts b/src/quant/parallel.ts
new file mode 100644
index 00000000..98d2eaac
--- /dev/null
+++ b/src/quant/parallel.ts
@@ -0,0 +1,15 @@
+/** Quant Parallel module — tsb analytics library. */
+export interface Quant parallelOptions { tol?: number; maxIter?: number; }
+export interface Quant parallelResult { values: number[]; converged: boolean; }
+export function computeQuant parallel(data: number[], opts: Quant parallelOptions = {}): Quant parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeQuant parallel };
diff --git a/src/quant/plus.ts b/src/quant/plus.ts
new file mode 100644
index 00000000..f74ecc33
--- /dev/null
+++ b/src/quant/plus.ts
@@ -0,0 +1,15 @@
+/** Quant Plus module — tsb analytics library. */
+export interface Quant plusOptions { tol?: number; maxIter?: number; }
+export interface Quant plusResult { values: number[]; converged: boolean; }
+export function computeQuant plus(data: number[], opts: Quant plusOptions = {}): Quant plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeQuant plus };
diff --git a/src/quant/portfolio_opt.ts b/src/quant/portfolio_opt.ts
new file mode 100644
index 00000000..dc7e7ce1
--- /dev/null
+++ b/src/quant/portfolio_opt.ts
@@ -0,0 +1,22 @@
+/** Portfolio Opt module — tsb analytics library. */
+
+/** Options for Portfolio Opt. */
+export interface PortfolioOptOptions { tol?: number; maxIter?: number; }
+
+/** Result from Portfolio Opt. */
+export interface PortfolioOptResult { values: number[]; converged: boolean; }
+
+/** Compute Portfolio Opt. */
+export function computePortfolioOpt(data: number[], opts: PortfolioOptOptions = {}): PortfolioOptResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePortfolioOpt };
diff --git a/src/quant/position_sizing.ts b/src/quant/position_sizing.ts
new file mode 100644
index 00000000..219af2a2
--- /dev/null
+++ b/src/quant/position_sizing.ts
@@ -0,0 +1,22 @@
+/** Position Sizing module — tsb analytics library. */
+
+/** Options for Position Sizing. */
+export interface PositionSizingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Position Sizing. */
+export interface PositionSizingResult { values: number[]; converged: boolean; }
+
+/** Compute Position Sizing. */
+export function computePositionSizing(data: number[], opts: PositionSizingOptions = {}): PositionSizingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePositionSizing };
diff --git a/src/quant/pro.ts b/src/quant/pro.ts
new file mode 100644
index 00000000..8874b87b
--- /dev/null
+++ b/src/quant/pro.ts
@@ -0,0 +1,15 @@
+/** Quant Pro module — tsb analytics library. */
+export interface Quant proOptions { tol?: number; maxIter?: number; }
+export interface Quant proResult { values: number[]; converged: boolean; }
+export function computeQuant pro(data: number[], opts: Quant proOptions = {}): Quant proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeQuant pro };
diff --git a/src/quant/profitability.ts b/src/quant/profitability.ts
new file mode 100644
index 00000000..982df22a
--- /dev/null
+++ b/src/quant/profitability.ts
@@ -0,0 +1,22 @@
+/** Profitability module — tsb analytics library. */
+
+/** Options for Profitability. */
+export interface ProfitabilityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Profitability. */
+export interface ProfitabilityResult { values: number[]; converged: boolean; }
+
+/** Compute Profitability. */
+export function computeProfitability(data: number[], opts: ProfitabilityOptions = {}): ProfitabilityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeProfitability };
diff --git a/src/quant/quality.ts b/src/quant/quality.ts
new file mode 100644
index 00000000..bf7f8c23
--- /dev/null
+++ b/src/quant/quality.ts
@@ -0,0 +1,22 @@
+/** Quality module — tsb analytics library. */
+
+/** Options for Quality. */
+export interface QualityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Quality. */
+export interface QualityResult { values: number[]; converged: boolean; }
+
+/** Compute Quality. */
+export function computeQuality(data: number[], opts: QualityOptions = {}): QualityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeQuality };
diff --git a/src/quant/rebalancing.ts b/src/quant/rebalancing.ts
new file mode 100644
index 00000000..ade20b4a
--- /dev/null
+++ b/src/quant/rebalancing.ts
@@ -0,0 +1,22 @@
+/** Rebalancing module — tsb analytics library. */
+
+/** Options for Rebalancing. */
+export interface RebalancingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Rebalancing. */
+export interface RebalancingResult { values: number[]; converged: boolean; }
+
+/** Compute Rebalancing. */
+export function computeRebalancing(data: number[], opts: RebalancingOptions = {}): RebalancingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRebalancing };
diff --git a/src/quant/risk_model.ts b/src/quant/risk_model.ts
new file mode 100644
index 00000000..71859c53
--- /dev/null
+++ b/src/quant/risk_model.ts
@@ -0,0 +1,22 @@
+/** Risk Model module — tsb analytics library. */
+
+/** Options for Risk Model. */
+export interface RiskModelOptions { tol?: number; maxIter?: number; }
+
+/** Result from Risk Model. */
+export interface RiskModelResult { values: number[]; converged: boolean; }
+
+/** Compute Risk Model. */
+export function computeRiskModel(data: number[], opts: RiskModelOptions = {}): RiskModelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRiskModel };
diff --git a/src/quant/robust.ts b/src/quant/robust.ts
new file mode 100644
index 00000000..6e1eed54
--- /dev/null
+++ b/src/quant/robust.ts
@@ -0,0 +1,15 @@
+/** Quant Robust module — tsb analytics library. */
+export interface Quant robustOptions { tol?: number; maxIter?: number; }
+export interface Quant robustResult { values: number[]; converged: boolean; }
+export function computeQuant robust(data: number[], opts: Quant robustOptions = {}): Quant robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeQuant robust };
diff --git a/src/quant/sentiment_quant.ts b/src/quant/sentiment_quant.ts
new file mode 100644
index 00000000..12e04dc8
--- /dev/null
+++ b/src/quant/sentiment_quant.ts
@@ -0,0 +1,22 @@
+/** Sentiment Quant module — tsb analytics library. */
+
+/** Options for Sentiment Quant. */
+export interface SentimentQuantOptions { tol?: number; maxIter?: number; }
+
+/** Result from Sentiment Quant. */
+export interface SentimentQuantResult { values: number[]; converged: boolean; }
+
+/** Compute Sentiment Quant. */
+export function computeSentimentQuant(data: number[], opts: SentimentQuantOptions = {}): SentimentQuantResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSentimentQuant };
diff --git a/src/quant/signal_combination.ts b/src/quant/signal_combination.ts
new file mode 100644
index 00000000..1057bf0e
--- /dev/null
+++ b/src/quant/signal_combination.ts
@@ -0,0 +1,22 @@
+/** Signal Combination module — tsb analytics library. */
+
+/** Options for Signal Combination. */
+export interface SignalCombinationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Signal Combination. */
+export interface SignalCombinationResult { values: number[]; converged: boolean; }
+
+/** Compute Signal Combination. */
+export function computeSignalCombination(data: number[], opts: SignalCombinationOptions = {}): SignalCombinationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSignalCombination };
diff --git a/src/quant/signal_generation.ts b/src/quant/signal_generation.ts
new file mode 100644
index 00000000..4c9f03bd
--- /dev/null
+++ b/src/quant/signal_generation.ts
@@ -0,0 +1,22 @@
+/** Signal Generation module — tsb analytics library. */
+
+/** Options for Signal Generation. */
+export interface SignalGenerationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Signal Generation. */
+export interface SignalGenerationResult { values: number[]; converged: boolean; }
+
+/** Compute Signal Generation. */
+export function computeSignalGeneration(data: number[], opts: SignalGenerationOptions = {}): SignalGenerationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSignalGeneration };
diff --git a/src/quant/size.ts b/src/quant/size.ts
new file mode 100644
index 00000000..1cadc097
--- /dev/null
+++ b/src/quant/size.ts
@@ -0,0 +1,22 @@
+/** Size module — tsb analytics library. */
+
+/** Options for Size. */
+export interface SizeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Size. */
+export interface SizeResult { values: number[]; converged: boolean; }
+
+/** Compute Size. */
+export function computeSize(data: number[], opts: SizeOptions = {}): SizeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSize };
diff --git a/src/quant/slippage.ts b/src/quant/slippage.ts
new file mode 100644
index 00000000..7012f595
--- /dev/null
+++ b/src/quant/slippage.ts
@@ -0,0 +1,22 @@
+/** Slippage module — tsb analytics library. */
+
+/** Options for Slippage. */
+export interface SlippageOptions { tol?: number; maxIter?: number; }
+
+/** Result from Slippage. */
+export interface SlippageResult { values: number[]; converged: boolean; }
+
+/** Compute Slippage. */
+export function computeSlippage(data: number[], opts: SlippageOptions = {}): SlippageResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSlippage };
diff --git a/src/quant/small.ts b/src/quant/small.ts
new file mode 100644
index 00000000..477583a8
--- /dev/null
+++ b/src/quant/small.ts
@@ -0,0 +1,15 @@
+/** Quant Small module — tsb analytics library. */
+export interface Quant smallOptions { tol?: number; maxIter?: number; }
+export interface Quant smallResult { values: number[]; converged: boolean; }
+export function computeQuant small(data: number[], opts: Quant smallOptions = {}): Quant smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeQuant small };
diff --git a/src/quant/sparse.ts b/src/quant/sparse.ts
new file mode 100644
index 00000000..6109c4c7
--- /dev/null
+++ b/src/quant/sparse.ts
@@ -0,0 +1,15 @@
+/** Quant Sparse module — tsb analytics library. */
+export interface Quant sparseOptions { tol?: number; maxIter?: number; }
+export interface Quant sparseResult { values: number[]; converged: boolean; }
+export function computeQuant sparse(data: number[], opts: Quant sparseOptions = {}): Quant sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeQuant sparse };
diff --git a/src/quant/stable.ts b/src/quant/stable.ts
new file mode 100644
index 00000000..5961257c
--- /dev/null
+++ b/src/quant/stable.ts
@@ -0,0 +1,15 @@
+/** Quant Stable module — tsb analytics library. */
+export interface Quant stableOptions { tol?: number; maxIter?: number; }
+export interface Quant stableResult { values: number[]; converged: boolean; }
+export function computeQuant stable(data: number[], opts: Quant stableOptions = {}): Quant stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeQuant stable };
diff --git a/src/quant/streaming.ts b/src/quant/streaming.ts
new file mode 100644
index 00000000..9ebed750
--- /dev/null
+++ b/src/quant/streaming.ts
@@ -0,0 +1,15 @@
+/** Quant Streaming module — tsb analytics library. */
+export interface Quant streamingOptions { tol?: number; maxIter?: number; }
+export interface Quant streamingResult { values: number[]; converged: boolean; }
+export function computeQuant streaming(data: number[], opts: Quant streamingOptions = {}): Quant streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeQuant streaming };
diff --git a/src/quant/transaction_cost.ts b/src/quant/transaction_cost.ts
new file mode 100644
index 00000000..fb2c79d8
--- /dev/null
+++ b/src/quant/transaction_cost.ts
@@ -0,0 +1,22 @@
+/** Transaction Cost module — tsb analytics library. */
+
+/** Options for Transaction Cost. */
+export interface TransactionCostOptions { tol?: number; maxIter?: number; }
+
+/** Result from Transaction Cost. */
+export interface TransactionCostResult { values: number[]; converged: boolean; }
+
+/** Compute Transaction Cost. */
+export function computeTransactionCost(data: number[], opts: TransactionCostOptions = {}): TransactionCostResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTransactionCost };
diff --git a/src/quant/turnover.ts b/src/quant/turnover.ts
new file mode 100644
index 00000000..123f1a69
--- /dev/null
+++ b/src/quant/turnover.ts
@@ -0,0 +1,22 @@
+/** Turnover module — tsb analytics library. */
+
+/** Options for Turnover. */
+export interface TurnoverOptions { tol?: number; maxIter?: number; }
+
+/** Result from Turnover. */
+export interface TurnoverResult { values: number[]; converged: boolean; }
+
+/** Compute Turnover. */
+export function computeTurnover(data: number[], opts: TurnoverOptions = {}): TurnoverResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTurnover };
diff --git a/src/quant/v2.ts b/src/quant/v2.ts
new file mode 100644
index 00000000..8b0f8d09
--- /dev/null
+++ b/src/quant/v2.ts
@@ -0,0 +1,15 @@
+/** Quant V2 module — tsb analytics library. */
+export interface Quant v2Options { tol?: number; maxIter?: number; }
+export interface Quant v2Result { values: number[]; converged: boolean; }
+export function computeQuant v2(data: number[], opts: Quant v2Options = {}): Quant v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeQuant v2 };
diff --git a/src/quant/v3.ts b/src/quant/v3.ts
new file mode 100644
index 00000000..4a4cbb2d
--- /dev/null
+++ b/src/quant/v3.ts
@@ -0,0 +1,15 @@
+/** Quant V3 module — tsb analytics library. */
+export interface Quant v3Options { tol?: number; maxIter?: number; }
+export interface Quant v3Result { values: number[]; converged: boolean; }
+export function computeQuant v3(data: number[], opts: Quant v3Options = {}): Quant v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeQuant v3 };
diff --git a/src/quant/value.ts b/src/quant/value.ts
new file mode 100644
index 00000000..d3aa702d
--- /dev/null
+++ b/src/quant/value.ts
@@ -0,0 +1,22 @@
+/** Value module — tsb analytics library. */
+
+/** Options for Value. */
+export interface ValueOptions { tol?: number; maxIter?: number; }
+
+/** Result from Value. */
+export interface ValueResult { values: number[]; converged: boolean; }
+
+/** Compute Value. */
+export function computeValue(data: number[], opts: ValueOptions = {}): ValueResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeValue };
diff --git a/src/quant/wasm.ts b/src/quant/wasm.ts
new file mode 100644
index 00000000..0436a8d8
--- /dev/null
+++ b/src/quant/wasm.ts
@@ -0,0 +1,15 @@
+/** Quant Wasm module — tsb analytics library. */
+export interface Quant wasmOptions { tol?: number; maxIter?: number; }
+export interface Quant wasmResult { values: number[]; converged: boolean; }
+export function computeQuant wasm(data: number[], opts: Quant wasmOptions = {}): Quant wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeQuant wasm };
diff --git a/src/quant/xlarge.ts b/src/quant/xlarge.ts
new file mode 100644
index 00000000..b43150c0
--- /dev/null
+++ b/src/quant/xlarge.ts
@@ -0,0 +1,15 @@
+/** Quant Xlarge module — tsb analytics library. */
+export interface Quant xlargeOptions { tol?: number; maxIter?: number; }
+export interface Quant xlargeResult { values: number[]; converged: boolean; }
+export function computeQuant xlarge(data: number[], opts: Quant xlargeOptions = {}): Quant xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeQuant xlarge };
diff --git a/src/risk2/advanced.ts b/src/risk2/advanced.ts
new file mode 100644
index 00000000..b103b155
--- /dev/null
+++ b/src/risk2/advanced.ts
@@ -0,0 +1,15 @@
+/** Risk2 Advanced module — tsb analytics library. */
+export interface Risk2 advancedOptions { tol?: number; maxIter?: number; }
+export interface Risk2 advancedResult { values: number[]; converged: boolean; }
+export function computeRisk2 advanced(data: number[], opts: Risk2 advancedOptions = {}): Risk2 advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeRisk2 advanced };
diff --git a/src/risk2/base2.ts b/src/risk2/base2.ts
new file mode 100644
index 00000000..7113631d
--- /dev/null
+++ b/src/risk2/base2.ts
@@ -0,0 +1,15 @@
+/** Risk2 Base2 module — tsb analytics library. */
+export interface Risk2 base2Options { tol?: number; maxIter?: number; }
+export interface Risk2 base2Result { values: number[]; converged: boolean; }
+export function computeRisk2 base2(data: number[], opts: Risk2 base2Options = {}): Risk2 base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeRisk2 base2 };
diff --git a/src/risk2/batch.ts b/src/risk2/batch.ts
new file mode 100644
index 00000000..935ef07a
--- /dev/null
+++ b/src/risk2/batch.ts
@@ -0,0 +1,15 @@
+/** Risk2 Batch module — tsb analytics library. */
+export interface Risk2 batchOptions { tol?: number; maxIter?: number; }
+export interface Risk2 batchResult { values: number[]; converged: boolean; }
+export function computeRisk2 batch(data: number[], opts: Risk2 batchOptions = {}): Risk2 batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeRisk2 batch };
diff --git a/src/risk2/beta.ts b/src/risk2/beta.ts
new file mode 100644
index 00000000..18e58fab
--- /dev/null
+++ b/src/risk2/beta.ts
@@ -0,0 +1,15 @@
+/** Risk2 Beta module — tsb analytics library. */
+export interface Risk2 betaOptions { tol?: number; maxIter?: number; }
+export interface Risk2 betaResult { values: number[]; converged: boolean; }
+export function computeRisk2 beta(data: number[], opts: Risk2 betaOptions = {}): Risk2 betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeRisk2 beta };
diff --git a/src/risk2/component.ts b/src/risk2/component.ts
new file mode 100644
index 00000000..228b339b
--- /dev/null
+++ b/src/risk2/component.ts
@@ -0,0 +1,22 @@
+/** Component module — tsb analytics library. */
+
+/** Options for Component. */
+export interface ComponentOptions { tol?: number; maxIter?: number; }
+
+/** Result from Component. */
+export interface ComponentResult { values: number[]; converged: boolean; }
+
+/** Compute Component. */
+export function computeComponent(data: number[], opts: ComponentOptions = {}): ComponentResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeComponent };
diff --git a/src/risk2/concentration.ts b/src/risk2/concentration.ts
new file mode 100644
index 00000000..08a00549
--- /dev/null
+++ b/src/risk2/concentration.ts
@@ -0,0 +1,22 @@
+/** Concentration module — tsb analytics library. */
+
+/** Options for Concentration. */
+export interface ConcentrationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Concentration. */
+export interface ConcentrationResult { values: number[]; converged: boolean; }
+
+/** Compute Concentration. */
+export function computeConcentration(data: number[], opts: ConcentrationOptions = {}): ConcentrationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeConcentration };
diff --git a/src/risk2/convexity.ts b/src/risk2/convexity.ts
new file mode 100644
index 00000000..ff7c0b04
--- /dev/null
+++ b/src/risk2/convexity.ts
@@ -0,0 +1,22 @@
+/** Convexity module — tsb analytics library. */
+
+/** Options for Convexity. */
+export interface ConvexityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Convexity. */
+export interface ConvexityResult { values: number[]; converged: boolean; }
+
+/** Compute Convexity. */
+export function computeConvexity(data: number[], opts: ConvexityOptions = {}): ConvexityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeConvexity };
diff --git a/src/risk2/copula_risk.ts b/src/risk2/copula_risk.ts
new file mode 100644
index 00000000..80f35625
--- /dev/null
+++ b/src/risk2/copula_risk.ts
@@ -0,0 +1,22 @@
+/** Copula Risk module — tsb analytics library. */
+
+/** Options for Copula Risk. */
+export interface CopulaRiskOptions { tol?: number; maxIter?: number; }
+
+/** Result from Copula Risk. */
+export interface CopulaRiskResult { values: number[]; converged: boolean; }
+
+/** Compute Copula Risk. */
+export function computeCopulaRisk(data: number[], opts: CopulaRiskOptions = {}): CopulaRiskResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCopulaRisk };
diff --git a/src/risk2/correlation.ts b/src/risk2/correlation.ts
new file mode 100644
index 00000000..2d82cc65
--- /dev/null
+++ b/src/risk2/correlation.ts
@@ -0,0 +1,22 @@
+/** Correlation module — tsb analytics library. */
+
+/** Options for Correlation. */
+export interface CorrelationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Correlation. */
+export interface CorrelationResult { values: number[]; converged: boolean; }
+
+/** Compute Correlation. */
+export function computeCorrelation(data: number[], opts: CorrelationOptions = {}): CorrelationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCorrelation };
diff --git a/src/risk2/cpu.ts b/src/risk2/cpu.ts
new file mode 100644
index 00000000..a74d8dd1
--- /dev/null
+++ b/src/risk2/cpu.ts
@@ -0,0 +1,15 @@
+/** Risk2 Cpu module — tsb analytics library. */
+export interface Risk2 cpuOptions { tol?: number; maxIter?: number; }
+export interface Risk2 cpuResult { values: number[]; converged: boolean; }
+export function computeRisk2 cpu(data: number[], opts: Risk2 cpuOptions = {}): Risk2 cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeRisk2 cpu };
diff --git a/src/risk2/credit_risk.ts b/src/risk2/credit_risk.ts
new file mode 100644
index 00000000..f2211fd5
--- /dev/null
+++ b/src/risk2/credit_risk.ts
@@ -0,0 +1,22 @@
+/** Credit Risk module — tsb analytics library. */
+
+/** Options for Credit Risk. */
+export interface CreditRiskOptions { tol?: number; maxIter?: number; }
+
+/** Result from Credit Risk. */
+export interface CreditRiskResult { values: number[]; converged: boolean; }
+
+/** Compute Credit Risk. */
+export function computeCreditRisk(data: number[], opts: CreditRiskOptions = {}): CreditRiskResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCreditRisk };
diff --git a/src/risk2/delta.ts b/src/risk2/delta.ts
new file mode 100644
index 00000000..8d39fa20
--- /dev/null
+++ b/src/risk2/delta.ts
@@ -0,0 +1,22 @@
+/** Delta module — tsb analytics library. */
+
+/** Options for Delta. */
+export interface DeltaOptions { tol?: number; maxIter?: number; }
+
+/** Result from Delta. */
+export interface DeltaResult { values: number[]; converged: boolean; }
+
+/** Compute Delta. */
+export function computeDelta(data: number[], opts: DeltaOptions = {}): DeltaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDelta };
diff --git a/src/risk2/dense.ts b/src/risk2/dense.ts
new file mode 100644
index 00000000..0978fab8
--- /dev/null
+++ b/src/risk2/dense.ts
@@ -0,0 +1,15 @@
+/** Risk2 Dense module — tsb analytics library. */
+export interface Risk2 denseOptions { tol?: number; maxIter?: number; }
+export interface Risk2 denseResult { values: number[]; converged: boolean; }
+export function computeRisk2 dense(data: number[], opts: Risk2 denseOptions = {}): Risk2 denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeRisk2 dense };
diff --git a/src/risk2/distributed.ts b/src/risk2/distributed.ts
new file mode 100644
index 00000000..389dc278
--- /dev/null
+++ b/src/risk2/distributed.ts
@@ -0,0 +1,15 @@
+/** Risk2 Distributed module — tsb analytics library. */
+export interface Risk2 distributedOptions { tol?: number; maxIter?: number; }
+export interface Risk2 distributedResult { values: number[]; converged: boolean; }
+export function computeRisk2 distributed(data: number[], opts: Risk2 distributedOptions = {}): Risk2 distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeRisk2 distributed };
diff --git a/src/risk2/diversification.ts b/src/risk2/diversification.ts
new file mode 100644
index 00000000..1806c7b0
--- /dev/null
+++ b/src/risk2/diversification.ts
@@ -0,0 +1,22 @@
+/** Diversification module — tsb analytics library. */
+
+/** Options for Diversification. */
+export interface DiversificationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Diversification. */
+export interface DiversificationResult { values: number[]; converged: boolean; }
+
+/** Compute Diversification. */
+export function computeDiversification(data: number[], opts: DiversificationOptions = {}): DiversificationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDiversification };
diff --git a/src/risk2/duration.ts b/src/risk2/duration.ts
new file mode 100644
index 00000000..e3e38295
--- /dev/null
+++ b/src/risk2/duration.ts
@@ -0,0 +1,22 @@
+/** Duration module — tsb analytics library. */
+
+/** Options for Duration. */
+export interface DurationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Duration. */
+export interface DurationResult { values: number[]; converged: boolean; }
+
+/** Compute Duration. */
+export function computeDuration(data: number[], opts: DurationOptions = {}): DurationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDuration };
diff --git a/src/risk2/experimental.ts b/src/risk2/experimental.ts
new file mode 100644
index 00000000..bf8ee5f1
--- /dev/null
+++ b/src/risk2/experimental.ts
@@ -0,0 +1,15 @@
+/** Risk2 Experimental module — tsb analytics library. */
+export interface Risk2 experimentalOptions { tol?: number; maxIter?: number; }
+export interface Risk2 experimentalResult { values: number[]; converged: boolean; }
+export function computeRisk2 experimental(data: number[], opts: Risk2 experimentalOptions = {}): Risk2 experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeRisk2 experimental };
diff --git a/src/risk2/extreme_value.ts b/src/risk2/extreme_value.ts
new file mode 100644
index 00000000..1548f4e9
--- /dev/null
+++ b/src/risk2/extreme_value.ts
@@ -0,0 +1,22 @@
+/** Extreme Value module — tsb analytics library. */
+
+/** Options for Extreme Value. */
+export interface ExtremeValueOptions { tol?: number; maxIter?: number; }
+
+/** Result from Extreme Value. */
+export interface ExtremeValueResult { values: number[]; converged: boolean; }
+
+/** Compute Extreme Value. */
+export function computeExtremeValue(data: number[], opts: ExtremeValueOptions = {}): ExtremeValueResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeExtremeValue };
diff --git a/src/risk2/factor_risk.ts b/src/risk2/factor_risk.ts
new file mode 100644
index 00000000..8dcbde1d
--- /dev/null
+++ b/src/risk2/factor_risk.ts
@@ -0,0 +1,22 @@
+/** Factor Risk module — tsb analytics library. */
+
+/** Options for Factor Risk. */
+export interface FactorRiskOptions { tol?: number; maxIter?: number; }
+
+/** Result from Factor Risk. */
+export interface FactorRiskResult { values: number[]; converged: boolean; }
+
+/** Compute Factor Risk. */
+export function computeFactorRisk(data: number[], opts: FactorRiskOptions = {}): FactorRiskResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFactorRisk };
diff --git a/src/risk2/fast.ts b/src/risk2/fast.ts
new file mode 100644
index 00000000..64e55777
--- /dev/null
+++ b/src/risk2/fast.ts
@@ -0,0 +1,15 @@
+/** Risk2 Fast module — tsb analytics library. */
+export interface Risk2 fastOptions { tol?: number; maxIter?: number; }
+export interface Risk2 fastResult { values: number[]; converged: boolean; }
+export function computeRisk2 fast(data: number[], opts: Risk2 fastOptions = {}): Risk2 fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeRisk2 fast };
diff --git a/src/risk2/future.ts b/src/risk2/future.ts
new file mode 100644
index 00000000..6f48be97
--- /dev/null
+++ b/src/risk2/future.ts
@@ -0,0 +1,15 @@
+/** Risk2 Future module — tsb analytics library. */
+export interface Risk2 futureOptions { tol?: number; maxIter?: number; }
+export interface Risk2 futureResult { values: number[]; converged: boolean; }
+export function computeRisk2 future(data: number[], opts: Risk2 futureOptions = {}): Risk2 futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeRisk2 future };
diff --git a/src/risk2/gamma.ts b/src/risk2/gamma.ts
new file mode 100644
index 00000000..cfbcb306
--- /dev/null
+++ b/src/risk2/gamma.ts
@@ -0,0 +1,22 @@
+/** Gamma module — tsb analytics library. */
+
+/** Options for Gamma. */
+export interface GammaOptions { tol?: number; maxIter?: number; }
+
+/** Result from Gamma. */
+export interface GammaResult { values: number[]; converged: boolean; }
+
+/** Compute Gamma. */
+export function computeGamma(data: number[], opts: GammaOptions = {}): GammaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGamma };
diff --git a/src/risk2/gpu.ts b/src/risk2/gpu.ts
new file mode 100644
index 00000000..17000906
--- /dev/null
+++ b/src/risk2/gpu.ts
@@ -0,0 +1,15 @@
+/** Risk2 Gpu module — tsb analytics library. */
+export interface Risk2 gpuOptions { tol?: number; maxIter?: number; }
+export interface Risk2 gpuResult { values: number[]; converged: boolean; }
+export function computeRisk2 gpu(data: number[], opts: Risk2 gpuOptions = {}): Risk2 gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeRisk2 gpu };
diff --git a/src/risk2/greeks.ts b/src/risk2/greeks.ts
new file mode 100644
index 00000000..a02d08cd
--- /dev/null
+++ b/src/risk2/greeks.ts
@@ -0,0 +1,22 @@
+/** Greeks module — tsb analytics library. */
+
+/** Options for Greeks. */
+export interface GreeksOptions { tol?: number; maxIter?: number; }
+
+/** Result from Greeks. */
+export interface GreeksResult { values: number[]; converged: boolean; }
+
+/** Compute Greeks. */
+export function computeGreeks(data: number[], opts: GreeksOptions = {}): GreeksResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGreeks };
diff --git a/src/risk2/hedging_risk.ts b/src/risk2/hedging_risk.ts
new file mode 100644
index 00000000..d1ffd995
--- /dev/null
+++ b/src/risk2/hedging_risk.ts
@@ -0,0 +1,22 @@
+/** Hedging Risk module — tsb analytics library. */
+
+/** Options for Hedging Risk. */
+export interface HedgingRiskOptions { tol?: number; maxIter?: number; }
+
+/** Result from Hedging Risk. */
+export interface HedgingRiskResult { values: number[]; converged: boolean; }
+
+/** Compute Hedging Risk. */
+export function computeHedgingRisk(data: number[], opts: HedgingRiskOptions = {}): HedgingRiskResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeHedgingRisk };
diff --git a/src/risk2/incremental.ts b/src/risk2/incremental.ts
new file mode 100644
index 00000000..3967fcc1
--- /dev/null
+++ b/src/risk2/incremental.ts
@@ -0,0 +1,22 @@
+/** Incremental module — tsb analytics library. */
+
+/** Options for Incremental. */
+export interface IncrementalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Incremental. */
+export interface IncrementalResult { values: number[]; converged: boolean; }
+
+/** Compute Incremental. */
+export function computeIncremental(data: number[], opts: IncrementalOptions = {}): IncrementalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeIncremental };
diff --git a/src/risk2/key_rate.ts b/src/risk2/key_rate.ts
new file mode 100644
index 00000000..264f2b3a
--- /dev/null
+++ b/src/risk2/key_rate.ts
@@ -0,0 +1,22 @@
+/** Key Rate module — tsb analytics library. */
+
+/** Options for Key Rate. */
+export interface KeyRateOptions { tol?: number; maxIter?: number; }
+
+/** Result from Key Rate. */
+export interface KeyRateResult { values: number[]; converged: boolean; }
+
+/** Compute Key Rate. */
+export function computeKeyRate(data: number[], opts: KeyRateOptions = {}): KeyRateResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeKeyRate };
diff --git a/src/risk2/large.ts b/src/risk2/large.ts
new file mode 100644
index 00000000..1845be50
--- /dev/null
+++ b/src/risk2/large.ts
@@ -0,0 +1,15 @@
+/** Risk2 Large module — tsb analytics library. */
+export interface Risk2 largeOptions { tol?: number; maxIter?: number; }
+export interface Risk2 largeResult { values: number[]; converged: boolean; }
+export function computeRisk2 large(data: number[], opts: Risk2 largeOptions = {}): Risk2 largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeRisk2 large };
diff --git a/src/risk2/legacy.ts b/src/risk2/legacy.ts
new file mode 100644
index 00000000..5bdebb6f
--- /dev/null
+++ b/src/risk2/legacy.ts
@@ -0,0 +1,15 @@
+/** Risk2 Legacy module — tsb analytics library. */
+export interface Risk2 legacyOptions { tol?: number; maxIter?: number; }
+export interface Risk2 legacyResult { values: number[]; converged: boolean; }
+export function computeRisk2 legacy(data: number[], opts: Risk2 legacyOptions = {}): Risk2 legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeRisk2 legacy };
diff --git a/src/risk2/liquidity_risk.ts b/src/risk2/liquidity_risk.ts
new file mode 100644
index 00000000..5ae3904c
--- /dev/null
+++ b/src/risk2/liquidity_risk.ts
@@ -0,0 +1,22 @@
+/** Liquidity Risk module — tsb analytics library. */
+
+/** Options for Liquidity Risk. */
+export interface LiquidityRiskOptions { tol?: number; maxIter?: number; }
+
+/** Result from Liquidity Risk. */
+export interface LiquidityRiskResult { values: number[]; converged: boolean; }
+
+/** Compute Liquidity Risk. */
+export function computeLiquidityRisk(data: number[], opts: LiquidityRiskOptions = {}): LiquidityRiskResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLiquidityRisk };
diff --git a/src/risk2/lite.ts b/src/risk2/lite.ts
new file mode 100644
index 00000000..41bd8959
--- /dev/null
+++ b/src/risk2/lite.ts
@@ -0,0 +1,15 @@
+/** Risk2 Lite module — tsb analytics library. */
+export interface Risk2 liteOptions { tol?: number; maxIter?: number; }
+export interface Risk2 liteResult { values: number[]; converged: boolean; }
+export function computeRisk2 lite(data: number[], opts: Risk2 liteOptions = {}): Risk2 liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeRisk2 lite };
diff --git a/src/risk2/marginal.ts b/src/risk2/marginal.ts
new file mode 100644
index 00000000..60dee892
--- /dev/null
+++ b/src/risk2/marginal.ts
@@ -0,0 +1,22 @@
+/** Marginal module — tsb analytics library. */
+
+/** Options for Marginal. */
+export interface MarginalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Marginal. */
+export interface MarginalResult { values: number[]; converged: boolean; }
+
+/** Compute Marginal. */
+export function computeMarginal(data: number[], opts: MarginalOptions = {}): MarginalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMarginal };
diff --git a/src/risk2/market_risk.ts b/src/risk2/market_risk.ts
new file mode 100644
index 00000000..5666c115
--- /dev/null
+++ b/src/risk2/market_risk.ts
@@ -0,0 +1,22 @@
+/** Market Risk module — tsb analytics library. */
+
+/** Options for Market Risk. */
+export interface MarketRiskOptions { tol?: number; maxIter?: number; }
+
+/** Result from Market Risk. */
+export interface MarketRiskResult { values: number[]; converged: boolean; }
+
+/** Compute Market Risk. */
+export function computeMarketRisk(data: number[], opts: MarketRiskOptions = {}): MarketRiskResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMarketRisk };
diff --git a/src/risk2/mini.ts b/src/risk2/mini.ts
new file mode 100644
index 00000000..b0913a5d
--- /dev/null
+++ b/src/risk2/mini.ts
@@ -0,0 +1,15 @@
+/** Risk2 Mini module — tsb analytics library. */
+export interface Risk2 miniOptions { tol?: number; maxIter?: number; }
+export interface Risk2 miniResult { values: number[]; converged: boolean; }
+export function computeRisk2 mini(data: number[], opts: Risk2 miniOptions = {}): Risk2 miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeRisk2 mini };
diff --git a/src/risk2/next.ts b/src/risk2/next.ts
new file mode 100644
index 00000000..a403609c
--- /dev/null
+++ b/src/risk2/next.ts
@@ -0,0 +1,15 @@
+/** Risk2 Next module — tsb analytics library. */
+export interface Risk2 nextOptions { tol?: number; maxIter?: number; }
+export interface Risk2 nextResult { values: number[]; converged: boolean; }
+export function computeRisk2 next(data: number[], opts: Risk2 nextOptions = {}): Risk2 nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeRisk2 next };
diff --git a/src/risk2/online.ts b/src/risk2/online.ts
new file mode 100644
index 00000000..42d991ec
--- /dev/null
+++ b/src/risk2/online.ts
@@ -0,0 +1,15 @@
+/** Risk2 Online module — tsb analytics library. */
+export interface Risk2 onlineOptions { tol?: number; maxIter?: number; }
+export interface Risk2 onlineResult { values: number[]; converged: boolean; }
+export function computeRisk2 online(data: number[], opts: Risk2 onlineOptions = {}): Risk2 onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeRisk2 online };
diff --git a/src/risk2/operational_risk.ts b/src/risk2/operational_risk.ts
new file mode 100644
index 00000000..1c19e753
--- /dev/null
+++ b/src/risk2/operational_risk.ts
@@ -0,0 +1,22 @@
+/** Operational Risk module — tsb analytics library. */
+
+/** Options for Operational Risk. */
+export interface OperationalRiskOptions { tol?: number; maxIter?: number; }
+
+/** Result from Operational Risk. */
+export interface OperationalRiskResult { values: number[]; converged: boolean; }
+
+/** Compute Operational Risk. */
+export function computeOperationalRisk(data: number[], opts: OperationalRiskOptions = {}): OperationalRiskResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeOperationalRisk };
diff --git a/src/risk2/parallel.ts b/src/risk2/parallel.ts
new file mode 100644
index 00000000..2dded6be
--- /dev/null
+++ b/src/risk2/parallel.ts
@@ -0,0 +1,15 @@
+/** Risk2 Parallel module — tsb analytics library. */
+export interface Risk2 parallelOptions { tol?: number; maxIter?: number; }
+export interface Risk2 parallelResult { values: number[]; converged: boolean; }
+export function computeRisk2 parallel(data: number[], opts: Risk2 parallelOptions = {}): Risk2 parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeRisk2 parallel };
diff --git a/src/risk2/plus.ts b/src/risk2/plus.ts
new file mode 100644
index 00000000..cd90e108
--- /dev/null
+++ b/src/risk2/plus.ts
@@ -0,0 +1,15 @@
+/** Risk2 Plus module — tsb analytics library. */
+export interface Risk2 plusOptions { tol?: number; maxIter?: number; }
+export interface Risk2 plusResult { values: number[]; converged: boolean; }
+export function computeRisk2 plus(data: number[], opts: Risk2 plusOptions = {}): Risk2 plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeRisk2 plus };
diff --git a/src/risk2/principal_component.ts b/src/risk2/principal_component.ts
new file mode 100644
index 00000000..45fec1a2
--- /dev/null
+++ b/src/risk2/principal_component.ts
@@ -0,0 +1,22 @@
+/** Principal Component module — tsb analytics library. */
+
+/** Options for Principal Component. */
+export interface PrincipalComponentOptions { tol?: number; maxIter?: number; }
+
+/** Result from Principal Component. */
+export interface PrincipalComponentResult { values: number[]; converged: boolean; }
+
+/** Compute Principal Component. */
+export function computePrincipalComponent(data: number[], opts: PrincipalComponentOptions = {}): PrincipalComponentResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePrincipalComponent };
diff --git a/src/risk2/pro.ts b/src/risk2/pro.ts
new file mode 100644
index 00000000..8e044189
--- /dev/null
+++ b/src/risk2/pro.ts
@@ -0,0 +1,15 @@
+/** Risk2 Pro module — tsb analytics library. */
+export interface Risk2 proOptions { tol?: number; maxIter?: number; }
+export interface Risk2 proResult { values: number[]; converged: boolean; }
+export function computeRisk2 pro(data: number[], opts: Risk2 proOptions = {}): Risk2 proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeRisk2 pro };
diff --git a/src/risk2/rho.ts b/src/risk2/rho.ts
new file mode 100644
index 00000000..50fdc452
--- /dev/null
+++ b/src/risk2/rho.ts
@@ -0,0 +1,22 @@
+/** Rho module — tsb analytics library. */
+
+/** Options for Rho. */
+export interface RhoOptions { tol?: number; maxIter?: number; }
+
+/** Result from Rho. */
+export interface RhoResult { values: number[]; converged: boolean; }
+
+/** Compute Rho. */
+export function computeRho(data: number[], opts: RhoOptions = {}): RhoResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRho };
diff --git a/src/risk2/robust.ts b/src/risk2/robust.ts
new file mode 100644
index 00000000..bf6a235a
--- /dev/null
+++ b/src/risk2/robust.ts
@@ -0,0 +1,15 @@
+/** Risk2 Robust module — tsb analytics library. */
+export interface Risk2 robustOptions { tol?: number; maxIter?: number; }
+export interface Risk2 robustResult { values: number[]; converged: boolean; }
+export function computeRisk2 robust(data: number[], opts: Risk2 robustOptions = {}): Risk2 robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeRisk2 robust };
diff --git a/src/risk2/scenario_analysis.ts b/src/risk2/scenario_analysis.ts
new file mode 100644
index 00000000..5070b21e
--- /dev/null
+++ b/src/risk2/scenario_analysis.ts
@@ -0,0 +1,22 @@
+/** Scenario Analysis module — tsb analytics library. */
+
+/** Options for Scenario Analysis. */
+export interface ScenarioAnalysisOptions { tol?: number; maxIter?: number; }
+
+/** Result from Scenario Analysis. */
+export interface ScenarioAnalysisResult { values: number[]; converged: boolean; }
+
+/** Compute Scenario Analysis. */
+export function computeScenarioAnalysis(data: number[], opts: ScenarioAnalysisOptions = {}): ScenarioAnalysisResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeScenarioAnalysis };
diff --git a/src/risk2/sensitivity.ts b/src/risk2/sensitivity.ts
new file mode 100644
index 00000000..2c021c56
--- /dev/null
+++ b/src/risk2/sensitivity.ts
@@ -0,0 +1,22 @@
+/** Sensitivity module — tsb analytics library. */
+
+/** Options for Sensitivity. */
+export interface SensitivityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Sensitivity. */
+export interface SensitivityResult { values: number[]; converged: boolean; }
+
+/** Compute Sensitivity. */
+export function computeSensitivity(data: number[], opts: SensitivityOptions = {}): SensitivityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSensitivity };
diff --git a/src/risk2/small.ts b/src/risk2/small.ts
new file mode 100644
index 00000000..e342c40e
--- /dev/null
+++ b/src/risk2/small.ts
@@ -0,0 +1,15 @@
+/** Risk2 Small module — tsb analytics library. */
+export interface Risk2 smallOptions { tol?: number; maxIter?: number; }
+export interface Risk2 smallResult { values: number[]; converged: boolean; }
+export function computeRisk2 small(data: number[], opts: Risk2 smallOptions = {}): Risk2 smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeRisk2 small };
diff --git a/src/risk2/sparse.ts b/src/risk2/sparse.ts
new file mode 100644
index 00000000..c9a696c5
--- /dev/null
+++ b/src/risk2/sparse.ts
@@ -0,0 +1,15 @@
+/** Risk2 Sparse module — tsb analytics library. */
+export interface Risk2 sparseOptions { tol?: number; maxIter?: number; }
+export interface Risk2 sparseResult { values: number[]; converged: boolean; }
+export function computeRisk2 sparse(data: number[], opts: Risk2 sparseOptions = {}): Risk2 sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeRisk2 sparse };
diff --git a/src/risk2/stable.ts b/src/risk2/stable.ts
new file mode 100644
index 00000000..6a7e0ebc
--- /dev/null
+++ b/src/risk2/stable.ts
@@ -0,0 +1,15 @@
+/** Risk2 Stable module — tsb analytics library. */
+export interface Risk2 stableOptions { tol?: number; maxIter?: number; }
+export interface Risk2 stableResult { values: number[]; converged: boolean; }
+export function computeRisk2 stable(data: number[], opts: Risk2 stableOptions = {}): Risk2 stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeRisk2 stable };
diff --git a/src/risk2/streaming.ts b/src/risk2/streaming.ts
new file mode 100644
index 00000000..0e43e9a6
--- /dev/null
+++ b/src/risk2/streaming.ts
@@ -0,0 +1,15 @@
+/** Risk2 Streaming module — tsb analytics library. */
+export interface Risk2 streamingOptions { tol?: number; maxIter?: number; }
+export interface Risk2 streamingResult { values: number[]; converged: boolean; }
+export function computeRisk2 streaming(data: number[], opts: Risk2 streamingOptions = {}): Risk2 streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeRisk2 streaming };
diff --git a/src/risk2/stress_testing.ts b/src/risk2/stress_testing.ts
new file mode 100644
index 00000000..a3849077
--- /dev/null
+++ b/src/risk2/stress_testing.ts
@@ -0,0 +1,22 @@
+/** Stress Testing module — tsb analytics library. */
+
+/** Options for Stress Testing. */
+export interface StressTestingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Stress Testing. */
+export interface StressTestingResult { values: number[]; converged: boolean; }
+
+/** Compute Stress Testing. */
+export function computeStressTesting(data: number[], opts: StressTestingOptions = {}): StressTestingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeStressTesting };
diff --git a/src/risk2/systemic_risk.ts b/src/risk2/systemic_risk.ts
new file mode 100644
index 00000000..6191ffb8
--- /dev/null
+++ b/src/risk2/systemic_risk.ts
@@ -0,0 +1,22 @@
+/** Systemic Risk module — tsb analytics library. */
+
+/** Options for Systemic Risk. */
+export interface SystemicRiskOptions { tol?: number; maxIter?: number; }
+
+/** Result from Systemic Risk. */
+export interface SystemicRiskResult { values: number[]; converged: boolean; }
+
+/** Compute Systemic Risk. */
+export function computeSystemicRisk(data: number[], opts: SystemicRiskOptions = {}): SystemicRiskResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSystemicRisk };
diff --git a/src/risk2/tail_risk.ts b/src/risk2/tail_risk.ts
new file mode 100644
index 00000000..aaf6dd0a
--- /dev/null
+++ b/src/risk2/tail_risk.ts
@@ -0,0 +1,22 @@
+/** Tail Risk module — tsb analytics library. */
+
+/** Options for Tail Risk. */
+export interface TailRiskOptions { tol?: number; maxIter?: number; }
+
+/** Result from Tail Risk. */
+export interface TailRiskResult { values: number[]; converged: boolean; }
+
+/** Compute Tail Risk. */
+export function computeTailRisk(data: number[], opts: TailRiskOptions = {}): TailRiskResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTailRisk };
diff --git a/src/risk2/theta.ts b/src/risk2/theta.ts
new file mode 100644
index 00000000..6c2db67f
--- /dev/null
+++ b/src/risk2/theta.ts
@@ -0,0 +1,22 @@
+/** Theta module — tsb analytics library. */
+
+/** Options for Theta. */
+export interface ThetaOptions { tol?: number; maxIter?: number; }
+
+/** Result from Theta. */
+export interface ThetaResult { values: number[]; converged: boolean; }
+
+/** Compute Theta. */
+export function computeTheta(data: number[], opts: ThetaOptions = {}): ThetaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTheta };
diff --git a/src/risk2/v2.ts b/src/risk2/v2.ts
new file mode 100644
index 00000000..74a7d95d
--- /dev/null
+++ b/src/risk2/v2.ts
@@ -0,0 +1,15 @@
+/** Risk2 V2 module — tsb analytics library. */
+export interface Risk2 v2Options { tol?: number; maxIter?: number; }
+export interface Risk2 v2Result { values: number[]; converged: boolean; }
+export function computeRisk2 v2(data: number[], opts: Risk2 v2Options = {}): Risk2 v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeRisk2 v2 };
diff --git a/src/risk2/v3.ts b/src/risk2/v3.ts
new file mode 100644
index 00000000..a576a599
--- /dev/null
+++ b/src/risk2/v3.ts
@@ -0,0 +1,15 @@
+/** Risk2 V3 module — tsb analytics library. */
+export interface Risk2 v3Options { tol?: number; maxIter?: number; }
+export interface Risk2 v3Result { values: number[]; converged: boolean; }
+export function computeRisk2 v3(data: number[], opts: Risk2 v3Options = {}): Risk2 v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeRisk2 v3 };
diff --git a/src/risk2/vega.ts b/src/risk2/vega.ts
new file mode 100644
index 00000000..281a1455
--- /dev/null
+++ b/src/risk2/vega.ts
@@ -0,0 +1,22 @@
+/** Vega module — tsb analytics library. */
+
+/** Options for Vega. */
+export interface VegaOptions { tol?: number; maxIter?: number; }
+
+/** Result from Vega. */
+export interface VegaResult { values: number[]; converged: boolean; }
+
+/** Compute Vega. */
+export function computeVega(data: number[], opts: VegaOptions = {}): VegaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeVega };
diff --git a/src/risk2/wasm.ts b/src/risk2/wasm.ts
new file mode 100644
index 00000000..c0fb6fab
--- /dev/null
+++ b/src/risk2/wasm.ts
@@ -0,0 +1,15 @@
+/** Risk2 Wasm module — tsb analytics library. */
+export interface Risk2 wasmOptions { tol?: number; maxIter?: number; }
+export interface Risk2 wasmResult { values: number[]; converged: boolean; }
+export function computeRisk2 wasm(data: number[], opts: Risk2 wasmOptions = {}): Risk2 wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeRisk2 wasm };
diff --git a/src/risk2/xlarge.ts b/src/risk2/xlarge.ts
new file mode 100644
index 00000000..261e75d2
--- /dev/null
+++ b/src/risk2/xlarge.ts
@@ -0,0 +1,15 @@
+/** Risk2 Xlarge module — tsb analytics library. */
+export interface Risk2 xlargeOptions { tol?: number; maxIter?: number; }
+export interface Risk2 xlargeResult { values: number[]; converged: boolean; }
+export function computeRisk2 xlarge(data: number[], opts: Risk2 xlargeOptions = {}): Risk2 xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeRisk2 xlarge };
diff --git a/src/signal/adaptive.ts b/src/signal/adaptive.ts
new file mode 100644
index 00000000..178519f4
--- /dev/null
+++ b/src/signal/adaptive.ts
@@ -0,0 +1,22 @@
+/** Adaptive module — tsb analytics library. */
+
+/** Options for Adaptive. */
+export interface AdaptiveOptions { tol?: number; maxIter?: number; }
+
+/** Result from Adaptive. */
+export interface AdaptiveResult { values: number[]; converged: boolean; }
+
+/** Compute Adaptive. */
+export function computeAdaptive(data: number[], opts: AdaptiveOptions = {}): AdaptiveResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAdaptive };
diff --git a/src/signal/advanced.ts b/src/signal/advanced.ts
new file mode 100644
index 00000000..939a208f
--- /dev/null
+++ b/src/signal/advanced.ts
@@ -0,0 +1,15 @@
+/** Signal Advanced module — tsb analytics library. */
+export interface Signal advancedOptions { tol?: number; maxIter?: number; }
+export interface Signal advancedResult { values: number[]; converged: boolean; }
+export function computeSignal advanced(data: number[], opts: Signal advancedOptions = {}): Signal advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSignal advanced };
diff --git a/src/signal/autocorrelation_signal.ts b/src/signal/autocorrelation_signal.ts
new file mode 100644
index 00000000..03ba4152
--- /dev/null
+++ b/src/signal/autocorrelation_signal.ts
@@ -0,0 +1,22 @@
+/** Autocorrelation Signal module — tsb analytics library. */
+
+/** Options for Autocorrelation Signal. */
+export interface AutocorrelationSignalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Autocorrelation Signal. */
+export interface AutocorrelationSignalResult { values: number[]; converged: boolean; }
+
+/** Compute Autocorrelation Signal. */
+export function computeAutocorrelationSignal(data: number[], opts: AutocorrelationSignalOptions = {}): AutocorrelationSignalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAutocorrelationSignal };
diff --git a/src/signal/bandpass.ts b/src/signal/bandpass.ts
new file mode 100644
index 00000000..9c15a34d
--- /dev/null
+++ b/src/signal/bandpass.ts
@@ -0,0 +1,22 @@
+/** Bandpass module — tsb analytics library. */
+
+/** Options for Bandpass. */
+export interface BandpassOptions { tol?: number; maxIter?: number; }
+
+/** Result from Bandpass. */
+export interface BandpassResult { values: number[]; converged: boolean; }
+
+/** Compute Bandpass. */
+export function computeBandpass(data: number[], opts: BandpassOptions = {}): BandpassResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBandpass };
diff --git a/src/signal/base2.ts b/src/signal/base2.ts
new file mode 100644
index 00000000..3918829e
--- /dev/null
+++ b/src/signal/base2.ts
@@ -0,0 +1,15 @@
+/** Signal Base2 module — tsb analytics library. */
+export interface Signal base2Options { tol?: number; maxIter?: number; }
+export interface Signal base2Result { values: number[]; converged: boolean; }
+export function computeSignal base2(data: number[], opts: Signal base2Options = {}): Signal base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSignal base2 };
diff --git a/src/signal/batch.ts b/src/signal/batch.ts
new file mode 100644
index 00000000..acf35b37
--- /dev/null
+++ b/src/signal/batch.ts
@@ -0,0 +1,15 @@
+/** Signal Batch module — tsb analytics library. */
+export interface Signal batchOptions { tol?: number; maxIter?: number; }
+export interface Signal batchResult { values: number[]; converged: boolean; }
+export function computeSignal batch(data: number[], opts: Signal batchOptions = {}): Signal batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSignal batch };
diff --git a/src/signal/beamforming.ts b/src/signal/beamforming.ts
new file mode 100644
index 00000000..ffee5e15
--- /dev/null
+++ b/src/signal/beamforming.ts
@@ -0,0 +1,22 @@
+/** Beamforming module — tsb analytics library. */
+
+/** Options for Beamforming. */
+export interface BeamformingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Beamforming. */
+export interface BeamformingResult { values: number[]; converged: boolean; }
+
+/** Compute Beamforming. */
+export function computeBeamforming(data: number[], opts: BeamformingOptions = {}): BeamformingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBeamforming };
diff --git a/src/signal/beta.ts b/src/signal/beta.ts
new file mode 100644
index 00000000..4a7c111b
--- /dev/null
+++ b/src/signal/beta.ts
@@ -0,0 +1,15 @@
+/** Signal Beta module — tsb analytics library. */
+export interface Signal betaOptions { tol?: number; maxIter?: number; }
+export interface Signal betaResult { values: number[]; converged: boolean; }
+export function computeSignal beta(data: number[], opts: Signal betaOptions = {}): Signal betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSignal beta };
diff --git a/src/signal/blind.ts b/src/signal/blind.ts
new file mode 100644
index 00000000..78a7209b
--- /dev/null
+++ b/src/signal/blind.ts
@@ -0,0 +1,22 @@
+/** Blind module — tsb analytics library. */
+
+/** Options for Blind. */
+export interface BlindOptions { tol?: number; maxIter?: number; }
+
+/** Result from Blind. */
+export interface BlindResult { values: number[]; converged: boolean; }
+
+/** Compute Blind. */
+export function computeBlind(data: number[], opts: BlindOptions = {}): BlindResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBlind };
diff --git a/src/signal/cepstrum.ts b/src/signal/cepstrum.ts
new file mode 100644
index 00000000..97fec717
--- /dev/null
+++ b/src/signal/cepstrum.ts
@@ -0,0 +1,22 @@
+/** Cepstrum module — tsb analytics library. */
+
+/** Options for Cepstrum. */
+export interface CepstrumOptions { tol?: number; maxIter?: number; }
+
+/** Result from Cepstrum. */
+export interface CepstrumResult { values: number[]; converged: boolean; }
+
+/** Compute Cepstrum. */
+export function computeCepstrum(data: number[], opts: CepstrumOptions = {}): CepstrumResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCepstrum };
diff --git a/src/signal/coherence.ts b/src/signal/coherence.ts
new file mode 100644
index 00000000..89352968
--- /dev/null
+++ b/src/signal/coherence.ts
@@ -0,0 +1,22 @@
+/** Coherence module — tsb analytics library. */
+
+/** Options for Coherence. */
+export interface CoherenceOptions { tol?: number; maxIter?: number; }
+
+/** Result from Coherence. */
+export interface CoherenceResult { values: number[]; converged: boolean; }
+
+/** Compute Coherence. */
+export function computeCoherence(data: number[], opts: CoherenceOptions = {}): CoherenceResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCoherence };
diff --git a/src/signal/compressed_sensing.ts b/src/signal/compressed_sensing.ts
new file mode 100644
index 00000000..72f93ace
--- /dev/null
+++ b/src/signal/compressed_sensing.ts
@@ -0,0 +1,22 @@
+/** Compressed Sensing module — tsb analytics library. */
+
+/** Options for Compressed Sensing. */
+export interface CompressedSensingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Compressed Sensing. */
+export interface CompressedSensingResult { values: number[]; converged: boolean; }
+
+/** Compute Compressed Sensing. */
+export function computeCompressedSensing(data: number[], opts: CompressedSensingOptions = {}): CompressedSensingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCompressedSensing };
diff --git a/src/signal/convolution_signal.ts b/src/signal/convolution_signal.ts
new file mode 100644
index 00000000..ed56ba3e
--- /dev/null
+++ b/src/signal/convolution_signal.ts
@@ -0,0 +1,22 @@
+/** Convolution Signal module — tsb analytics library. */
+
+/** Options for Convolution Signal. */
+export interface ConvolutionSignalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Convolution Signal. */
+export interface ConvolutionSignalResult { values: number[]; converged: boolean; }
+
+/** Compute Convolution Signal. */
+export function computeConvolutionSignal(data: number[], opts: ConvolutionSignalOptions = {}): ConvolutionSignalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeConvolutionSignal };
diff --git a/src/signal/cpu.ts b/src/signal/cpu.ts
new file mode 100644
index 00000000..52a7c512
--- /dev/null
+++ b/src/signal/cpu.ts
@@ -0,0 +1,15 @@
+/** Signal Cpu module — tsb analytics library. */
+export interface Signal cpuOptions { tol?: number; maxIter?: number; }
+export interface Signal cpuResult { values: number[]; converged: boolean; }
+export function computeSignal cpu(data: number[], opts: Signal cpuOptions = {}): Signal cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSignal cpu };
diff --git a/src/signal/cross_correlation.ts b/src/signal/cross_correlation.ts
new file mode 100644
index 00000000..fa34b43e
--- /dev/null
+++ b/src/signal/cross_correlation.ts
@@ -0,0 +1,22 @@
+/** Cross Correlation module — tsb analytics library. */
+
+/** Options for Cross Correlation. */
+export interface CrossCorrelationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Cross Correlation. */
+export interface CrossCorrelationResult { values: number[]; converged: boolean; }
+
+/** Compute Cross Correlation. */
+export function computeCrossCorrelation(data: number[], opts: CrossCorrelationOptions = {}): CrossCorrelationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCrossCorrelation };
diff --git a/src/signal/cwt.ts b/src/signal/cwt.ts
new file mode 100644
index 00000000..cad49f91
--- /dev/null
+++ b/src/signal/cwt.ts
@@ -0,0 +1,22 @@
+/** Cwt module — tsb analytics library. */
+
+/** Options for Cwt. */
+export interface CwtOptions { tol?: number; maxIter?: number; }
+
+/** Result from Cwt. */
+export interface CwtResult { values: number[]; converged: boolean; }
+
+/** Compute Cwt. */
+export function computeCwt(data: number[], opts: CwtOptions = {}): CwtResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCwt };
diff --git a/src/signal/deconvolution.ts b/src/signal/deconvolution.ts
new file mode 100644
index 00000000..be64a323
--- /dev/null
+++ b/src/signal/deconvolution.ts
@@ -0,0 +1,22 @@
+/** Deconvolution module — tsb analytics library. */
+
+/** Options for Deconvolution. */
+export interface DeconvolutionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Deconvolution. */
+export interface DeconvolutionResult { values: number[]; converged: boolean; }
+
+/** Compute Deconvolution. */
+export function computeDeconvolution(data: number[], opts: DeconvolutionOptions = {}): DeconvolutionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDeconvolution };
diff --git a/src/signal/demodulation.ts b/src/signal/demodulation.ts
new file mode 100644
index 00000000..e2c113a8
--- /dev/null
+++ b/src/signal/demodulation.ts
@@ -0,0 +1,22 @@
+/** Demodulation module — tsb analytics library. */
+
+/** Options for Demodulation. */
+export interface DemodulationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Demodulation. */
+export interface DemodulationResult { values: number[]; converged: boolean; }
+
+/** Compute Demodulation. */
+export function computeDemodulation(data: number[], opts: DemodulationOptions = {}): DemodulationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDemodulation };
diff --git a/src/signal/dense.ts b/src/signal/dense.ts
new file mode 100644
index 00000000..aa6164a1
--- /dev/null
+++ b/src/signal/dense.ts
@@ -0,0 +1,15 @@
+/** Signal Dense module — tsb analytics library. */
+export interface Signal denseOptions { tol?: number; maxIter?: number; }
+export interface Signal denseResult { values: number[]; converged: boolean; }
+export function computeSignal dense(data: number[], opts: Signal denseOptions = {}): Signal denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSignal dense };
diff --git a/src/signal/distributed.ts b/src/signal/distributed.ts
new file mode 100644
index 00000000..f950201f
--- /dev/null
+++ b/src/signal/distributed.ts
@@ -0,0 +1,15 @@
+/** Signal Distributed module — tsb analytics library. */
+export interface Signal distributedOptions { tol?: number; maxIter?: number; }
+export interface Signal distributedResult { values: number[]; converged: boolean; }
+export function computeSignal distributed(data: number[], opts: Signal distributedOptions = {}): Signal distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSignal distributed };
diff --git a/src/signal/dwt.ts b/src/signal/dwt.ts
new file mode 100644
index 00000000..c4157682
--- /dev/null
+++ b/src/signal/dwt.ts
@@ -0,0 +1,22 @@
+/** Dwt module — tsb analytics library. */
+
+/** Options for Dwt. */
+export interface DwtOptions { tol?: number; maxIter?: number; }
+
+/** Result from Dwt. */
+export interface DwtResult { values: number[]; converged: boolean; }
+
+/** Compute Dwt. */
+export function computeDwt(data: number[], opts: DwtOptions = {}): DwtResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDwt };
diff --git a/src/signal/envelope.ts b/src/signal/envelope.ts
new file mode 100644
index 00000000..d6f373f7
--- /dev/null
+++ b/src/signal/envelope.ts
@@ -0,0 +1,22 @@
+/** Envelope module — tsb analytics library. */
+
+/** Options for Envelope. */
+export interface EnvelopeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Envelope. */
+export interface EnvelopeResult { values: number[]; converged: boolean; }
+
+/** Compute Envelope. */
+export function computeEnvelope(data: number[], opts: EnvelopeOptions = {}): EnvelopeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEnvelope };
diff --git a/src/signal/experimental.ts b/src/signal/experimental.ts
new file mode 100644
index 00000000..dcb64188
--- /dev/null
+++ b/src/signal/experimental.ts
@@ -0,0 +1,15 @@
+/** Signal Experimental module — tsb analytics library. */
+export interface Signal experimentalOptions { tol?: number; maxIter?: number; }
+export interface Signal experimentalResult { values: number[]; converged: boolean; }
+export function computeSignal experimental(data: number[], opts: Signal experimentalOptions = {}): Signal experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSignal experimental };
diff --git a/src/signal/fast.ts b/src/signal/fast.ts
new file mode 100644
index 00000000..0d50733c
--- /dev/null
+++ b/src/signal/fast.ts
@@ -0,0 +1,15 @@
+/** Signal Fast module — tsb analytics library. */
+export interface Signal fastOptions { tol?: number; maxIter?: number; }
+export interface Signal fastResult { values: number[]; converged: boolean; }
+export function computeSignal fast(data: number[], opts: Signal fastOptions = {}): Signal fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSignal fast };
diff --git a/src/signal/fft.ts b/src/signal/fft.ts
new file mode 100644
index 00000000..ef1f4831
--- /dev/null
+++ b/src/signal/fft.ts
@@ -0,0 +1,22 @@
+/** Fft module — tsb analytics library. */
+
+/** Options for Fft. */
+export interface FftOptions { tol?: number; maxIter?: number; }
+
+/** Result from Fft. */
+export interface FftResult { values: number[]; converged: boolean; }
+
+/** Compute Fft. */
+export function computeFft(data: number[], opts: FftOptions = {}): FftResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFft };
diff --git a/src/signal/filter_design.ts b/src/signal/filter_design.ts
new file mode 100644
index 00000000..676ac536
--- /dev/null
+++ b/src/signal/filter_design.ts
@@ -0,0 +1,22 @@
+/** Filter Design module — tsb analytics library. */
+
+/** Options for Filter Design. */
+export interface FilterDesignOptions { tol?: number; maxIter?: number; }
+
+/** Result from Filter Design. */
+export interface FilterDesignResult { values: number[]; converged: boolean; }
+
+/** Compute Filter Design. */
+export function computeFilterDesign(data: number[], opts: FilterDesignOptions = {}): FilterDesignResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFilterDesign };
diff --git a/src/signal/future.ts b/src/signal/future.ts
new file mode 100644
index 00000000..6de1d8a8
--- /dev/null
+++ b/src/signal/future.ts
@@ -0,0 +1,15 @@
+/** Signal Future module — tsb analytics library. */
+export interface Signal futureOptions { tol?: number; maxIter?: number; }
+export interface Signal futureResult { values: number[]; converged: boolean; }
+export function computeSignal future(data: number[], opts: Signal futureOptions = {}): Signal futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSignal future };
diff --git a/src/signal/gpu.ts b/src/signal/gpu.ts
new file mode 100644
index 00000000..fe3473fb
--- /dev/null
+++ b/src/signal/gpu.ts
@@ -0,0 +1,15 @@
+/** Signal Gpu module — tsb analytics library. */
+export interface Signal gpuOptions { tol?: number; maxIter?: number; }
+export interface Signal gpuResult { values: number[]; converged: boolean; }
+export function computeSignal gpu(data: number[], opts: Signal gpuOptions = {}): Signal gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSignal gpu };
diff --git a/src/signal/highpass.ts b/src/signal/highpass.ts
new file mode 100644
index 00000000..03c30f8e
--- /dev/null
+++ b/src/signal/highpass.ts
@@ -0,0 +1,22 @@
+/** Highpass module — tsb analytics library. */
+
+/** Options for Highpass. */
+export interface HighpassOptions { tol?: number; maxIter?: number; }
+
+/** Result from Highpass. */
+export interface HighpassResult { values: number[]; converged: boolean; }
+
+/** Compute Highpass. */
+export function computeHighpass(data: number[], opts: HighpassOptions = {}): HighpassResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeHighpass };
diff --git a/src/signal/ifft.ts b/src/signal/ifft.ts
new file mode 100644
index 00000000..b44039a1
--- /dev/null
+++ b/src/signal/ifft.ts
@@ -0,0 +1,22 @@
+/** Ifft module — tsb analytics library. */
+
+/** Options for Ifft. */
+export interface IfftOptions { tol?: number; maxIter?: number; }
+
+/** Result from Ifft. */
+export interface IfftResult { values: number[]; converged: boolean; }
+
+/** Compute Ifft. */
+export function computeIfft(data: number[], opts: IfftOptions = {}): IfftResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeIfft };
diff --git a/src/signal/kalman_signal.ts b/src/signal/kalman_signal.ts
new file mode 100644
index 00000000..a4f8c25b
--- /dev/null
+++ b/src/signal/kalman_signal.ts
@@ -0,0 +1,22 @@
+/** Kalman Signal module — tsb analytics library. */
+
+/** Options for Kalman Signal. */
+export interface KalmanSignalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Kalman Signal. */
+export interface KalmanSignalResult { values: number[]; converged: boolean; }
+
+/** Compute Kalman Signal. */
+export function computeKalmanSignal(data: number[], opts: KalmanSignalOptions = {}): KalmanSignalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeKalmanSignal };
diff --git a/src/signal/large.ts b/src/signal/large.ts
new file mode 100644
index 00000000..68bd4f17
--- /dev/null
+++ b/src/signal/large.ts
@@ -0,0 +1,15 @@
+/** Signal Large module — tsb analytics library. */
+export interface Signal largeOptions { tol?: number; maxIter?: number; }
+export interface Signal largeResult { values: number[]; converged: boolean; }
+export function computeSignal large(data: number[], opts: Signal largeOptions = {}): Signal largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSignal large };
diff --git a/src/signal/legacy.ts b/src/signal/legacy.ts
new file mode 100644
index 00000000..3f011780
--- /dev/null
+++ b/src/signal/legacy.ts
@@ -0,0 +1,15 @@
+/** Signal Legacy module — tsb analytics library. */
+export interface Signal legacyOptions { tol?: number; maxIter?: number; }
+export interface Signal legacyResult { values: number[]; converged: boolean; }
+export function computeSignal legacy(data: number[], opts: Signal legacyOptions = {}): Signal legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSignal legacy };
diff --git a/src/signal/lite.ts b/src/signal/lite.ts
new file mode 100644
index 00000000..a66b9697
--- /dev/null
+++ b/src/signal/lite.ts
@@ -0,0 +1,15 @@
+/** Signal Lite module — tsb analytics library. */
+export interface Signal liteOptions { tol?: number; maxIter?: number; }
+export interface Signal liteResult { values: number[]; converged: boolean; }
+export function computeSignal lite(data: number[], opts: Signal liteOptions = {}): Signal liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSignal lite };
diff --git a/src/signal/lowpass.ts b/src/signal/lowpass.ts
new file mode 100644
index 00000000..9c6adeee
--- /dev/null
+++ b/src/signal/lowpass.ts
@@ -0,0 +1,22 @@
+/** Lowpass module — tsb analytics library. */
+
+/** Options for Lowpass. */
+export interface LowpassOptions { tol?: number; maxIter?: number; }
+
+/** Result from Lowpass. */
+export interface LowpassResult { values: number[]; converged: boolean; }
+
+/** Compute Lowpass. */
+export function computeLowpass(data: number[], opts: LowpassOptions = {}): LowpassResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLowpass };
diff --git a/src/signal/mini.ts b/src/signal/mini.ts
new file mode 100644
index 00000000..d2a52670
--- /dev/null
+++ b/src/signal/mini.ts
@@ -0,0 +1,15 @@
+/** Signal Mini module — tsb analytics library. */
+export interface Signal miniOptions { tol?: number; maxIter?: number; }
+export interface Signal miniResult { values: number[]; converged: boolean; }
+export function computeSignal mini(data: number[], opts: Signal miniOptions = {}): Signal miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSignal mini };
diff --git a/src/signal/modulation.ts b/src/signal/modulation.ts
new file mode 100644
index 00000000..84565de9
--- /dev/null
+++ b/src/signal/modulation.ts
@@ -0,0 +1,22 @@
+/** Modulation module — tsb analytics library. */
+
+/** Options for Modulation. */
+export interface ModulationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Modulation. */
+export interface ModulationResult { values: number[]; converged: boolean; }
+
+/** Compute Modulation. */
+export function computeModulation(data: number[], opts: ModulationOptions = {}): ModulationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeModulation };
diff --git a/src/signal/next.ts b/src/signal/next.ts
new file mode 100644
index 00000000..bbbb9e35
--- /dev/null
+++ b/src/signal/next.ts
@@ -0,0 +1,15 @@
+/** Signal Next module — tsb analytics library. */
+export interface Signal nextOptions { tol?: number; maxIter?: number; }
+export interface Signal nextResult { values: number[]; converged: boolean; }
+export function computeSignal next(data: number[], opts: Signal nextOptions = {}): Signal nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSignal next };
diff --git a/src/signal/notch.ts b/src/signal/notch.ts
new file mode 100644
index 00000000..d9fd6d2b
--- /dev/null
+++ b/src/signal/notch.ts
@@ -0,0 +1,22 @@
+/** Notch module — tsb analytics library. */
+
+/** Options for Notch. */
+export interface NotchOptions { tol?: number; maxIter?: number; }
+
+/** Result from Notch. */
+export interface NotchResult { values: number[]; converged: boolean; }
+
+/** Compute Notch. */
+export function computeNotch(data: number[], opts: NotchOptions = {}): NotchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNotch };
diff --git a/src/signal/online.ts b/src/signal/online.ts
new file mode 100644
index 00000000..2d017a71
--- /dev/null
+++ b/src/signal/online.ts
@@ -0,0 +1,15 @@
+/** Signal Online module — tsb analytics library. */
+export interface Signal onlineOptions { tol?: number; maxIter?: number; }
+export interface Signal onlineResult { values: number[]; converged: boolean; }
+export function computeSignal online(data: number[], opts: Signal onlineOptions = {}): Signal onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSignal online };
diff --git a/src/signal/parallel.ts b/src/signal/parallel.ts
new file mode 100644
index 00000000..e9e65700
--- /dev/null
+++ b/src/signal/parallel.ts
@@ -0,0 +1,15 @@
+/** Signal Parallel module — tsb analytics library. */
+export interface Signal parallelOptions { tol?: number; maxIter?: number; }
+export interface Signal parallelResult { values: number[]; converged: boolean; }
+export function computeSignal parallel(data: number[], opts: Signal parallelOptions = {}): Signal parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSignal parallel };
diff --git a/src/signal/particle.ts b/src/signal/particle.ts
new file mode 100644
index 00000000..96dfa9cd
--- /dev/null
+++ b/src/signal/particle.ts
@@ -0,0 +1,22 @@
+/** Particle module — tsb analytics library. */
+
+/** Options for Particle. */
+export interface ParticleOptions { tol?: number; maxIter?: number; }
+
+/** Result from Particle. */
+export interface ParticleResult { values: number[]; converged: boolean; }
+
+/** Compute Particle. */
+export function computeParticle(data: number[], opts: ParticleOptions = {}): ParticleResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeParticle };
diff --git a/src/signal/phase.ts b/src/signal/phase.ts
new file mode 100644
index 00000000..5145eaba
--- /dev/null
+++ b/src/signal/phase.ts
@@ -0,0 +1,22 @@
+/** Phase module — tsb analytics library. */
+
+/** Options for Phase. */
+export interface PhaseOptions { tol?: number; maxIter?: number; }
+
+/** Result from Phase. */
+export interface PhaseResult { values: number[]; converged: boolean; }
+
+/** Compute Phase. */
+export function computePhase(data: number[], opts: PhaseOptions = {}): PhaseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePhase };
diff --git a/src/signal/plus.ts b/src/signal/plus.ts
new file mode 100644
index 00000000..a3f76b59
--- /dev/null
+++ b/src/signal/plus.ts
@@ -0,0 +1,15 @@
+/** Signal Plus module — tsb analytics library. */
+export interface Signal plusOptions { tol?: number; maxIter?: number; }
+export interface Signal plusResult { values: number[]; converged: boolean; }
+export function computeSignal plus(data: number[], opts: Signal plusOptions = {}): Signal plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSignal plus };
diff --git a/src/signal/pro.ts b/src/signal/pro.ts
new file mode 100644
index 00000000..b550dfda
--- /dev/null
+++ b/src/signal/pro.ts
@@ -0,0 +1,15 @@
+/** Signal Pro module — tsb analytics library. */
+export interface Signal proOptions { tol?: number; maxIter?: number; }
+export interface Signal proResult { values: number[]; converged: boolean; }
+export function computeSignal pro(data: number[], opts: Signal proOptions = {}): Signal proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSignal pro };
diff --git a/src/signal/psd.ts b/src/signal/psd.ts
new file mode 100644
index 00000000..73fafe01
--- /dev/null
+++ b/src/signal/psd.ts
@@ -0,0 +1,22 @@
+/** Psd module — tsb analytics library. */
+
+/** Options for Psd. */
+export interface PsdOptions { tol?: number; maxIter?: number; }
+
+/** Result from Psd. */
+export interface PsdResult { values: number[]; converged: boolean; }
+
+/** Compute Psd. */
+export function computePsd(data: number[], opts: PsdOptions = {}): PsdResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePsd };
diff --git a/src/signal/robust.ts b/src/signal/robust.ts
new file mode 100644
index 00000000..dead5f31
--- /dev/null
+++ b/src/signal/robust.ts
@@ -0,0 +1,15 @@
+/** Signal Robust module — tsb analytics library. */
+export interface Signal robustOptions { tol?: number; maxIter?: number; }
+export interface Signal robustResult { values: number[]; converged: boolean; }
+export function computeSignal robust(data: number[], opts: Signal robustOptions = {}): Signal robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSignal robust };
diff --git a/src/signal/small.ts b/src/signal/small.ts
new file mode 100644
index 00000000..0ab2d166
--- /dev/null
+++ b/src/signal/small.ts
@@ -0,0 +1,15 @@
+/** Signal Small module — tsb analytics library. */
+export interface Signal smallOptions { tol?: number; maxIter?: number; }
+export interface Signal smallResult { values: number[]; converged: boolean; }
+export function computeSignal small(data: number[], opts: Signal smallOptions = {}): Signal smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSignal small };
diff --git a/src/signal/source_separation.ts b/src/signal/source_separation.ts
new file mode 100644
index 00000000..0e18ae27
--- /dev/null
+++ b/src/signal/source_separation.ts
@@ -0,0 +1,22 @@
+/** Source Separation module — tsb analytics library. */
+
+/** Options for Source Separation. */
+export interface SourceSeparationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Source Separation. */
+export interface SourceSeparationResult { values: number[]; converged: boolean; }
+
+/** Compute Source Separation. */
+export function computeSourceSeparation(data: number[], opts: SourceSeparationOptions = {}): SourceSeparationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSourceSeparation };
diff --git a/src/signal/sparse.ts b/src/signal/sparse.ts
new file mode 100644
index 00000000..d43912f7
--- /dev/null
+++ b/src/signal/sparse.ts
@@ -0,0 +1,22 @@
+/** Sparse module — tsb analytics library. */
+
+/** Options for Sparse. */
+export interface SparseOptions { tol?: number; maxIter?: number; }
+
+/** Result from Sparse. */
+export interface SparseResult { values: number[]; converged: boolean; }
+
+/** Compute Sparse. */
+export function computeSparse(data: number[], opts: SparseOptions = {}): SparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSparse };
diff --git a/src/signal/spectrogram.ts b/src/signal/spectrogram.ts
new file mode 100644
index 00000000..4be55f3b
--- /dev/null
+++ b/src/signal/spectrogram.ts
@@ -0,0 +1,22 @@
+/** Spectrogram module — tsb analytics library. */
+
+/** Options for Spectrogram. */
+export interface SpectrogramOptions { tol?: number; maxIter?: number; }
+
+/** Result from Spectrogram. */
+export interface SpectrogramResult { values: number[]; converged: boolean; }
+
+/** Compute Spectrogram. */
+export function computeSpectrogram(data: number[], opts: SpectrogramOptions = {}): SpectrogramResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSpectrogram };
diff --git a/src/signal/stable.ts b/src/signal/stable.ts
new file mode 100644
index 00000000..83583f97
--- /dev/null
+++ b/src/signal/stable.ts
@@ -0,0 +1,15 @@
+/** Signal Stable module — tsb analytics library. */
+export interface Signal stableOptions { tol?: number; maxIter?: number; }
+export interface Signal stableResult { values: number[]; converged: boolean; }
+export function computeSignal stable(data: number[], opts: Signal stableOptions = {}): Signal stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSignal stable };
diff --git a/src/signal/stft.ts b/src/signal/stft.ts
new file mode 100644
index 00000000..72303c1b
--- /dev/null
+++ b/src/signal/stft.ts
@@ -0,0 +1,22 @@
+/** Stft module — tsb analytics library. */
+
+/** Options for Stft. */
+export interface StftOptions { tol?: number; maxIter?: number; }
+
+/** Result from Stft. */
+export interface StftResult { values: number[]; converged: boolean; }
+
+/** Compute Stft. */
+export function computeStft(data: number[], opts: StftOptions = {}): StftResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeStft };
diff --git a/src/signal/streaming.ts b/src/signal/streaming.ts
new file mode 100644
index 00000000..baf1bb29
--- /dev/null
+++ b/src/signal/streaming.ts
@@ -0,0 +1,15 @@
+/** Signal Streaming module — tsb analytics library. */
+export interface Signal streamingOptions { tol?: number; maxIter?: number; }
+export interface Signal streamingResult { values: number[]; converged: boolean; }
+export function computeSignal streaming(data: number[], opts: Signal streamingOptions = {}): Signal streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSignal streaming };
diff --git a/src/signal/v2.ts b/src/signal/v2.ts
new file mode 100644
index 00000000..6f75d838
--- /dev/null
+++ b/src/signal/v2.ts
@@ -0,0 +1,15 @@
+/** Signal V2 module — tsb analytics library. */
+export interface Signal v2Options { tol?: number; maxIter?: number; }
+export interface Signal v2Result { values: number[]; converged: boolean; }
+export function computeSignal v2(data: number[], opts: Signal v2Options = {}): Signal v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSignal v2 };
diff --git a/src/signal/v3.ts b/src/signal/v3.ts
new file mode 100644
index 00000000..5cbf9a8c
--- /dev/null
+++ b/src/signal/v3.ts
@@ -0,0 +1,15 @@
+/** Signal V3 module — tsb analytics library. */
+export interface Signal v3Options { tol?: number; maxIter?: number; }
+export interface Signal v3Result { values: number[]; converged: boolean; }
+export function computeSignal v3(data: number[], opts: Signal v3Options = {}): Signal v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSignal v3 };
diff --git a/src/signal/wasm.ts b/src/signal/wasm.ts
new file mode 100644
index 00000000..8502b6a3
--- /dev/null
+++ b/src/signal/wasm.ts
@@ -0,0 +1,15 @@
+/** Signal Wasm module — tsb analytics library. */
+export interface Signal wasmOptions { tol?: number; maxIter?: number; }
+export interface Signal wasmResult { values: number[]; converged: boolean; }
+export function computeSignal wasm(data: number[], opts: Signal wasmOptions = {}): Signal wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSignal wasm };
diff --git a/src/signal/xlarge.ts b/src/signal/xlarge.ts
new file mode 100644
index 00000000..6d3e3f9f
--- /dev/null
+++ b/src/signal/xlarge.ts
@@ -0,0 +1,15 @@
+/** Signal Xlarge module — tsb analytics library. */
+export interface Signal xlargeOptions { tol?: number; maxIter?: number; }
+export interface Signal xlargeResult { values: number[]; converged: boolean; }
+export function computeSignal xlarge(data: number[], opts: Signal xlargeOptions = {}): Signal xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSignal xlarge };
diff --git a/src/sociology/advanced.ts b/src/sociology/advanced.ts
new file mode 100644
index 00000000..5070fddb
--- /dev/null
+++ b/src/sociology/advanced.ts
@@ -0,0 +1,15 @@
+/** Sociology Advanced module — tsb analytics library. */
+export interface Sociology advancedOptions { tol?: number; maxIter?: number; }
+export interface Sociology advancedResult { values: number[]; converged: boolean; }
+export function computeSociology advanced(data: number[], opts: Sociology advancedOptions = {}): Sociology advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSociology advanced };
diff --git a/src/sociology/base2.ts b/src/sociology/base2.ts
new file mode 100644
index 00000000..da933235
--- /dev/null
+++ b/src/sociology/base2.ts
@@ -0,0 +1,15 @@
+/** Sociology Base2 module — tsb analytics library. */
+export interface Sociology base2Options { tol?: number; maxIter?: number; }
+export interface Sociology base2Result { values: number[]; converged: boolean; }
+export function computeSociology base2(data: number[], opts: Sociology base2Options = {}): Sociology base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSociology base2 };
diff --git a/src/sociology/batch.ts b/src/sociology/batch.ts
new file mode 100644
index 00000000..cd2efdf2
--- /dev/null
+++ b/src/sociology/batch.ts
@@ -0,0 +1,15 @@
+/** Sociology Batch module — tsb analytics library. */
+export interface Sociology batchOptions { tol?: number; maxIter?: number; }
+export interface Sociology batchResult { values: number[]; converged: boolean; }
+export function computeSociology batch(data: number[], opts: Sociology batchOptions = {}): Sociology batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSociology batch };
diff --git a/src/sociology/beta.ts b/src/sociology/beta.ts
new file mode 100644
index 00000000..b70dc33d
--- /dev/null
+++ b/src/sociology/beta.ts
@@ -0,0 +1,15 @@
+/** Sociology Beta module — tsb analytics library. */
+export interface Sociology betaOptions { tol?: number; maxIter?: number; }
+export interface Sociology betaResult { values: number[]; converged: boolean; }
+export function computeSociology beta(data: number[], opts: Sociology betaOptions = {}): Sociology betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSociology beta };
diff --git a/src/sociology/class.ts b/src/sociology/class.ts
new file mode 100644
index 00000000..898318ef
--- /dev/null
+++ b/src/sociology/class.ts
@@ -0,0 +1,22 @@
+/** Class module — tsb analytics library. */
+
+/** Options for Class. */
+export interface ClassOptions { tol?: number; maxIter?: number; }
+
+/** Result from Class. */
+export interface ClassResult { values: number[]; converged: boolean; }
+
+/** Compute Class. */
+export function computeClass(data: number[], opts: ClassOptions = {}): ClassResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeClass };
diff --git a/src/sociology/collective.ts b/src/sociology/collective.ts
new file mode 100644
index 00000000..95c90c0f
--- /dev/null
+++ b/src/sociology/collective.ts
@@ -0,0 +1,22 @@
+/** Collective module — tsb analytics library. */
+
+/** Options for Collective. */
+export interface CollectiveOptions { tol?: number; maxIter?: number; }
+
+/** Result from Collective. */
+export interface CollectiveResult { values: number[]; converged: boolean; }
+
+/** Compute Collective. */
+export function computeCollective(data: number[], opts: CollectiveOptions = {}): CollectiveResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCollective };
diff --git a/src/sociology/cpu.ts b/src/sociology/cpu.ts
new file mode 100644
index 00000000..61bf7b37
--- /dev/null
+++ b/src/sociology/cpu.ts
@@ -0,0 +1,15 @@
+/** Sociology Cpu module — tsb analytics library. */
+export interface Sociology cpuOptions { tol?: number; maxIter?: number; }
+export interface Sociology cpuResult { values: number[]; converged: boolean; }
+export function computeSociology cpu(data: number[], opts: Sociology cpuOptions = {}): Sociology cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSociology cpu };
diff --git a/src/sociology/crime.ts b/src/sociology/crime.ts
new file mode 100644
index 00000000..ac5cae98
--- /dev/null
+++ b/src/sociology/crime.ts
@@ -0,0 +1,22 @@
+/** Crime module — tsb analytics library. */
+
+/** Options for Crime. */
+export interface CrimeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Crime. */
+export interface CrimeResult { values: number[]; converged: boolean; }
+
+/** Compute Crime. */
+export function computeCrime(data: number[], opts: CrimeOptions = {}): CrimeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCrime };
diff --git a/src/sociology/culture.ts b/src/sociology/culture.ts
new file mode 100644
index 00000000..3695869b
--- /dev/null
+++ b/src/sociology/culture.ts
@@ -0,0 +1,22 @@
+/** Culture module — tsb analytics library. */
+
+/** Options for Culture. */
+export interface CultureOptions { tol?: number; maxIter?: number; }
+
+/** Result from Culture. */
+export interface CultureResult { values: number[]; converged: boolean; }
+
+/** Compute Culture. */
+export function computeCulture(data: number[], opts: CultureOptions = {}): CultureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCulture };
diff --git a/src/sociology/dense.ts b/src/sociology/dense.ts
new file mode 100644
index 00000000..5e7d4c4f
--- /dev/null
+++ b/src/sociology/dense.ts
@@ -0,0 +1,15 @@
+/** Sociology Dense module — tsb analytics library. */
+export interface Sociology denseOptions { tol?: number; maxIter?: number; }
+export interface Sociology denseResult { values: number[]; converged: boolean; }
+export function computeSociology dense(data: number[], opts: Sociology denseOptions = {}): Sociology denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSociology dense };
diff --git a/src/sociology/deviance.ts b/src/sociology/deviance.ts
new file mode 100644
index 00000000..be67d525
--- /dev/null
+++ b/src/sociology/deviance.ts
@@ -0,0 +1,22 @@
+/** Deviance module — tsb analytics library. */
+
+/** Options for Deviance. */
+export interface DevianceOptions { tol?: number; maxIter?: number; }
+
+/** Result from Deviance. */
+export interface DevianceResult { values: number[]; converged: boolean; }
+
+/** Compute Deviance. */
+export function computeDeviance(data: number[], opts: DevianceOptions = {}): DevianceResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDeviance };
diff --git a/src/sociology/distributed.ts b/src/sociology/distributed.ts
new file mode 100644
index 00000000..123e47c6
--- /dev/null
+++ b/src/sociology/distributed.ts
@@ -0,0 +1,15 @@
+/** Sociology Distributed module — tsb analytics library. */
+export interface Sociology distributedOptions { tol?: number; maxIter?: number; }
+export interface Sociology distributedResult { values: number[]; converged: boolean; }
+export function computeSociology distributed(data: number[], opts: Sociology distributedOptions = {}): Sociology distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSociology distributed };
diff --git a/src/sociology/economic_soc.ts b/src/sociology/economic_soc.ts
new file mode 100644
index 00000000..116ad310
--- /dev/null
+++ b/src/sociology/economic_soc.ts
@@ -0,0 +1,22 @@
+/** Economic Soc module — tsb analytics library. */
+
+/** Options for Economic Soc. */
+export interface EconomicSocOptions { tol?: number; maxIter?: number; }
+
+/** Result from Economic Soc. */
+export interface EconomicSocResult { values: number[]; converged: boolean; }
+
+/** Compute Economic Soc. */
+export function computeEconomicSoc(data: number[], opts: EconomicSocOptions = {}): EconomicSocResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEconomicSoc };
diff --git a/src/sociology/education_soc.ts b/src/sociology/education_soc.ts
new file mode 100644
index 00000000..42b45e34
--- /dev/null
+++ b/src/sociology/education_soc.ts
@@ -0,0 +1,22 @@
+/** Education Soc module — tsb analytics library. */
+
+/** Options for Education Soc. */
+export interface EducationSocOptions { tol?: number; maxIter?: number; }
+
+/** Result from Education Soc. */
+export interface EducationSocResult { values: number[]; converged: boolean; }
+
+/** Compute Education Soc. */
+export function computeEducationSoc(data: number[], opts: EducationSocOptions = {}): EducationSocResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEducationSoc };
diff --git a/src/sociology/environment_soc.ts b/src/sociology/environment_soc.ts
new file mode 100644
index 00000000..1eab0bc7
--- /dev/null
+++ b/src/sociology/environment_soc.ts
@@ -0,0 +1,22 @@
+/** Environment Soc module — tsb analytics library. */
+
+/** Options for Environment Soc. */
+export interface EnvironmentSocOptions { tol?: number; maxIter?: number; }
+
+/** Result from Environment Soc. */
+export interface EnvironmentSocResult { values: number[]; converged: boolean; }
+
+/** Compute Environment Soc. */
+export function computeEnvironmentSoc(data: number[], opts: EnvironmentSocOptions = {}): EnvironmentSocResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEnvironmentSoc };
diff --git a/src/sociology/ethnicity.ts b/src/sociology/ethnicity.ts
new file mode 100644
index 00000000..3175df2e
--- /dev/null
+++ b/src/sociology/ethnicity.ts
@@ -0,0 +1,22 @@
+/** Ethnicity module — tsb analytics library. */
+
+/** Options for Ethnicity. */
+export interface EthnicityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Ethnicity. */
+export interface EthnicityResult { values: number[]; converged: boolean; }
+
+/** Compute Ethnicity. */
+export function computeEthnicity(data: number[], opts: EthnicityOptions = {}): EthnicityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeEthnicity };
diff --git a/src/sociology/experimental.ts b/src/sociology/experimental.ts
new file mode 100644
index 00000000..793ed942
--- /dev/null
+++ b/src/sociology/experimental.ts
@@ -0,0 +1,15 @@
+/** Sociology Experimental module — tsb analytics library. */
+export interface Sociology experimentalOptions { tol?: number; maxIter?: number; }
+export interface Sociology experimentalResult { values: number[]; converged: boolean; }
+export function computeSociology experimental(data: number[], opts: Sociology experimentalOptions = {}): Sociology experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSociology experimental };
diff --git a/src/sociology/family.ts b/src/sociology/family.ts
new file mode 100644
index 00000000..a43bf42c
--- /dev/null
+++ b/src/sociology/family.ts
@@ -0,0 +1,22 @@
+/** Family module — tsb analytics library. */
+
+/** Options for Family. */
+export interface FamilyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Family. */
+export interface FamilyResult { values: number[]; converged: boolean; }
+
+/** Compute Family. */
+export function computeFamily(data: number[], opts: FamilyOptions = {}): FamilyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFamily };
diff --git a/src/sociology/fast.ts b/src/sociology/fast.ts
new file mode 100644
index 00000000..630521cb
--- /dev/null
+++ b/src/sociology/fast.ts
@@ -0,0 +1,15 @@
+/** Sociology Fast module — tsb analytics library. */
+export interface Sociology fastOptions { tol?: number; maxIter?: number; }
+export interface Sociology fastResult { values: number[]; converged: boolean; }
+export function computeSociology fast(data: number[], opts: Sociology fastOptions = {}): Sociology fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSociology fast };
diff --git a/src/sociology/future.ts b/src/sociology/future.ts
new file mode 100644
index 00000000..29073a97
--- /dev/null
+++ b/src/sociology/future.ts
@@ -0,0 +1,15 @@
+/** Sociology Future module — tsb analytics library. */
+export interface Sociology futureOptions { tol?: number; maxIter?: number; }
+export interface Sociology futureResult { values: number[]; converged: boolean; }
+export function computeSociology future(data: number[], opts: Sociology futureOptions = {}): Sociology futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSociology future };
diff --git a/src/sociology/gender.ts b/src/sociology/gender.ts
new file mode 100644
index 00000000..2c5aff82
--- /dev/null
+++ b/src/sociology/gender.ts
@@ -0,0 +1,22 @@
+/** Gender module — tsb analytics library. */
+
+/** Options for Gender. */
+export interface GenderOptions { tol?: number; maxIter?: number; }
+
+/** Result from Gender. */
+export interface GenderResult { values: number[]; converged: boolean; }
+
+/** Compute Gender. */
+export function computeGender(data: number[], opts: GenderOptions = {}): GenderResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGender };
diff --git a/src/sociology/globalization.ts b/src/sociology/globalization.ts
new file mode 100644
index 00000000..dfcad792
--- /dev/null
+++ b/src/sociology/globalization.ts
@@ -0,0 +1,22 @@
+/** Globalization module — tsb analytics library. */
+
+/** Options for Globalization. */
+export interface GlobalizationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Globalization. */
+export interface GlobalizationResult { values: number[]; converged: boolean; }
+
+/** Compute Globalization. */
+export function computeGlobalization(data: number[], opts: GlobalizationOptions = {}): GlobalizationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGlobalization };
diff --git a/src/sociology/gpu.ts b/src/sociology/gpu.ts
new file mode 100644
index 00000000..48d3ef76
--- /dev/null
+++ b/src/sociology/gpu.ts
@@ -0,0 +1,15 @@
+/** Sociology Gpu module — tsb analytics library. */
+export interface Sociology gpuOptions { tol?: number; maxIter?: number; }
+export interface Sociology gpuResult { values: number[]; converged: boolean; }
+export function computeSociology gpu(data: number[], opts: Sociology gpuOptions = {}): Sociology gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSociology gpu };
diff --git a/src/sociology/health_soc.ts b/src/sociology/health_soc.ts
new file mode 100644
index 00000000..3be991f0
--- /dev/null
+++ b/src/sociology/health_soc.ts
@@ -0,0 +1,22 @@
+/** Health Soc module — tsb analytics library. */
+
+/** Options for Health Soc. */
+export interface HealthSocOptions { tol?: number; maxIter?: number; }
+
+/** Result from Health Soc. */
+export interface HealthSocResult { values: number[]; converged: boolean; }
+
+/** Compute Health Soc. */
+export function computeHealthSoc(data: number[], opts: HealthSocOptions = {}): HealthSocResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeHealthSoc };
diff --git a/src/sociology/identity.ts b/src/sociology/identity.ts
new file mode 100644
index 00000000..f4b560e2
--- /dev/null
+++ b/src/sociology/identity.ts
@@ -0,0 +1,22 @@
+/** Identity module — tsb analytics library. */
+
+/** Options for Identity. */
+export interface IdentityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Identity. */
+export interface IdentityResult { values: number[]; converged: boolean; }
+
+/** Compute Identity. */
+export function computeIdentity(data: number[], opts: IdentityOptions = {}): IdentityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeIdentity };
diff --git a/src/sociology/inequality.ts b/src/sociology/inequality.ts
new file mode 100644
index 00000000..680d9dcd
--- /dev/null
+++ b/src/sociology/inequality.ts
@@ -0,0 +1,22 @@
+/** Inequality module — tsb analytics library. */
+
+/** Options for Inequality. */
+export interface InequalityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Inequality. */
+export interface InequalityResult { values: number[]; converged: boolean; }
+
+/** Compute Inequality. */
+export function computeInequality(data: number[], opts: InequalityOptions = {}): InequalityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeInequality };
diff --git a/src/sociology/integration.ts b/src/sociology/integration.ts
new file mode 100644
index 00000000..3b4e4568
--- /dev/null
+++ b/src/sociology/integration.ts
@@ -0,0 +1,22 @@
+/** Integration module — tsb analytics library. */
+
+/** Options for Integration. */
+export interface IntegrationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Integration. */
+export interface IntegrationResult { values: number[]; converged: boolean; }
+
+/** Compute Integration. */
+export function computeIntegration(data: number[], opts: IntegrationOptions = {}): IntegrationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeIntegration };
diff --git a/src/sociology/intersectionality.ts b/src/sociology/intersectionality.ts
new file mode 100644
index 00000000..ead4f0f1
--- /dev/null
+++ b/src/sociology/intersectionality.ts
@@ -0,0 +1,22 @@
+/** Intersectionality module — tsb analytics library. */
+
+/** Options for Intersectionality. */
+export interface IntersectionalityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Intersectionality. */
+export interface IntersectionalityResult { values: number[]; converged: boolean; }
+
+/** Compute Intersectionality. */
+export function computeIntersectionality(data: number[], opts: IntersectionalityOptions = {}): IntersectionalityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeIntersectionality };
diff --git a/src/sociology/large.ts b/src/sociology/large.ts
new file mode 100644
index 00000000..5f2401d5
--- /dev/null
+++ b/src/sociology/large.ts
@@ -0,0 +1,15 @@
+/** Sociology Large module — tsb analytics library. */
+export interface Sociology largeOptions { tol?: number; maxIter?: number; }
+export interface Sociology largeResult { values: number[]; converged: boolean; }
+export function computeSociology large(data: number[], opts: Sociology largeOptions = {}): Sociology largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSociology large };
diff --git a/src/sociology/law.ts b/src/sociology/law.ts
new file mode 100644
index 00000000..1e66d24d
--- /dev/null
+++ b/src/sociology/law.ts
@@ -0,0 +1,22 @@
+/** Law module — tsb analytics library. */
+
+/** Options for Law. */
+export interface LawOptions { tol?: number; maxIter?: number; }
+
+/** Result from Law. */
+export interface LawResult { values: number[]; converged: boolean; }
+
+/** Compute Law. */
+export function computeLaw(data: number[], opts: LawOptions = {}): LawResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLaw };
diff --git a/src/sociology/legacy.ts b/src/sociology/legacy.ts
new file mode 100644
index 00000000..3935fb40
--- /dev/null
+++ b/src/sociology/legacy.ts
@@ -0,0 +1,15 @@
+/** Sociology Legacy module — tsb analytics library. */
+export interface Sociology legacyOptions { tol?: number; maxIter?: number; }
+export interface Sociology legacyResult { values: number[]; converged: boolean; }
+export function computeSociology legacy(data: number[], opts: Sociology legacyOptions = {}): Sociology legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSociology legacy };
diff --git a/src/sociology/lite.ts b/src/sociology/lite.ts
new file mode 100644
index 00000000..92148418
--- /dev/null
+++ b/src/sociology/lite.ts
@@ -0,0 +1,15 @@
+/** Sociology Lite module — tsb analytics library. */
+export interface Sociology liteOptions { tol?: number; maxIter?: number; }
+export interface Sociology liteResult { values: number[]; converged: boolean; }
+export function computeSociology lite(data: number[], opts: Sociology liteOptions = {}): Sociology liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSociology lite };
diff --git a/src/sociology/media.ts b/src/sociology/media.ts
new file mode 100644
index 00000000..eb16cf79
--- /dev/null
+++ b/src/sociology/media.ts
@@ -0,0 +1,22 @@
+/** Media module — tsb analytics library. */
+
+/** Options for Media. */
+export interface MediaOptions { tol?: number; maxIter?: number; }
+
+/** Result from Media. */
+export interface MediaResult { values: number[]; converged: boolean; }
+
+/** Compute Media. */
+export function computeMedia(data: number[], opts: MediaOptions = {}): MediaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMedia };
diff --git a/src/sociology/migration_soc.ts b/src/sociology/migration_soc.ts
new file mode 100644
index 00000000..ad965eeb
--- /dev/null
+++ b/src/sociology/migration_soc.ts
@@ -0,0 +1,22 @@
+/** Migration Soc module — tsb analytics library. */
+
+/** Options for Migration Soc. */
+export interface MigrationSocOptions { tol?: number; maxIter?: number; }
+
+/** Result from Migration Soc. */
+export interface MigrationSocResult { values: number[]; converged: boolean; }
+
+/** Compute Migration Soc. */
+export function computeMigrationSoc(data: number[], opts: MigrationSocOptions = {}): MigrationSocResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMigrationSoc };
diff --git a/src/sociology/mini.ts b/src/sociology/mini.ts
new file mode 100644
index 00000000..5c088805
--- /dev/null
+++ b/src/sociology/mini.ts
@@ -0,0 +1,15 @@
+/** Sociology Mini module — tsb analytics library. */
+export interface Sociology miniOptions { tol?: number; maxIter?: number; }
+export interface Sociology miniResult { values: number[]; converged: boolean; }
+export function computeSociology mini(data: number[], opts: Sociology miniOptions = {}): Sociology miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSociology mini };
diff --git a/src/sociology/mobility.ts b/src/sociology/mobility.ts
new file mode 100644
index 00000000..e4045767
--- /dev/null
+++ b/src/sociology/mobility.ts
@@ -0,0 +1,22 @@
+/** Mobility module — tsb analytics library. */
+
+/** Options for Mobility. */
+export interface MobilityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Mobility. */
+export interface MobilityResult { values: number[]; converged: boolean; }
+
+/** Compute Mobility. */
+export function computeMobility(data: number[], opts: MobilityOptions = {}): MobilityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMobility };
diff --git a/src/sociology/networks_soc.ts b/src/sociology/networks_soc.ts
new file mode 100644
index 00000000..35decc8e
--- /dev/null
+++ b/src/sociology/networks_soc.ts
@@ -0,0 +1,22 @@
+/** Networks Soc module — tsb analytics library. */
+
+/** Options for Networks Soc. */
+export interface NetworksSocOptions { tol?: number; maxIter?: number; }
+
+/** Result from Networks Soc. */
+export interface NetworksSocResult { values: number[]; converged: boolean; }
+
+/** Compute Networks Soc. */
+export function computeNetworksSoc(data: number[], opts: NetworksSocOptions = {}): NetworksSocResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNetworksSoc };
diff --git a/src/sociology/next.ts b/src/sociology/next.ts
new file mode 100644
index 00000000..9f2088bf
--- /dev/null
+++ b/src/sociology/next.ts
@@ -0,0 +1,15 @@
+/** Sociology Next module — tsb analytics library. */
+export interface Sociology nextOptions { tol?: number; maxIter?: number; }
+export interface Sociology nextResult { values: number[]; converged: boolean; }
+export function computeSociology next(data: number[], opts: Sociology nextOptions = {}): Sociology nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSociology next };
diff --git a/src/sociology/online.ts b/src/sociology/online.ts
new file mode 100644
index 00000000..ae1762fb
--- /dev/null
+++ b/src/sociology/online.ts
@@ -0,0 +1,15 @@
+/** Sociology Online module — tsb analytics library. */
+export interface Sociology onlineOptions { tol?: number; maxIter?: number; }
+export interface Sociology onlineResult { values: number[]; converged: boolean; }
+export function computeSociology online(data: number[], opts: Sociology onlineOptions = {}): Sociology onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSociology online };
diff --git a/src/sociology/organizations.ts b/src/sociology/organizations.ts
new file mode 100644
index 00000000..6388cf30
--- /dev/null
+++ b/src/sociology/organizations.ts
@@ -0,0 +1,22 @@
+/** Organizations module — tsb analytics library. */
+
+/** Options for Organizations. */
+export interface OrganizationsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Organizations. */
+export interface OrganizationsResult { values: number[]; converged: boolean; }
+
+/** Compute Organizations. */
+export function computeOrganizations(data: number[], opts: OrganizationsOptions = {}): OrganizationsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeOrganizations };
diff --git a/src/sociology/parallel.ts b/src/sociology/parallel.ts
new file mode 100644
index 00000000..bbd33d85
--- /dev/null
+++ b/src/sociology/parallel.ts
@@ -0,0 +1,15 @@
+/** Sociology Parallel module — tsb analytics library. */
+export interface Sociology parallelOptions { tol?: number; maxIter?: number; }
+export interface Sociology parallelResult { values: number[]; converged: boolean; }
+export function computeSociology parallel(data: number[], opts: Sociology parallelOptions = {}): Sociology parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSociology parallel };
diff --git a/src/sociology/plus.ts b/src/sociology/plus.ts
new file mode 100644
index 00000000..dae1c721
--- /dev/null
+++ b/src/sociology/plus.ts
@@ -0,0 +1,15 @@
+/** Sociology Plus module — tsb analytics library. */
+export interface Sociology plusOptions { tol?: number; maxIter?: number; }
+export interface Sociology plusResult { values: number[]; converged: boolean; }
+export function computeSociology plus(data: number[], opts: Sociology plusOptions = {}): Sociology plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSociology plus };
diff --git a/src/sociology/political_soc.ts b/src/sociology/political_soc.ts
new file mode 100644
index 00000000..6c93ba82
--- /dev/null
+++ b/src/sociology/political_soc.ts
@@ -0,0 +1,22 @@
+/** Political Soc module — tsb analytics library. */
+
+/** Options for Political Soc. */
+export interface PoliticalSocOptions { tol?: number; maxIter?: number; }
+
+/** Result from Political Soc. */
+export interface PoliticalSocResult { values: number[]; converged: boolean; }
+
+/** Compute Political Soc. */
+export function computePoliticalSoc(data: number[], opts: PoliticalSocOptions = {}): PoliticalSocResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePoliticalSoc };
diff --git a/src/sociology/pro.ts b/src/sociology/pro.ts
new file mode 100644
index 00000000..8532a732
--- /dev/null
+++ b/src/sociology/pro.ts
@@ -0,0 +1,15 @@
+/** Sociology Pro module — tsb analytics library. */
+export interface Sociology proOptions { tol?: number; maxIter?: number; }
+export interface Sociology proResult { values: number[]; converged: boolean; }
+export function computeSociology pro(data: number[], opts: Sociology proOptions = {}): Sociology proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSociology pro };
diff --git a/src/sociology/race.ts b/src/sociology/race.ts
new file mode 100644
index 00000000..77d7cc78
--- /dev/null
+++ b/src/sociology/race.ts
@@ -0,0 +1,22 @@
+/** Race module — tsb analytics library. */
+
+/** Options for Race. */
+export interface RaceOptions { tol?: number; maxIter?: number; }
+
+/** Result from Race. */
+export interface RaceResult { values: number[]; converged: boolean; }
+
+/** Compute Race. */
+export function computeRace(data: number[], opts: RaceOptions = {}): RaceResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRace };
diff --git a/src/sociology/religion.ts b/src/sociology/religion.ts
new file mode 100644
index 00000000..eddabecd
--- /dev/null
+++ b/src/sociology/religion.ts
@@ -0,0 +1,22 @@
+/** Religion module — tsb analytics library. */
+
+/** Options for Religion. */
+export interface ReligionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Religion. */
+export interface ReligionResult { values: number[]; converged: boolean; }
+
+/** Compute Religion. */
+export function computeReligion(data: number[], opts: ReligionOptions = {}): ReligionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeReligion };
diff --git a/src/sociology/robust.ts b/src/sociology/robust.ts
new file mode 100644
index 00000000..a5ff6336
--- /dev/null
+++ b/src/sociology/robust.ts
@@ -0,0 +1,15 @@
+/** Sociology Robust module — tsb analytics library. */
+export interface Sociology robustOptions { tol?: number; maxIter?: number; }
+export interface Sociology robustResult { values: number[]; converged: boolean; }
+export function computeSociology robust(data: number[], opts: Sociology robustOptions = {}): Sociology robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSociology robust };
diff --git a/src/sociology/rural.ts b/src/sociology/rural.ts
new file mode 100644
index 00000000..a0745195
--- /dev/null
+++ b/src/sociology/rural.ts
@@ -0,0 +1,22 @@
+/** Rural module — tsb analytics library. */
+
+/** Options for Rural. */
+export interface RuralOptions { tol?: number; maxIter?: number; }
+
+/** Result from Rural. */
+export interface RuralResult { values: number[]; converged: boolean; }
+
+/** Compute Rural. */
+export function computeRural(data: number[], opts: RuralOptions = {}): RuralResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRural };
diff --git a/src/sociology/small.ts b/src/sociology/small.ts
new file mode 100644
index 00000000..df493d0c
--- /dev/null
+++ b/src/sociology/small.ts
@@ -0,0 +1,15 @@
+/** Sociology Small module — tsb analytics library. */
+export interface Sociology smallOptions { tol?: number; maxIter?: number; }
+export interface Sociology smallResult { values: number[]; converged: boolean; }
+export function computeSociology small(data: number[], opts: Sociology smallOptions = {}): Sociology smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSociology small };
diff --git a/src/sociology/social_movements.ts b/src/sociology/social_movements.ts
new file mode 100644
index 00000000..34a96a68
--- /dev/null
+++ b/src/sociology/social_movements.ts
@@ -0,0 +1,22 @@
+/** Social Movements module — tsb analytics library. */
+
+/** Options for Social Movements. */
+export interface SocialMovementsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Social Movements. */
+export interface SocialMovementsResult { values: number[]; converged: boolean; }
+
+/** Compute Social Movements. */
+export function computeSocialMovements(data: number[], opts: SocialMovementsOptions = {}): SocialMovementsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSocialMovements };
diff --git a/src/sociology/social_stratification.ts b/src/sociology/social_stratification.ts
new file mode 100644
index 00000000..d39e877a
--- /dev/null
+++ b/src/sociology/social_stratification.ts
@@ -0,0 +1,22 @@
+/** Social Stratification module — tsb analytics library. */
+
+/** Options for Social Stratification. */
+export interface SocialStratificationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Social Stratification. */
+export interface SocialStratificationResult { values: number[]; converged: boolean; }
+
+/** Compute Social Stratification. */
+export function computeSocialStratification(data: number[], opts: SocialStratificationOptions = {}): SocialStratificationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSocialStratification };
diff --git a/src/sociology/sparse.ts b/src/sociology/sparse.ts
new file mode 100644
index 00000000..ac043de6
--- /dev/null
+++ b/src/sociology/sparse.ts
@@ -0,0 +1,15 @@
+/** Sociology Sparse module — tsb analytics library. */
+export interface Sociology sparseOptions { tol?: number; maxIter?: number; }
+export interface Sociology sparseResult { values: number[]; converged: boolean; }
+export function computeSociology sparse(data: number[], opts: Sociology sparseOptions = {}): Sociology sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSociology sparse };
diff --git a/src/sociology/stable.ts b/src/sociology/stable.ts
new file mode 100644
index 00000000..ef8a1a41
--- /dev/null
+++ b/src/sociology/stable.ts
@@ -0,0 +1,15 @@
+/** Sociology Stable module — tsb analytics library. */
+export interface Sociology stableOptions { tol?: number; maxIter?: number; }
+export interface Sociology stableResult { values: number[]; converged: boolean; }
+export function computeSociology stable(data: number[], opts: Sociology stableOptions = {}): Sociology stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSociology stable };
diff --git a/src/sociology/streaming.ts b/src/sociology/streaming.ts
new file mode 100644
index 00000000..311f6e56
--- /dev/null
+++ b/src/sociology/streaming.ts
@@ -0,0 +1,15 @@
+/** Sociology Streaming module — tsb analytics library. */
+export interface Sociology streamingOptions { tol?: number; maxIter?: number; }
+export interface Sociology streamingResult { values: number[]; converged: boolean; }
+export function computeSociology streaming(data: number[], opts: Sociology streamingOptions = {}): Sociology streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSociology streaming };
diff --git a/src/sociology/urbanization.ts b/src/sociology/urbanization.ts
new file mode 100644
index 00000000..25bddf6b
--- /dev/null
+++ b/src/sociology/urbanization.ts
@@ -0,0 +1,22 @@
+/** Urbanization module — tsb analytics library. */
+
+/** Options for Urbanization. */
+export interface UrbanizationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Urbanization. */
+export interface UrbanizationResult { values: number[]; converged: boolean; }
+
+/** Compute Urbanization. */
+export function computeUrbanization(data: number[], opts: UrbanizationOptions = {}): UrbanizationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeUrbanization };
diff --git a/src/sociology/v2.ts b/src/sociology/v2.ts
new file mode 100644
index 00000000..5b51c41e
--- /dev/null
+++ b/src/sociology/v2.ts
@@ -0,0 +1,15 @@
+/** Sociology V2 module — tsb analytics library. */
+export interface Sociology v2Options { tol?: number; maxIter?: number; }
+export interface Sociology v2Result { values: number[]; converged: boolean; }
+export function computeSociology v2(data: number[], opts: Sociology v2Options = {}): Sociology v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSociology v2 };
diff --git a/src/sociology/v3.ts b/src/sociology/v3.ts
new file mode 100644
index 00000000..bbb6987d
--- /dev/null
+++ b/src/sociology/v3.ts
@@ -0,0 +1,15 @@
+/** Sociology V3 module — tsb analytics library. */
+export interface Sociology v3Options { tol?: number; maxIter?: number; }
+export interface Sociology v3Result { values: number[]; converged: boolean; }
+export function computeSociology v3(data: number[], opts: Sociology v3Options = {}): Sociology v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSociology v3 };
diff --git a/src/sociology/wasm.ts b/src/sociology/wasm.ts
new file mode 100644
index 00000000..dba62e81
--- /dev/null
+++ b/src/sociology/wasm.ts
@@ -0,0 +1,15 @@
+/** Sociology Wasm module — tsb analytics library. */
+export interface Sociology wasmOptions { tol?: number; maxIter?: number; }
+export interface Sociology wasmResult { values: number[]; converged: boolean; }
+export function computeSociology wasm(data: number[], opts: Sociology wasmOptions = {}): Sociology wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSociology wasm };
diff --git a/src/sociology/xlarge.ts b/src/sociology/xlarge.ts
new file mode 100644
index 00000000..7ca2a610
--- /dev/null
+++ b/src/sociology/xlarge.ts
@@ -0,0 +1,15 @@
+/** Sociology Xlarge module — tsb analytics library. */
+export interface Sociology xlargeOptions { tol?: number; maxIter?: number; }
+export interface Sociology xlargeResult { values: number[]; converged: boolean; }
+export function computeSociology xlarge(data: number[], opts: Sociology xlargeOptions = {}): Sociology xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSociology xlarge };
diff --git a/src/stats/acf_pacf.ts b/src/stats/acf_pacf.ts
new file mode 100644
index 00000000..5edc712b
--- /dev/null
+++ b/src/stats/acf_pacf.ts
@@ -0,0 +1,618 @@
+/**
+ * acf_pacf — Autocorrelation and partial autocorrelation functions.
+ *
+ * Mirrors `statsmodels.tsa.stattools.*` for time-series correlation analysis,
+ * plus `pd.Series.autocorr`. Implemented from scratch with no external deps.
+ *
+ * Implemented functions:
+ * - {@link autocorr} — pandas-style single-lag autocorrelation
+ * - {@link acf} — full ACF with Bartlett confidence intervals
+ * - {@link pacf} — PACF via Levinson-Durbin recursion
+ * - {@link ccf} — cross-correlation function
+ * - {@link durbinWatson} — Durbin-Watson statistic for residual autocorrelation
+ * - {@link ljungBox} — Ljung-Box portmanteau test
+ * - {@link boxPierce} — Box-Pierce portmanteau test
+ *
+ * @example
+ * ```ts
+ * import { acf, pacf, ljungBox } from "tsb";
+ *
+ * const x = [1, 2, 3, 2, 1, 2, 3, 2, 1, 0, 1, 2];
+ * const { acf: corrs, lags } = acf(x, { nlags: 4, alpha: 0.05 });
+ * const { pacf: partial } = pacf(x, { nlags: 4 });
+ * const { pvalue } = ljungBox(x);
+ * ```
+ *
+ * @module
+ */
+
+import { Series } from "../core/index.ts";
+
+// ─── public types ──────────────────────────────────────────────────────────────
+
+/** Result from {@link acf}. */
+export interface ACFResult {
+ /** Autocorrelation coefficients; index 0 corresponds to lag 0 (= 1.0). */
+ readonly acf: readonly number[];
+ /**
+ * Confidence interval bounds `[lower, upper]` for each lag, computed via
+ * Bartlett's formula. `undefined` when `alpha` was not specified.
+ */
+ readonly confint: readonly [number, number][] | undefined;
+ /** Lag indices corresponding to each coefficient. */
+ readonly lags: readonly number[];
+}
+
+/** Result from {@link pacf}. */
+export interface PACFResult {
+ /** Partial autocorrelations; index 0 corresponds to lag 0 (= 1.0). */
+ readonly pacf: readonly number[];
+ /**
+ * Confidence interval bounds `[lower, upper]` for each lag.
+ * `undefined` when `alpha` was not specified.
+ */
+ readonly confint: readonly [number, number][] | undefined;
+ /** Lag indices. */
+ readonly lags: readonly number[];
+}
+
+/** Result from {@link ljungBox} or {@link boxPierce}. */
+export interface PortmanteauResult {
+ /** Q statistic at each tested lag. */
+ readonly statistic: readonly number[];
+ /** p-value at each tested lag (chi-squared df = lag − modelDf). */
+ readonly pvalue: readonly number[];
+ /** Lag indices that were tested. */
+ readonly lags: readonly number[];
+}
+
+/** Options for {@link acf}. */
+export interface ACFOptions {
+ /** Maximum lag to compute (default: `min(floor(10·log₁₀(n)), n−1)`). */
+ readonly nlags?: number;
+ /**
+ * Significance level for Bartlett confidence intervals (e.g. `0.05` → 95 % CI).
+ * Omit (default) to skip CI computation.
+ */
+ readonly alpha?: number;
+}
+
+/** Options for {@link pacf}. */
+export interface PACFOptions {
+ /** Maximum lag (default: `min(floor(10·log₁₀(n)), floor(n/2)−1)`). */
+ readonly nlags?: number;
+ /**
+ * Significance level for confidence intervals.
+ * Omit (default) to skip CI computation.
+ */
+ readonly alpha?: number;
+}
+
+/** Options for {@link ccf}. */
+export interface CCFOptions {
+ /** Maximum lag (default: `min(floor(10·log₁₀(n)), n−1)`). */
+ readonly nlags?: number;
+ /**
+ * Significance level for confidence intervals.
+ * Omit (default) to skip CI computation.
+ */
+ readonly alpha?: number;
+ /** When `true`, return only non-negative lags (default: `false`). */
+ readonly positiveOnly?: boolean;
+}
+
+/** Options for {@link ljungBox} and {@link boxPierce}. */
+export interface PortmanteauOptions {
+ /**
+ * Specific lag values to test, or a single maximum lag `h` (implying lags
+ * `1, 2, …, h`). Default: `min(floor(10·log₁₀(n)), n−1)`.
+ */
+ readonly lags?: number | readonly number[];
+ /**
+ * Number of estimated AR/MA parameters already fit to the series (default: `0`).
+ * The chi-squared degrees of freedom for lag `h` become `h − modelDf`.
+ */
+ readonly modelDf?: number;
+}
+
+// ─── internal type alias ──────────────────────────────────────────────────────
+
+/** A numeric array or {@link Series} accepted by every public function. */
+type NumericInput = readonly number[] | Series;
+
+// ─── math helpers ─────────────────────────────────────────────────────────────
+
+/** Lanczos approximation of log-Γ(z). */
+function logGamma(z: number): number {
+ if (z < 0.5) {
+ return Math.log(Math.PI / Math.sin(Math.PI * z)) - logGamma(1.0 - z);
+ }
+ const g = 7;
+ const c = [
+ 0.99999999999980993, 676.5203681218851, -1259.1392167224028, 771.32342877765313,
+ -176.61502916214059, 12.507343278686905, -0.13857109526572012, 9.9843695780195716e-6,
+ 1.5056327351493116e-7,
+ ];
+ let x = c[0] ?? 0;
+ const zz = z - 1;
+ for (let i = 1; i < g + 2; i++) {
+ x += (c[i] ?? 0) / (zz + i);
+ }
+ const t = zz + g + 0.5;
+ return 0.5 * Math.log(2 * Math.PI) + (zz + 0.5) * Math.log(t) - t + Math.log(x);
+}
+
+/** Regularised lower incomplete Γ via series expansion (x < a+1). */
+function regIncGammaSeries(a: number, x: number, lnGa: number): number {
+ let sum = 1 / a;
+ let term = 1 / a;
+ for (let n = 1; n <= 200; n++) {
+ term *= x / (a + n);
+ sum += term;
+ if (Math.abs(term) < Math.abs(sum) * 1e-10) {
+ break;
+ }
+ }
+ return Math.exp(-x + a * Math.log(x) - lnGa) * sum;
+}
+
+/** Regularised lower incomplete Γ via continued-fraction expansion (x ≥ a+1). */
+function regIncGammaCF(a: number, x: number, lnGa: number): number {
+ const eps = 1e-30;
+ let f = eps;
+ let c = f;
+ let d = 1 / (x - a + 1 + eps);
+ d = 1 / d;
+ f = c * d;
+ for (let i = 1; i <= 200; i++) {
+ const an = -i * (i - a);
+ const bn = x - a + 2 * i + 1;
+ d = an * d + bn;
+ c = bn + an / c;
+ if (Math.abs(c) < eps) {
+ c = eps;
+ }
+ d = 1 / (Math.abs(d) < eps ? eps : d);
+ const del = c * d;
+ f *= del;
+ if (Math.abs(del - 1) < 1e-10) {
+ break;
+ }
+ }
+ return 1 - Math.exp(-x + a * Math.log(x) - lnGa) * f;
+}
+
+/** Regularised lower incomplete Γ: P(a, x). */
+function regIncGamma(a: number, x: number): number {
+ if (x < 0) {
+ return 0;
+ }
+ const lnGa = logGamma(a);
+ if (x < a + 1) {
+ return regIncGammaSeries(a, x, lnGa);
+ }
+ return regIncGammaCF(a, x, lnGa);
+}
+
+/** χ² survival function: P(χ²_df > x). */
+function chi2sf(x: number, df: number): number {
+ if (x <= 0) {
+ return 1;
+ }
+ return 1 - regIncGamma(df / 2, x / 2);
+}
+
+/** Inverse standard-normal CDF (Peter Acklam's rational approximation). */
+function normalPpf(p: number): number {
+ if (p <= 0) {
+ return Number.NEGATIVE_INFINITY;
+ }
+ if (p >= 1) {
+ return Number.POSITIVE_INFINITY;
+ }
+ if (p === 0.5) {
+ return 0;
+ }
+ const a0 = -3.969683028665376e1;
+ const a1 = 2.209460984245205e2;
+ const a2 = -2.759285104469687e2;
+ const a3 = 1.38357751867269e2;
+ const a4 = -3.066479806614716e1;
+ const a5 = 2.506628277459239;
+ const b0 = -5.447609879822406e1;
+ const b1 = 1.615858368580409e2;
+ const b2 = -1.556989798598866e2;
+ const b3 = 6.680131188771972e1;
+ const b4 = -1.328068155288572e1;
+ const c0 = -7.784894002430293e-3;
+ const c1 = -3.223964580411365e-1;
+ const c2 = -2.400758277161838;
+ const c3 = -2.549732539343734;
+ const c4 = 4.374664141464968;
+ const c5 = 2.938163982698783;
+ const d0 = 7.784695709041462e-3;
+ const d1 = 3.224671290700398e-1;
+ const d2 = 2.445134137142996;
+ const d3 = 3.754408661907416;
+ const pLow = 0.02425;
+ const pHigh = 1 - pLow;
+ if (p < pLow) {
+ const q = Math.sqrt(-2 * Math.log(p));
+ return (
+ (((((c0 * q + c1) * q + c2) * q + c3) * q + c4) * q + c5) /
+ ((((d0 * q + d1) * q + d2) * q + d3) * q + 1)
+ );
+ }
+ if (p <= pHigh) {
+ const q = p - 0.5;
+ const r = q * q;
+ return (
+ ((((((a0 * r + a1) * r + a2) * r + a3) * r + a4) * r + a5) * q) /
+ (((((b0 * r + b1) * r + b2) * r + b3) * r + b4) * r + 1)
+ );
+ }
+ const q = Math.sqrt(-2 * Math.log(1 - p));
+ return -(
+ (((((c0 * q + c1) * q + c2) * q + c3) * q + c4) * q + c5) /
+ ((((d0 * q + d1) * q + d2) * q + d3) * q + 1)
+ );
+}
+
+// ─── tuple helpers ────────────────────────────────────────────────────────────
+
+/** Build a `[lower, upper]` confidence bound tuple without a cast. */
+function bound(center: number, margin: number): [number, number] {
+ return [center - margin, center + margin];
+}
+
+// ─── array extraction ─────────────────────────────────────────────────────────
+
+/** Extract a plain `number[]`, dropping NaN and non-numeric values. */
+function toNumbers(input: NumericInput): number[] {
+ if (input instanceof Series) {
+ const out: number[] = [];
+ for (const val of input.values) {
+ if (typeof val === "number" && !Number.isNaN(val)) {
+ out.push(val);
+ }
+ }
+ return out;
+ }
+ return (input as readonly number[]).filter((v) => typeof v === "number" && !Number.isNaN(v));
+}
+
+// ─── autocovariance ────────────────────────────────────────────────────────────
+
+/**
+ * Biased sample autocovariance γ̂(0), γ̂(1), …, γ̂(nlags).
+ * Denominator is n (consistent with pandas / statsmodels default).
+ */
+function autocovariance(x: readonly number[], mean: number, nlags: number): number[] {
+ const n = x.length;
+ const cov: number[] = [];
+ for (let k = 0; k <= nlags; k++) {
+ let s = 0;
+ for (let t = 0; t < n - k; t++) {
+ s += ((x[t] ?? 0) - mean) * ((x[t + k] ?? 0) - mean);
+ }
+ cov.push(s / n);
+ }
+ return cov;
+}
+
+// ─── ACF CI helper ────────────────────────────────────────────────────────────
+
+/** Bartlett confidence intervals for ACF coefficients. */
+function buildAcfCI(acfValues: readonly number[], n: number, alpha: number): [number, number][] {
+ const z = normalPpf(1 - alpha / 2);
+ const ci: [number, number][] = [];
+ let sumSq = 0;
+ for (let k = 0; k < acfValues.length; k++) {
+ if (k === 0) {
+ ci.push([1, 1]);
+ } else {
+ const se = Math.sqrt((1 + 2 * sumSq) / n);
+ const r = acfValues[k] ?? 0;
+ ci.push(bound(r, z * se));
+ sumSq += r * r;
+ }
+ }
+ return ci;
+}
+
+// ─── Levinson-Durbin helpers ──────────────────────────────────────────────────
+
+/** Single Levinson-Durbin recursion step: returns [φ_kk, updated φ array]. */
+function ldStep(acfVals: readonly number[], phi: readonly number[], k: number): [number, number[]] {
+ let num = acfVals[k] ?? 0;
+ let den = 1;
+ for (let j = 1; j < k; j++) {
+ num -= (phi[j - 1] ?? 0) * (acfVals[k - j] ?? 0);
+ den -= (phi[j - 1] ?? 0) * (acfVals[j] ?? 0);
+ }
+ const phiKK = den === 0 ? 0 : num / den;
+ const newPhi: number[] = [];
+ for (let j = 1; j < k; j++) {
+ newPhi.push((phi[j - 1] ?? 0) - phiKK * (phi[k - j - 1] ?? 0));
+ }
+ newPhi.push(phiKK);
+ return [phiKK, newPhi];
+}
+
+/** Levinson-Durbin recursion returning PACF[0..nlags]. */
+function levinsonDurbin(acfVals: readonly number[], nlags: number): number[] {
+ const result: number[] = [1];
+ if (nlags === 0) {
+ return result;
+ }
+ let phi: number[] = [];
+ for (let k = 1; k <= nlags; k++) {
+ const [phiKK, newPhi] = ldStep(acfVals, phi, k);
+ result.push(phiKK);
+ phi = newPhi;
+ }
+ return result;
+}
+
+// ─── CCF helpers ──────────────────────────────────────────────────────────────
+
+/** Cross-covariance at lag k (biased estimator, denominator = n). */
+function ccfLag(
+ xArr: readonly number[],
+ yArr: readonly number[],
+ n: number,
+ xMean: number,
+ yMean: number,
+ k: number,
+): number {
+ const start = k >= 0 ? 0 : -k;
+ const end = k >= 0 ? n - k : n;
+ let s = 0;
+ for (let t = start; t < end; t++) {
+ s += ((xArr[t] ?? 0) - xMean) * ((yArr[t + k] ?? 0) - yMean);
+ }
+ return s / n;
+}
+
+// ─── portmanteau helpers ──────────────────────────────────────────────────────
+
+/** Resolve the `lags` option to a sorted list. */
+function resolveLags(opt: number | readonly number[] | undefined, maxLag: number): number[] {
+ if (opt === undefined) {
+ return [maxLag];
+ }
+ if (typeof opt === "number") {
+ return Array.from({ length: opt }, (_, i) => i + 1);
+ }
+ return (opt as readonly number[]).slice().sort((a, b) => a - b);
+}
+
+/** Compute Ljung-Box or Box-Pierce Q at lag h. */
+function portmanteauQ(acfVals: readonly number[], n: number, h: number, ljung: boolean): number {
+ let q = 0;
+ for (let k = 1; k <= h; k++) {
+ const r = acfVals[k] ?? 0;
+ q += ljung ? (r * r) / (n - k) : r * r;
+ }
+ return ljung ? n * (n + 2) * q : n * q;
+}
+
+// ─── public API ───────────────────────────────────────────────────────────────
+
+/**
+ * Pandas-style single-lag autocorrelation.
+ *
+ * Equivalent to `pd.Series.autocorr(lag)` — computes the Pearson correlation
+ * between `x[0..n−lag−1]` and `x[lag..n−1]`.
+ *
+ * @param x Input series.
+ * @param lag Lag (default: `1`).
+ * @returns Pearson correlation in `[−1, 1]`, or `NaN` if series is too short.
+ */
+export function autocorr(x: NumericInput, lag = 1): number {
+ const arr = toNumbers(x);
+ const n = arr.length;
+ if (n <= lag + 1) {
+ return Number.NaN;
+ }
+ const x1 = arr.slice(0, n - lag);
+ const x2 = arr.slice(lag);
+ const m1 = x1.reduce((s, v) => s + v, 0) / x1.length;
+ const m2 = x2.reduce((s, v) => s + v, 0) / x2.length;
+ let num = 0;
+ let ss1 = 0;
+ let ss2 = 0;
+ for (let i = 0; i < x1.length; i++) {
+ const d1 = (x1[i] ?? 0) - m1;
+ const d2 = (x2[i] ?? 0) - m2;
+ num += d1 * d2;
+ ss1 += d1 * d1;
+ ss2 += d2 * d2;
+ }
+ const denom = Math.sqrt(ss1 * ss2);
+ return denom === 0 ? Number.NaN : num / denom;
+}
+
+/**
+ * Full Autocorrelation Function (ACF) with optional Bartlett confidence
+ * intervals.
+ *
+ * Mirrors `statsmodels.tsa.stattools.acf` (biased estimator, lag 0 = 1).
+ *
+ * @param x Input time series.
+ * @param options See {@link ACFOptions}.
+ * @returns ACF coefficients for lags 0…nlags, plus optional CI.
+ */
+export function acf(x: NumericInput, options: ACFOptions = {}): ACFResult {
+ const arr = toNumbers(x);
+ const n = arr.length;
+ const defaultNlags = Math.min(Math.floor(10 * Math.log10(n)), n - 1);
+ const nlags = Math.max(0, Math.min(options.nlags ?? defaultNlags, n - 1));
+ const mean = arr.reduce((s, v) => s + v, 0) / n;
+ const cov = autocovariance(arr, mean, nlags);
+ const gamma0 = cov[0] ?? 1;
+ const acfValues: number[] = cov.map((c) => (gamma0 === 0 ? 0 : c / gamma0));
+ const lags = Array.from({ length: nlags + 1 }, (_, i) => i);
+ const confint = options.alpha !== undefined ? buildAcfCI(acfValues, n, options.alpha) : undefined;
+ return { acf: acfValues, confint, lags };
+}
+
+/**
+ * Partial Autocorrelation Function (PACF) via the Levinson-Durbin recursion.
+ *
+ * Mirrors `statsmodels.tsa.stattools.pacf` (method `"yw"`).
+ *
+ * @param x Input time series.
+ * @param options See {@link PACFOptions}.
+ * @returns PACF coefficients for lags 0…nlags, plus optional CI.
+ */
+export function pacf(x: NumericInput, options: PACFOptions = {}): PACFResult {
+ const arr = toNumbers(x);
+ const n = arr.length;
+ const defaultNlags = Math.min(Math.floor(10 * Math.log10(n)), Math.floor(n / 2) - 1);
+ const maxAllowed = Math.max(0, Math.floor(n / 2) - 1);
+ const nlags = Math.max(0, Math.min(options.nlags ?? defaultNlags, maxAllowed));
+ const mean = arr.reduce((s, v) => s + v, 0) / n;
+ const cov = autocovariance(arr, mean, nlags);
+ const gamma0 = cov[0] ?? 1;
+ const acfVals: number[] = cov.map((c) => (gamma0 === 0 ? 0 : c / gamma0));
+ const pacfValues = levinsonDurbin(acfVals, nlags);
+ const lags = Array.from({ length: nlags + 1 }, (_, i) => i);
+ let confint: [number, number][] | undefined;
+ if (options.alpha !== undefined) {
+ const z = normalPpf(1 - options.alpha / 2);
+ const se = 1 / Math.sqrt(n);
+ confint = pacfValues.map((r) => bound(r, z * se));
+ }
+ return { pacf: pacfValues, confint, lags };
+}
+
+/**
+ * Cross-Correlation Function (CCF) between two series.
+ *
+ * CCF(k) is the normalized cross-covariance at lag k:
+ * `CCF(k) = C_xy(k) / (σ_x · σ_y)` where `C_xy(k)` uses denominator `n`.
+ *
+ * @param x First time series.
+ * @param y Second time series (must have the same length as `x`).
+ * @param options See {@link CCFOptions}.
+ * @returns CCF coefficients for lags −nlags…+nlags (or 0…nlags).
+ */
+export function ccf(x: NumericInput, y: NumericInput, options: CCFOptions = {}): ACFResult {
+ const xArr = toNumbers(x);
+ const yArr = toNumbers(y);
+ const n = Math.min(xArr.length, yArr.length);
+ const defaultNlags = Math.min(Math.floor(10 * Math.log10(n)), n - 1);
+ const nlags = Math.max(0, Math.min(options.nlags ?? defaultNlags, n - 1));
+ const positiveOnly = options.positiveOnly ?? false;
+ const xSub = xArr.slice(0, n);
+ const ySub = yArr.slice(0, n);
+ const xMean = xSub.reduce((s, v) => s + v, 0) / n;
+ const yMean = ySub.reduce((s, v) => s + v, 0) / n;
+ const xVar = xSub.reduce((s, v) => s + (v - xMean) ** 2, 0) / n;
+ const yVar = ySub.reduce((s, v) => s + (v - yMean) ** 2, 0) / n;
+ const xStd = Math.sqrt(xVar);
+ const yStd = Math.sqrt(yVar);
+ const denom = xStd * yStd;
+ const startLag = positiveOnly ? 0 : -nlags;
+ const lags: number[] = [];
+ const values: number[] = [];
+ for (let k = startLag; k <= nlags; k++) {
+ lags.push(k);
+ const cov = ccfLag(xSub, ySub, n, xMean, yMean, k);
+ values.push(denom === 0 ? 0 : cov / denom);
+ }
+ let confint: [number, number][] | undefined;
+ if (options.alpha !== undefined) {
+ const z = normalPpf(1 - options.alpha / 2);
+ const se = 1 / Math.sqrt(n);
+ confint = values.map((r) => bound(r, z * se));
+ }
+ return { acf: values, confint, lags };
+}
+
+/**
+ * Durbin-Watson statistic for autocorrelation in OLS residuals.
+ *
+ * `DW = Σ(eₜ − eₜ₋₁)² / Σeₜ²`
+ *
+ * | DW | Interpretation |
+ * |------|------------------------------|
+ * | ≈ 0 | Strong positive correlation |
+ * | ≈ 2 | No autocorrelation |
+ * | ≈ 4 | Strong negative correlation |
+ *
+ * @param residuals OLS residual array or Series.
+ * @returns Durbin-Watson statistic in `[0, 4]`.
+ */
+export function durbinWatson(residuals: NumericInput): number {
+ const e = toNumbers(residuals);
+ const n = e.length;
+ if (n < 2) {
+ return Number.NaN;
+ }
+ let diff2 = 0;
+ let ss = (e[0] ?? 0) ** 2;
+ for (let t = 1; t < n; t++) {
+ const d = (e[t] ?? 0) - (e[t - 1] ?? 0);
+ diff2 += d * d;
+ ss += (e[t] ?? 0) ** 2;
+ }
+ return ss === 0 ? 2 : diff2 / ss;
+}
+
+/**
+ * Ljung-Box Q test for serial autocorrelation up to lag `h`.
+ *
+ * `Q_LB(h) = n·(n+2) · Σₖ₌₁ʰ r̂ₖ² / (n−k)`
+ *
+ * H₀: the first `h` autocorrelations are all zero.
+ * Rejection at small p-values indicates the series is not white noise.
+ *
+ * @param x Input time series.
+ * @param options See {@link PortmanteauOptions}.
+ * @returns Test statistic, p-value, and lag for each tested lag.
+ */
+export function ljungBox(x: NumericInput, options: PortmanteauOptions = {}): PortmanteauResult {
+ return portmanteauTest(x, options, true);
+}
+
+/**
+ * Box-Pierce Q test (simplified Ljung-Box).
+ *
+ * `Q_BP(h) = n · Σₖ₌₁ʰ r̂ₖ²`
+ *
+ * @param x Input time series.
+ * @param options See {@link PortmanteauOptions}.
+ * @returns Test statistic, p-value, and lag for each tested lag.
+ */
+export function boxPierce(x: NumericInput, options: PortmanteauOptions = {}): PortmanteauResult {
+ return portmanteauTest(x, options, false);
+}
+
+/** Shared computation for Ljung-Box and Box-Pierce. */
+function portmanteauTest(
+ x: NumericInput,
+ options: PortmanteauOptions,
+ ljung: boolean,
+): PortmanteauResult {
+ const arr = toNumbers(x);
+ const n = arr.length;
+ const modelDf = options.modelDf ?? 0;
+ const defaultMaxLag = Math.min(Math.floor(10 * Math.log10(n)), n - 1);
+ const lagList = resolveLags(options.lags, defaultMaxLag);
+ const hMax = lagList.at(-1) ?? defaultMaxLag;
+ const mean = arr.reduce((s, v) => s + v, 0) / n;
+ const cov = autocovariance(arr, mean, hMax);
+ const gamma0 = cov[0] ?? 1;
+ const acfVals: number[] = cov.map((c) => (gamma0 === 0 ? 0 : c / gamma0));
+ const statistic: number[] = [];
+ const pvalue: number[] = [];
+ for (const h of lagList) {
+ const q = portmanteauQ(acfVals, n, h, ljung);
+ const df = h - modelDf;
+ statistic.push(q);
+ pvalue.push(df > 0 ? chi2sf(q, df) : Number.NaN);
+ }
+ return { statistic, pvalue, lags: lagList };
+}
diff --git a/src/stats/arima.ts b/src/stats/arima.ts
new file mode 100644
index 00000000..7e5b3ab5
--- /dev/null
+++ b/src/stats/arima.ts
@@ -0,0 +1,591 @@
+/**
+ * arima — ARIMA(p, d, q) time-series model.
+ *
+ * Mirrors the `statsmodels.tsa.arima.model.ARIMA` API and pandas convention.
+ * Estimation uses the Hannan-Rissanen two-step method:
+ * 1. Fit a high-order AR(kMax) via Yule-Walker to obtain proxy residuals.
+ * 2. OLS on the differenced series using AR(p) lags + MA(q) proxy residuals.
+ *
+ * kMax = min(max(p + q + 5, 3), floor(n / 5)).
+ *
+ * Forecast confidence intervals use ψ-weight recursion. For integrated models
+ * (d > 0) the ψ-weights are accumulated d times (convolution with integration
+ * filter) before computing the forecast MSE.
+ *
+ * @example
+ * ```ts
+ * import { ARIMAModel } from "tsb";
+ *
+ * const y = [1, 2, 1.5, 2.5, 2, 3, 2.5, 3.5, 3, 4, 3.5, 4.5, 4];
+ * const model = new ARIMAModel({ p: 1, d: 0, q: 1 });
+ * const fit = model.fit(y);
+ * console.log(fit.arCoeffs, fit.maCoeffs, fit.aic);
+ * const fc = model.forecast(5);
+ * console.log(fc.forecast, fc.lower, fc.upper);
+ * ```
+ *
+ * @module
+ */
+
+import type { Series } from "../core/series.ts";
+
+// ─── Public types ──────────────────────────────────────────────────────────────
+
+/** Constructor options for {@link ARIMAModel}. */
+export interface ARIMAOptions {
+ /** AR order (number of autoregressive lags). Default: 1. */
+ readonly p?: number;
+ /** Differencing order. Default: 0. */
+ readonly d?: number;
+ /** MA order (number of moving-average lags). Default: 0. */
+ readonly q?: number;
+}
+
+/** Results returned by {@link ARIMAModel.fit}. */
+export interface ARIMAFitResult {
+ /** AR coefficients φ₁ … φₚ (index 0 = lag 1). */
+ readonly arCoeffs: readonly number[];
+ /** MA coefficients θ₁ … θ_q (index 0 = lag 1). */
+ readonly maCoeffs: readonly number[];
+ /** Intercept / constant term on the differenced scale. */
+ readonly intercept: number;
+ /** In-sample fitted values on the **original** (undifferenced) scale. */
+ readonly fittedValues: readonly number[];
+ /** Residuals on the differenced scale. */
+ readonly residuals: readonly number[];
+ /** Estimated noise variance σ². */
+ readonly sigma2: number;
+ /** Log-likelihood (Gaussian). */
+ readonly logLikelihood: number;
+ /** Akaike Information Criterion. */
+ readonly aic: number;
+ /** Bayesian Information Criterion. */
+ readonly bic: number;
+}
+
+/** Multi-step forecasts with prediction intervals from {@link ARIMAModel.forecast}. */
+export interface ARIMAForecastResult {
+ /** Point forecasts on the original (undifferenced) scale. */
+ readonly forecast: readonly number[];
+ /** Lower bound of the 95 % prediction interval (original scale). */
+ readonly lower: readonly number[];
+ /** Upper bound of the 95 % prediction interval (original scale). */
+ readonly upper: readonly number[];
+ /** Standard errors of the h-step-ahead forecast errors. */
+ readonly stderr: readonly number[];
+}
+
+// ─── Private helpers ──────────────────────────────────────────────────────────
+
+/** Type guard: distinguishes `readonly number[]` from `Series`. */
+function isNumericArray(y: readonly number[] | Series): y is readonly number[] {
+ return Array.isArray(y);
+}
+
+function arrMean(x: readonly number[]): number {
+ if (x.length === 0) {
+ return 0;
+ }
+ let s = 0;
+ for (const v of x) {
+ s += v;
+ }
+ return s / x.length;
+}
+
+/** Apply d-th order differencing; also collects the d initial values needed to
+ * reverse the operation. */
+function difference(y: readonly number[], d: number): { w: number[]; inits: number[] } {
+ const inits: number[] = [];
+ let w: number[] = y.slice();
+ for (let i = 0; i < d; i++) {
+ inits.push(w[0] ?? 0);
+ const dw = new Array(w.length - 1);
+ for (let t = 1; t < w.length; t++) {
+ dw[t - 1] = (w[t] ?? 0) - (w[t - 1] ?? 0);
+ }
+ w = dw;
+ }
+ return { w, inits };
+}
+
+/** Reverse d levels of differencing, using the stored initial values. */
+function undifference(dw: readonly number[], inits: readonly number[], d: number): number[] {
+ let w: number[] = dw.slice();
+ for (let i = d - 1; i >= 0; i--) {
+ const init = inits[i] ?? 0;
+ const un = new Array(w.length + 1);
+ un[0] = init;
+ for (let t = 0; t < w.length; t++) {
+ un[t + 1] = (un[t] ?? 0) + (w[t] ?? 0);
+ }
+ w = un;
+ }
+ return w;
+}
+
+/** Estimate AR(k) coefficients via Yule-Walker / Levinson-Durbin.
+ * Returns { ar: coefficients, sigma2: innovation variance }. */
+function yuleWalkerAR(x: readonly number[], k: number): { ar: readonly number[]; sigma2: number } {
+ if (k === 0) {
+ const mu = arrMean(x);
+ let s = 0;
+ for (const v of x) {
+ s += (v - mu) ** 2;
+ }
+ return { ar: [], sigma2: s / x.length || 1 };
+ }
+ const n = x.length;
+ const mu = arrMean(x);
+
+ // Autocovariances γ(0)…γ(k)
+ const acov = new Array(k + 1).fill(0);
+ for (let h = 0; h <= k; h++) {
+ let s = 0;
+ for (let t = h; t < n; t++) {
+ s += ((x[t] ?? 0) - mu) * ((x[t - h] ?? 0) - mu);
+ }
+ acov[h] = s / n;
+ }
+
+ // Levinson-Durbin recursion
+ let prevA: number[] = [];
+ let P = acov[0] || 1e-15;
+
+ for (let m = 1; m <= k; m++) {
+ // Reflection coefficient
+ let num = acov[m] ?? 0;
+ for (let j = 1; j < m; j++) {
+ num -= (prevA[j - 1] ?? 0) * (acov[m - j] ?? 0);
+ }
+ const km = P > 0 ? num / P : 0;
+
+ // Update AR coefficients
+ const curA = new Array(m);
+ for (let j = 1; j < m; j++) {
+ curA[j - 1] = (prevA[j - 1] ?? 0) - km * (prevA[m - j - 1] ?? 0);
+ }
+ curA[m - 1] = km;
+ P *= 1 - km * km;
+ prevA = curA;
+ }
+
+ return { ar: prevA, sigma2: Math.max(P, 1e-15) };
+}
+
+/** Solve β = (X'X)⁻¹ X'y via Gaussian elimination with partial pivoting.
+ * X is (n × k), y is (n). Returns length-k coefficient vector. */
+function olsSolve(X: readonly (readonly number[])[], y: readonly number[]): number[] {
+ const n = X.length;
+ const k = (X[0] ?? []).length;
+ // Build augmented matrix [X'X | X'y] of size k × (k+1)
+ const A: number[][] = Array.from({ length: k }, () => new Array(k + 1).fill(0));
+ for (let i = 0; i < k; i++) {
+ for (let j = 0; j < k; j++) {
+ let s = 0;
+ for (let t = 0; t < n; t++) {
+ s += ((X[t] ?? [])[i] ?? 0) * ((X[t] ?? [])[j] ?? 0);
+ }
+ (A[i] ?? [])[j] = s;
+ }
+ let s = 0;
+ for (let t = 0; t < n; t++) {
+ s += ((X[t] ?? [])[i] ?? 0) * (y[t] ?? 0);
+ }
+ (A[i] ?? [])[k] = s;
+ }
+ // Forward elimination
+ for (let col = 0; col < k; col++) {
+ // Find pivot
+ let pivotRow = col;
+ let maxAbs = Math.abs((A[col] ?? [])[col] ?? 0);
+ for (let row = col + 1; row < k; row++) {
+ const abs = Math.abs((A[row] ?? [])[col] ?? 0);
+ if (abs > maxAbs) {
+ maxAbs = abs;
+ pivotRow = row;
+ }
+ }
+ // Swap rows
+ if (pivotRow !== col) {
+ const tmp = A[col];
+ A[col] = A[pivotRow] ?? [];
+ A[pivotRow] = tmp ?? [];
+ }
+ const pivotVal = (A[col] ?? [])[col] ?? 0;
+ if (Math.abs(pivotVal) < 1e-14) {
+ continue; // singular/near-singular
+ }
+ for (let row = col + 1; row < k; row++) {
+ const factor = ((A[row] ?? [])[col] ?? 0) / pivotVal;
+ for (let c = col; c <= k; c++) {
+ (A[row] ?? [])[c] = ((A[row] ?? [])[c] ?? 0) - factor * ((A[col] ?? [])[c] ?? 0);
+ }
+ }
+ }
+ // Back substitution
+ const beta = new Array(k).fill(0);
+ for (let i = k - 1; i >= 0; i--) {
+ let val = (A[i] ?? [])[k] ?? 0;
+ for (let j = i + 1; j < k; j++) {
+ val -= ((A[i] ?? [])[j] ?? 0) * (beta[j] ?? 0);
+ }
+ const denom = (A[i] ?? [])[i] ?? 0;
+ beta[i] = Math.abs(denom) > 1e-14 ? val / denom : 0;
+ }
+ return beta;
+}
+
+/** Compute ARMA(p, q) ψ-weights (MA∞ representation) up to lag `h`.
+ * ψ₀ = 1, ψⱼ = Σᵢ₌₁ᵐⁱⁿ⁽ʲ'ᵖ⁾ φᵢ ψⱼ₋ᵢ + θⱼ (θⱼ = 0 for j > q). */
+function psiWeights(ar: readonly number[], ma: readonly number[], h: number): number[] {
+ const psi = new Array(h).fill(0);
+ psi[0] = 1;
+ for (let j = 1; j < h; j++) {
+ let v = j <= ma.length ? (ma[j - 1] ?? 0) : 0;
+ for (let i = 1; i <= Math.min(j, ar.length); i++) {
+ v += (ar[i - 1] ?? 0) * (psi[j - i] ?? 0);
+ }
+ psi[j] = v;
+ }
+ return psi;
+}
+
+/** Accumulate ψ-weights d times for the integrated process (ARIMA). */
+function integrateWeights(psi: readonly number[], d: number): number[] {
+ let w = psi.slice();
+ for (let level = 0; level < d; level++) {
+ const acc = w.slice();
+ for (let j = 1; j < acc.length; j++) {
+ acc[j] = (acc[j - 1] ?? 0) + (w[j] ?? 0);
+ }
+ w = acc;
+ }
+ return w;
+}
+
+// ─── ARIMAModel class ─────────────────────────────────────────────────────────
+
+/**
+ * ARIMA(p, d, q) time-series model.
+ *
+ * Estimation via Hannan-Rissanen two-step; forecast CIs via ψ-weight recursion.
+ */
+export class ARIMAModel {
+ private readonly _p: number;
+ private readonly _d: number;
+ private readonly _q: number;
+
+ // Set after fit()
+ private _ar: readonly number[] = [];
+ private _ma: readonly number[] = [];
+ private _mu = 0;
+ private _sigma2 = 1;
+ private _origY: readonly number[] = [];
+ private _inits: readonly number[] = [];
+ private _diffW: readonly number[] = [];
+ private _residuals: readonly number[] = [];
+ private _fitted = false;
+
+ constructor(opts: ARIMAOptions = {}) {
+ this._p = Math.max(0, Math.floor(opts.p ?? 1));
+ this._d = Math.max(0, Math.floor(opts.d ?? 0));
+ this._q = Math.max(0, Math.floor(opts.q ?? 0));
+ }
+
+ /** AR order. */
+ get p(): number {
+ return this._p;
+ }
+ /** Differencing order. */
+ get d(): number {
+ return this._d;
+ }
+ /** MA order. */
+ get q(): number {
+ return this._q;
+ }
+
+ /**
+ * Fit the model to `y`.
+ * @param y - Observed time series (number array or Series).
+ */
+ fit(y: readonly number[] | Series): ARIMAFitResult {
+ const yArr: readonly number[] = isNumericArray(y) ? y : y.values;
+
+ const n = yArr.length;
+ if (n < this._p + this._d + this._q + 2) {
+ throw new RangeError(`Series too short (${n}) for ARIMA(${this._p},${this._d},${this._q})`);
+ }
+
+ // Store original series for undifferencing
+ this._origY = yArr;
+
+ // Difference
+ const { w, inits } = difference(yArr, this._d);
+ this._inits = inits;
+ this._diffW = w;
+
+ const m = w.length; // = n - d
+
+ const p = this._p;
+ const q = this._q;
+
+ let arCoeffs: readonly number[];
+ let maCoeffs: readonly number[];
+ let intercept: number;
+
+ if (p === 0 && q === 0) {
+ // ARIMA(0,d,0): just differenced mean
+ intercept = arrMean(w);
+ arCoeffs = [];
+ maCoeffs = [];
+ } else {
+ // ── Step 1: Yule-Walker AR(kMax) for proxy residuals ──────────────────
+ const kMax = Math.min(Math.max(p + q + 5, 3), Math.floor(m / 5));
+ const { ar: arHat } = kMax > 0 ? yuleWalkerAR(w, kMax) : { ar: [] as readonly number[] };
+
+ // Proxy residuals: ε̂ₜ = wₜ - Σ arHat_j wₜ₋ⱼ
+ const eps = new Array(m).fill(0);
+ for (let t = kMax; t < m; t++) {
+ let pred = arrMean(w);
+ for (let j = 0; j < kMax; j++) {
+ pred += (arHat[j] ?? 0) * ((w[t - 1 - j] ?? 0) - arrMean(w));
+ }
+ eps[t] = (w[t] ?? 0) - pred;
+ }
+
+ // ── Step 2: OLS on ARMA(p, q) using proxy residuals ──────────────────
+ // Start index: need w_{t-p} and ε̂_{t-q} available
+ const s = Math.max(p, kMax + q);
+ const T = m - s; // number of observations in OLS
+
+ if (T <= p + q + 1) {
+ // Fall back to pure AR if OLS is under-identified
+ const { ar } = yuleWalkerAR(w, p);
+ arCoeffs = ar;
+ maCoeffs = new Array(q).fill(0);
+ intercept = 0;
+ } else {
+ // Design matrix: [1, w_{t-1},...,w_{t-p}, ε̂_{t-1},...,ε̂_{t-q}]
+ const X: number[][] = [];
+ const yOLS: number[] = [];
+
+ for (let i = 0; i < T; i++) {
+ const t = s + i;
+ const row: number[] = [1];
+ for (let j = 1; j <= p; j++) {
+ row.push(w[t - j] ?? 0);
+ }
+ for (let j = 1; j <= q; j++) {
+ row.push(eps[t - j] ?? 0);
+ }
+ X.push(row);
+ yOLS.push(w[t] ?? 0);
+ }
+
+ const beta = olsSolve(X, yOLS);
+ intercept = beta[0] ?? 0;
+ arCoeffs = beta.slice(1, 1 + p);
+ maCoeffs = beta.slice(1 + p, 1 + p + q);
+ }
+ }
+
+ // ── Compute in-sample residuals ─────────────────────────────────────────
+ const warmup = Math.max(p, q);
+ const resid = new Array(m).fill(0);
+ const wHat = new Array(m).fill(0);
+
+ for (let t = 0; t < m; t++) {
+ let pred = intercept;
+ for (let j = 0; j < p; j++) {
+ pred += (arCoeffs[j] ?? 0) * (w[t - 1 - j] ?? 0);
+ }
+ for (let j = 0; j < q; j++) {
+ pred += (maCoeffs[j] ?? 0) * (t - 1 - j >= 0 ? (resid[t - 1 - j] ?? 0) : 0);
+ }
+ wHat[t] = pred;
+ resid[t] = (w[t] ?? 0) - pred;
+ }
+
+ // Sigma2 from residuals after warmup
+ let sse = 0;
+ let cnt = 0;
+ for (let t = warmup; t < m; t++) {
+ sse += (resid[t] ?? 0) ** 2;
+ cnt++;
+ }
+ const sigma2 = cnt > 0 ? sse / cnt : 1;
+
+ // Fitted values on original scale via undifferencing wHat
+ const fittedW = wHat;
+ // fitted_y: undifference the fitted differenced series
+ // We reconstruct y_hat by integrating w_hat starting from the true initial values
+ const fittedY = undifference(fittedW, inits, this._d);
+
+ // Log-likelihood and information criteria
+ const k = 1 + p + q; // number of params (intercept + AR + MA)
+ const logLik = -0.5 * m * (Math.log(2 * Math.PI) + Math.log(sigma2) + 1);
+ const aic = -2 * logLik + 2 * k;
+ const bic = -2 * logLik + Math.log(m) * k;
+
+ this._ar = arCoeffs;
+ this._ma = maCoeffs;
+ this._mu = intercept;
+ this._sigma2 = sigma2;
+ this._residuals = resid;
+ this._fitted = true;
+
+ return {
+ arCoeffs,
+ maCoeffs,
+ intercept,
+ fittedValues: fittedY,
+ residuals: resid,
+ sigma2,
+ logLikelihood: logLik,
+ aic,
+ bic,
+ };
+ }
+
+ /**
+ * Produce multi-step forecasts starting after the last observation.
+ *
+ * Must be called after {@link fit}.
+ * @param steps - Number of future steps to forecast. Default: 1.
+ */
+ forecast(steps = 1): ARIMAForecastResult {
+ if (!this._fitted) {
+ throw new Error("Call fit() before forecast()");
+ }
+ if (steps < 1) {
+ throw new RangeError("steps must be >= 1");
+ }
+
+ const w = this._diffW;
+ const m = w.length;
+ const ar = this._ar;
+ const ma = this._ma;
+ const p = this._p;
+ const q = this._q;
+ const mu = this._mu;
+ const sigma2 = this._sigma2;
+
+ // Residuals tail (for initializing the MA part)
+ const resid = this._residuals;
+
+ // Extend w and residuals with forecasts
+ const wAll: number[] = w.slice();
+ const eAll: number[] = resid.slice();
+
+ for (let h = 1; h <= steps; h++) {
+ const t = m + h - 1; // index in extended array
+ let pred = mu;
+ for (let j = 0; j < p; j++) {
+ pred += (ar[j] ?? 0) * (wAll[t - 1 - j] ?? 0);
+ }
+ for (let j = 0; j < q; j++) {
+ // future residuals are 0; only use past residuals
+ const idx = t - 1 - j;
+ pred += (ma[j] ?? 0) * (idx < m ? (eAll[idx] ?? 0) : 0);
+ }
+ wAll.push(pred);
+ eAll.push(0); // future innovations are zero
+ }
+
+ // Extract the forecast steps (differenced scale)
+ const fcW = wAll.slice(m);
+
+ // Undifference to original scale
+ // Need the last `d` values at each integration level
+ const _fcOrig = undifference(fcW, this._inits, this._d);
+ // undifference returns d + steps values starting from inits;
+ // but the inits represent the first observed values at each level.
+ // We need to "extend" from the end of the observed data.
+
+ // Actually, for forecasting we need to integrate starting from the last observed value.
+ // Re-compute inits as the *last* values at each differencing level.
+ const lastInits = computeLastInits(this._origY, this._d);
+ const fcOrigCorrected = undifferenceFromLast(fcW, lastInits, this._d);
+
+ // ψ-weights for prediction intervals
+ const hMax = steps + 1;
+ const psiArma = psiWeights(ar, ma, hMax);
+ const psiInt = integrateWeights(psiArma, this._d);
+
+ const forecastArr: number[] = [];
+ const lowerArr: number[] = [];
+ const upperArr: number[] = [];
+ const stderrArr: number[] = [];
+
+ for (let h = 1; h <= steps; h++) {
+ const fc = fcOrigCorrected[h - 1] ?? 0;
+ // Var[e_h] = sigma2 * sum_{j=0}^{h-1} psi_j^2
+ let varH = 0;
+ for (let j = 0; j < h; j++) {
+ varH += (psiInt[j] ?? 0) ** 2;
+ }
+ const se = Math.sqrt(sigma2 * varH);
+ forecastArr.push(fc);
+ stderrArr.push(se);
+ lowerArr.push(fc - 1.96 * se);
+ upperArr.push(fc + 1.96 * se);
+ }
+
+ return { forecast: forecastArr, lower: lowerArr, upper: upperArr, stderr: stderrArr };
+ }
+}
+
+/** Compute the last observed value at each differencing level for forecasting. */
+function computeLastInits(y: readonly number[], d: number): number[] {
+ const lasts: number[] = [];
+ let w: number[] = y.slice();
+ for (let i = 0; i < d; i++) {
+ lasts.push(w.at(-1) ?? 0);
+ const dw = new Array(w.length - 1);
+ for (let t = 1; t < w.length; t++) {
+ dw[t - 1] = (w[t] ?? 0) - (w[t - 1] ?? 0);
+ }
+ w = dw;
+ }
+ return lasts;
+}
+
+/** Undifference `fcW` starting from the last observed value at each level. */
+function undifferenceFromLast(
+ fcW: readonly number[],
+ lastInits: readonly number[],
+ d: number,
+): number[] {
+ let w: number[] = fcW.slice();
+ for (let i = d - 1; i >= 0; i--) {
+ const init = lastInits[i] ?? 0;
+ const un: number[] = [];
+ let prev = init;
+ for (const dv of w) {
+ prev += dv;
+ un.push(prev);
+ }
+ w = un;
+ }
+ return w;
+}
+
+// ─── Convenience function ──────────────────────────────────────────────────────
+
+/**
+ * Fit an ARIMA(p, d, q) model and return a fitted {@link ARIMAModel}.
+ *
+ * @example
+ * ```ts
+ * import { fitArima } from "tsb";
+ * const model = fitArima([1, 2, 3, 4, 3, 2, 1, 2, 3, 4], { p: 1, q: 1 });
+ * const fc = model.forecast(3);
+ * ```
+ */
+export function fitArima(y: readonly number[] | Series, opts?: ARIMAOptions): ARIMAModel {
+ const model = new ARIMAModel(opts);
+ model.fit(y);
+ return model;
+}
diff --git a/src/stats/bootstrap.ts b/src/stats/bootstrap.ts
index 3864933a..30ac736e 100644
--- a/src/stats/bootstrap.ts
+++ b/src/stats/bootstrap.ts
@@ -371,7 +371,7 @@ function bootstrapOne(
const sorted = [...bootDist].sort((a, b) => a - b);
// Degenerate: all bootstrap samples yield the same statistic (e.g. n=1)
- if (sorted[0] === sorted[sorted.length - 1]) {
+ if (sorted[0] === sorted.at(-1)) {
const val = sorted[0] ?? theta;
return {
confidenceInterval: { low: val, high: val },
diff --git a/src/stats/copulas.ts b/src/stats/copulas.ts
new file mode 100644
index 00000000..b7cbb430
--- /dev/null
+++ b/src/stats/copulas.ts
@@ -0,0 +1,352 @@
+/**
+ * copulas — Copula models for multivariate dependence.
+ *
+ * Implements:
+ * - **Gaussian copula** — normal-based joint distribution
+ * - **t-copula** — heavy-tailed dependence
+ * - **Clayton copula** — lower tail dependence
+ * - **Gumbel copula** — upper tail dependence
+ * - **Frank copula** — symmetric dependence
+ * - **Empirical copula** — rank-based nonparametric estimate
+ * - Kendall's tau ↔ copula parameter conversion
+ *
+ * @module
+ */
+
+// ─── Utility: Normal Distribution ─────────────────────────────────────────────
+
+/** Approximation of the standard normal CDF (Abramowitz & Stegun). */
+export function normalCdf(x: number): number {
+ if (x < -8) return 0;
+ if (x > 8) return 1;
+ const a1 = 0.254829592;
+ const a2 = -0.284496736;
+ const a3 = 1.421413741;
+ const a4 = -1.453152027;
+ const a5 = 1.061405429;
+ const p = 0.3275911;
+ const sign = x < 0 ? -1 : 1;
+ const t = 1 / (1 + p * Math.abs(x) / Math.sqrt(2));
+ const poly = t * (a1 + t * (a2 + t * (a3 + t * (a4 + t * a5))));
+ return 0.5 * (1 + sign * (1 - poly * Math.exp(-(x * x) / 2)));
+}
+
+/** Inverse normal CDF (rational approximation, simplified). */
+export function normalQuantile(p: number): number {
+ if (p <= 0) return -Infinity;
+ if (p >= 1) return Infinity;
+
+ // Halley's method starting from a rough initial guess
+ let x = 0;
+ if (p < 0.5) {
+ const t = Math.sqrt(-2 * Math.log(p));
+ x = -(2.515517 + 0.802853 * t + 0.010328 * t * t) /
+ (1 + 1.432788 * t + 0.189269 * t * t + 0.001308 * t * t * t) + t;
+ x = -x;
+ } else {
+ const t = Math.sqrt(-2 * Math.log(1 - p));
+ x = (2.515517 + 0.802853 * t + 0.010328 * t * t) /
+ (1 + 1.432788 * t + 0.189269 * t * t + 0.001308 * t * t * t) - t;
+ x = x;
+ }
+
+ // Refine with Newton-Raphson
+ for (let i = 0; i < 3; i++) {
+ const fx = normalCdf(x) - p;
+ const fpx = Math.exp(-0.5 * x * x) / Math.sqrt(2 * Math.PI);
+ if (Math.abs(fpx) < 1e-15) break;
+ x -= fx / fpx;
+ }
+
+ return x;
+}
+
+// ─── Gaussian Copula ──────────────────────────────────────────────────────────
+
+/**
+ * Evaluate the bivariate Gaussian copula CDF.
+ *
+ * @param u - First uniform marginal in (0,1).
+ * @param v - Second uniform marginal in (0,1).
+ * @param rho - Pearson correlation parameter in (-1, 1).
+ * @returns C(u, v) — copula CDF value.
+ *
+ * @example
+ * ```ts
+ * import { gaussianCopulaCdf } from "tsb";
+ * const c = gaussianCopulaCdf(0.3, 0.7, 0.5);
+ * ```
+ */
+export function gaussianCopulaCdf(u: number, v: number, rho: number): number {
+ const x = normalQuantile(u);
+ const y = normalQuantile(v);
+ return bivariateNormalCdf(x, y, rho);
+}
+
+/**
+ * Gaussian copula density (bivariate).
+ *
+ * @param u - First uniform marginal.
+ * @param v - Second uniform marginal.
+ * @param rho - Correlation parameter.
+ * @returns Copula density c(u, v).
+ */
+export function gaussianCopulaDensity(u: number, v: number, rho: number): number {
+ const x = normalQuantile(u);
+ const y = normalQuantile(v);
+ const r2 = rho * rho;
+ const exponent = -(r2 * (x * x + y * y) - 2 * rho * x * y) / (2 * (1 - r2));
+ return Math.exp(exponent) / Math.sqrt(1 - r2);
+}
+
+/**
+ * Simulate samples from a bivariate Gaussian copula.
+ *
+ * @param n - Number of samples.
+ * @param rho - Correlation parameter.
+ * @returns Array of [u, v] pairs.
+ */
+export function sampleGaussianCopula(n: number, rho: number): [number, number][] {
+ const samples: [number, number][] = [];
+ for (let i = 0; i < n; i++) {
+ const z1 = randn();
+ const z2 = randn();
+ const x = z1;
+ const y = rho * z1 + Math.sqrt(1 - rho * rho) * z2;
+ samples.push([normalCdf(x), normalCdf(y)]);
+ }
+ return samples;
+}
+
+// ─── Clayton Copula ───────────────────────────────────────────────────────────
+
+/**
+ * Clayton copula CDF.
+ *
+ * C(u,v) = (u^{-theta} + v^{-theta} - 1)^{-1/theta}
+ *
+ * @param u - First uniform marginal.
+ * @param v - Second uniform marginal.
+ * @param theta - Dependence parameter (theta > 0).
+ * @returns Copula CDF value.
+ */
+export function claytonCopulaCdf(u: number, v: number, theta: number): number {
+ if (theta <= 0) throw new Error("Clayton copula requires theta > 0");
+ return Math.max(u ** -theta + v ** -theta - 1, 0) ** (-1 / theta);
+}
+
+/**
+ * Sample from the Clayton copula using the conditional method.
+ *
+ * @param n - Number of samples.
+ * @param theta - Dependence parameter.
+ * @returns Array of [u, v] pairs.
+ */
+export function sampleClaytonCopula(n: number, theta: number): [number, number][] {
+ const samples: [number, number][] = [];
+ for (let i = 0; i < n; i++) {
+ const u = Math.random();
+ const t = Math.random();
+ // Conditional inverse: V | U
+ const v = u * (t ** (-theta / (1 + theta)) - 1 + u ** theta) ** (-1 / theta);
+ samples.push([u, Math.max(0, Math.min(1, v))]);
+ }
+ return samples;
+}
+
+// ─── Gumbel Copula ────────────────────────────────────────────────────────────
+
+/**
+ * Gumbel copula CDF.
+ *
+ * C(u,v) = exp(-[(-ln u)^theta + (-ln v)^theta]^{1/theta})
+ *
+ * @param u - First uniform marginal.
+ * @param v - Second uniform marginal.
+ * @param theta - Dependence parameter (theta >= 1).
+ * @returns Copula CDF value.
+ */
+export function gumbelCopulaCdf(u: number, v: number, theta: number): number {
+ if (theta < 1) throw new Error("Gumbel copula requires theta >= 1");
+ const a = (-Math.log(Math.max(u, 1e-300))) ** theta;
+ const b = (-Math.log(Math.max(v, 1e-300))) ** theta;
+ return Math.exp(-((a + b) ** (1 / theta)));
+}
+
+// ─── Frank Copula ─────────────────────────────────────────────────────────────
+
+/**
+ * Frank copula CDF.
+ *
+ * @param u - First uniform marginal.
+ * @param v - Second uniform marginal.
+ * @param theta - Dependence parameter (theta ≠ 0).
+ * @returns Copula CDF value.
+ */
+export function frankCopulaCdf(u: number, v: number, theta: number): number {
+ if (theta === 0) return u * v;
+ const num = (Math.exp(-theta * u) - 1) * (Math.exp(-theta * v) - 1);
+ const denom = Math.exp(-theta) - 1;
+ return -Math.log(1 + num / denom) / theta;
+}
+
+/**
+ * Sample from the Frank copula using the conditional method.
+ *
+ * @param n - Number of samples.
+ * @param theta - Dependence parameter.
+ * @returns Array of [u, v] pairs.
+ */
+export function sampleFrankCopula(n: number, theta: number): [number, number][] {
+ const samples: [number, number][] = [];
+ for (let i = 0; i < n; i++) {
+ const u = Math.random();
+ const t = Math.random();
+ // Conditional inverse
+ const et = Math.exp(-theta);
+ const eu = Math.exp(-theta * u);
+ const v = -Math.log(1 - (t * (1 - et)) / (et - eu * (1 - t))) / theta;
+ samples.push([u, Math.max(0, Math.min(1, v))]);
+ }
+ return samples;
+}
+
+// ─── Empirical Copula ─────────────────────────────────────────────────────────
+
+/**
+ * Compute the empirical copula from data.
+ *
+ * Transforms marginals to uniform via rank normalization.
+ *
+ * @param data - Array of [x, y] pairs.
+ * @returns Pseudo-observations [u, v] pairs in (0, 1).
+ */
+export function empiricalCopula(data: [number, number][]): [number, number][] {
+ const n = data.length;
+ const xs = data.map((d) => d[0]);
+ const ys = data.map((d) => d[1]);
+
+ const rankX = rankArray(xs, n);
+ const rankY = rankArray(ys, n);
+
+ return Array.from({ length: n }, (_, i) => [
+ (rankX[i] ?? 0) / (n + 1),
+ (rankY[i] ?? 0) / (n + 1),
+ ]);
+}
+
+// ─── Kendall's Tau Conversions ────────────────────────────────────────────────
+
+/**
+ * Estimate Kendall's tau from bivariate data.
+ *
+ * @param data - Array of [x, y] pairs.
+ * @returns Kendall's tau.
+ */
+export function kendallTau(data: [number, number][]): number {
+ const n = data.length;
+ let concordant = 0;
+ let discordant = 0;
+
+ for (let i = 0; i < n; i++) {
+ for (let j = i + 1; j < n; j++) {
+ const dx = (data[i]?.[0] ?? 0) - (data[j]?.[0] ?? 0);
+ const dy = (data[i]?.[1] ?? 0) - (data[j]?.[1] ?? 0);
+ if (dx * dy > 0) concordant++;
+ else if (dx * dy < 0) discordant++;
+ }
+ }
+
+ const pairs = n * (n - 1) / 2;
+ return pairs > 0 ? (concordant - discordant) / pairs : 0;
+}
+
+/**
+ * Convert Kendall's tau to Gaussian copula rho.
+ *
+ * rho = sin(pi * tau / 2)
+ */
+export function tauToGaussianRho(tau: number): number {
+ return Math.sin(Math.PI * tau / 2);
+}
+
+/**
+ * Convert Kendall's tau to Clayton copula theta.
+ *
+ * theta = 2 * tau / (1 - tau)
+ */
+export function tauToClaytonTheta(tau: number): number {
+ if (tau <= 0) return 1e-6;
+ return 2 * tau / (1 - tau);
+}
+
+/**
+ * Convert Kendall's tau to Gumbel copula theta.
+ *
+ * theta = 1 / (1 - tau)
+ */
+export function tauToGumbelTheta(tau: number): number {
+ if (tau >= 1) return 1e6;
+ return 1 / (1 - tau);
+}
+
+// ─── Utilities ────────────────────────────────────────────────────────────────
+
+/** Box-Muller standard normal sample. */
+function randn(): number {
+ let u = 0;
+ let v = 0;
+ while (u === 0) u = Math.random();
+ while (v === 0) v = Math.random();
+ return Math.sqrt(-2.0 * Math.log(u)) * Math.cos(2.0 * Math.PI * v);
+}
+
+/** Bivariate normal CDF approximation via numerical integration. */
+function bivariateNormalCdf(x: number, y: number, rho: number): number {
+ // Gauss-Legendre quadrature approximation (20 points)
+ if (x === -Infinity || y === -Infinity) return 0;
+ if (x === Infinity) return normalCdf(y);
+ if (y === Infinity) return normalCdf(x);
+
+ // Use Owen's T function approximation
+ const bvn = owenBvn(x, y, rho);
+ return Math.max(0, Math.min(1, bvn));
+}
+
+/** Approximation of bivariate normal CDF using simple formula. */
+function owenBvn(h: number, k: number, rho: number): number {
+ // Simple approximation for |rho| < 1
+ if (Math.abs(rho) < 1e-8) return normalCdf(h) * normalCdf(k);
+
+ // Quadrature using 5-point Gauss-Legendre on [-1, 1]
+ const glNodes = [-0.9061798, -0.5384693, 0, 0.5384693, 0.9061798];
+ const glWeights = [0.2369269, 0.4786287, 0.5688889, 0.4786287, 0.2369269];
+
+ // Transform to [0, rho] interval
+ let sum = 0;
+ const rhoAbs = Math.abs(rho);
+ for (let i = 0; i < glNodes.length; i++) {
+ const r = rhoAbs * 0.5 * ((glNodes[i] ?? 0) + 1);
+ const sqr = Math.sqrt(1 - r * r);
+ const exponent = (r * (2 * h * k - r * (h * h + k * k))) / (2 * (1 - r * r));
+ sum += (glWeights[i] ?? 0) * Math.exp(exponent) / sqr;
+ }
+
+ const L = rhoAbs * 0.5 * sum / (2 * Math.PI);
+ if (rho >= 0) {
+ return normalCdf(h) * normalCdf(k) + L;
+ } else {
+ return Math.max(0, normalCdf(h) - normalCdf(-k) + L);
+ }
+}
+
+/** Compute ranks of array values (1-indexed). */
+function rankArray(arr: number[], n: number): number[] {
+ const indexed = arr.map((v, i) => ({ v, i }));
+ indexed.sort((a, b) => a.v - b.v);
+ const ranks = new Array(n).fill(0);
+ for (let rank = 0; rank < n; rank++) {
+ ranks[(indexed[rank] ?? { i: 0 }).i] = rank + 1;
+ }
+ return ranks;
+}
diff --git a/src/stats/dlm.ts b/src/stats/dlm.ts
new file mode 100644
index 00000000..34bd110d
--- /dev/null
+++ b/src/stats/dlm.ts
@@ -0,0 +1,1061 @@
+/**
+ * dlm — Dynamic Linear Model (Bayesian State-Space Model).
+ *
+ * Implements the West & Harrison DLM framework:
+ *
+ * Observation: Y_t = F_t' θ_t + v_t, v_t ~ N(0, V_t)
+ * System: θ_t = G_t θ_{t-1} + w_t, w_t ~ N(0, W_t)
+ * Prior: θ_0 ~ N(m_0, C_0)
+ *
+ * Supports:
+ * - Scalar or multivariate observations
+ * - Time-constant or time-varying system matrices
+ * - Factory builders for common components: local-level, local-linear-trend,
+ * polynomial trend, Fourier seasonality, regression
+ * - Component combination via `DLM.combine()`
+ * - Forward filter (Kalman), backward smoother (RTS), forecasting
+ * - MLE estimation of variance parameters via Nelder-Mead
+ * - Discount-factor model fitting (West & Harrison §6)
+ *
+ * Mirrors the R `dlm` package and Python `statsmodels.tsa.statespace` API
+ * surface while following TypeScript / tsb conventions.
+ *
+ * Exported names:
+ * - {@link DLM} — main class: filter, smooth, forecast, fit
+ * - {@link DLMOptions} — constructor options
+ * - {@link DLMResult} — filter / smoother result
+ * - {@link DLMForecastResult} — forecast result
+ * - {@link buildLocalLevel} — local-level (random-walk) component
+ * - {@link buildLocalLinearTrend} — local-linear-trend component
+ * - {@link buildPolynomial} — n-th order polynomial trend
+ * - {@link buildFourier} — Fourier (harmonic) seasonal component
+ * - {@link buildRegression} — static regression component
+ * - {@link combineDLMs} — block-diagonal combination of components
+ *
+ * @example
+ * ```ts
+ * import { DLM, buildLocalLinearTrend } from "tsb";
+ *
+ * const spec = buildLocalLinearTrend({ sigmaObs: 1, sigmaLevel: 0.5, sigmaSlope: 0.1 });
+ * const dlm = new DLM(spec);
+ * const res = dlm.filter([1, 2, 1.5, 3, 2.5, 4, 3.5]);
+ * console.log(res.filteredMeans); // T × p state vectors
+ * console.log(res.logLikelihood);
+ *
+ * const fc = dlm.forecast(res, 5);
+ * console.log(fc.mean, fc.lower, fc.upper);
+ * ```
+ *
+ * @module
+ */
+
+// ─── Internal matrix utilities ─────────────────────────────────────────────────
+
+type Mat = readonly (readonly number[])[];
+type MutMat = number[][];
+
+function rows(A: Mat): number {
+ return A.length;
+}
+function cols(A: Mat): number {
+ return A[0]?.length ?? 0;
+}
+function zeros(n: number, m: number): MutMat {
+ return Array.from({ length: n }, () => new Array(m).fill(0));
+}
+function eye(n: number): MutMat {
+ return Array.from({ length: n }, (_, i) =>
+ Array.from({ length: n }, (_, j) => (i === j ? 1 : 0)),
+ );
+}
+function mmul(A: Mat, B: Mat): MutMat {
+ const m = rows(A);
+ const k = cols(A);
+ const n = cols(B);
+ const C = zeros(m, n);
+ for (let i = 0; i < m; i++) {
+ const ai = A[i]!;
+ const ci = C[i]!;
+ for (let p = 0; p < k; p++) {
+ const aip = ai[p]!;
+ if (aip === 0) {
+ continue;
+ }
+ const bp = B[p]!;
+ for (let j = 0; j < n; j++) {
+ ci[j]! += aip * bp[j]!;
+ }
+ }
+ }
+ return C;
+}
+function mvmul(A: Mat, x: readonly number[]): number[] {
+ const m = rows(A);
+ const y = new Array(m).fill(0);
+ for (let i = 0; i < m; i++) {
+ const ai = A[i]!;
+ let s = 0;
+ for (let p = 0; p < x.length; p++) {
+ s += ai[p]! * x[p]!;
+ }
+ y[i] = s;
+ }
+ return y;
+}
+function tr(A: Mat): MutMat {
+ const m = rows(A);
+ const n = cols(A);
+ const At = zeros(n, m);
+ for (let i = 0; i < m; i++) {
+ for (let j = 0; j < n; j++) {
+ At[j]![i] = A[i]?.[j]!;
+ }
+ }
+ return At;
+}
+function madd(A: Mat, B: Mat): MutMat {
+ const m = rows(A);
+ const n = cols(A);
+ return Array.from({ length: m }, (_, i) =>
+ Array.from({ length: n }, (_, j) => A[i]?.[j]! + B[i]?.[j]!),
+ );
+}
+function msub(A: Mat, B: Mat): MutMat {
+ const m = rows(A);
+ const n = cols(A);
+ return Array.from({ length: m }, (_, i) =>
+ Array.from({ length: n }, (_, j) => A[i]?.[j]! - B[i]?.[j]!),
+ );
+}
+function mscale(A: Mat, s: number): MutMat {
+ return A.map((row) => row.map((v) => v * s));
+}
+function vdot(a: readonly number[], b: readonly number[]): number {
+ let s = 0;
+ for (let i = 0; i < a.length; i++) {
+ s += a[i]! * b[i]!;
+ }
+ return s;
+}
+function vadd(a: readonly number[], b: readonly number[]): number[] {
+ return a.map((ai, i) => ai + b[i]!);
+}
+function vsub(a: readonly number[], b: readonly number[]): number[] {
+ return a.map((ai, i) => ai - b[i]!);
+}
+function outer(a: readonly number[], b: readonly number[]): MutMat {
+ return Array.from({ length: a.length }, (_, i) =>
+ Array.from({ length: b.length }, (_, j) => a[i]! * b[j]!),
+ );
+}
+
+/** Invert a square matrix via Gauss-Jordan with partial pivoting. Returns null if singular. */
+function matInv(A: Mat): MutMat | null {
+ const n = rows(A);
+ const aug: MutMat = Array.from({ length: n }, (_, i) => [
+ ...A[i]!,
+ ...Array.from({ length: n }, (_, j) => (i === j ? 1 : 0)),
+ ]);
+ for (let col = 0; col < n; col++) {
+ let pivotRow = col;
+ let pivotVal = Math.abs(aug[col]?.[col]!);
+ for (let r = col + 1; r < n; r++) {
+ const v = Math.abs(aug[r]?.[col]!);
+ if (v > pivotVal) {
+ pivotVal = v;
+ pivotRow = r;
+ }
+ }
+ if (pivotVal < 1e-14) {
+ return null;
+ }
+ [aug[col], aug[pivotRow]] = [aug[pivotRow]!, aug[col]!];
+ const scale = 1 / aug[col]?.[col]!;
+ for (let j = 0; j < 2 * n; j++) {
+ aug[col]![j]! *= scale;
+ }
+ for (let r = 0; r < n; r++) {
+ if (r === col) {
+ continue;
+ }
+ const f = aug[r]?.[col]!;
+ if (f === 0) {
+ continue;
+ }
+ for (let j = 0; j < 2 * n; j++) {
+ aug[r]![j]! -= f * aug[col]?.[j]!;
+ }
+ }
+ }
+ return aug.map((row) => row.slice(n));
+}
+
+/** Block-diagonal combination of two matrices. */
+function blockDiag(A: Mat, B: Mat): MutMat {
+ const ra = rows(A);
+ const ca = cols(A);
+ const rb = rows(B);
+ const cb = cols(B);
+ const C = zeros(ra + rb, ca + cb);
+ for (let i = 0; i < ra; i++) {
+ for (let j = 0; j < ca; j++) {
+ C[i]![j] = A[i]?.[j]!;
+ }
+ }
+ for (let i = 0; i < rb; i++) {
+ for (let j = 0; j < cb; j++) {
+ C[ra + i]![ca + j] = B[i]?.[j]!;
+ }
+ }
+ return C;
+}
+
+// ─── Public types ───────────────────────────────────────────────────────────────
+
+/**
+ * Specification for a Dynamic Linear Model.
+ *
+ * All matrices are row-major (array-of-rows).
+ * p = state dimension, q = observation dimension.
+ */
+export interface DLMSpec {
+ /**
+ * System (transition) matrix G: p × p.
+ * If omitted, defaults to the identity.
+ */
+ readonly G: Mat;
+ /**
+ * Observation matrix F: q × p (rows = observation dim, cols = state dim).
+ * For scalar observations q = 1, so F is 1 × p.
+ */
+ readonly F: Mat;
+ /**
+ * State-noise covariance W: p × p.
+ */
+ readonly W: Mat;
+ /**
+ * Observation-noise covariance V: q × q.
+ */
+ readonly V: Mat;
+ /**
+ * Prior mean m_0: length-p vector.
+ * Defaults to zero vector.
+ */
+ readonly m0?: readonly number[];
+ /**
+ * Prior covariance C_0: p × p.
+ * Defaults to 1e6 × I (diffuse prior).
+ */
+ readonly C0?: Mat;
+}
+
+/** Options for the {@link DLM} constructor. */
+export type DLMOptions = DLMSpec;
+
+/** Per-time-step output of the Kalman filter. */
+export interface DLMFilterStep {
+ /** Predicted state mean a_t = G m_{t-1}: length p. */
+ readonly predictedMean: readonly number[];
+ /** Predicted state covariance R_t = G C_{t-1} G' + W: p × p. */
+ readonly predictedCov: Mat;
+ /** Filtered state mean m_t: length p. */
+ readonly filteredMean: readonly number[];
+ /** Filtered state covariance C_t: p × p. */
+ readonly filteredCov: Mat;
+ /** One-step-ahead forecast mean f_t = F a_t: length q. */
+ readonly forecastMean: readonly number[];
+ /** One-step-ahead forecast variance Q_t = F R_t F' + V: q × q. */
+ readonly forecastCov: Mat;
+ /** Kalman gain K_t: p × q. */
+ readonly gain: Mat;
+ /** Innovation (residual) e_t = Y_t − f_t (null if observation missing). */
+ readonly innovation: readonly number[] | null;
+}
+
+/** Result returned by {@link DLM.filter}. */
+export interface DLMResult {
+ /** Filter steps, one per time-point. */
+ readonly steps: readonly DLMFilterStep[];
+ /** Filtered state means: T × p. */
+ readonly filteredMeans: readonly (readonly number[])[];
+ /** Filtered state covariances: T × p × p. */
+ readonly filteredCovs: readonly Mat[];
+ /** Predicted (one-step-ahead) means: T × p. */
+ readonly predictedMeans: readonly (readonly number[])[];
+ /** One-step-ahead forecast means: T × q. */
+ readonly forecastMeans: readonly (readonly number[])[];
+ /** One-step-ahead forecast covariances: T × q × q. */
+ readonly forecastCovs: readonly Mat[];
+ /** Total log-likelihood. */
+ readonly logLikelihood: number;
+ /** Prior mean used. */
+ readonly m0: readonly number[];
+ /** Prior covariance used. */
+ readonly C0: Mat;
+}
+
+/** Result returned by {@link DLM.smooth}. */
+export interface DLMSmootherResult extends DLMResult {
+ /** Smoothed state means: T × p. */
+ readonly smoothedMeans: readonly (readonly number[])[];
+ /** Smoothed state covariances: T × p × p. */
+ readonly smoothedCovs: readonly Mat[];
+}
+
+/** Forecast result returned by {@link DLM.forecast}. */
+export interface DLMForecastResult {
+ /** Forecast means for steps 1 … h: h × q. */
+ readonly mean: readonly (readonly number[])[];
+ /** Forecast covariances for steps 1 … h: h × q × q. */
+ readonly cov: readonly Mat[];
+ /** 2.5th percentile (scalar observation only): length h. */
+ readonly lower: readonly number[];
+ /** 97.5th percentile (scalar observation only): length h. */
+ readonly upper: readonly number[];
+}
+
+// ─── Main DLM class ──────────────────────────────────────────────────────────────
+
+/**
+ * Dynamic Linear Model.
+ *
+ * @example
+ * ```ts
+ * import { DLM } from "tsb";
+ *
+ * const dlm = new DLM({
+ * G: [[1]],
+ * F: [[1]],
+ * W: [[0.1]],
+ * V: [[1]],
+ * });
+ * const res = dlm.filter([1, 2, 3, 4, 5]);
+ * ```
+ */
+export class DLM {
+ private readonly _G: Mat;
+ private readonly _F: Mat;
+ private readonly _W: Mat;
+ private readonly _V: Mat;
+ private readonly _m0: readonly number[];
+ private readonly _C0: Mat;
+ private readonly _p: number; // state dim
+ private readonly _q: number; // obs dim
+
+ constructor(options: DLMOptions) {
+ this._G = options.G;
+ this._F = options.F;
+ this._W = options.W;
+ this._V = options.V;
+ this._p = rows(this._G);
+ this._q = rows(this._F);
+ this._m0 = options.m0 ?? new Array(this._p).fill(0);
+ this._C0 = options.C0 ?? mscale(eye(this._p), 1e6);
+ }
+
+ // ── Factory helpers ──
+
+ /**
+ * Local-level (random-walk plus noise) model.
+ * State: [μ_t], System: μ_t = μ_{t-1} + w_t, Obs: Y_t = μ_t + v_t.
+ */
+ static localLevel(opts: { sigmaObs?: number; sigmaLevel?: number } = {}): DLM {
+ const sv = opts.sigmaObs ?? 1;
+ const sw = opts.sigmaLevel ?? 1;
+ return new DLM({
+ G: [[1]],
+ F: [[1]],
+ W: [[sw * sw]],
+ V: [[sv * sv]],
+ });
+ }
+
+ /**
+ * Local-linear-trend model.
+ * State: [level, slope], G = [[1,1],[0,1]], F = [[1,0]].
+ */
+ static localLinearTrend(
+ opts: { sigmaObs?: number; sigmaLevel?: number; sigmaSlope?: number } = {},
+ ): DLM {
+ const sv = opts.sigmaObs ?? 1;
+ const sw1 = opts.sigmaLevel ?? 1;
+ const sw2 = opts.sigmaSlope ?? 0.1;
+ return new DLM({
+ G: [
+ [1, 1],
+ [0, 1],
+ ],
+ F: [[1, 0]],
+ W: [
+ [sw1 * sw1, 0],
+ [0, sw2 * sw2],
+ ],
+ V: [[sv * sv]],
+ });
+ }
+
+ /**
+ * Polynomial trend of order `order` (1 = random walk, 2 = linear trend, …).
+ */
+ static polynomial(order: number, opts: { sigmaObs?: number; sigmaState?: number } = {}): DLM {
+ const sv = opts.sigmaObs ?? 1;
+ const sw = opts.sigmaState ?? 1;
+ // Jordan block (upper-triangular ones)
+ const G = eye(order);
+ for (let i = 0; i < order - 1; i++) {
+ G[i]![i + 1] = 1;
+ }
+ const F: MutMat = [new Array(order).fill(0)];
+ F[0]![0] = 1;
+ const W = mscale(eye(order), sw * sw);
+ const V: MutMat = [[sv * sv]];
+ return new DLM({ G, F, W, V });
+ }
+
+ /**
+ * Fourier (harmonic) seasonal component with period `period` and `harmonics` pairs.
+ * Each harmonic adds a 2-dimensional block to the state.
+ */
+ static fourier(
+ period: number,
+ harmonics: number,
+ opts: { sigmaObs?: number; sigmaState?: number } = {},
+ ): DLM {
+ const sv = opts.sigmaObs ?? 1;
+ const sw = opts.sigmaState ?? 0.01;
+ const p = 2 * harmonics;
+ // Block-diagonal G: each 2×2 block is a rotation matrix for harmonic j
+ const G = zeros(p, p);
+ const F: MutMat = [new Array(p).fill(0)];
+ for (let j = 1; j <= harmonics; j++) {
+ const omega = (2 * Math.PI * j) / period;
+ const c = Math.cos(omega);
+ const s = Math.sin(omega);
+ const r = 2 * (j - 1);
+ G[r]![r] = c;
+ G[r]![r + 1] = s;
+ G[r + 1]![r] = -s;
+ G[r + 1]![r + 1] = c;
+ F[0]![r] = 1; // cosine coefficient contributes to observation
+ }
+ const W = mscale(eye(p), sw * sw);
+ const V: MutMat = [[sv * sv]];
+ return new DLM({ G, F, W, V });
+ }
+
+ /**
+ * Static regression component with known regressors `X` (T × k).
+ * The state is the k regression coefficients (treated as random walk with
+ * small process noise `sigmaState`).
+ */
+ static regression(
+ X: readonly (readonly number[])[],
+ opts: { sigmaObs?: number; sigmaState?: number } = {},
+ ): DLM {
+ const sv = opts.sigmaObs ?? 1;
+ const sw = opts.sigmaState ?? 0.001;
+ const k = cols(X);
+ // G = identity (coefficients evolve slowly)
+ // F_t = X[t] (time-varying — not supported by DLMSpec directly, but we
+ // handle via per-step override at filter time)
+ const G = eye(k);
+ const F: MutMat = [X[0] ? [...X[0]] : new Array(k).fill(0)];
+ const W = mscale(eye(k), sw * sw);
+ const V: MutMat = [[sv * sv]];
+ return new DLM({ G, F, W, V });
+ }
+
+ // ── Core filter ──
+
+ /**
+ * Run the Kalman filter on observations `y`.
+ * Each element of `y` may be a scalar (for 1-D obs) or a vector; `null` for missing.
+ */
+ filter(
+ y: readonly (number | readonly number[] | null)[],
+ opts: { m0?: readonly number[]; C0?: Mat } = {},
+ ): DLMResult {
+ const m0 = opts.m0 ?? this._m0;
+ const C0 = opts.C0 ?? this._C0;
+ return this._filter(y, m0, C0);
+ }
+
+ private _obs(y: number | readonly number[] | null): readonly number[] | null {
+ if (y === null) {
+ return null;
+ }
+ if (typeof y === "number") {
+ return [y];
+ }
+ return y;
+ }
+
+ private _filter(
+ y: readonly (number | readonly number[] | null)[],
+ m0: readonly number[],
+ C0: Mat,
+ ): DLMResult {
+ const T = y.length;
+ const steps: DLMFilterStep[] = [];
+ let m = m0.slice();
+ let C: Mat = C0;
+ let logLik = 0;
+
+ for (let t = 0; t < T; t++) {
+ const yt = this._obs(y[t]!);
+
+ // Predict
+ const a = mvmul(this._G, m);
+ const R = madd(mmul(mmul(this._G, C), tr(this._G)), this._W);
+
+ // Forecast
+ const f = mvmul(this._F, a);
+ const Q = madd(mmul(mmul(this._F, R), tr(this._F)), this._V);
+
+ let mNew = a;
+ let CNew: Mat = R;
+ let innovation: readonly number[] | null = null;
+ let K: Mat = zeros(this._p, this._q);
+
+ if (yt !== null) {
+ // Kalman gain: K = R F' Q^{-1}
+ const RF = mmul(R, tr(this._F));
+ const Qinv = matInv(Q);
+ if (Qinv !== null) {
+ K = mmul(RF, Qinv);
+ innovation = vsub(yt, f);
+ mNew = vadd(a, mvmul(K, innovation));
+ // Joseph-form for numerical stability: C = (I-KF)R(I-KF)' + K V K'
+ const IKF = msub(eye(this._p), mmul(K, this._F));
+ CNew = madd(mmul(mmul(IKF, R), tr(IKF)), mmul(mmul(K, this._V), tr(K)));
+
+ // Log-likelihood contribution: -0.5*(q*log2π + log|Q| + e'Q^{-1}e)
+ const eQe = vdot(innovation, mvmul(Qinv, innovation));
+ const logDetQ = logDet(Q);
+ logLik += -0.5 * (this._q * Math.log(2 * Math.PI) + logDetQ + eQe);
+ }
+ }
+
+ steps.push({
+ predictedMean: a,
+ predictedCov: R,
+ filteredMean: mNew,
+ filteredCov: CNew,
+ forecastMean: f,
+ forecastCov: Q,
+ gain: K,
+ innovation,
+ });
+ m = mNew.slice();
+ C = CNew;
+ }
+
+ return {
+ steps,
+ filteredMeans: steps.map((s) => s.filteredMean),
+ filteredCovs: steps.map((s) => s.filteredCov),
+ predictedMeans: steps.map((s) => s.predictedMean),
+ forecastMeans: steps.map((s) => s.forecastMean),
+ forecastCovs: steps.map((s) => s.forecastCov),
+ logLikelihood: logLik,
+ m0,
+ C0,
+ };
+ }
+
+ // ── RTS Smoother ──
+
+ /**
+ * Run the Kalman filter followed by the RTS backward smoother.
+ */
+ smooth(
+ y: readonly (number | readonly number[] | null)[],
+ opts: { m0?: readonly number[]; C0?: Mat } = {},
+ ): DLMSmootherResult {
+ const filterResult = this.filter(y, opts);
+ return this._smooth(filterResult);
+ }
+
+ private _smooth(res: DLMResult): DLMSmootherResult {
+ const T = res.steps.length;
+ const sMeans: MutMat = res.filteredMeans.map((m) => m.slice());
+ const sCovs: MutMat[] = res.filteredCovs.map((C) => C.map((r) => r.slice()));
+
+ for (let t = T - 2; t >= 0; t--) {
+ const step = res.steps[t]!;
+ const stepNext = res.steps[t + 1]!;
+ const Rt1 = stepNext.predictedCov;
+ const Rt1inv = matInv(Rt1);
+ if (Rt1inv === null) {
+ continue;
+ }
+ // Smoother gain: J_t = C_t G' R_{t+1}^{-1}
+ const Jt = mmul(mmul(step.filteredCov, tr(this._G)), Rt1inv);
+ // Smoothed mean: s_t = m_t + J_t (s_{t+1} - a_{t+1})
+ const diff = vsub(sMeans[t + 1]!, stepNext.predictedMean);
+ sMeans[t] = vadd(step.filteredMean, mvmul(Jt, diff));
+ // Smoothed covariance: S_t = C_t + J_t (S_{t+1} - R_{t+1}) J_t'
+ const covDiff = msub(sCovs[t + 1]!, stepNext.predictedCov);
+ sCovs[t] = madd(step.filteredCov, mmul(mmul(Jt, covDiff), tr(Jt)));
+ }
+
+ return {
+ ...res,
+ smoothedMeans: sMeans,
+ smoothedCovs: sCovs,
+ };
+ }
+
+ // ── Forecasting ──
+
+ /**
+ * Forecast `h` steps ahead from the end of the filter result.
+ * Returns means and 95% prediction intervals (scalar obs only).
+ */
+ forecast(filterResult: DLMResult, h: number): DLMForecastResult {
+ const T = filterResult.steps.length;
+ const lastStep = filterResult.steps[T - 1]!;
+ let m = lastStep.filteredMean.slice();
+ let C: Mat = lastStep.filteredCov;
+
+ const meanArr: (readonly number[])[] = [];
+ const covArr: Mat[] = [];
+ const lower: number[] = [];
+ const upper: number[] = [];
+ const z975 = 1.959963985;
+
+ for (let h_i = 0; h_i < h; h_i++) {
+ // Predict one step
+ const a = mvmul(this._G, m);
+ const R = madd(mmul(mmul(this._G, C), tr(this._G)), this._W);
+ // Forecast mean and variance
+ const f = mvmul(this._F, a);
+ const Q = madd(mmul(mmul(this._F, R), tr(this._F)), this._V);
+ meanArr.push(f);
+ covArr.push(Q);
+ if (this._q === 1) {
+ const sd = Math.sqrt(Math.max(0, Q[0]?.[0]!));
+ lower.push(f[0]! - z975 * sd);
+ upper.push(f[0]! + z975 * sd);
+ }
+ m = a;
+ C = R;
+ }
+
+ return { mean: meanArr, cov: covArr, lower, upper };
+ }
+
+ // ── MLE estimation ──
+
+ /**
+ * Estimate variance parameters (log V, log W diagonal) by maximum likelihood.
+ * Returns a new DLM with the fitted parameters.
+ *
+ * Uses Nelder-Mead minimisation of negative log-likelihood.
+ */
+ fitMLE(y: readonly (number | readonly number[] | null)[]): DLM {
+ const p = this._p;
+ const q = this._q;
+ // Pack: theta = [log V entries (q*q), log diag(W) entries (p)]
+ // Only diagonal elements of V and W are fitted; off-diagonal kept fixed.
+ const packV = Array.from({ length: q }, (_, i) => Math.log(Math.max(1e-8, this._V[i]?.[i]!)));
+ const packW = Array.from({ length: p }, (_, i) => Math.log(Math.max(1e-8, this._W[i]?.[i]!)));
+ const x0 = [...packV, ...packW];
+
+ const objective = (x: readonly number[]): number => {
+ const V2: MutMat = this._V.map((row) => row.slice());
+ for (let i = 0; i < q; i++) {
+ V2[i]![i] = Math.exp(x[i]!);
+ }
+ const W2: MutMat = this._W.map((row) => row.slice());
+ for (let i = 0; i < p; i++) {
+ W2[i]![i] = Math.exp(x[q + i]!);
+ }
+ const dlm2 = new DLM({ G: this._G, F: this._F, W: W2, V: V2, m0: this._m0, C0: this._C0 });
+ const res = dlm2._filter(y, this._m0, this._C0);
+ return -res.logLikelihood;
+ };
+
+ const xOpt = nelderMead(objective, x0, { maxIter: 500, tol: 1e-6 });
+
+ const Vfit: MutMat = this._V.map((row) => row.slice());
+ for (let i = 0; i < q; i++) {
+ Vfit[i]![i] = Math.exp(xOpt[i]!);
+ }
+ const Wfit: MutMat = this._W.map((row) => row.slice());
+ for (let i = 0; i < p; i++) {
+ Wfit[i]![i] = Math.exp(xOpt[q + i]!);
+ }
+
+ return new DLM({ G: this._G, F: this._F, W: Wfit, V: Vfit, m0: this._m0, C0: this._C0 });
+ }
+
+ /**
+ * Fit with discount factors (West & Harrison §6).
+ * The process covariance is replaced by W_t = ((1-δ)/δ) C_{t-1},
+ * where δ ∈ (0,1] is the discount factor (close to 1 = small evolution).
+ * Returns a new DLM plus the filter result under the discounted model.
+ */
+ filterDiscount(y: readonly (number | readonly number[] | null)[], delta: number): DLMResult {
+ const m0 = this._m0;
+ const C0 = this._C0;
+ const T = y.length;
+ const steps: DLMFilterStep[] = [];
+ let m = m0.slice();
+ let C: Mat = C0;
+ let logLik = 0;
+
+ for (let t = 0; t < T; t++) {
+ const yt = this._obs(y[t]!);
+
+ // Predict with discount: R_t = G C_{t-1} G' / delta
+ const GCGt = mmul(mmul(this._G, C), tr(this._G));
+ const R = mscale(GCGt, 1 / delta);
+ const a = mvmul(this._G, m);
+
+ const f = mvmul(this._F, a);
+ const Q = madd(mmul(mmul(this._F, R), tr(this._F)), this._V);
+
+ let mNew = a;
+ let CNew: Mat = R;
+ let innovation: readonly number[] | null = null;
+ let K: Mat = zeros(this._p, this._q);
+
+ if (yt !== null) {
+ const Qinv = matInv(Q);
+ if (Qinv !== null) {
+ K = mmul(mmul(R, tr(this._F)), Qinv);
+ innovation = vsub(yt, f);
+ mNew = vadd(a, mvmul(K, innovation));
+ const IKF = msub(eye(this._p), mmul(K, this._F));
+ CNew = madd(mmul(mmul(IKF, R), tr(IKF)), mmul(mmul(K, this._V), tr(K)));
+ const eQe = vdot(innovation, mvmul(Qinv, innovation));
+ const logDetQ = logDet(Q);
+ logLik += -0.5 * (this._q * Math.log(2 * Math.PI) + logDetQ + eQe);
+ }
+ }
+
+ steps.push({
+ predictedMean: a,
+ predictedCov: R,
+ filteredMean: mNew,
+ filteredCov: CNew,
+ forecastMean: f,
+ forecastCov: Q,
+ gain: K,
+ innovation,
+ });
+ m = mNew.slice();
+ C = CNew;
+ }
+
+ return {
+ steps,
+ filteredMeans: steps.map((s) => s.filteredMean),
+ filteredCovs: steps.map((s) => s.filteredCov),
+ predictedMeans: steps.map((s) => s.predictedMean),
+ forecastMeans: steps.map((s) => s.forecastMean),
+ forecastCovs: steps.map((s) => s.forecastCov),
+ logLikelihood: logLik,
+ m0,
+ C0,
+ };
+ }
+}
+
+// ─── Factory helpers ────────────────────────────────────────────────────────────
+
+/** Build a local-level DLM (random walk + noise). */
+export function buildLocalLevel(opts: { sigmaObs?: number; sigmaLevel?: number } = {}): DLMSpec {
+ return {
+ G: [[1]],
+ F: [[1]],
+ W: [[(opts.sigmaLevel ?? 1) ** 2]],
+ V: [[(opts.sigmaObs ?? 1) ** 2]],
+ };
+}
+
+/** Build a local-linear-trend DLM. */
+export function buildLocalLinearTrend(
+ opts: {
+ sigmaObs?: number;
+ sigmaLevel?: number;
+ sigmaSlope?: number;
+ } = {},
+): DLMSpec {
+ const sv = opts.sigmaObs ?? 1;
+ const sw1 = opts.sigmaLevel ?? 1;
+ const sw2 = opts.sigmaSlope ?? 0.1;
+ return {
+ G: [
+ [1, 1],
+ [0, 1],
+ ],
+ F: [[1, 0]],
+ W: [
+ [sw1 * sw1, 0],
+ [0, sw2 * sw2],
+ ],
+ V: [[sv * sv]],
+ };
+}
+
+/** Build a polynomial (Jordan-block) trend DLM of a given order. */
+export function buildPolynomial(
+ order: number,
+ opts: { sigmaObs?: number; sigmaState?: number } = {},
+): DLMSpec {
+ const sv = opts.sigmaObs ?? 1;
+ const sw = opts.sigmaState ?? 1;
+ const G = eye(order);
+ for (let i = 0; i < order - 1; i++) {
+ G[i]![i + 1] = 1;
+ }
+ const F: MutMat = [new Array(order).fill(0)];
+ F[0]![0] = 1;
+ return { G, F, W: mscale(eye(order), sw * sw), V: [[sv * sv]] };
+}
+
+/** Build a Fourier seasonal DLM. */
+export function buildFourier(
+ period: number,
+ harmonics: number,
+ opts: { sigmaObs?: number; sigmaState?: number } = {},
+): DLMSpec {
+ const sv = opts.sigmaObs ?? 1;
+ const sw = opts.sigmaState ?? 0.01;
+ const p = 2 * harmonics;
+ const G = zeros(p, p);
+ const F: MutMat = [new Array(p).fill(0)];
+ for (let j = 1; j <= harmonics; j++) {
+ const omega = (2 * Math.PI * j) / period;
+ const c = Math.cos(omega);
+ const s = Math.sin(omega);
+ const r = 2 * (j - 1);
+ G[r]![r] = c;
+ G[r]![r + 1] = s;
+ G[r + 1]![r] = -s;
+ G[r + 1]![r + 1] = c;
+ F[0]![r] = 1;
+ }
+ return { G, F, W: mscale(eye(p), sw * sw), V: [[sv * sv]] };
+}
+
+/** Build a static regression DLM with k predictors (slow-varying coefficients). */
+export function buildRegression(
+ k: number,
+ opts: { sigmaObs?: number; sigmaState?: number } = {},
+): DLMSpec {
+ const sv = opts.sigmaObs ?? 1;
+ const sw = opts.sigmaState ?? 0.001;
+ return {
+ G: eye(k),
+ F: [new Array(k).fill(0)], // placeholder; rows are set externally
+ W: mscale(eye(k), sw * sw),
+ V: [[sv * sv]],
+ };
+}
+
+/**
+ * Combine multiple DLM specs into a single block-diagonal model.
+ * The combined observation matrix is the horizontal concatenation of the
+ * individual F matrices (summing each component's contribution to the scalar
+ * observation). All component V matrices must have the same dimension.
+ */
+export function combineDLMs(...specs: DLMSpec[]): DLMSpec {
+ if (specs.length === 0) {
+ throw new RangeError("combineDLMs requires at least one spec");
+ }
+ const first = specs[0];
+ let G: Mat = first.G;
+ let W: Mat = first.W;
+ let C0: Mat | undefined = first.C0;
+ let m0: number[] = first.m0 ? [...first.m0] : new Array(rows(first.G)).fill(0);
+
+ // Combined F: 1 × (p1+p2+…) — horizontal concat of F rows
+ let Fcombined: number[] = first.F[0] ? [...first.F[0]] : [];
+
+ for (let i = 1; i < specs.length; i++) {
+ const s = specs[i]!;
+ G = blockDiag(G, s.G);
+ W = blockDiag(W, s.W);
+ if (C0 !== undefined && s.C0 !== undefined) {
+ C0 = blockDiag(C0, s.C0);
+ } else {
+ C0 = undefined;
+ }
+ const fi = s.F[0] ? [...s.F[0]] : [];
+ Fcombined = [...Fcombined, ...fi];
+ const pm = s.m0 ? [...s.m0] : new Array(rows(s.G)).fill(0);
+ m0 = [...m0, ...pm];
+ }
+
+ // Use the first component's V (they must agree)
+ const V = specs[0]?.V;
+ const spec: DLMSpec = {
+ G,
+ F: [Fcombined],
+ W,
+ V,
+ m0,
+ ...(C0 !== undefined ? { C0 } : {}),
+ };
+ return spec;
+}
+
+// ─── Log-determinant helper ─────────────────────────────────────────────────────
+
+/** Log-determinant of a positive-definite matrix via Cholesky. Falls back to LU. */
+function logDet(A: Mat): number {
+ const n = rows(A);
+ // Try Cholesky L L' = A
+ const L = zeros(n, n);
+ for (let i = 0; i < n; i++) {
+ for (let j = 0; j <= i; j++) {
+ let s = A[i]?.[j]!;
+ for (let k2 = 0; k2 < j; k2++) {
+ s -= L[i]?.[k2]! * L[j]?.[k2]!;
+ }
+ if (i === j) {
+ if (s < 0) {
+ return _logDetLU(A); // fall back
+ }
+ L[i]![j] = Math.sqrt(s);
+ } else {
+ L[i]![j] = s / (L[j]?.[j] ?? 1);
+ }
+ }
+ }
+ let ld = 0;
+ for (let i = 0; i < n; i++) {
+ ld += 2 * Math.log(Math.abs(L[i]?.[i]!));
+ }
+ return ld;
+}
+
+function _logDetLU(A: Mat): number {
+ const n = rows(A);
+ const U: MutMat = A.map((r) => r.slice());
+ let sign = 1;
+ for (let col = 0; col < n; col++) {
+ let maxVal = Math.abs(U[col]?.[col]!);
+ let maxRow = col;
+ for (let r = col + 1; r < n; r++) {
+ const v = Math.abs(U[r]?.[col]!);
+ if (v > maxVal) {
+ maxVal = v;
+ maxRow = r;
+ }
+ }
+ if (maxRow !== col) {
+ [U[col], U[maxRow]] = [U[maxRow]!, U[col]!];
+ sign *= -1;
+ }
+ const pivot = U[col]?.[col]!;
+ if (Math.abs(pivot) < 1e-14) {
+ return Number.NEGATIVE_INFINITY;
+ }
+ for (let r = col + 1; r < n; r++) {
+ const f = U[r]?.[col]! / pivot;
+ for (let j = col; j < n; j++) {
+ U[r]![j]! -= f * U[col]?.[j]!;
+ }
+ }
+ }
+ let ld = Math.log(Math.abs(sign));
+ for (let i = 0; i < n; i++) {
+ ld += Math.log(Math.abs(U[i]?.[i]!));
+ }
+ return ld;
+}
+
+// ─── Nelder-Mead optimizer ──────────────────────────────────────────────────────
+
+interface NelderMeadOptions {
+ maxIter?: number;
+ tol?: number;
+}
+
+function nelderMead(
+ f: (x: readonly number[]) => number,
+ x0: readonly number[],
+ opts: NelderMeadOptions = {},
+): number[] {
+ const maxIter = opts.maxIter ?? 1000;
+ const tol = opts.tol ?? 1e-8;
+ const n = x0.length;
+ // Build initial simplex
+ const simplex: number[][] = [x0.slice()];
+ for (let i = 0; i < n; i++) {
+ const xi = x0.slice();
+ xi[i]! += xi[i]! !== 0 ? 0.05 * Math.abs(xi[i]!) : 0.00025;
+ simplex.push(xi);
+ }
+ const fvals = simplex.map((x) => f(x));
+
+ const alpha = 1.0;
+ const gamma = 2.0;
+ const rho = 0.5;
+ const sigma = 0.5;
+
+ for (let iter = 0; iter < maxIter; iter++) {
+ // Sort
+ const order = Array.from({ length: n + 1 }, (_, i) => i).sort((a, b) => fvals[a]! - fvals[b]!);
+ const sx = order.map((i) => simplex[i]!);
+ const sf = order.map((i) => fvals[i]!);
+
+ // Convergence check
+ const range = sf[n]! - sf[0]!;
+ if (range < tol) {
+ return sx[0]?.slice();
+ }
+
+ // Centroid of all but worst
+ const xbar = new Array(n).fill(0);
+ for (let i = 0; i < n; i++) {
+ for (let j = 0; j < n; j++) {
+ xbar[j]! += sx[i]?.[j]! / n;
+ }
+ }
+
+ // Reflect
+ const xr = xbar.map((xi, j) => xi + alpha * (xi - sx[n]?.[j]!));
+ const fr = f(xr);
+
+ if (fr < sf[0]!) {
+ // Expand
+ const xe = xbar.map((xi, j) => xi + gamma * (xr[j]! - xi));
+ const fe = f(xe);
+ simplex[order[n]!] = fe < fr ? xe : xr;
+ fvals[order[n]!] = fe < fr ? fe : fr;
+ } else if (fr < sf[n - 1]!) {
+ simplex[order[n]!] = xr;
+ fvals[order[n]!] = fr;
+ } else {
+ // Contract
+ const xc = xbar.map((xi, j) => xi + rho * (sx[n]?.[j]! - xi));
+ const fc = f(xc);
+ if (fc < sf[n]!) {
+ simplex[order[n]!] = xc;
+ fvals[order[n]!] = fc;
+ } else {
+ // Shrink
+ for (let i = 1; i <= n; i++) {
+ for (let j = 0; j < n; j++) {
+ simplex[order[i]!]![j] = sx[0]?.[j]! + sigma * (sx[i]?.[j]! - sx[0]?.[j]!);
+ }
+ fvals[order[i]!] = f(simplex[order[i]!]!);
+ }
+ }
+ }
+ }
+
+ // Return best found
+ let best = 0;
+ for (let i = 1; i < simplex.length; i++) {
+ if (fvals[i]! < fvals[best]!) {
+ best = i;
+ }
+ }
+ return simplex[best]?.slice();
+}
diff --git a/src/stats/ets.ts b/src/stats/ets.ts
new file mode 100644
index 00000000..e5296998
--- /dev/null
+++ b/src/stats/ets.ts
@@ -0,0 +1,1335 @@
+/**
+ * ets — Exponential Smoothing / Holt-Winters ETS models.
+ *
+ * Implements the classical additive-error ETS state-space framework:
+ * - **SimpleExpSmoothing** (SES / ETS(A,N,N)): single level parameter α.
+ * - **Holt** (ETS(A,A,N) / ETS(A,Ad,N)): level α + trend β, optional damping φ.
+ * - **ExponentialSmoothing** (Holt-Winters ETS(·,·,·)): full model with additive
+ * or multiplicative trend and seasonal components.
+ *
+ * Parameter estimation minimises SSE via Nelder-Mead simplex.
+ * Heuristic initialisation follows statsmodels conventions.
+ *
+ * API mirrors `statsmodels.tsa.holtwinters`.
+ *
+ * @example
+ * ```ts
+ * import { ExponentialSmoothing } from "tsb";
+ *
+ * const sales = [17, 21, 23, 18, 22, 26, 19, 24, 27, 20, 25, 28];
+ * const model = new ExponentialSmoothing({ trend: "add", seasonal: "add", seasonalPeriods: 4 });
+ * const fit = model.fit(sales);
+ * console.log(fit.alpha, fit.beta, fit.gamma);
+ * console.log(model.forecast(4)); // 4-step ahead forecasts
+ * ```
+ *
+ * @module
+ */
+
+import type { Series } from "../core/series.ts";
+
+// ─── Public types ──────────────────────────────────────────────────────────────
+
+/** Trend component type: additive, multiplicative, or absent. */
+export type ETSTrend = "add" | "mul" | null;
+
+/** Seasonal component type: additive, multiplicative, or absent. */
+export type ETSSeasonal = "add" | "mul" | null;
+
+/** Initialisation strategy for state variables. */
+export type ETSInit = "heuristic" | "known";
+
+// ── SimpleExpSmoothing ────────────────────────────────────────────────────────
+
+/** Options for {@link SimpleExpSmoothing}. */
+export interface SESOptions {
+ /**
+ * Smoothing level parameter (0 < α < 1).
+ * If omitted the parameter is estimated by minimising SSE.
+ */
+ readonly alpha?: number;
+ /** Initial level value. If omitted, set to `y[0]`. */
+ readonly initialLevel?: number;
+}
+
+/** Result returned by {@link SimpleExpSmoothing.fit}. */
+export interface SESFitResult {
+ /** Estimated smoothing level. */
+ readonly alpha: number;
+ /** Initial level l₀. */
+ readonly initialLevel: number;
+ /** In-sample one-step-ahead fitted values. */
+ readonly fittedValues: readonly number[];
+ /** In-sample residuals e_t = y_t − ŷ_t. */
+ readonly residuals: readonly number[];
+ /** Sum of squared errors. */
+ readonly sse: number;
+ /** Akaike Information Criterion. */
+ readonly aic: number;
+ /** Bayesian Information Criterion. */
+ readonly bic: number;
+ /** Corrected AIC. */
+ readonly aicc: number;
+}
+
+// ── Holt ─────────────────────────────────────────────────────────────────────
+
+/** Options for {@link Holt}. */
+export interface HoltOptions {
+ /** Smoothing level (0 < α < 1). Auto-estimated if omitted. */
+ readonly alpha?: number;
+ /** Smoothing trend (0 < β < 1). Auto-estimated if omitted. */
+ readonly beta?: number;
+ /** Whether to apply a damped trend. Default `false`. */
+ readonly damped?: boolean;
+ /**
+ * Damping coefficient (0 < φ < 1).
+ * Only used when `damped` is `true`. Auto-estimated if omitted.
+ */
+ readonly dampingSlope?: number;
+ /** Initial level l₀. Heuristic if omitted. */
+ readonly initialLevel?: number;
+ /** Initial trend b₀. Heuristic if omitted. */
+ readonly initialTrend?: number;
+}
+
+/** Result returned by {@link Holt.fit}. */
+export interface HoltFitResult {
+ /** Estimated level smoothing parameter. */
+ readonly alpha: number;
+ /** Estimated trend smoothing parameter. */
+ readonly beta: number;
+ /** Damping slope φ (1.0 when not damped). */
+ readonly phi: number;
+ /** Initial level l₀. */
+ readonly initialLevel: number;
+ /** Initial trend b₀. */
+ readonly initialTrend: number;
+ /** In-sample one-step-ahead fitted values. */
+ readonly fittedValues: readonly number[];
+ /** In-sample residuals. */
+ readonly residuals: readonly number[];
+ /** Sum of squared errors. */
+ readonly sse: number;
+ /** Akaike Information Criterion. */
+ readonly aic: number;
+ /** Bayesian Information Criterion. */
+ readonly bic: number;
+ /** Corrected AIC. */
+ readonly aicc: number;
+}
+
+// ── ExponentialSmoothing ─────────────────────────────────────────────────────
+
+/** Options for {@link ExponentialSmoothing}. */
+export interface ExponentialSmoothingOptions {
+ /** Trend component. `"add"` = additive, `"mul"` = multiplicative, `null` = none. */
+ readonly trend?: ETSTrend;
+ /** Whether to use a damped trend. Default `false`. */
+ readonly damped?: boolean;
+ /** Seasonal component. `"add"` = additive, `"mul"` = multiplicative, `null` = none. */
+ readonly seasonal?: ETSSeasonal;
+ /** Number of periods in one seasonal cycle (e.g. 12 for monthly, 4 for quarterly). */
+ readonly seasonalPeriods?: number;
+ /** Smoothing level parameter (0 < α < 1). Auto-estimated if omitted. */
+ readonly alpha?: number;
+ /** Smoothing trend parameter (0 < β < 1). Auto-estimated if omitted. */
+ readonly beta?: number;
+ /** Smoothing seasonal parameter (0 < γ < 1). Auto-estimated if omitted. */
+ readonly gamma?: number;
+ /** Damping slope (0 < φ < 1). Auto-estimated when `damped = true` and omitted. */
+ readonly phi?: number;
+ /** How to initialise the state: `"heuristic"` (default) or `"known"`. */
+ readonly initializationMethod?: ETSInit;
+ /** Known initial level (only used when `initializationMethod = "known"`). */
+ readonly initialLevel?: number;
+ /** Known initial trend (only used when `initializationMethod = "known"`). */
+ readonly initialTrend?: number;
+ /** Known initial seasonal indices (only used when `initializationMethod = "known"`). */
+ readonly initialSeasons?: readonly number[];
+}
+
+/** Result returned by {@link ExponentialSmoothing.fit}. */
+export interface ExponentialSmoothingFitResult {
+ /** Estimated level smoothing parameter. */
+ readonly alpha: number;
+ /** Estimated trend smoothing parameter (`null` when no trend component). */
+ readonly beta: number | null;
+ /** Estimated seasonal smoothing parameter (`null` when no seasonal component). */
+ readonly gamma: number | null;
+ /** Damping slope φ (1.0 when not damped). */
+ readonly phi: number;
+ /** Initial level l₀. */
+ readonly initialLevel: number;
+ /** Initial trend b₀ (`null` when no trend). */
+ readonly initialTrend: number | null;
+ /** Initial seasonal indices s₁…s_m (`null` when no seasonal). */
+ readonly initialSeasons: readonly number[] | null;
+ /** In-sample one-step-ahead fitted values. */
+ readonly fittedValues: readonly number[];
+ /** In-sample residuals. */
+ readonly residuals: readonly number[];
+ /** Sum of squared errors. */
+ readonly sse: number;
+ /** Log-likelihood. */
+ readonly logLikelihood: number;
+ /** Akaike Information Criterion. */
+ readonly aic: number;
+ /** Bayesian Information Criterion. */
+ readonly bic: number;
+ /** Corrected AIC. */
+ readonly aicc: number;
+}
+
+/** Forecast result with prediction intervals. */
+export interface ETSForecastResult {
+ /** Point forecasts h = 1, 2, … steps. */
+ readonly forecast: readonly number[];
+ /** Lower bound of (1 − α_ci) % prediction interval. */
+ readonly lower: readonly number[];
+ /** Upper bound of (1 − α_ci) % prediction interval. */
+ readonly upper: readonly number[];
+ /** Standard errors of h-step-ahead forecast errors. */
+ readonly stderr: readonly number[];
+}
+
+// ─── Private helpers ──────────────────────────────────────────────────────────
+
+/** Extract numeric array from Series or array. */
+function toArr(y: readonly number[] | Series): readonly number[] {
+ if ("dtype" in y) {
+ return y.values;
+ }
+ return y;
+}
+
+/** Clamp value to [lo, hi]. */
+function clamp(v: number, lo: number, hi: number): number {
+ return Math.min(Math.max(v, lo), hi);
+}
+
+/** Clamp all elements of a param vector to their respective bounds. */
+function clampParams(params: readonly number[], bounds: readonly [number, number][]): number[] {
+ return params.map((v, i) => clamp(v, (bounds[i] ?? [0, 1])[0], (bounds[i] ?? [0, 1])[1]));
+}
+
+/**
+ * Nelder-Mead simplex optimiser (unconstrained; bounds enforced by clamping).
+ * Minimises `fn(params)` starting from `x0`.
+ */
+function nelderMead(
+ fn: (params: readonly number[]) => number,
+ x0: readonly number[],
+ bounds: readonly [number, number][],
+ maxIter = 3000,
+): { params: number[]; value: number } {
+ const n = x0.length;
+ if (n === 0) {
+ return { params: [], value: fn([]) };
+ }
+
+ const EPS = 1e-12;
+ const ALPHA_NM = 1.0; // reflection
+ const BETA_NM = 0.5; // contraction
+ const GAMMA_NM = 2.0; // expansion
+ const SIGMA_NM = 0.5; // shrinkage
+
+ const clamp1 = (p: readonly number[]): number[] => clampParams(p, bounds);
+
+ // Build initial simplex
+ const simplex: number[][] = [clamp1(x0)];
+ for (let i = 0; i < n; i++) {
+ const pt = clamp1(x0);
+ const lo = (bounds[i] ?? [0, 1])[0];
+ const hi = (bounds[i] ?? [0, 1])[1];
+ const delta = Math.max((hi - lo) * 0.1, 0.01);
+ pt[i] = clamp((pt[i] ?? 0) + delta, lo + EPS, hi - EPS);
+ simplex.push(pt);
+ }
+
+ const fvals: number[] = simplex.map((p) => fn(p));
+
+ for (let iter = 0; iter < maxIter; iter++) {
+ // Sort indices by fval
+ const ord = Array.from({ length: n + 1 }, (_, i) => i).sort(
+ (a, b) => (fvals[a] ?? 0) - (fvals[b] ?? 0),
+ );
+
+ const fBest = fvals[ord[0] ?? 0] ?? 0;
+ const fWorst = fvals[ord[n] ?? 0] ?? 0;
+ if (fWorst - fBest < EPS) {
+ break;
+ }
+
+ // Centroid of best n points
+ const cent = new Array(n).fill(0);
+ for (let i = 0; i < n; i++) {
+ const row = simplex[ord[i] ?? 0] ?? [];
+ for (let j = 0; j < n; j++) {
+ cent[j] = (cent[j] ?? 0) + (row[j] ?? 0);
+ }
+ }
+ for (let j = 0; j < n; j++) {
+ cent[j] = (cent[j] ?? 0) / n;
+ }
+
+ const worstPt = simplex[ord[n] ?? 0] ?? [];
+
+ // Reflection
+ const xr = clamp1(cent.map((c, j) => (1 + ALPHA_NM) * c - ALPHA_NM * (worstPt[j] ?? 0)));
+ const fr = fn(xr);
+
+ const fSecondWorst = fvals[ord[n - 1] ?? 0] ?? 0;
+
+ if (fr < fBest) {
+ // Expansion
+ const xe = clamp1(cent.map((c, j) => (1 + GAMMA_NM) * c - GAMMA_NM * (worstPt[j] ?? 0)));
+ const fe = fn(xe);
+ if (fe < fr) {
+ simplex[ord[n] ?? 0] = xe;
+ fvals[ord[n] ?? 0] = fe;
+ } else {
+ simplex[ord[n] ?? 0] = xr;
+ fvals[ord[n] ?? 0] = fr;
+ }
+ } else if (fr < fSecondWorst) {
+ simplex[ord[n] ?? 0] = xr;
+ fvals[ord[n] ?? 0] = fr;
+ } else {
+ // Contraction
+ const inside = fr >= fWorst;
+ const src = inside ? worstPt : xr;
+ const xc = clamp1(cent.map((c, j) => BETA_NM * c + (1 - BETA_NM) * (src[j] ?? 0)));
+ const fc = fn(xc);
+ const compareVal = inside ? fWorst : fr;
+ if (fc < compareVal) {
+ simplex[ord[n] ?? 0] = xc;
+ fvals[ord[n] ?? 0] = fc;
+ } else {
+ // Shrink
+ const bestPt = simplex[ord[0] ?? 0] ?? [];
+ for (let i = 1; i <= n; i++) {
+ const row = simplex[ord[i] ?? 0] ?? [];
+ const newRow = clamp1(row.map((v, j) => SIGMA_NM * (v + (bestPt[j] ?? 0))));
+ simplex[ord[i] ?? 0] = newRow;
+ fvals[ord[i] ?? 0] = fn(newRow);
+ }
+ }
+ }
+ }
+
+ // Return best
+ let bestIdx = 0;
+ for (let i = 1; i <= n; i++) {
+ if ((fvals[i] ?? Number.POSITIVE_INFINITY) < (fvals[bestIdx] ?? Number.POSITIVE_INFINITY)) {
+ bestIdx = i;
+ }
+ }
+ return { params: simplex[bestIdx] ?? [], value: fvals[bestIdx] ?? Number.POSITIVE_INFINITY };
+}
+
+/** Compute AIC, BIC, AICc from SSE, n, k. */
+function infoGaussian(
+ sse: number,
+ n: number,
+ k: number,
+): { logLikelihood: number; aic: number; bic: number; aicc: number } {
+ const sigma2 = Math.max(sse / n, 1e-15);
+ const logL = -0.5 * n * (Math.log(2 * Math.PI * sigma2) + 1);
+ const aic = -2 * logL + 2 * k;
+ const bic = -2 * logL + k * Math.log(n);
+ const denom = n - k - 1;
+ const aicc = denom > 0 ? aic + (2 * k * (k + 1)) / denom : aic;
+ return { logLikelihood: logL, aic, bic, aicc };
+}
+
+// ─── SES internals ────────────────────────────────────────────────────────────
+
+/**
+ * Run one SES pass. Returns { fitted, residuals, sse }.
+ * l0 = initial level.
+ */
+function sesPass(
+ y: readonly number[],
+ alpha: number,
+ l0: number,
+): { fitted: number[]; residuals: number[]; sse: number } {
+ const n = y.length;
+ const fitted: number[] = new Array(n);
+ const residuals: number[] = new Array(n);
+ let sse = 0;
+ let l = l0;
+ for (let t = 0; t < n; t++) {
+ fitted[t] = l;
+ const e = (y[t] ?? 0) - l;
+ residuals[t] = e;
+ sse += e * e;
+ l = alpha * (y[t] ?? 0) + (1 - alpha) * l;
+ }
+ return { fitted, residuals, sse };
+}
+
+// ─── Holt internals ───────────────────────────────────────────────────────────
+
+/**
+ * Run one Holt pass.
+ * Returns { fitted, residuals, sse, levels, trends }.
+ */
+function holtPass(
+ y: readonly number[],
+ alpha: number,
+ beta: number,
+ phi: number,
+ l0: number,
+ b0: number,
+): { fitted: number[]; residuals: number[]; sse: number } {
+ const n = y.length;
+ const fitted: number[] = new Array(n);
+ const residuals: number[] = new Array(n);
+ let sse = 0;
+ let l = l0;
+ let b = b0;
+ for (let t = 0; t < n; t++) {
+ const yhat = l + phi * b;
+ fitted[t] = yhat;
+ const yt = y[t] ?? 0;
+ const e = yt - yhat;
+ residuals[t] = e;
+ sse += e * e;
+ const lNew = alpha * yt + (1 - alpha) * (l + phi * b);
+ b = beta * (lNew - l) + (1 - beta) * phi * b;
+ l = lNew;
+ }
+ return { fitted, residuals, sse };
+}
+
+/** Holt h-step forecast (damped or not). */
+function holtForecast(steps: number, l: number, b: number, phi: number): number[] {
+ const out: number[] = [];
+ let phiH = phi; // φ¹
+ let phiSum = phi; // φ + φ² + … + φ^h
+ for (let h = 1; h <= steps; h++) {
+ out.push(l + phiSum * b);
+ phiH *= phi;
+ phiSum += phiH;
+ }
+ return out;
+}
+
+// ─── ETS (Holt-Winters) internals ─────────────────────────────────────────────
+
+interface ETSState {
+ l: number;
+ b: number;
+ s: number[]; // length m circular buffer, s[0] = s_{t-m+1}, … , s[m-1] = s_t
+}
+
+/**
+ * Run one Holt-Winters pass. Returns fitted values, residuals, SSE, and the
+ * final state (l, b, last m seasonal indices).
+ */
+function hwPass(
+ y: readonly number[],
+ alpha: number,
+ beta: number | null,
+ gamma: number | null,
+ phi: number,
+ l0: number,
+ b0: number | null,
+ s0: readonly number[] | null,
+ trend: ETSTrend,
+ seasonal: ETSSeasonal,
+ m: number,
+): {
+ fitted: number[];
+ residuals: number[];
+ sse: number;
+ finalL: number;
+ finalB: number;
+ finalS: number[];
+} {
+ const n = y.length;
+ const fitted: number[] = new Array(n);
+ const residuals: number[] = new Array(n);
+ let sse = 0;
+
+ let l = l0;
+ let b = b0 ?? 0;
+
+ // seasonal buffer: seasonals[t % m] = s_{t+1-m}
+ const seasonals: number[] = s0 ? s0.slice() : new Array(m).fill(0);
+
+ for (let t = 0; t < n; t++) {
+ const yt = y[t] ?? 0;
+ const sIdx = ((t % m) + m) % m; // index into seasonal buffer
+ const st_m = seasonals[sIdx] ?? 0; // s_{t+1-m}
+
+ // One-step-ahead forecast
+ let yhat: number;
+ if (trend === null && seasonal === null) {
+ yhat = l;
+ } else if (trend !== null && seasonal === null) {
+ yhat = l + phi * b;
+ } else if (trend === null && seasonal === "add") {
+ yhat = l + st_m;
+ } else if (trend === null && seasonal === "mul") {
+ yhat = l * st_m;
+ } else if (trend === "add" && seasonal === "add") {
+ yhat = l + phi * b + st_m;
+ } else if (trend === "add" && seasonal === "mul") {
+ yhat = (l + phi * b) * st_m;
+ } else if (trend === "mul" && seasonal === "add") {
+ yhat = l * (phi === 1 ? b : b ** phi) + st_m;
+ } else {
+ // mul trend + mul seasonal
+ yhat = l * (phi === 1 ? b : b ** phi) * st_m;
+ }
+
+ fitted[t] = yhat;
+ const e = yt - yhat;
+ residuals[t] = e;
+ sse += e * e;
+
+ // State update
+ const lPrev = l;
+ const bPrev = b;
+
+ if (trend === null && seasonal === null) {
+ l = alpha * yt + (1 - alpha) * l;
+ } else if (trend !== null && seasonal === null) {
+ l = alpha * yt + (1 - alpha) * (l + phi * b);
+ if (beta !== null) {
+ b = beta * (l - lPrev) + (1 - beta) * phi * bPrev;
+ }
+ } else if (trend === null && seasonal === "add") {
+ l = alpha * (yt - st_m) + (1 - alpha) * l;
+ if (gamma !== null) {
+ seasonals[sIdx] = gamma * (yt - l) + (1 - gamma) * st_m;
+ }
+ } else if (trend === null && seasonal === "mul") {
+ l = alpha * (st_m !== 0 ? yt / st_m : yt) + (1 - alpha) * l;
+ if (gamma !== null) {
+ seasonals[sIdx] = gamma * (l !== 0 ? yt / l : 1) + (1 - gamma) * st_m;
+ }
+ } else if (trend === "add" && seasonal === "add") {
+ l = alpha * (yt - st_m) + (1 - alpha) * (lPrev + phi * bPrev);
+ if (beta !== null) {
+ b = beta * (l - lPrev) + (1 - beta) * phi * bPrev;
+ }
+ if (gamma !== null) {
+ seasonals[sIdx] = gamma * (yt - l) + (1 - gamma) * st_m;
+ }
+ } else if (trend === "add" && seasonal === "mul") {
+ l = alpha * (st_m !== 0 ? yt / st_m : yt) + (1 - alpha) * (lPrev + phi * bPrev);
+ if (beta !== null) {
+ b = beta * (l - lPrev) + (1 - beta) * phi * bPrev;
+ }
+ if (gamma !== null) {
+ seasonals[sIdx] = gamma * (l + phi * b !== 0 ? yt / (l + phi * b) : 1) + (1 - gamma) * st_m;
+ }
+ } else {
+ // multiplicative trend — approximate as additive for stability
+ l = alpha * yt + (1 - alpha) * (lPrev + phi * bPrev);
+ if (beta !== null) {
+ b = beta * (l - lPrev) + (1 - beta) * phi * bPrev;
+ }
+ if (gamma !== null) {
+ seasonals[sIdx] = gamma * (yt - l) + (1 - gamma) * st_m;
+ }
+ }
+ }
+
+ return {
+ fitted,
+ residuals,
+ sse,
+ finalL: l,
+ finalB: b,
+ finalS: seasonals.slice(),
+ };
+}
+
+/**
+ * Generate h-step forecasts from the final ETS state.
+ * `finalS` is the circular buffer of the last m seasonal indices where
+ * `finalS[t % m]` = s_{t+1-m} (same convention as hwPass).
+ */
+function hwForecast(
+ steps: number,
+ l: number,
+ b: number,
+ finalS: readonly number[],
+ phi: number,
+ trend: ETSTrend,
+ seasonal: ETSSeasonal,
+ m: number,
+ n: number, // length of training series (to compute season offsets)
+): number[] {
+ const out: number[] = [];
+ let phiH = phi;
+ let phiSum = phi;
+
+ // At t = n-1 (last training obs), seasonals[t % m] has just been updated.
+ // For forecast step h, the seasonal index corresponds to position (n-1+h) % m in
+ // the buffer (shifted by 1 because sIdx = (t % m) in hwPass at time t = n-1+h).
+ for (let h = 1; h <= steps; h++) {
+ const sIdx = (((n - 1 + h) % m) + m) % m;
+ const sVal = finalS[sIdx] ?? 1;
+
+ let yhat: number;
+ if (trend === null && seasonal === null) {
+ yhat = l;
+ } else if (trend !== null && seasonal === null) {
+ yhat = l + phiSum * b;
+ } else if (trend === null && seasonal === "add") {
+ yhat = l + sVal;
+ } else if (trend === null && seasonal === "mul") {
+ yhat = l * sVal;
+ } else if (trend === "add" && seasonal === "add") {
+ yhat = l + phiSum * b + sVal;
+ } else if (trend === "add" && seasonal === "mul") {
+ yhat = (l + phiSum * b) * sVal;
+ } else if (trend === "mul" && seasonal === "add") {
+ yhat = l * b ** phiSum + sVal;
+ } else {
+ yhat = l * b ** phiSum * sVal;
+ }
+
+ out.push(yhat);
+
+ phiH *= phi;
+ phiSum += phiH;
+ }
+ return out;
+}
+
+// ─── Heuristic initialisation ─────────────────────────────────────────────────
+
+/**
+ * Compute heuristic initial level and trend.
+ * Uses mean of first season + linear regression slope on first two seasons.
+ */
+function heuristicInit(
+ y: readonly number[],
+ m: number,
+ hasTrend: boolean,
+): { l0: number; b0: number } {
+ const n = y.length;
+ if (!hasTrend) {
+ return { l0: y[0] ?? 0, b0: 0 };
+ }
+ // Use first season average as l0, slope between first two seasons as b0
+ const k = Math.min(m, n);
+ let s1 = 0;
+ for (let i = 0; i < k; i++) {
+ s1 += y[i] ?? 0;
+ }
+ const l0 = s1 / k;
+
+ if (n >= 2 * m) {
+ let s2 = 0;
+ for (let i = m; i < 2 * m; i++) {
+ s2 += y[i] ?? 0;
+ }
+ const b0 = (s2 / m - l0) / m;
+ return { l0, b0: b0 || (((y[1] ?? 0) - (y[0] ?? 0)) * m) / m };
+ }
+ // Fallback: slope between y[0] and y[n-1]
+ const b0 = n > 1 ? ((y[n - 1] ?? 0) - (y[0] ?? 0)) / (n - 1) : 0;
+ return { l0, b0 };
+}
+
+/**
+ * Compute heuristic seasonal indices.
+ * Additive: s_j = avg(y_j, y_{j+m}, …) − overall mean
+ * Multiplicative: s_j = avg(y_j, y_{j+m}, …) / overall mean
+ */
+function heuristicSeasons(
+ y: readonly number[],
+ m: number,
+ seasonal: "add" | "mul",
+ l0: number,
+ b0: number,
+): number[] {
+ const n = y.length;
+ const cycles = Math.max(1, Math.floor(n / m));
+
+ // Detrended values for each position in the seasonal cycle
+ const byPos: number[][] = Array.from({ length: m }, () => []);
+ for (let t = 0; t < cycles * m && t < n; t++) {
+ const trend = l0 + b0 * t;
+ const raw = y[t] ?? 0;
+ const pos = t % m;
+ const posArr = byPos[pos];
+ if (posArr !== undefined) {
+ if (seasonal === "add") {
+ posArr.push(raw - trend);
+ } else {
+ posArr.push(trend !== 0 ? raw / trend : 1);
+ }
+ }
+ }
+
+ const rawS = byPos.map((vals) => {
+ if (vals.length === 0) {
+ return seasonal === "add" ? 0 : 1;
+ }
+ return vals.reduce((a, b) => a + b, 0) / vals.length;
+ });
+
+ // Normalise so seasonal indices sum to 0 (additive) or m (multiplicative)
+ if (seasonal === "add") {
+ const mean = rawS.reduce((a, b) => a + b, 0) / m;
+ return rawS.map((v) => v - mean);
+ }
+ const mean = rawS.reduce((a, b) => a + b, 0) / m;
+ return rawS.map((v) => (mean !== 0 ? (v / mean) * 1 : 1));
+}
+
+// ─── SimpleExpSmoothing ──────────────────────────────────────────────────────
+
+/**
+ * Simple Exponential Smoothing (SES / ETS(A,N,N)).
+ *
+ * Produces level-only forecasts; all future forecasts equal the final level.
+ *
+ * @example
+ * ```ts
+ * import { SimpleExpSmoothing } from "tsb";
+ * const model = new SimpleExpSmoothing();
+ * const fit = model.fit([3, 5, 4, 6, 5, 8, 7]);
+ * console.log(fit.alpha, fit.sse);
+ * console.log(model.forecast(3)); // [level, level, level]
+ * ```
+ */
+export class SimpleExpSmoothing {
+ private _fit: SESFitResult | null = null;
+ private _finalLevel = 0;
+
+ /**
+ * Fit the SES model to observed data.
+ * @param y - Time series observations.
+ * @param opts - Optional fixed parameters.
+ */
+ fit(y: readonly number[] | Series, opts?: SESOptions): SESFitResult {
+ const arr = toArr(y);
+ const n = arr.length;
+ if (n < 2) {
+ throw new RangeError("SimpleExpSmoothing requires at least 2 observations");
+ }
+
+ const l0Init = opts?.initialLevel ?? arr[0] ?? 0;
+
+ let alpha: number;
+ let l0: number;
+
+ if (opts?.alpha !== undefined) {
+ alpha = clamp(opts.alpha, 1e-6, 1 - 1e-6);
+ l0 = l0Init;
+ } else {
+ // Optimise α (and optionally l0)
+ const result = nelderMead(
+ ([a, l]: readonly number[]) => sesPass(arr, a ?? 0.3, l ?? arr[0] ?? 0).sse,
+ [0.3, l0Init],
+ [
+ [1e-6, 1 - 1e-6],
+ [
+ (arr[0] ?? 0) - Math.abs(arr[0] ?? 0) * 5 - 1,
+ (arr[0] ?? 0) + Math.abs(arr[0] ?? 0) * 5 + 1,
+ ],
+ ],
+ );
+ alpha = result.params[0] ?? 0.3;
+ l0 = result.params[1] ?? l0Init;
+ }
+
+ const { fitted, residuals, sse } = sesPass(arr, alpha, l0);
+
+ // Final level for forecasting
+ let lFinal = l0;
+ for (const yt of arr) {
+ lFinal = alpha * yt + (1 - alpha) * lFinal;
+ }
+ this._finalLevel = lFinal;
+
+ const k = 2; // alpha + l0
+ const { aic, bic, aicc } = infoGaussian(sse, n, k);
+
+ const result: SESFitResult = {
+ alpha,
+ initialLevel: l0,
+ fittedValues: fitted,
+ residuals,
+ sse,
+ aic,
+ bic,
+ aicc,
+ };
+ this._fit = result;
+ return result;
+ }
+
+ /**
+ * Generate `steps` forecasts from the last fitted state.
+ * All forecasts equal the final level (flat forecast).
+ * Must call {@link fit} first.
+ */
+ forecast(steps: number): number[] {
+ if (this._fit === null) {
+ throw new Error("Call fit() before forecast()");
+ }
+ return new Array(steps).fill(this._finalLevel);
+ }
+}
+
+// ─── Holt ─────────────────────────────────────────────────────────────────────
+
+/**
+ * Holt's linear (double) exponential smoothing (ETS(A,A,N) / ETS(A,Ad,N)).
+ *
+ * Extends SES with a trend component; supports optional damping.
+ *
+ * @example
+ * ```ts
+ * import { Holt } from "tsb";
+ * const model = new Holt();
+ * const fit = model.fit([3, 5, 4, 6, 5, 8, 7, 9, 8, 11]);
+ * console.log(fit.alpha, fit.beta, fit.phi);
+ * console.log(model.forecast(5));
+ * ```
+ */
+export class Holt {
+ private _opts: HoltOptions = {};
+ private _fit: HoltFitResult | null = null;
+ private _finalL = 0;
+ private _finalB = 0;
+ private _phi = 1;
+
+ constructor(opts?: HoltOptions) {
+ this._opts = opts ?? {};
+ }
+
+ /**
+ * Fit Holt's model to observed data.
+ * @param y - Time series observations.
+ * @param opts - Optional parameter overrides (merged with constructor opts).
+ */
+ fit(y: readonly number[] | Series, opts?: HoltOptions): HoltFitResult {
+ const arr = toArr(y);
+ const n = arr.length;
+ if (n < 3) {
+ throw new RangeError("Holt requires at least 3 observations");
+ }
+
+ const merged: HoltOptions = { ...this._opts, ...opts };
+ const damped = merged.damped ?? false;
+
+ const { l0: l0h, b0: b0h } = heuristicInit(arr, n, true);
+
+ const alphaFixed = merged.alpha;
+ const betaFixed = merged.beta;
+ const phiFixed = merged.dampingSlope;
+ const l0Fixed = merged.initialLevel ?? l0h;
+ const b0Fixed = merged.initialTrend ?? b0h;
+
+ // Build optimisation bounds and initial point
+ type Bound = [number, number];
+ const paramNames: string[] = [];
+ const x0: number[] = [];
+ const bounds: Bound[] = [];
+
+ if (alphaFixed === undefined) {
+ paramNames.push("alpha");
+ x0.push(0.3);
+ bounds.push([1e-6, 1 - 1e-6]);
+ }
+ if (betaFixed === undefined) {
+ paramNames.push("beta");
+ x0.push(0.1);
+ bounds.push([1e-6, 1 - 1e-6]);
+ }
+ if (damped && phiFixed === undefined) {
+ paramNames.push("phi");
+ x0.push(0.98);
+ bounds.push([0.8, 1 - 1e-6]);
+ }
+ // Always optimise l0 and b0 if not fixed
+ const optimL0 = merged.initialLevel === undefined;
+ const optimB0 = merged.initialTrend === undefined;
+ if (optimL0) {
+ paramNames.push("l0");
+ x0.push(l0h);
+ const spread = Math.abs(l0h) * 2 + 10;
+ bounds.push([l0h - spread, l0h + spread]);
+ }
+ if (optimB0) {
+ paramNames.push("b0");
+ x0.push(b0h);
+ const spread = Math.abs(b0h) * 5 + 1;
+ bounds.push([b0h - spread, b0h + spread]);
+ }
+
+ let alpha = alphaFixed ?? 0.3;
+ let beta = betaFixed ?? 0.1;
+ let phi = damped ? (phiFixed ?? 0.98) : 1.0;
+ let l0 = l0Fixed;
+ let b0 = b0Fixed;
+
+ if (x0.length > 0) {
+ const result = nelderMead(
+ (params: readonly number[]): number => {
+ let a = alphaFixed ?? params[paramNames.indexOf("alpha")] ?? 0.3;
+ let bta = betaFixed ?? params[paramNames.indexOf("beta")] ?? 0.1;
+ let ph = damped ? (phiFixed ?? params[paramNames.indexOf("phi")] ?? 0.98) : 1.0;
+ const ll0 = optimL0 ? (params[paramNames.indexOf("l0")] ?? l0h) : l0Fixed;
+ const lb0 = optimB0 ? (params[paramNames.indexOf("b0")] ?? b0h) : b0Fixed;
+ a = clamp(a, 1e-6, 1 - 1e-6);
+ bta = clamp(bta, 1e-6, 1 - 1e-6);
+ ph = clamp(ph, 0.8, 1 - 1e-6);
+ return holtPass(arr, a, bta, ph, ll0, lb0).sse;
+ },
+ x0,
+ bounds,
+ );
+ const p = result.params;
+ alpha = alphaFixed ?? p[paramNames.indexOf("alpha")] ?? alpha;
+ beta = betaFixed ?? p[paramNames.indexOf("beta")] ?? beta;
+ phi = damped ? (phiFixed ?? p[paramNames.indexOf("phi")] ?? phi) : 1.0;
+ l0 = optimL0 ? (p[paramNames.indexOf("l0")] ?? l0) : l0Fixed;
+ b0 = optimB0 ? (p[paramNames.indexOf("b0")] ?? b0) : b0Fixed;
+ }
+
+ alpha = clamp(alpha, 1e-6, 1 - 1e-6);
+ beta = clamp(beta, 1e-6, 1 - 1e-6);
+ if (damped) {
+ phi = clamp(phi, 0.8, 1 - 1e-6);
+ }
+
+ const { fitted, residuals, sse } = holtPass(arr, alpha, beta, phi, l0, b0);
+
+ // Final state for forecasting
+ let lF = l0;
+ let bF = b0;
+ for (const yt of arr) {
+ const lNew = alpha * yt + (1 - alpha) * (lF + phi * bF);
+ bF = beta * (lNew - lF) + (1 - beta) * phi * bF;
+ lF = lNew;
+ }
+ this._finalL = lF;
+ this._finalB = bF;
+ this._phi = phi;
+
+ const k = 2 + (damped ? 1 : 0) + 2; // alpha + beta + phi? + l0 + b0
+ const { aic, bic, aicc } = infoGaussian(sse, n, k);
+
+ const result: HoltFitResult = {
+ alpha,
+ beta,
+ phi,
+ initialLevel: l0,
+ initialTrend: b0,
+ fittedValues: fitted,
+ residuals,
+ sse,
+ aic,
+ bic,
+ aicc,
+ };
+ this._fit = result;
+ return result;
+ }
+
+ /**
+ * Generate `steps` forecasts from the last fitted state.
+ * Must call {@link fit} first.
+ */
+ forecast(steps: number): number[] {
+ if (this._fit === null) {
+ throw new Error("Call fit() before forecast()");
+ }
+ return holtForecast(steps, this._finalL, this._finalB, this._phi);
+ }
+}
+
+// ─── ExponentialSmoothing (Holt-Winters) ─────────────────────────────────────
+
+/**
+ * Holt-Winters Exponential Smoothing — full ETS model.
+ *
+ * Supports all combinations of additive / multiplicative trend and seasonal
+ * components with optional damped trend.
+ *
+ * @example
+ * ```ts
+ * import { ExponentialSmoothing } from "tsb";
+ *
+ * // Monthly data with additive seasonal component
+ * const y = [17, 21, 23, 18, 22, 26, 19, 24, 27, 20, 25, 28,
+ * 18, 23, 25, 20, 24, 28, 21, 26, 29, 22, 27, 30];
+ * const model = new ExponentialSmoothing({ trend: "add", seasonal: "add", seasonalPeriods: 12 });
+ * const fit = model.fit(y);
+ * console.log(fit.alpha, fit.beta, fit.gamma, fit.aic);
+ * console.log(model.forecast(12));
+ * ```
+ */
+export class ExponentialSmoothing {
+ private _opts: ExponentialSmoothingOptions;
+ private _fit: ExponentialSmoothingFitResult | null = null;
+ private _finalL = 0;
+ private _finalB = 0;
+ private _finalS: number[] = [];
+ private _phi = 1;
+ private _trend: ETSTrend = null;
+ private _seasonal: ETSSeasonal = null;
+ private _m = 1;
+ private _n = 0;
+ private _sigma2 = 1;
+
+ constructor(opts?: ExponentialSmoothingOptions) {
+ this._opts = opts ?? {};
+ }
+
+ /**
+ * Fit the Holt-Winters model to observed data.
+ * @param y - Time series observations.
+ * @param opts - Optional parameter overrides.
+ */
+ fit(
+ y: readonly number[] | Series,
+ opts?: ExponentialSmoothingOptions,
+ ): ExponentialSmoothingFitResult {
+ const arr = toArr(y);
+ const n = arr.length;
+ if (n < 3) {
+ throw new RangeError("ExponentialSmoothing requires at least 3 observations");
+ }
+
+ const merged: ExponentialSmoothingOptions = { ...this._opts, ...opts };
+ const trend = merged.trend ?? null;
+ const seasonal = merged.seasonal ?? null;
+ const damped = merged.damped ?? false;
+ const m = merged.seasonalPeriods ?? (seasonal !== null ? 2 : 1);
+
+ this._trend = trend;
+ this._seasonal = seasonal;
+ this._m = m;
+ this._n = n;
+
+ if (seasonal !== null && n < 2 * m) {
+ throw new RangeError(
+ `ExponentialSmoothing: need at least 2 full seasonal periods (${2 * m} obs), got ${n}`,
+ );
+ }
+
+ const initMethod = merged.initializationMethod ?? "heuristic";
+
+ // Heuristic initialisation
+ const { l0: l0h, b0: b0h } = heuristicInit(arr, m, trend !== null);
+ const s0h = seasonal !== null ? heuristicSeasons(arr, m, seasonal, l0h, b0h) : null;
+
+ // Determine which params to optimise
+ const alphaFixed = merged.alpha;
+ const betaFixed = merged.beta;
+ const gammaFixed = merged.gamma;
+ const phiFixed = merged.phi;
+ const l0Fixed = initMethod === "known" ? (merged.initialLevel ?? l0h) : undefined;
+ const b0Fixed = initMethod === "known" ? (merged.initialTrend ?? b0h) : undefined;
+ const s0Fixed =
+ initMethod === "known" && merged.initialSeasons !== undefined
+ ? merged.initialSeasons.slice()
+ : undefined;
+
+ type Bound = [number, number];
+ const paramNames: string[] = [];
+ const x0: number[] = [];
+ const bounds: Bound[] = [];
+
+ if (alphaFixed === undefined) {
+ paramNames.push("alpha");
+ x0.push(0.3);
+ bounds.push([1e-6, 1 - 1e-6]);
+ }
+ if (trend !== null && betaFixed === undefined) {
+ paramNames.push("beta");
+ x0.push(0.1);
+ bounds.push([1e-6, 1 - 1e-6]);
+ }
+ if (seasonal !== null && gammaFixed === undefined) {
+ paramNames.push("gamma");
+ x0.push(0.1);
+ bounds.push([1e-6, 1 - 1e-6]);
+ }
+ if (damped && phiFixed === undefined) {
+ paramNames.push("phi");
+ x0.push(0.98);
+ bounds.push([0.8, 1 - 1e-6]);
+ }
+
+ const optimL0 = initMethod !== "known" && merged.initialLevel === undefined;
+ const optimB0 = trend !== null && initMethod !== "known" && merged.initialTrend === undefined;
+ const optimS0 =
+ seasonal !== null && initMethod !== "known" && merged.initialSeasons === undefined;
+
+ if (optimL0) {
+ paramNames.push("l0");
+ x0.push(l0h);
+ const sp = Math.abs(l0h) * 2 + 10;
+ bounds.push([l0h - sp, l0h + sp]);
+ }
+ if (optimB0) {
+ paramNames.push("b0");
+ x0.push(b0h);
+ const sp = Math.abs(b0h) * 5 + 1;
+ bounds.push([b0h - sp, b0h + sp]);
+ }
+ if (optimS0 && s0h !== null) {
+ for (let j = 0; j < m; j++) {
+ paramNames.push(`s0_${j}`);
+ x0.push(s0h[j] ?? 0);
+ const sp = Math.abs(s0h[j] ?? 0) * 5 + 1;
+ bounds.push([(s0h[j] ?? 0) - sp, (s0h[j] ?? 0) + sp]);
+ }
+ }
+
+ let alpha = alphaFixed ?? 0.3;
+ let beta = trend !== null ? (betaFixed ?? 0.1) : null;
+ let gamma = seasonal !== null ? (gammaFixed ?? 0.1) : null;
+ let phi = damped ? (phiFixed ?? 0.98) : 1.0;
+ let l0 = l0Fixed ?? l0h;
+ let b0 = b0Fixed ?? b0h;
+ let s0 = s0Fixed ?? s0h;
+
+ if (x0.length > 0) {
+ const res = nelderMead(
+ (params: readonly number[]): number => {
+ let a = alphaFixed ?? params[paramNames.indexOf("alpha")] ?? 0.3;
+ let bt = trend !== null ? (betaFixed ?? params[paramNames.indexOf("beta")] ?? 0.1) : null;
+ let gm =
+ seasonal !== null ? (gammaFixed ?? params[paramNames.indexOf("gamma")] ?? 0.1) : null;
+ let ph = damped ? (phiFixed ?? params[paramNames.indexOf("phi")] ?? 0.98) : 1.0;
+ a = clamp(a, 1e-6, 1 - 1e-6);
+ if (bt !== null) {
+ bt = clamp(bt, 1e-6, 1 - 1e-6);
+ }
+ if (gm !== null) {
+ gm = clamp(gm, 1e-6, 1 - 1e-6);
+ }
+ if (damped) {
+ ph = clamp(ph, 0.8, 1 - 1e-6);
+ }
+
+ const ll0 = optimL0 ? (params[paramNames.indexOf("l0")] ?? l0h) : (l0Fixed ?? l0h);
+ const lb0 = optimB0 ? (params[paramNames.indexOf("b0")] ?? b0h) : (b0Fixed ?? b0h);
+ let ss0: number[] | null = null;
+ if (optimS0) {
+ ss0 = [];
+ for (let j = 0; j < m; j++) {
+ ss0.push(params[paramNames.indexOf(`s0_${j}`)] ?? s0h?.[j] ?? 0);
+ }
+ } else {
+ ss0 = s0Fixed ?? s0h;
+ }
+
+ return hwPass(arr, a, bt, gm, ph, ll0, lb0, ss0, trend, seasonal, m).sse;
+ },
+ x0,
+ bounds,
+ seasonal !== null ? 5000 : 3000,
+ );
+ const p = res.params;
+ alpha = alphaFixed ?? p[paramNames.indexOf("alpha")] ?? alpha;
+ beta = trend !== null ? (betaFixed ?? p[paramNames.indexOf("beta")] ?? beta ?? 0.1) : null;
+ gamma =
+ seasonal !== null ? (gammaFixed ?? p[paramNames.indexOf("gamma")] ?? gamma ?? 0.1) : null;
+ phi = damped ? (phiFixed ?? p[paramNames.indexOf("phi")] ?? phi) : 1.0;
+ l0 = optimL0 ? (p[paramNames.indexOf("l0")] ?? l0) : (l0Fixed ?? l0);
+ b0 = optimB0 ? (p[paramNames.indexOf("b0")] ?? b0) : (b0Fixed ?? b0);
+ if (optimS0) {
+ s0 = [];
+ for (let j = 0; j < m; j++) {
+ s0.push(p[paramNames.indexOf(`s0_${j}`)] ?? s0h?.[j] ?? 0);
+ }
+ }
+ }
+
+ // Final clamp
+ alpha = clamp(alpha, 1e-6, 1 - 1e-6);
+ if (beta !== null) {
+ beta = clamp(beta, 1e-6, 1 - 1e-6);
+ }
+ if (gamma !== null) {
+ gamma = clamp(gamma, 1e-6, 1 - 1e-6);
+ }
+ if (damped) {
+ phi = clamp(phi, 0.8, 1 - 1e-6);
+ }
+
+ const { fitted, residuals, sse, finalL, finalB, finalS } = hwPass(
+ arr,
+ alpha,
+ beta,
+ gamma,
+ phi,
+ l0,
+ b0,
+ s0,
+ trend,
+ seasonal,
+ m,
+ );
+
+ this._finalL = finalL;
+ this._finalB = finalB;
+ this._finalS = finalS;
+ this._phi = phi;
+ this._sigma2 = Math.max(sse / n, 1e-15);
+
+ // Number of free parameters for information criteria
+ const nParams =
+ 1 + // alpha
+ (trend !== null ? 1 : 0) + // beta
+ (seasonal !== null ? 1 : 0) + // gamma
+ (damped ? 1 : 0) + // phi
+ 1 + // l0
+ (trend !== null ? 1 : 0) + // b0
+ (seasonal !== null ? m : 0); // seasonal indices
+
+ const { logLikelihood, aic, bic, aicc } = infoGaussian(sse, n, nParams);
+
+ const result: ExponentialSmoothingFitResult = {
+ alpha,
+ beta,
+ gamma,
+ phi,
+ initialLevel: l0,
+ initialTrend: trend !== null ? b0 : null,
+ initialSeasons: seasonal !== null ? (s0 ?? null) : null,
+ fittedValues: fitted,
+ residuals,
+ sse,
+ logLikelihood,
+ aic,
+ bic,
+ aicc,
+ };
+ this._fit = result;
+ return result;
+ }
+
+ /**
+ * Generate `steps` point forecasts from the final state.
+ * Must call {@link fit} first.
+ */
+ forecast(steps: number): number[] {
+ if (this._fit === null) {
+ throw new Error("Call fit() before forecast()");
+ }
+ return hwForecast(
+ steps,
+ this._finalL,
+ this._finalB,
+ this._finalS,
+ this._phi,
+ this._trend,
+ this._seasonal,
+ this._m,
+ this._n,
+ );
+ }
+
+ /**
+ * Generate `steps` forecasts with (1 − `alpha_ci`) prediction intervals.
+ * Uses additive-error variance approximation (constant σ² scaled by h).
+ * Must call {@link fit} first.
+ *
+ * @param steps - Number of steps ahead.
+ * @param alpha_ci - Significance level (default 0.05 → 95 % intervals).
+ */
+ forecastWithCI(steps: number, alpha_ci = 0.05): ETSForecastResult {
+ const fc = this.forecast(steps);
+ // Normal quantile for (1 - alpha_ci/2)
+ const z = normalQuantile(1 - alpha_ci / 2);
+ const sigma = Math.sqrt(this._sigma2);
+ const lower: number[] = [];
+ const upper: number[] = [];
+ const stderr: number[] = [];
+ for (let h = 1; h <= steps; h++) {
+ // Variance grows linearly with h for additive-error models
+ const se = sigma * Math.sqrt(h);
+ stderr.push(se);
+ lower.push((fc[h - 1] ?? 0) - z * se);
+ upper.push((fc[h - 1] ?? 0) + z * se);
+ }
+ return { forecast: fc, lower, upper, stderr };
+ }
+}
+
+/**
+ * Rational approximation of the normal quantile function (Abramowitz & Stegun).
+ * @internal
+ */
+function normalQuantile(p: number): number {
+ if (p <= 0) {
+ return Number.NEGATIVE_INFINITY;
+ }
+ if (p >= 1) {
+ return Number.POSITIVE_INFINITY;
+ }
+ const a = [2.515517, 0.802853, 0.010328];
+ const b = [1.432788, 0.189269, 0.001308];
+ const t = Math.sqrt(-2 * Math.log(p < 0.5 ? p : 1 - p));
+ const num = (a[0] ?? 0) + (a[1] ?? 0) * t + (a[2] ?? 0) * t * t;
+ const den = 1 + (b[0] ?? 0) * t + (b[1] ?? 0) * t * t + (b[2] ?? 0) * t * t * t;
+ const x = t - num / den;
+ return p < 0.5 ? -x : x;
+}
+
+// ─── Convenience functions ────────────────────────────────────────────────────
+
+/**
+ * Fit a Simple Exponential Smoothing model and return the result.
+ *
+ * @example
+ * ```ts
+ * import { simpleExpSmoothing } from "tsb";
+ * const { alpha, fittedValues, sse } = simpleExpSmoothing([3, 5, 4, 6, 5]);
+ * ```
+ */
+export function simpleExpSmoothing(
+ y: readonly number[] | Series,
+ opts?: SESOptions,
+): SESFitResult {
+ return new SimpleExpSmoothing().fit(y, opts);
+}
+
+/**
+ * Fit a Holt linear trend model and return the result.
+ *
+ * @example
+ * ```ts
+ * import { holt } from "tsb";
+ * const fit = holt([3, 5, 4, 6, 5, 8, 7, 9]);
+ * console.log(fit.alpha, fit.beta, fit.sse);
+ * ```
+ */
+export function holt(y: readonly number[] | Series, opts?: HoltOptions): HoltFitResult {
+ return new Holt(opts).fit(y);
+}
+
+/**
+ * Fit a full Holt-Winters Exponential Smoothing model and return the result.
+ *
+ * @example
+ * ```ts
+ * import { fitEts } from "tsb";
+ * const y = [17, 21, 23, 18, 22, 26, 19, 24, 27, 20, 25, 28];
+ * const fit = fitEts(y, { trend: "add", seasonal: "add", seasonalPeriods: 4 });
+ * console.log(fit.alpha, fit.beta, fit.gamma, fit.aic);
+ * ```
+ */
+export function fitEts(
+ y: readonly number[] | Series,
+ opts?: ExponentialSmoothingOptions,
+): ExponentialSmoothingFitResult {
+ return new ExponentialSmoothing(opts).fit(y);
+}
diff --git a/src/stats/extreme_value.ts b/src/stats/extreme_value.ts
new file mode 100644
index 00000000..3e5423b5
--- /dev/null
+++ b/src/stats/extreme_value.ts
@@ -0,0 +1,445 @@
+/**
+ * extreme_value — Extreme Value Theory (EVT) distributions and analysis.
+ *
+ * Implements:
+ * - **Generalized Extreme Value (GEV)** distribution (Gumbel, Fréchet, Weibull)
+ * - **Generalized Pareto Distribution (GPD)** for Peaks Over Threshold
+ * - **Block maxima** method (GEV fitting via L-moments)
+ * - **Peaks Over Threshold (POT)** method (GPD fitting via MLE)
+ * - **Return level** and **return period** calculations
+ * - **Gumbel**, **Fréchet**, **Weibull** special-case distributions
+ *
+ * @module
+ */
+
+// ─── GEV Distribution ─────────────────────────────────────────────────────────
+
+/**
+ * Parameters of the Generalized Extreme Value distribution.
+ */
+export interface GEVParams {
+ /** Location parameter (mu). */
+ mu: number;
+ /** Scale parameter (sigma > 0). */
+ sigma: number;
+ /** Shape parameter (xi). xi=0: Gumbel, xi>0: Fréchet, xi<0: Weibull. */
+ xi: number;
+}
+
+/**
+ * GEV probability density function.
+ *
+ * @param x - Value.
+ * @param params - GEV parameters.
+ * @returns Density f(x).
+ *
+ * @example
+ * ```ts
+ * import { gevPdf } from "tsb";
+ * const p = gevPdf(2.5, { mu: 0, sigma: 1, xi: 0.1 });
+ * ```
+ */
+export function gevPdf(x: number, params: GEVParams): number {
+ const { mu, sigma, xi } = params;
+ if (sigma <= 0) return 0;
+
+ const z = (x - mu) / sigma;
+
+ if (Math.abs(xi) < 1e-8) {
+ // Gumbel case (xi → 0)
+ const t = Math.exp(-z);
+ return (1 / sigma) * Math.exp(-z - t);
+ }
+
+ const t = 1 + xi * z;
+ if (t <= 0) return 0;
+
+ return (1 / sigma) * t ** (-1 / xi - 1) * Math.exp(-(t ** (-1 / xi)));
+}
+
+/**
+ * GEV cumulative distribution function.
+ *
+ * @param x - Value.
+ * @param params - GEV parameters.
+ * @returns CDF F(x).
+ */
+export function gevCdf(x: number, params: GEVParams): number {
+ const { mu, sigma, xi } = params;
+ if (sigma <= 0) return 0;
+
+ const z = (x - mu) / sigma;
+
+ if (Math.abs(xi) < 1e-8) {
+ // Gumbel case
+ return Math.exp(-Math.exp(-z));
+ }
+
+ const t = 1 + xi * z;
+ if (t <= 0) {
+ return xi > 0 ? 0 : 1;
+ }
+
+ return Math.exp(-(t ** (-1 / xi)));
+}
+
+/**
+ * GEV quantile function (inverse CDF).
+ *
+ * @param p - Probability in (0, 1).
+ * @param params - GEV parameters.
+ * @returns Quantile x such that F(x) = p.
+ */
+export function gevQuantile(p: number, params: GEVParams): number {
+ if (p <= 0) return -Infinity;
+ if (p >= 1) return Infinity;
+
+ const { mu, sigma, xi } = params;
+
+ if (Math.abs(xi) < 1e-8) {
+ // Gumbel
+ return mu - sigma * Math.log(-Math.log(p));
+ }
+
+ return mu + (sigma / xi) * ((-Math.log(p)) ** (-xi) - 1);
+}
+
+/**
+ * Compute return level for a given return period (years/blocks).
+ *
+ * @param returnPeriod - Return period (e.g., 100 for 100-year event).
+ * @param params - GEV parameters.
+ * @returns Return level x such that P(X > x) = 1/returnPeriod.
+ */
+export function gevReturnLevel(returnPeriod: number, params: GEVParams): number {
+ const p = 1 - 1 / returnPeriod;
+ return gevQuantile(p, params);
+}
+
+// ─── GEV Fitting (L-moments) ──────────────────────────────────────────────────
+
+/**
+ * Fit GEV distribution to block maxima using L-moments.
+ *
+ * @param maxima - Array of block maxima (e.g., annual maxima).
+ * @returns Estimated GEV parameters.
+ *
+ * @example
+ * ```ts
+ * import { fitGEV } from "tsb";
+ * const annualMaxima = [12.3, 15.1, 9.8, 18.2, 11.4, 14.7];
+ * const params = fitGEV(annualMaxima);
+ * ```
+ */
+export function fitGEV(maxima: number[]): GEVParams {
+ const n = maxima.length;
+ if (n < 3) return { mu: 0, sigma: 1, xi: 0 };
+
+ const sorted = [...maxima].sort((a, b) => a - b);
+
+ // Compute L-moments via probability-weighted moments
+ let b0 = 0;
+ let b1 = 0;
+ let b2 = 0;
+
+ for (let i = 0; i < n; i++) {
+ b0 += sorted[i] ?? 0;
+ b1 += ((i) / (n - 1)) * (sorted[i] ?? 0);
+ b2 += ((i) * (i - 1) / ((n - 1) * (n - 2))) * (sorted[i] ?? 0);
+ }
+ b0 /= n;
+ b1 /= n;
+ b2 /= n;
+
+ const l1 = b0;
+ const l2 = 2 * b1 - b0;
+ const l3 = 6 * b2 - 6 * b1 + b0;
+
+ // L-skewness
+ const tau3 = l2 > 1e-10 ? l3 / l2 : 0;
+
+ // Estimate xi from L-skewness using approximation
+ let xi: number;
+ if (Math.abs(tau3) < 1e-8) {
+ xi = 0;
+ } else {
+ // Rational approximation for xi from tau3
+ xi = estimateXiFromTau3(tau3);
+ }
+
+ let sigma: number;
+ let mu: number;
+
+ if (Math.abs(xi) < 1e-6) {
+ // Gumbel
+ sigma = l2 / Math.log(2);
+ mu = l1 - 0.5772156649 * sigma;
+ } else {
+ const g1 = gamma(1 - xi);
+ sigma = (l2 * xi) / ((1 - 2 ** (-xi)) * g1);
+ mu = l1 - sigma * (g1 - 1) / xi;
+ }
+
+ return {
+ mu,
+ sigma: Math.max(sigma, 1e-8),
+ xi,
+ };
+}
+
+// ─── GPD Distribution ─────────────────────────────────────────────────────────
+
+/**
+ * Parameters of the Generalized Pareto Distribution.
+ */
+export interface GPDParams {
+ /** Threshold (u). */
+ threshold: number;
+ /** Scale parameter (sigma > 0). */
+ sigma: number;
+ /** Shape parameter (xi). */
+ xi: number;
+}
+
+/**
+ * GPD probability density function.
+ *
+ * @param x - Value (must be >= threshold).
+ * @param params - GPD parameters.
+ * @returns Density f(x).
+ */
+export function gpdPdf(x: number, params: GPDParams): number {
+ const { threshold, sigma, xi } = params;
+ if (sigma <= 0 || x < threshold) return 0;
+
+ const z = (x - threshold) / sigma;
+
+ if (Math.abs(xi) < 1e-8) {
+ return (1 / sigma) * Math.exp(-z);
+ }
+
+ const t = 1 + xi * z;
+ if (t <= 0) return 0;
+
+ return (1 / sigma) * t ** (-1 / xi - 1);
+}
+
+/**
+ * GPD cumulative distribution function.
+ *
+ * @param x - Value.
+ * @param params - GPD parameters.
+ * @returns CDF value.
+ */
+export function gpdCdf(x: number, params: GPDParams): number {
+ const { threshold, sigma, xi } = params;
+ if (x < threshold) return 0;
+
+ const z = (x - threshold) / sigma;
+
+ if (Math.abs(xi) < 1e-8) {
+ return 1 - Math.exp(-z);
+ }
+
+ const t = 1 + xi * z;
+ if (t <= 0) return xi > 0 ? 0 : 1;
+
+ return 1 - t ** (-1 / xi);
+}
+
+/**
+ * GPD quantile function.
+ *
+ * @param p - Probability in (0, 1).
+ * @param params - GPD parameters.
+ * @returns Quantile.
+ */
+export function gpdQuantile(p: number, params: GPDParams): number {
+ if (p <= 0) return params.threshold;
+ if (p >= 1) return Infinity;
+
+ const { threshold, sigma, xi } = params;
+
+ if (Math.abs(xi) < 1e-8) {
+ return threshold - sigma * Math.log(1 - p);
+ }
+
+ return threshold + (sigma / xi) * ((1 - p) ** (-xi) - 1);
+}
+
+// ─── GPD Fitting (MLE) ────────────────────────────────────────────────────────
+
+/**
+ * Fit GPD to exceedances above a threshold using MLE.
+ *
+ * @param data - Full dataset.
+ * @param threshold - Threshold u (only exceedances x > u are used).
+ * @returns Estimated GPD parameters.
+ *
+ * @example
+ * ```ts
+ * import { fitGPD } from "tsb";
+ * const data = [1.2, 0.5, 3.4, 8.1, 0.2, 5.6, 12.3, 0.8, 4.1, 7.2];
+ * const params = fitGPD(data, 4.0);
+ * ```
+ */
+export function fitGPD(data: number[], threshold: number): GPDParams {
+ const exceedances = data.filter((x) => x > threshold).map((x) => x - threshold);
+
+ if (exceedances.length < 2) {
+ return { threshold, sigma: 1, xi: 0 };
+ }
+
+ const n = exceedances.length;
+ const mean = exceedances.reduce((s, x) => s + x, 0) / n;
+ const variance = exceedances.reduce((s, x) => s + (x - mean) ** 2, 0) / (n - 1);
+
+ // Method of moments starting values
+ let sigmaInit = mean * (mean * mean / variance + 1) / 2;
+ let xiInit = (mean * mean / variance - 1) / 2;
+
+ sigmaInit = Math.max(sigmaInit, 1e-6);
+
+ // Simple gradient-free optimization (Nelder-Mead would be ideal, use grid search)
+ const { sigma, xi } = optimizeGPD(exceedances, sigmaInit, xiInit);
+
+ return { threshold, sigma, xi };
+}
+
+// ─── Peaks Over Threshold Analysis ───────────────────────────────────────────
+
+/**
+ * Extract exceedances above a threshold.
+ *
+ * @param data - Time series data.
+ * @param threshold - Threshold value.
+ * @returns Array of exceedance values.
+ */
+export function extractExceedances(data: number[], threshold: number): number[] {
+ return data.filter((x) => x > threshold);
+}
+
+/**
+ * Compute return level from GPD fit.
+ *
+ * @param returnPeriod - Return period.
+ * @param params - Fitted GPD parameters.
+ * @param lambda - Rate of threshold exceedances (exceedances per time unit).
+ * @returns Return level.
+ */
+export function gpdReturnLevel(
+ returnPeriod: number,
+ params: GPDParams,
+ lambda: number,
+): number {
+ const p = 1 - 1 / (returnPeriod * lambda);
+ return gpdQuantile(Math.max(0, Math.min(1, p)), params);
+}
+
+// ─── Gumbel Distribution ──────────────────────────────────────────────────────
+
+/** Parameters for the Gumbel distribution (GEV with xi=0). */
+export interface GumbelParams {
+ /** Location (mu). */
+ mu: number;
+ /** Scale (beta > 0). */
+ beta: number;
+}
+
+/** Gumbel PDF. */
+export function gumbelPdf(x: number, params: GumbelParams): number {
+ const z = (x - params.mu) / params.beta;
+ return (1 / params.beta) * Math.exp(-(z + Math.exp(-z)));
+}
+
+/** Gumbel CDF. */
+export function gumbelCdf(x: number, params: GumbelParams): number {
+ const z = (x - params.mu) / params.beta;
+ return Math.exp(-Math.exp(-z));
+}
+
+/** Gumbel quantile. */
+export function gumbelQuantile(p: number, params: GumbelParams): number {
+ return params.mu - params.beta * Math.log(-Math.log(p));
+}
+
+// ─── Utilities ────────────────────────────────────────────────────────────────
+
+/** Lanczos approximation of the gamma function. */
+function gamma(z: number): number {
+ if (z < 0.5) {
+ return Math.PI / (Math.sin(Math.PI * z) * gamma(1 - z));
+ }
+ const g = 7;
+ const c = [
+ 0.99999999999980993, 676.5203681218851, -1259.1392167224028,
+ 771.32342877765313, -176.61502916214059, 12.507343278686905,
+ -0.13857109526572012, 9.9843695780195716e-6, 1.5056327351493116e-7,
+ ];
+ let x = c[0] ?? 0;
+ const zr = z - 1;
+ for (let i = 1; i < g + 2; i++) {
+ x += (c[i] ?? 0) / (zr + i);
+ }
+ const t = zr + g + 0.5;
+ return Math.sqrt(2 * Math.PI) * t ** (zr + 0.5) * Math.exp(-t) * x;
+}
+
+/** Estimate GEV shape parameter from L-skewness. */
+function estimateXiFromTau3(tau3: number): number {
+ // Approximation from Hosking (1985)
+ // For Gumbel: tau3 = 0.1699 (log(3/2) / log(2) - 2)
+ // Valid for -0.5 < xi < 0.5
+ const c = 2 / (3 + tau3) - Math.log(2) / Math.log(3);
+ return 7.8590 * c + 2.9554 * c * c;
+}
+
+/** Simple optimization for GPD parameters. */
+function optimizeGPD(
+ exceedances: number[],
+ sigmaInit: number,
+ xiInit: number,
+): { sigma: number; xi: number } {
+ let sigma = sigmaInit;
+ let xi = xiInit;
+
+ const logLik = (s: number, x: number): number => {
+ const n = exceedances.length;
+ if (s <= 0) return -Infinity;
+ let ll = -n * Math.log(s);
+ for (const e of exceedances) {
+ if (Math.abs(x) < 1e-8) {
+ ll -= e / s;
+ } else {
+ const t = 1 + x * e / s;
+ if (t <= 0) return -Infinity;
+ ll -= (1 / x + 1) * Math.log(t);
+ }
+ }
+ return ll;
+ };
+
+ // Simple coordinate ascent
+ let bestLl = logLik(sigma, xi);
+ for (let iter = 0; iter < 100; iter++) {
+ const stepS = sigma * 0.1;
+ const stepX = 0.05;
+
+ for (const ds of [-stepS, stepS]) {
+ const ll = logLik(sigma + ds, xi);
+ if (ll > bestLl) {
+ bestLl = ll;
+ sigma = sigma + ds;
+ }
+ }
+ for (const dx of [-stepX, stepX]) {
+ const ll = logLik(sigma, xi + dx);
+ if (ll > bestLl) {
+ bestLl = ll;
+ xi = xi + dx;
+ }
+ }
+ }
+
+ return { sigma: Math.max(sigma, 1e-8), xi };
+}
diff --git a/src/stats/filters.ts b/src/stats/filters.ts
new file mode 100644
index 00000000..76136a51
--- /dev/null
+++ b/src/stats/filters.ts
@@ -0,0 +1,757 @@
+/**
+ * filters — Digital filter design and application.
+ *
+ * Mirrors `scipy.signal` filter utilities. Implemented from scratch with no
+ * external dependencies.
+ *
+ * Filter design:
+ * - {@link firwin} — FIR filter (windowed-sinc method)
+ * - {@link butter} — Butterworth IIR digital filter
+ *
+ * Frequency response:
+ * - {@link freqz} — frequency response of an FIR/IIR filter
+ * - {@link sosfreqz} — frequency response of SOS filter
+ *
+ * Filter application:
+ * - {@link lfilter} — causal FIR/IIR filter (direct-form II transposed)
+ * - {@link filtfilt} — zero-phase forward-backward filter
+ * - {@link sosfilt} — second-order-sections filter
+ *
+ * @example
+ * ```ts
+ * import { firwin, lfilter, butter, sosfilt } from "tsb";
+ *
+ * // Low-pass FIR with 29 taps, cutoff 0.25 (Nyquist = 0.5)
+ * const b = firwin(29, 0.25);
+ * const y = lfilter(b, [1], signal);
+ *
+ * // Butterworth low-pass, order 4, cutoff 0.2
+ * const { sos } = butter(4, 0.2, "lowpass");
+ * const filtered = sosfilt(sos, signal);
+ * ```
+ *
+ * @module
+ */
+
+import {
+ type Complex,
+ type WindowName,
+ blackmanWindow,
+ cAbs,
+ complex,
+ hammingWindow,
+ hannWindow,
+ kaiserWindow,
+} from "./signal.ts";
+
+// ─── internal helpers ─────────────────────────────────────────────────────────
+
+/** sinc(x) = sin(πx) / (πx), sinc(0) = 1 (normalised). */
+function sinc(x: number): number {
+ if (x === 0) {
+ return 1;
+ }
+ const px = Math.PI * x;
+ return Math.sin(px) / px;
+}
+
+/** Polynomial multiplication (convolution). */
+function polyMul(a: readonly number[], b: readonly number[]): number[] {
+ const out = new Array(a.length + b.length - 1).fill(0);
+ for (let i = 0; i < a.length; i++) {
+ for (let j = 0; j < b.length; j++) {
+ out[i + j] = (out[i + j] ?? 0) + (a[i] ?? 0) * (b[j] ?? 0);
+ }
+ }
+ return out;
+}
+
+// ─── FIR filter design ────────────────────────────────────────────────────────
+
+/** Options for {@link firwin}. */
+export interface FirwinOptions {
+ /**
+ * Window to apply after ideal filter: name string or pre-computed array.
+ * Default `"hamming"`.
+ */
+ window?: WindowName | readonly number[];
+ /** If `true`, design a high-pass filter (default `false` = low-pass). */
+ pass_zero?: boolean;
+ /** Sampling rate used to normalise `cutoff` (default `2` so `cutoff ∈ [0, 1]`). */
+ fs?: number;
+}
+
+/**
+ * Design a low- or high-pass FIR filter using the windowed-sinc method.
+ *
+ * Mirrors `scipy.signal.firwin`.
+ *
+ * @param numtaps - Number of filter coefficients (must be odd for pass_zero=false).
+ * @param cutoff - Cutoff frequency. With default `fs=2`, cutoff is normalised
+ * so `1.0` equals the Nyquist frequency.
+ * @param options - {@link FirwinOptions}.
+ * @returns - FIR filter coefficients `b` (length `numtaps`).
+ *
+ * @example
+ * ```ts
+ * import { firwin, lfilter } from "tsb";
+ * const b = firwin(51, 0.3); // 51-tap 150 Hz LPF (fs=1000)
+ * const y = lfilter(b, [1], signal);
+ * ```
+ */
+export function firwin(
+ numtaps: number,
+ cutoff: number | readonly [number, number],
+ options: FirwinOptions = {},
+): number[] {
+ const fs = options.fs ?? 2;
+ const passZero = options.pass_zero ?? true;
+ const nyq = fs / 2;
+
+ // Normalise cutoff(s) to [0..1] where 1 = Nyquist
+ const rawCuts = Array.isArray(cutoff)
+ ? (cutoff as readonly [number, number])
+ : ([cutoff] as const);
+ const cuts = (rawCuts as readonly number[]).map((c) => c / nyq);
+
+ const M = numtaps - 1;
+
+ // Build window
+ let win: number[];
+ if (options.window !== undefined) {
+ win =
+ typeof options.window === "string"
+ ? buildFirWindow(options.window, numtaps)
+ : Array.from(options.window);
+ } else {
+ win = hammingWindow(numtaps);
+ }
+
+ // Ideal sinc coefficients
+ const h = new Array(numtaps).fill(0);
+
+ if (cuts.length === 1) {
+ const fc = cuts[0] ?? 0;
+ if (passZero) {
+ // Low-pass: h[n] = fc * sinc(fc * (n - M/2))
+ for (let n = 0; n < numtaps; n++) {
+ h[n] = fc * sinc(fc * (n - M / 2)) * (win[n] ?? 1);
+ }
+ } else {
+ // High-pass: h[n] = delta(n - M/2) - fc * sinc(fc * (n - M/2))
+ for (let n = 0; n < numtaps; n++) {
+ const delta = n === M / 2 ? 1 : 0;
+ h[n] = (delta - fc * sinc(fc * (n - M / 2))) * (win[n] ?? 1);
+ }
+ }
+ } else {
+ // Band-pass or band-stop
+ const [f1, f2] = cuts as [number, number];
+ if (passZero) {
+ // Band-stop (notch): LP(f1) + HP(f2)
+ for (let n = 0; n < numtaps; n++) {
+ const mid = M / 2;
+ const delta = n === mid ? 1 : 0;
+ h[n] = (f1 * sinc(f1 * (n - mid)) + (delta - f2 * sinc(f2 * (n - mid)))) * (win[n] ?? 1);
+ }
+ } else {
+ // Band-pass: BP(f1, f2) = LP(f2) - LP(f1)
+ for (let n = 0; n < numtaps; n++) {
+ const mid = M / 2;
+ h[n] = (f2 * sinc(f2 * (n - mid)) - f1 * sinc(f1 * (n - mid))) * (win[n] ?? 1);
+ }
+ }
+ }
+
+ // Normalise DC gain
+ const dcGain = h.reduce((s, v) => s + v, 0);
+ if (Math.abs(dcGain) > 1e-12 && passZero && cuts.length === 1) {
+ // Low-pass: normalise DC to 1
+ const scale = 1 / dcGain;
+ return h.map((v) => v * scale);
+ }
+ return h;
+}
+
+/** Build a named window for FIR design. */
+function buildFirWindow(name: WindowName, n: number): number[] {
+ switch (name) {
+ case "hamming":
+ return hammingWindow(n);
+ case "hann":
+ return hannWindow(n);
+ case "blackman":
+ return blackmanWindow(n);
+ case "kaiser":
+ return kaiserWindow(n, 14);
+ default:
+ return hammingWindow(n);
+ }
+}
+
+// ─── frequency response ───────────────────────────────────────────────────────
+
+/** Result of {@link freqz} and {@link sosfreqz}. */
+export interface FreqzResult {
+ /** Angular frequencies in radians/sample (0 to π). */
+ w: number[];
+ /** Complex frequency response H(e^jω). */
+ H: Complex[];
+}
+
+/**
+ * Compute the frequency response H(e^jω) of a digital filter.
+ *
+ * Mirrors `scipy.signal.freqz`.
+ *
+ * @param b - Numerator polynomial coefficients.
+ * @param a - Denominator polynomial coefficients (default `[1]` = FIR).
+ * @param worN - Number of frequency points, or array of specific radian frequencies.
+ * @returns - `{ w, H }` where `w` is in radians/sample and `H` is complex.
+ *
+ * @example
+ * ```ts
+ * import { firwin, freqz } from "tsb";
+ * const b = firwin(31, 0.3);
+ * const { w, H } = freqz(b, [1], 512);
+ * const mag = H.map(h => cAbs(h));
+ * ```
+ */
+export function freqz(
+ b: readonly number[],
+ a: readonly number[] = [1],
+ worN: number | readonly number[] = 512,
+): FreqzResult {
+ const ws: number[] = Array.isArray(worN)
+ ? Array.from(worN as readonly number[])
+ : Array.from({ length: worN as number }, (_, i) => (Math.PI * i) / (worN as number));
+
+ const H: Complex[] = ws.map((w) => {
+ // H(e^jw) = B(e^jw) / A(e^jw)
+ // Evaluate using Horner at z = e^jw
+ const z: Complex = { re: Math.cos(w), im: Math.sin(w) };
+ const Bw = evalPolyZ(b, z);
+ const Aw = evalPolyZ(a, z);
+ return divComplex(Bw, Aw);
+ });
+
+ return { w: ws, H };
+}
+
+/** Evaluate polynomial with coefficients `p` at complex `z` (b[0]*z^N + ... + b[N]). */
+function evalPolyZ(p: readonly number[], z: Complex): Complex {
+ let acc: Complex = complex(0, 0);
+ for (let i = 0; i < p.length; i++) {
+ // acc = acc * z + p[i]
+ acc = {
+ re: acc.re * z.re - acc.im * z.im + (p[i] ?? 0),
+ im: acc.re * z.im + acc.im * z.re,
+ };
+ }
+ return acc;
+}
+
+/** Divide two complex numbers (b / a), returns 0 when |a| < eps. */
+function divComplex(b: Complex, a: Complex): Complex {
+ const denom = a.re * a.re + a.im * a.im;
+ if (denom < 1e-300) {
+ return complex(0, 0);
+ }
+ return {
+ re: (b.re * a.re + b.im * a.im) / denom,
+ im: (b.im * a.re - b.re * a.im) / denom,
+ };
+}
+
+// ─── Butterworth IIR filter ───────────────────────────────────────────────────
+
+/** A second-order section: `[b0, b1, b2, 1, a1, a2]`. */
+export type SOSSection = [number, number, number, number, number, number];
+
+/** Result of {@link butter}. */
+export interface ButterResult {
+ /** Second-order sections (numerically preferred for high orders). */
+ sos: SOSSection[];
+ /** Numerator polynomial (may lose precision for high orders). */
+ b: number[];
+ /** Denominator polynomial (may lose precision for high orders). */
+ a: number[];
+}
+
+/** Butter filter type. */
+export type FilterType = "lowpass" | "highpass" | "bandpass" | "bandstop";
+
+/**
+ * Design an N-th order Butterworth digital filter (bilinear transform).
+ *
+ * Mirrors `scipy.signal.butter`.
+ *
+ * Returns both the SOS form (use {@link sosfilt} — numerically stable) and
+ * the b/a form (use {@link lfilter} — may have numerical issues for N > 4).
+ *
+ * @param N - Filter order (1–8 recommended; high orders lose precision in b/a form).
+ * @param Wn - Critical frequency. Normalised to `[0, 1]` where `1 = Nyquist`.
+ * Provide `[low, high]` for band-pass or band-stop.
+ * @param type - Filter type (default `"lowpass"`).
+ * @returns - `{ sos, b, a }`.
+ *
+ * @example
+ * ```ts
+ * import { butter, sosfilt } from "tsb";
+ * const { sos } = butter(4, 0.2);
+ * const y = sosfilt(sos, signal);
+ * ```
+ */
+// biome-ignore lint/complexity/noExcessiveCognitiveComplexity: filter design algebra
+export function butter(
+ N: number,
+ Wn: number | readonly [number, number],
+ type: FilterType = "lowpass",
+): ButterResult {
+ // Validate
+ if (N < 1 || N > 20 || !Number.isInteger(N)) {
+ throw new RangeError("Order N must be an integer 1–20");
+ }
+
+ const _nyq = 1; // Normalised: Nyquist = 1
+ const isBand = type === "bandpass" || type === "bandstop";
+
+ if (isBand && !Array.isArray(Wn)) {
+ throw new TypeError("Band filters require Wn = [low, high]");
+ }
+ if (!isBand && Array.isArray(Wn)) {
+ throw new TypeError("Low/high-pass filters require scalar Wn");
+ }
+
+ // Pre-warp critical frequency(ies) using bilinear transform
+ const warpedWn: number | [number, number] = Array.isArray(Wn)
+ ? ([
+ 2 * Math.tan((Math.PI * (Wn as readonly [number, number])[0]) / 2),
+ 2 * Math.tan((Math.PI * (Wn as readonly [number, number])[1]) / 2),
+ ] as [number, number])
+ : 2 * Math.tan((Math.PI * (Wn as number)) / 2);
+
+ // Analog Butterworth prototype poles at unit circle (left half-plane)
+ // p_k = exp(j * pi * (2k + N - 1) / (2N)) for k = 0..N-1
+ const poles: Complex[] = Array.from({ length: N }, (_, k) => {
+ const ang = (Math.PI * (2 * k + N - 1)) / (2 * N);
+ return complex(Math.cos(ang), Math.sin(ang));
+ });
+
+ // Scale poles to the desired cutoff frequency
+ let scaledPoles: Complex[];
+ let scaledZeros: Complex[];
+ let scaledGain: number;
+
+ if (type === "lowpass") {
+ const omega = warpedWn as number;
+ scaledPoles = poles.map((p) => ({ re: p.re * omega, im: p.im * omega }));
+ scaledZeros = []; // all zeros at s = ∞
+ scaledGain = omega ** N;
+ } else if (type === "highpass") {
+ const omega = warpedWn as number;
+ // LP → HP: s → omega / s ⟹ pole at s=p_k maps to omega/p_k
+ scaledPoles = poles.map((p) => {
+ const mag2 = p.re * p.re + p.im * p.im;
+ return { re: (omega * p.re) / mag2, im: -(omega * p.im) / mag2 };
+ });
+ scaledZeros = Array.from({ length: N }, () => complex(0, 0)); // N zeros at s=0
+ scaledGain = 1;
+ } else {
+ // Bandpass / bandstop: use direct bilinear transform below
+ scaledPoles = poles;
+ scaledZeros = [];
+ scaledGain = 1;
+ }
+
+ // Convert analog poles/zeros to digital via bilinear transform: z = (2+s)/(2-s)
+ // For band filters, handle separately
+ if (type === "bandpass" || type === "bandstop") {
+ return butterBand(N, warpedWn as [number, number], type, poles);
+ }
+
+ const digPoles: Complex[] = scaledPoles.map(bilinearPole);
+ const digZeros: Complex[] = scaledZeros.map(bilinearPole);
+ // LP numerator: N zeros at z=-1 after bilinear (from s=∞ mapping)
+ const lpZerosAtMinusOne = type === "lowpass" ? N : 0;
+
+ // Build SOS sections
+ const sos = buildSOS(digPoles, digZeros, lpZerosAtMinusOne, scaledGain, type);
+ const { b, a } = sosToBA(sos);
+
+ return { sos, b, a };
+}
+
+/** Bilinear transform: analog pole s → digital pole z = (2+s)/(2-s). */
+function bilinearPole(s: Complex): Complex {
+ // z = (2+s)/(2-s)
+ const num: Complex = { re: 2 + s.re, im: s.im };
+ const den: Complex = { re: 2 - s.re, im: -s.im };
+ const denom = den.re * den.re + den.im * den.im;
+ return {
+ re: (num.re * den.re + num.im * den.im) / denom,
+ im: (num.im * den.re - num.re * den.im) / denom,
+ };
+}
+
+/** Build second-order sections from digital poles, zeros, and gain. */
+// biome-ignore lint/complexity/noExcessiveCognitiveComplexity: SOS construction
+function buildSOS(
+ poles: Complex[],
+ explicitZeros: Complex[],
+ nZerosAtMinusOne: number,
+ gain: number,
+ type: FilterType,
+): SOSSection[] {
+ const N = poles.length;
+ const sections: SOSSection[] = [];
+
+ // Pair up conjugate poles (sort by imaginary part descending to pair conjugates)
+ const sortedPoles = [...poles].sort((a, b) => Math.abs(b.im) - Math.abs(a.im));
+ const usedPoles = new Array(N).fill(false);
+ const pairedPoles: [Complex, Complex | null][] = [];
+
+ for (let i = 0; i < N; i++) {
+ if (usedPoles[i]) {
+ continue;
+ }
+ const p = sortedPoles[i]!;
+ if (Math.abs(p.im) < 1e-10) {
+ // Real pole — stand alone
+ usedPoles[i] = true;
+ pairedPoles.push([p, null]);
+ } else {
+ // Find conjugate
+ let found = false;
+ for (let j = i + 1; j < N; j++) {
+ if (!usedPoles[j]) {
+ const q = sortedPoles[j]!;
+ if (Math.abs(p.re - q.re) < 1e-10 && Math.abs(p.im + q.im) < 1e-10) {
+ usedPoles[i] = usedPoles[j] = true;
+ pairedPoles.push([p, q]);
+ found = true;
+ break;
+ }
+ }
+ }
+ if (!found) {
+ usedPoles[i] = true;
+ pairedPoles.push([p, null]);
+ }
+ }
+ }
+
+ // Build sections: pair poles with zeros
+ let zerosRemaining = nZerosAtMinusOne;
+ let expZerosIdx = 0;
+ const nSections = pairedPoles.length;
+ const gainPerSection = gain > 0 ? gain ** (1 / nSections) : 1;
+
+ for (const [p1, p2] of pairedPoles) {
+ let b0: number;
+ let b1: number;
+ let b2: number;
+ let a1: number;
+ let a2: number;
+
+ if (p2 !== null) {
+ // Conjugate pair: (z - p1)(z - p2) = z^2 - 2*Re(p1)*z + |p1|^2
+ a1 = -2 * p1.re;
+ a2 = p1.re * p1.re + p1.im * p1.im;
+ if (type === "lowpass" && zerosRemaining >= 2) {
+ // Two zeros at z = -1: (z+1)^2 = z^2 + 2z + 1
+ b0 = 1;
+ b1 = 2;
+ b2 = 1;
+ zerosRemaining -= 2;
+ } else if (type === "highpass" && expZerosIdx < explicitZeros.length - 1) {
+ // Two zeros at z = 0: z^2 = z^2 + 0*z + 0
+ b0 = 1;
+ b1 = 0;
+ b2 = 0;
+ expZerosIdx += 2;
+ } else {
+ b0 = 1;
+ b1 = 0;
+ b2 = 0;
+ }
+ } else {
+ // Single real pole: (z - p1) = z - p1.re
+ a1 = -p1.re;
+ a2 = 0;
+ if (type === "lowpass" && zerosRemaining >= 1) {
+ // One zero at z = -1: z + 1
+ b0 = 1;
+ b1 = 1;
+ b2 = 0;
+ zerosRemaining -= 1;
+ } else if (type === "highpass" && expZerosIdx < explicitZeros.length) {
+ // One zero at z = 0: z
+ b0 = 1;
+ b1 = 0;
+ b2 = 0;
+ expZerosIdx += 1;
+ } else {
+ b0 = 1;
+ b1 = 0;
+ b2 = 0;
+ }
+ }
+
+ // Normalise section gain
+ const secGain = gainPerSection;
+ sections.push([b0 * secGain, b1 * secGain, b2 * secGain, 1, a1, a2]);
+ }
+
+ // Normalise so H(z=1) = 1 for lowpass, H(z=-1) = 1 for highpass
+ return normaliseSOS(sections, type);
+}
+
+/** Normalise SOS sections so the passband gain equals 1. */
+function normaliseSOS(sections: SOSSection[], type: FilterType): SOSSection[] {
+ // Evaluate H(z) at passband frequency: z=1 for LP, z=-1 for HP
+ const z = type === "highpass" ? -1 : 1;
+ const totalGain = sections.reduce((prod, sec) => {
+ const [b0, b1, b2, , a1, a2] = sec;
+ const num = b0 * z ** 2 + b1 * z + b2;
+ const den = z ** 2 + a1 * z + a2;
+ return prod * (Math.abs(den) > 1e-10 ? num / den : 1);
+ }, 1);
+
+ if (Math.abs(totalGain) < 1e-12) {
+ return sections;
+ }
+ const scale = 1 / totalGain;
+
+ // Apply scale to first section numerator only
+ const result: SOSSection[] = [...sections];
+ if (result.length > 0) {
+ const [b0, b1, b2, one, a1, a2] = result[0]!;
+ result[0] = [b0 * scale, b1 * scale, b2 * scale, one, a1, a2];
+ }
+ return result;
+}
+
+/** Handle band-pass and band-stop Butterworth filters. */
+// biome-ignore lint/complexity/noExcessiveCognitiveComplexity: band filter design
+function butterBand(
+ _N: number,
+ warped: [number, number],
+ type: "bandpass" | "bandstop",
+ protoPoles: Complex[],
+): ButterResult {
+ const [w1, w2] = warped;
+ const bw = w2 - w1;
+ const w0 = Math.sqrt(w1 * w2); // geometric centre frequency
+
+ const sos: SOSSection[] = [];
+
+ // For each prototype pole, apply LP→BP or LP→BS transform
+ // LP→BP: s → (s^2 + w0^2) / (bw * s)
+ // Each LP pole becomes two BP poles
+ for (const p of protoPoles) {
+ // LP pole p_k: s → (s^2 + w0^2) / (bw * s) = p_k
+ // bw * s * p_k = s^2 + w0^2
+ // s^2 - bw * p_k * s + w0^2 = 0
+ // Solutions: s = (bw * p_k ± sqrt((bw * p_k)^2 - 4 * w0^2)) / 2
+ const a = bw * p.re;
+ const b = bw * p.im;
+ // discriminant = (bw*p)^2 - 4*w0^2 = (a+jb)^2 - 4*w0^2
+ const discRe = a * a - b * b - 4 * w0 * w0;
+ const discIm = 2 * a * b;
+ // sqrt of (discRe + j*discIm)
+ const [sqrtRe, sqrtIm] = complexSqrt(discRe, discIm);
+ const s1: Complex = { re: (a + sqrtRe) / 2, im: (b + sqrtIm) / 2 };
+ const s2: Complex = { re: (a - sqrtRe) / 2, im: (b - sqrtIm) / 2 };
+
+ let z1: Complex;
+ let z2: Complex;
+ if (type === "bandpass") {
+ z1 = bilinearPole(s1);
+ z2 = bilinearPole(s2);
+ } else {
+ // BP→BS transform: s → bw*w0 / (s^2 + w0^2)... simplify using direct analog BS poles
+ // LP→BS: s → bw*s / (s^2 + w0^2)
+ // Similar computation
+ z1 = bilinearPole(s1);
+ z2 = bilinearPole(s2);
+ }
+
+ // Each pair of complex poles contributes a 2nd-order section
+ const a1 = -(z1.re + z2.re);
+ const a2 = z1.re * z2.re - z1.im * z2.im; // assume z1, z2 are conjugates
+
+ const [b0, b1, b2] =
+ type === "bandpass"
+ ? [1, 0, -1] // bandpass: zeros at z=+1 and z=-1
+ : [
+ 1,
+ -2 * Math.cos(Math.acos(Math.max(-1, Math.min(1, (w0 * w0 + 1) / (w0 * w0 + 1))))),
+ 1,
+ ]; // bandstop: zeros at e^±jw0
+
+ sos.push([b0, b1, b2, 1, a1, a2]);
+ }
+
+ const normalised = normaliseSOS(sos, type);
+ const { b, a } = sosToBA(normalised);
+ return { sos: normalised, b, a };
+}
+
+/** Real and imaginary parts of sqrt(re + j*im). */
+function complexSqrt(re: number, im: number): [number, number] {
+ const r = Math.sqrt(re * re + im * im);
+ const sr = Math.sqrt((r + re) / 2);
+ const si = Math.sign(im) * Math.sqrt((r - re) / 2);
+ return [sr, si];
+}
+
+/** Convert SOS to b/a transfer function via polynomial multiplication. */
+function sosToBA(sections: readonly SOSSection[]): { b: number[]; a: number[] } {
+ let b: number[] = [1];
+ let a: number[] = [1];
+ for (const [b0, b1, b2, , a1, a2] of sections) {
+ b = polyMul(b, [b0, b1, b2]);
+ a = polyMul(a, [1, a1, a2]);
+ }
+ return { b, a };
+}
+
+/**
+ * Compute the frequency response of a SOS filter.
+ *
+ * @param sos - SOS sections as from {@link butter}.
+ * @param worN - Number of frequency points or explicit frequencies.
+ * @returns - `{ w, H }`.
+ */
+export function sosfreqz(
+ sos: readonly SOSSection[],
+ worN: number | readonly number[] = 512,
+): FreqzResult {
+ const ws: number[] = Array.isArray(worN)
+ ? Array.from(worN as readonly number[])
+ : Array.from({ length: worN as number }, (_, i) => (Math.PI * i) / (worN as number));
+
+ const H: Complex[] = ws.map((w) => {
+ const z: Complex = { re: Math.cos(w), im: Math.sin(w) };
+ let acc: Complex = complex(1, 0);
+ for (const [b0, b1, b2, , a1, a2] of sos) {
+ const num = evalPolyZ([b0, b1, b2], z);
+ const den = evalPolyZ([1, a1, a2], z);
+ const secH = divComplex(num, den);
+ acc = { re: acc.re * secH.re - acc.im * secH.im, im: acc.re * secH.im + acc.im * secH.re };
+ }
+ return acc;
+ });
+
+ return { w: ws, H };
+}
+
+// ─── filter application ───────────────────────────────────────────────────────
+
+/**
+ * Apply an IIR or FIR filter using direct-form II transposed.
+ *
+ * Mirrors `scipy.signal.lfilter`. Computes `y[n] = b[0]*x[n] + b[1]*x[n-1] + ...
+ * - a[1]*y[n-1] - a[2]*y[n-2] - ...` (a[0] is assumed to be 1 or is normalised).
+ *
+ * @param b - Numerator coefficients (length M+1).
+ * @param a - Denominator coefficients (length N+1, a[0] normalised to 1).
+ * @param x - Input signal.
+ * @returns - Filtered signal (same length as `x`).
+ *
+ * @example
+ * ```ts
+ * import { firwin, lfilter } from "tsb";
+ * const b = firwin(21, 0.3);
+ * const y = lfilter(b, [1], x);
+ * ```
+ */
+export function lfilter(
+ b: readonly number[],
+ a: readonly number[],
+ x: readonly number[],
+): number[] {
+ const nb = b.length;
+ const na = a.length;
+ const n = x.length;
+
+ // Normalise a[0]
+ const a0 = a[0] ?? 1;
+ const bn = b.map((v) => v / a0);
+ const an = a.map((v) => v / a0);
+
+ const m = Math.max(nb, na);
+ const z = new Float64Array(m); // state buffer
+ const y = new Array(n);
+
+ for (let i = 0; i < n; i++) {
+ const xi = x[i] ?? 0;
+ const yi = (bn[0] ?? 0) * xi + (z[0] ?? 0);
+ y[i] = yi;
+ for (let j = 0; j < m - 1; j++) {
+ z[j] = (bn[j + 1] ?? 0) * xi - (an[j + 1] ?? 0) * yi + (z[j + 1] ?? 0);
+ }
+ z[m - 1] = (bn[m] ?? 0) * xi - (an[m] ?? 0) * yi;
+ }
+
+ return y;
+}
+
+/**
+ * Zero-phase forward-backward filter. Applies the filter twice — once forward
+ * and once backward — eliminating phase distortion.
+ *
+ * Mirrors `scipy.signal.filtfilt`.
+ *
+ * @param b - Numerator coefficients.
+ * @param a - Denominator coefficients.
+ * @param x - Input signal.
+ * @returns - Zero-phase filtered signal (same length as `x`).
+ */
+export function filtfilt(
+ b: readonly number[],
+ a: readonly number[],
+ x: readonly number[],
+): number[] {
+ const forward = lfilter(b, a, x);
+ const reversed = [...forward].reverse();
+ const backward = lfilter(b, a, reversed);
+ return backward.reverse();
+}
+
+/**
+ * Apply a second-order-sections filter.
+ *
+ * Numerically more stable than {@link lfilter} for high-order IIR filters.
+ * Mirrors `scipy.signal.sosfilt`.
+ *
+ * @param sos - SOS sections from {@link butter}.
+ * @param x - Input signal.
+ * @returns - Filtered signal (same length as `x`).
+ */
+export function sosfilt(sos: readonly SOSSection[], x: readonly number[]): number[] {
+ let signal = Array.from(x);
+ for (const [b0, b1, b2, , a1, a2] of sos) {
+ signal = lfilter([b0, b1, b2], [1, a1, a2], signal);
+ }
+ return signal;
+}
+
+/**
+ * Zero-phase SOS filter (applies each section forward then backward).
+ *
+ * @param sos - SOS sections from {@link butter}.
+ * @param x - Input signal.
+ * @returns - Zero-phase filtered signal.
+ */
+export function sosfiltfilt(sos: readonly SOSSection[], x: readonly number[]): number[] {
+ let signal = Array.from(x);
+ for (const [b0, b1, b2, , a1, a2] of sos) {
+ signal = filtfilt([b0, b1, b2], [1, a1, a2], signal);
+ }
+ return signal;
+}
+
+// Re-export cAbs for convenience
+export { cAbs };
diff --git a/src/stats/hmm.ts b/src/stats/hmm.ts
new file mode 100644
index 00000000..931ae6fb
--- /dev/null
+++ b/src/stats/hmm.ts
@@ -0,0 +1,776 @@
+/**
+ * hmm — Hidden Markov Model (HMM) with discrete and Gaussian emissions.
+ *
+ * Implements:
+ * - **Forward-Backward** algorithm (log-space for numerical stability)
+ * - **Baum-Welch** EM parameter estimation
+ * - **Viterbi** algorithm for most-likely state sequence decoding
+ * - **GaussianHMM**: continuous observations with Gaussian emission distributions
+ * - **MultinomialHMM**: discrete observations with categorical emission distributions
+ *
+ * Mirrors `hmmlearn.hmm.GaussianHMM` and `MultinomialHMM` APIs.
+ *
+ * @example
+ * ```ts
+ * import { GaussianHMM } from "tsb";
+ *
+ * const model = new GaussianHMM({ nComponents: 2, nIter: 100 });
+ * const obs = [0.1, 0.2, 0.15, 2.1, 2.3, 2.0, 0.05, 0.1, 2.5, 2.2];
+ * model.fit(obs);
+ * const states = model.predict(obs);
+ * const logProb = model.score(obs);
+ * ```
+ *
+ * @module
+ */
+
+// ─── Constants ─────────────────────────────────────────────────────────────────
+
+const LOG_ZERO = Number.NEGATIVE_INFINITY;
+
+// ─── Utility helpers ──────────────────────────────────────────────────────────
+
+/** log(exp(a) + exp(b)) with numerical stability. */
+function logSumExp(a: number, b: number): number {
+ if (a === LOG_ZERO) {
+ return b;
+ }
+ if (b === LOG_ZERO) {
+ return a;
+ }
+ const m = Math.max(a, b);
+ return m + Math.log(Math.exp(a - m) + Math.exp(b - m));
+}
+
+/** Stable log-sum-exp over an array. */
+function logSumExpArr(arr: readonly number[]): number {
+ let result = LOG_ZERO;
+ for (const v of arr) {
+ result = logSumExp(result, v);
+ }
+ return result;
+}
+
+/** Safe log (returns LOG_ZERO for non-positive). */
+function safeLog(x: number): number {
+ return x > 0 ? Math.log(x) : LOG_ZERO;
+}
+
+/** Normalise an array in-place so it sums to 1. Returns sum before normalisation. */
+function normalise(arr: number[]): number {
+ const s = arr.reduce((a, b) => a + b, 0);
+ if (s > 0) {
+ for (let i = 0; i < arr.length; i++) {
+ arr[i] = (arr[i] ?? 0) / s;
+ }
+ }
+ return s;
+}
+
+// ─── Public types ─────────────────────────────────────────────────────────────
+
+/** Parameters for a Gaussian HMM. */
+export interface GaussianHMMParams {
+ /** Number of hidden states. */
+ nComponents: number;
+ /** Maximum EM iterations. Default 100. */
+ nIter?: number;
+ /** Convergence tolerance on log-likelihood. Default 1e-4. */
+ tol?: number;
+ /** Random seed for initialisation (unused in deterministic init). */
+ randomState?: number;
+}
+
+/** Fitted Gaussian HMM result. */
+export interface GaussianHMMFit {
+ /** Initial state probabilities (shape: nComponents). */
+ startProb: number[];
+ /** Transition matrix (shape: nComponents × nComponents). Row i → state i, column j → state j. */
+ transmat: number[][];
+ /** Emission means (shape: nComponents). */
+ means: number[];
+ /** Emission variances (shape: nComponents). */
+ covars: number[];
+ /** Log-likelihood of training data at convergence. */
+ logProb: number;
+ /** Number of EM iterations completed. */
+ nIterDone: number;
+}
+
+/** Parameters for a Multinomial HMM. */
+export interface MultinomialHMMParams {
+ /** Number of hidden states. */
+ nComponents: number;
+ /** Number of distinct observation symbols. */
+ nFeatures: number;
+ /** Maximum EM iterations. Default 100. */
+ nIter?: number;
+ /** Convergence tolerance on log-likelihood. Default 1e-4. */
+ tol?: number;
+}
+
+/** Fitted Multinomial HMM result. */
+export interface MultinomialHMMFit {
+ /** Initial state probabilities (shape: nComponents). */
+ startProb: number[];
+ /** Transition matrix (shape: nComponents × nComponents). */
+ transmat: number[][];
+ /** Emission probability matrix (shape: nComponents × nFeatures). */
+ emissionProb: number[][];
+ /** Log-likelihood of training data at convergence. */
+ logProb: number;
+ /** Number of EM iterations completed. */
+ nIterDone: number;
+}
+
+// ─── Forward-Backward (log-space) ─────────────────────────────────────────────
+
+/**
+ * Compute log-forward variables.
+ * @param logStartProb - log initial probabilities (nStates)
+ * @param logTransmat - log transition matrix (nStates × nStates)
+ * @param logEmit - log emission probabilities (T × nStates)
+ * @returns logAlpha (T × nStates)
+ */
+function logForward(
+ logStartProb: readonly number[],
+ logTransmat: readonly (readonly number[])[],
+ logEmit: readonly (readonly number[])[],
+): number[][] {
+ const T = logEmit.length;
+ const K = logStartProb.length;
+ const logAlpha: number[][] = Array.from({ length: T }, () => new Array(K).fill(LOG_ZERO));
+
+ for (let j = 0; j < K; j++) {
+ logAlpha[0]![j] = (logStartProb[j] ?? LOG_ZERO) + (logEmit[0]?.[j] ?? LOG_ZERO);
+ }
+ for (let t = 1; t < T; t++) {
+ for (let j = 0; j < K; j++) {
+ let s = LOG_ZERO;
+ for (let i = 0; i < K; i++) {
+ s = logSumExp(s, (logAlpha[t - 1]?.[i] ?? LOG_ZERO) + (logTransmat[i]?.[j] ?? LOG_ZERO));
+ }
+ logAlpha[t]![j] = s + (logEmit[t]?.[j] ?? LOG_ZERO);
+ }
+ }
+ return logAlpha;
+}
+
+/**
+ * Compute log-backward variables.
+ * @param logTransmat - log transition matrix (nStates × nStates)
+ * @param logEmit - log emission probabilities (T × nStates)
+ * @returns logBeta (T × nStates)
+ */
+function logBackward(
+ logTransmat: readonly (readonly number[])[],
+ logEmit: readonly (readonly number[])[],
+): number[][] {
+ const T = logEmit.length;
+ const K = logTransmat.length;
+ const logBeta: number[][] = Array.from({ length: T }, () => new Array(K).fill(0));
+
+ for (let t = T - 2; t >= 0; t--) {
+ for (let i = 0; i < K; i++) {
+ let s = LOG_ZERO;
+ for (let j = 0; j < K; j++) {
+ s = logSumExp(
+ s,
+ (logTransmat[i]?.[j] ?? LOG_ZERO) +
+ (logEmit[t + 1]?.[j] ?? LOG_ZERO) +
+ (logBeta[t + 1]?.[j] ?? 0),
+ );
+ }
+ logBeta[t]![i] = s;
+ }
+ }
+ return logBeta;
+}
+
+// ─── Viterbi ──────────────────────────────────────────────────────────────────
+
+/**
+ * Viterbi algorithm to find the most-likely state sequence.
+ * @returns decoded state sequence (length T)
+ */
+function viterbi(
+ logStartProb: readonly number[],
+ logTransmat: readonly (readonly number[])[],
+ logEmit: readonly (readonly number[])[],
+): number[] {
+ const T = logEmit.length;
+ const K = logStartProb.length;
+ const delta: number[][] = Array.from({ length: T }, () => new Array(K).fill(LOG_ZERO));
+ const psi: number[][] = Array.from({ length: T }, () => new Array(K).fill(0));
+
+ for (let j = 0; j < K; j++) {
+ delta[0]![j] = (logStartProb[j] ?? LOG_ZERO) + (logEmit[0]?.[j] ?? LOG_ZERO);
+ }
+ for (let t = 1; t < T; t++) {
+ for (let j = 0; j < K; j++) {
+ let best = LOG_ZERO;
+ let bestI = 0;
+ for (let i = 0; i < K; i++) {
+ const v = (delta[t - 1]?.[i] ?? LOG_ZERO) + (logTransmat[i]?.[j] ?? LOG_ZERO);
+ if (v > best) {
+ best = v;
+ bestI = i;
+ }
+ }
+ delta[t]![j] = best + (logEmit[t]?.[j] ?? LOG_ZERO);
+ psi[t]![j] = bestI;
+ }
+ }
+
+ // Backtrack
+ const path = new Array(T).fill(0);
+ let s = 0;
+ let best = LOG_ZERO;
+ for (let j = 0; j < K; j++) {
+ const v = delta[T - 1]?.[j] ?? LOG_ZERO;
+ if (v > best) {
+ best = v;
+ s = j;
+ }
+ }
+ path[T - 1] = s;
+ for (let t = T - 2; t >= 0; t--) {
+ path[t] = psi[t + 1]?.[path[t + 1] ?? 0] ?? 0;
+ }
+ return path;
+}
+
+// ─── GaussianHMM ──────────────────────────────────────────────────────────────
+
+/** Log probability of x under N(mu, sigma^2). */
+function gaussianLogProb(x: number, mu: number, sigma2: number): number {
+ if (sigma2 <= 0) {
+ return LOG_ZERO;
+ }
+ return -0.5 * (Math.log(2 * Math.PI * sigma2) + ((x - mu) * (x - mu)) / sigma2);
+}
+
+/**
+ * Hidden Markov Model with univariate Gaussian emission distributions.
+ *
+ * Uses Baum-Welch EM for parameter estimation, Viterbi for decoding.
+ */
+export class GaussianHMM {
+ private readonly K: number;
+ private readonly nIter: number;
+ private readonly tol: number;
+
+ // Parameters (set after fit)
+ private _startProb: number[] = [];
+ private _transmat: number[][] = [];
+ private _means: number[] = [];
+ private _covars: number[] = [];
+ private _fitted = false;
+
+ constructor(params: GaussianHMMParams) {
+ this.K = params.nComponents;
+ this.nIter = params.nIter ?? 100;
+ this.tol = params.tol ?? 1e-4;
+ }
+
+ /** Fit the HMM to a sequence of observations using Baum-Welch EM. */
+ fit(obs: readonly number[]): GaussianHMMFit {
+ const T = obs.length;
+ const K = this.K;
+ if (T < 2) {
+ throw new Error("Need at least 2 observations");
+ }
+
+ // ── Initialise ───────────────────────────────────────────────────────────
+ // k-means-style: split sorted observations into K equal buckets
+ const sorted = [...obs].sort((a, b) => a - b);
+ const means = new Array(K).fill(0);
+ const covars = new Array(K).fill(1);
+ for (let k = 0; k < K; k++) {
+ const lo = Math.floor((k * T) / K);
+ const hi = Math.floor(((k + 1) * T) / K);
+ const slice = sorted.slice(lo, hi);
+ const mu = slice.reduce((a, b) => a + b, 0) / slice.length;
+ const v = slice.reduce((a, b) => a + (b - mu) ** 2, 0) / Math.max(slice.length - 1, 1);
+ means[k] = mu;
+ covars[k] = Math.max(v, 1e-6);
+ }
+
+ // Uniform start and transition
+ const startProb = new Array(K).fill(1 / K);
+ const transmat: number[][] = Array.from({ length: K }, () => new Array(K).fill(1 / K));
+
+ let prevLogProb = Number.NEGATIVE_INFINITY;
+ let nIterDone = 0;
+
+ for (let iter = 0; iter < this.nIter; iter++) {
+ // ── E-step ─────────────────────────────────────────────────────────────
+ const logEmit: number[][] = Array.from({ length: T }, (_, t) =>
+ Array.from({ length: K }, (__, k) =>
+ gaussianLogProb(obs[t] ?? 0, means[k] ?? 0, covars[k] ?? 1),
+ ),
+ );
+
+ const logStartProb = startProb.map(safeLog);
+ const logTransmat = transmat.map((row) => row.map(safeLog));
+
+ const logAlpha = logForward(logStartProb, logTransmat, logEmit);
+ const logBeta = logBackward(logTransmat, logEmit);
+
+ // Log-likelihood
+ const logProb = logSumExpArr(logAlpha[T - 1] ?? []);
+
+ if (Math.abs(logProb - prevLogProb) < this.tol) {
+ nIterDone = iter + 1;
+ break;
+ }
+ prevLogProb = logProb;
+ nIterDone = iter + 1;
+
+ // γ_t(k) = P(z_t = k | obs, θ) in log space
+ const logGamma: number[][] = Array.from({ length: T }, (_, t) => {
+ const row = Array.from(
+ { length: K },
+ (__, k) => (logAlpha[t]?.[k] ?? LOG_ZERO) + (logBeta[t]?.[k] ?? 0),
+ );
+ const z = logSumExpArr(row);
+ return row.map((v) => v - z);
+ });
+
+ // ξ_t(i,j) = P(z_t=i, z_{t+1}=j | obs, θ) — sum over t
+ const logXiSum: number[][] = Array.from({ length: K }, () =>
+ new Array(K).fill(LOG_ZERO),
+ );
+ for (let t = 0; t < T - 1; t++) {
+ for (let i = 0; i < K; i++) {
+ for (let j = 0; j < K; j++) {
+ const v =
+ (logAlpha[t]?.[i] ?? LOG_ZERO) +
+ (logTransmat[i]?.[j] ?? LOG_ZERO) +
+ (logEmit[t + 1]?.[j] ?? LOG_ZERO) +
+ (logBeta[t + 1]?.[j] ?? 0) -
+ logProb;
+ logXiSum[i]![j] = logSumExp(logXiSum[i]?.[j] ?? LOG_ZERO, v);
+ }
+ }
+ }
+
+ // ── M-step ─────────────────────────────────────────────────────────────
+ // Update startProb
+ for (let k = 0; k < K; k++) {
+ startProb[k] = Math.exp(logGamma[0]?.[k] ?? LOG_ZERO);
+ }
+ normalise(startProb);
+
+ // Update transmat
+ for (let i = 0; i < K; i++) {
+ for (let j = 0; j < K; j++) {
+ transmat[i]![j] = Math.exp(logXiSum[i]?.[j] ?? LOG_ZERO);
+ }
+ normalise(transmat[i]!);
+ }
+
+ // Update means
+ for (let k = 0; k < K; k++) {
+ let num = 0;
+ let den = 0;
+ for (let t = 0; t < T; t++) {
+ const g = Math.exp(logGamma[t]?.[k] ?? LOG_ZERO);
+ num += g * (obs[t] ?? 0);
+ den += g;
+ }
+ means[k] = den > 0 ? num / den : (means[k] ?? 0);
+ }
+
+ // Update covars
+ for (let k = 0; k < K; k++) {
+ let num = 0;
+ let den = 0;
+ for (let t = 0; t < T; t++) {
+ const g = Math.exp(logGamma[t]?.[k] ?? LOG_ZERO);
+ const diff = (obs[t] ?? 0) - (means[k] ?? 0);
+ num += g * diff * diff;
+ den += g;
+ }
+ covars[k] = Math.max(den > 0 ? num / den : (covars[k] ?? 1), 1e-6);
+ }
+ }
+
+ this._startProb = [...startProb];
+ this._transmat = transmat.map((row) => [...row]);
+ this._means = [...means];
+ this._covars = [...covars];
+ this._fitted = true;
+
+ return {
+ startProb: [...startProb],
+ transmat: transmat.map((row) => [...row]),
+ means: [...means],
+ covars: [...covars],
+ logProb: prevLogProb,
+ nIterDone,
+ };
+ }
+
+ /** Decode the most-likely state sequence using the Viterbi algorithm. */
+ predict(obs: readonly number[]): number[] {
+ this._checkFitted();
+ const K = this.K;
+ const logEmit = obs.map((x) =>
+ Array.from({ length: K }, (_, k) =>
+ gaussianLogProb(x, this._means[k] ?? 0, this._covars[k] ?? 1),
+ ),
+ );
+ return viterbi(
+ this._startProb.map(safeLog),
+ this._transmat.map((row) => row.map(safeLog)),
+ logEmit,
+ );
+ }
+
+ /** Compute the log-probability of the observation sequence. */
+ score(obs: readonly number[]): number {
+ this._checkFitted();
+ const K = this.K;
+ const logEmit = obs.map((x) =>
+ Array.from({ length: K }, (_, k) =>
+ gaussianLogProb(x, this._means[k] ?? 0, this._covars[k] ?? 1),
+ ),
+ );
+ const logAlpha = logForward(
+ this._startProb.map(safeLog),
+ this._transmat.map((row) => row.map(safeLog)),
+ logEmit,
+ );
+ return logSumExpArr(logAlpha.at(-1) ?? []);
+ }
+
+ /** Compute posterior state probabilities (T × nComponents). */
+ predictProba(obs: readonly number[]): number[][] {
+ this._checkFitted();
+ const K = this.K;
+ const T = obs.length;
+ const logEmit = obs.map((x) =>
+ Array.from({ length: K }, (_, k) =>
+ gaussianLogProb(x, this._means[k] ?? 0, this._covars[k] ?? 1),
+ ),
+ );
+ const logStartProb = this._startProb.map(safeLog);
+ const logTransmat = this._transmat.map((row) => row.map(safeLog));
+ const logAlpha = logForward(logStartProb, logTransmat, logEmit);
+ const logBeta = logBackward(logTransmat, logEmit);
+ return Array.from({ length: T }, (_, t) => {
+ const row = Array.from(
+ { length: K },
+ (__, k) => (logAlpha[t]?.[k] ?? LOG_ZERO) + (logBeta[t]?.[k] ?? 0),
+ );
+ const z = logSumExpArr(row);
+ return row.map((v) => Math.exp(v - z));
+ });
+ }
+
+ /** Sample a sequence of states and observations. */
+ sample(length: number): { states: number[]; obs: number[] } {
+ this._checkFitted();
+ const K = this.K;
+ const states: number[] = [];
+ const obs: number[] = [];
+
+ // Sample initial state
+ let cumProb = 0;
+ const r0 = Math.random();
+ let state = K - 1;
+ for (let k = 0; k < K; k++) {
+ cumProb += this._startProb[k] ?? 0;
+ if (r0 < cumProb) {
+ state = k;
+ break;
+ }
+ }
+
+ for (let t = 0; t < length; t++) {
+ states.push(state);
+ const mu = this._means[state] ?? 0;
+ const sigma = Math.sqrt(this._covars[state] ?? 1);
+ // Box-Muller for Gaussian sample
+ const u1 = Math.random();
+ const u2 = Math.random();
+ const z = Math.sqrt(-2 * Math.log(Math.max(u1, 1e-15))) * Math.cos(2 * Math.PI * u2);
+ obs.push(mu + sigma * z);
+
+ // Transition
+ const row = this._transmat[state]!;
+ let cum = 0;
+ const rv = Math.random();
+ let nextState = K - 1;
+ for (let k = 0; k < K; k++) {
+ cum += row[k] ?? 0;
+ if (rv < cum) {
+ nextState = k;
+ break;
+ }
+ }
+ state = nextState;
+ }
+ return { states, obs };
+ }
+
+ get startProb(): number[] {
+ this._checkFitted();
+ return [...this._startProb];
+ }
+ get transmat(): number[][] {
+ this._checkFitted();
+ return this._transmat.map((r) => [...r]);
+ }
+ get means(): number[] {
+ this._checkFitted();
+ return [...this._means];
+ }
+ get covars(): number[] {
+ this._checkFitted();
+ return [...this._covars];
+ }
+
+ private _checkFitted(): void {
+ if (!this._fitted) {
+ throw new Error("GaussianHMM is not fitted yet");
+ }
+ }
+}
+
+// ─── MultinomialHMM ───────────────────────────────────────────────────────────
+
+/**
+ * Hidden Markov Model with discrete (multinomial/categorical) emission distributions.
+ *
+ * Observations are non-negative integer symbol indices in [0, nFeatures).
+ */
+export class MultinomialHMM {
+ private readonly K: number;
+ private readonly nFeatures: number;
+ private readonly nIter: number;
+ private readonly tol: number;
+
+ private _startProb: number[] = [];
+ private _transmat: number[][] = [];
+ private _emissionProb: number[][] = [];
+ private _fitted = false;
+
+ constructor(params: MultinomialHMMParams) {
+ this.K = params.nComponents;
+ this.nFeatures = params.nFeatures;
+ this.nIter = params.nIter ?? 100;
+ this.tol = params.tol ?? 1e-4;
+ }
+
+ /** Fit the HMM to a sequence of integer observations using Baum-Welch EM. */
+ fit(obs: readonly number[]): MultinomialHMMFit {
+ const T = obs.length;
+ const K = this.K;
+ const V = this.nFeatures;
+ if (T < 2) {
+ throw new Error("Need at least 2 observations");
+ }
+
+ // Uniform initialisation with small random perturbation
+ const startProb = new Array(K).fill(1 / K);
+ const transmat: number[][] = Array.from({ length: K }, () =>
+ Array.from({ length: K }, () => 1 / K + (Math.random() * 0.1 - 0.05) / K),
+ );
+ const emissionProb: number[][] = Array.from({ length: K }, () =>
+ Array.from({ length: V }, () => 1 / V + (Math.random() * 0.1 - 0.05) / V),
+ );
+ // Normalise
+ for (let k = 0; k < K; k++) {
+ normalise(transmat[k]!);
+ normalise(emissionProb[k]!);
+ }
+
+ let prevLogProb = Number.NEGATIVE_INFINITY;
+ let nIterDone = 0;
+
+ for (let iter = 0; iter < this.nIter; iter++) {
+ // Log-emission: logEmit[t][k] = log P(obs[t] | z_t = k)
+ const logEmit: number[][] = Array.from({ length: T }, (_, t) =>
+ Array.from({ length: K }, (__, k) => safeLog(emissionProb[k]?.[obs[t] ?? 0] ?? 0)),
+ );
+
+ const logStartProb = startProb.map(safeLog);
+ const logTransmat = transmat.map((row) => row.map(safeLog));
+
+ const logAlpha = logForward(logStartProb, logTransmat, logEmit);
+ const logBeta = logBackward(logTransmat, logEmit);
+
+ const logProb = logSumExpArr(logAlpha[T - 1] ?? []);
+ if (Math.abs(logProb - prevLogProb) < this.tol) {
+ nIterDone = iter + 1;
+ break;
+ }
+ prevLogProb = logProb;
+ nIterDone = iter + 1;
+
+ // γ_t(k)
+ const logGamma: number[][] = Array.from({ length: T }, (_, t) => {
+ const row = Array.from(
+ { length: K },
+ (__, k) => (logAlpha[t]?.[k] ?? LOG_ZERO) + (logBeta[t]?.[k] ?? 0),
+ );
+ const z = logSumExpArr(row);
+ return row.map((v) => v - z);
+ });
+
+ // ξ sum
+ const logXiSum: number[][] = Array.from({ length: K }, () =>
+ new Array(K).fill(LOG_ZERO),
+ );
+ for (let t = 0; t < T - 1; t++) {
+ for (let i = 0; i < K; i++) {
+ for (let j = 0; j < K; j++) {
+ const v =
+ (logAlpha[t]?.[i] ?? LOG_ZERO) +
+ (logTransmat[i]?.[j] ?? LOG_ZERO) +
+ (logEmit[t + 1]?.[j] ?? LOG_ZERO) +
+ (logBeta[t + 1]?.[j] ?? 0) -
+ logProb;
+ logXiSum[i]![j] = logSumExp(logXiSum[i]?.[j] ?? LOG_ZERO, v);
+ }
+ }
+ }
+
+ // M-step: startProb
+ for (let k = 0; k < K; k++) {
+ startProb[k] = Math.exp(logGamma[0]?.[k] ?? LOG_ZERO);
+ }
+ normalise(startProb);
+
+ // transmat
+ for (let i = 0; i < K; i++) {
+ for (let j = 0; j < K; j++) {
+ transmat[i]![j] = Math.exp(logXiSum[i]?.[j] ?? LOG_ZERO);
+ }
+ normalise(transmat[i]!);
+ }
+
+ // emissionProb
+ for (let k = 0; k < K; k++) {
+ for (let v = 0; v < V; v++) {
+ emissionProb[k]![v] = 0;
+ }
+ for (let t = 0; t < T; t++) {
+ const sym = obs[t] ?? 0;
+ emissionProb[k]![sym] =
+ (emissionProb[k]?.[sym] ?? 0) + Math.exp(logGamma[t]?.[k] ?? LOG_ZERO);
+ }
+ normalise(emissionProb[k]!);
+ }
+ }
+
+ this._startProb = [...startProb];
+ this._transmat = transmat.map((row) => [...row]);
+ this._emissionProb = emissionProb.map((row) => [...row]);
+ this._fitted = true;
+
+ return {
+ startProb: [...startProb],
+ transmat: transmat.map((row) => [...row]),
+ emissionProb: emissionProb.map((row) => [...row]),
+ logProb: prevLogProb,
+ nIterDone,
+ };
+ }
+
+ /** Decode the most-likely state sequence. */
+ predict(obs: readonly number[]): number[] {
+ this._checkFitted();
+ const K = this.K;
+ const logEmit = obs.map((sym) =>
+ Array.from({ length: K }, (_, k) => safeLog(this._emissionProb[k]?.[sym] ?? 0)),
+ );
+ return viterbi(
+ this._startProb.map(safeLog),
+ this._transmat.map((row) => row.map(safeLog)),
+ logEmit,
+ );
+ }
+
+ /** Compute log-probability of the observation sequence. */
+ score(obs: readonly number[]): number {
+ this._checkFitted();
+ const K = this.K;
+ const logEmit = obs.map((sym) =>
+ Array.from({ length: K }, (_, k) => safeLog(this._emissionProb[k]?.[sym] ?? 0)),
+ );
+ const logAlpha = logForward(
+ this._startProb.map(safeLog),
+ this._transmat.map((row) => row.map(safeLog)),
+ logEmit,
+ );
+ return logSumExpArr(logAlpha.at(-1) ?? []);
+ }
+
+ get startProb(): number[] {
+ this._checkFitted();
+ return [...this._startProb];
+ }
+ get transmat(): number[][] {
+ this._checkFitted();
+ return this._transmat.map((r) => [...r]);
+ }
+ get emissionProb(): number[][] {
+ this._checkFitted();
+ return this._emissionProb.map((r) => [...r]);
+ }
+
+ private _checkFitted(): void {
+ if (!this._fitted) {
+ throw new Error("MultinomialHMM is not fitted yet");
+ }
+ }
+}
+
+// ─── Standalone functions ──────────────────────────────────────────────────────
+
+/**
+ * Convenience function: fit a GaussianHMM and return the fitted model.
+ *
+ * @example
+ * ```ts
+ * const model = fitGaussianHMM([0.1, 0.2, 2.1, 2.3, 0.05, 2.5], 2);
+ * ```
+ */
+export function fitGaussianHMM(
+ obs: readonly number[],
+ nComponents: number,
+ nIter = 100,
+): GaussianHMM {
+ const model = new GaussianHMM({ nComponents, nIter });
+ model.fit(obs);
+ return model;
+}
+
+/**
+ * Convenience function: Viterbi decoding with explicit parameters (no fitting).
+ *
+ * @param startProb - Initial state probabilities (length K).
+ * @param transmat - Transition matrix (K × K).
+ * @param emissionProb - Emission probabilities (K × V).
+ * @param obs - Integer observation sequence.
+ * @returns Most-likely state sequence.
+ */
+export function hmmViterbi(
+ startProb: readonly number[],
+ transmat: readonly (readonly number[])[],
+ emissionProb: readonly (readonly number[])[],
+ obs: readonly number[],
+): number[] {
+ const K = startProb.length;
+ const logEmit = obs.map((sym) =>
+ Array.from({ length: K }, (_, k) => safeLog(emissionProb[k]?.[sym] ?? 0)),
+ );
+ return viterbi(
+ startProb.map(safeLog),
+ transmat.map((row) => [...row].map(safeLog)),
+ logEmit,
+ );
+}
diff --git a/src/stats/index.ts b/src/stats/index.ts
index 78767ee1..dbfb531d 100644
--- a/src/stats/index.ts
+++ b/src/stats/index.ts
@@ -575,3 +575,136 @@ export {
tsallisEntropy,
} from "./information.ts";
export type { PMF, NMIMethod } from "./information.ts";
+export {
+ complex,
+ cAbs,
+ cArg,
+ fft,
+ ifft,
+ rfft,
+ irfft,
+ fftFreq,
+ rfftFreq,
+ fftshift,
+ ifftshift,
+ rectangularWindow,
+ bartlettWindow,
+ hannWindow,
+ hammingWindow,
+ blackmanWindow,
+ blackmanHarrisWindow,
+ flatTopWindow,
+ kaiserWindow,
+ getWindow,
+ stft,
+ istft,
+ welch,
+ periodogram,
+} from "./signal.ts";
+export type {
+ Complex,
+ WindowName,
+ STFTOptions,
+ STFTResult,
+ ISTFTOptions,
+ WelchOptions,
+ PSDResult,
+ PeriodogramOptions,
+} from "./signal.ts";
+export {
+ firwin,
+ freqz,
+ sosfreqz,
+ lfilter,
+ filtfilt,
+ sosfilt,
+ sosfiltfilt,
+ butter,
+} from "./filters.ts";
+export type {
+ FirwinOptions,
+ FreqzResult,
+ SOSSection,
+ ButterResult,
+ FilterType,
+} from "./filters.ts";
+export {
+ autocorr,
+ acf,
+ pacf,
+ ccf,
+ durbinWatson,
+ ljungBox,
+ boxPierce,
+} from "./acf_pacf.ts";
+export type {
+ ACFResult,
+ PACFResult,
+ PortmanteauResult,
+ ACFOptions,
+ PACFOptions,
+ CCFOptions,
+ PortmanteauOptions,
+} from "./acf_pacf.ts";
+export {
+ ARIMAModel,
+ fitArima,
+} from "./arima.ts";
+export type {
+ ARIMAOptions,
+ ARIMAFitResult,
+ ARIMAForecastResult,
+} from "./arima.ts";
+export {
+ KalmanFilter,
+ StateSpaceModel,
+ kalmanFilter1D,
+ kalmanSmooth1D,
+ extractScalarMeans,
+ extractScalarVariances,
+ filteredPredictionInterval,
+} from "./kalman.ts";
+export type {
+ KalmanFilterOptions,
+ LocalLevelOptions,
+ LocalLinearTrendOptions,
+ KalmanFilterResult,
+ KalmanSmootherResult,
+} from "./kalman.ts";
+export {
+ SimpleExpSmoothing,
+ Holt,
+ ExponentialSmoothing,
+ simpleExpSmoothing,
+ holt,
+ fitEts,
+} from "./ets.ts";
+export type {
+ ETSTrend,
+ ETSSeasonal,
+ ETSInit,
+ SESOptions,
+ SESFitResult,
+ HoltOptions,
+ HoltFitResult,
+ ExponentialSmoothingOptions,
+ ExponentialSmoothingFitResult,
+ ETSForecastResult,
+} from "./ets.ts";
+export {
+ DLM,
+ buildLocalLevel,
+ buildLocalLinearTrend,
+ buildPolynomial,
+ buildFourier,
+ buildRegression,
+ combineDLMs,
+} from "./dlm.ts";
+export type {
+ DLMSpec,
+ DLMOptions,
+ DLMFilterStep,
+ DLMResult,
+ DLMSmootherResult,
+ DLMForecastResult,
+} from "./dlm.ts";
diff --git a/src/stats/kalman.ts b/src/stats/kalman.ts
new file mode 100644
index 00000000..a0493a78
--- /dev/null
+++ b/src/stats/kalman.ts
@@ -0,0 +1,809 @@
+/**
+ * kalman — Linear Gaussian State-Space Model: Kalman Filter & RTS Smoother.
+ *
+ * Implements the standard discrete-time Kalman filter (forward pass) and the
+ * Rauch-Tung-Striebel (RTS) smoother (backward pass) for linear dynamical
+ * systems:
+ *
+ * x_t = F·x_{t-1} + w_t, w_t ~ N(0, Q) (state equation)
+ * y_t = H·x_t + v_t, v_t ~ N(0, R) (observation equation)
+ * x_0 ~ N(m0, P0)
+ *
+ * Missing observations (null) are handled by skipping the update step —
+ * the filtered state reverts to the predicted state for that time-step.
+ *
+ * Mirrors the `statsmodels.tsa.statespace.kalman_filter.KalmanFilter` and
+ * `pykalman.KalmanFilter` APIs; factory helpers match common pandas patterns.
+ *
+ * Exported names:
+ * - {@link KalmanFilter} — main class (filter + smooth)
+ * - {@link KalmanFilterOptions} — constructor options
+ * - {@link KalmanFilterResult} — forward-pass output
+ * - {@link KalmanSmootherResult} — backward-pass output
+ *
+ * @example
+ * ```ts
+ * import { KalmanFilter } from "tsb";
+ *
+ * // Local-level model (random walk observed with noise)
+ * const kf = KalmanFilter.localLevel({ processNoise: 1, observationNoise: 2 });
+ * const res = kf.filter([[1], [2], [1.5], [null], [3], [2.5]]);
+ * console.log(res.filteredStateMeans); // [[…], …] T × 1
+ * console.log(res.logLikelihood);
+ *
+ * const sm = kf.smooth([[1], [2], [1.5], [null], [3], [2.5]]);
+ * console.log(sm.smoothedStateMeans);
+ * ```
+ *
+ * @module
+ */
+
+// ─── Internal matrix helpers ───────────────────────────────────────────────────
+
+/** Read-only row-major matrix. */
+type Mat = readonly (readonly number[])[];
+/** Mutable row-major matrix. */
+type MutMat = number[][];
+
+/** Rows of A (no checks — callers must ensure dimensions). */
+function rows(A: Mat): number {
+ return A.length;
+}
+/** Columns of A (0 if empty). */
+function cols(A: Mat): number {
+ return A[0]?.length ?? 0;
+}
+
+/** Create an n×m zero matrix. */
+function zeros(n: number, m: number): MutMat {
+ return Array.from({ length: n }, () => new Array(m).fill(0));
+}
+
+/** Create an n×n identity matrix. */
+function eye(n: number): MutMat {
+ return Array.from({ length: n }, (_, i) =>
+ Array.from({ length: n }, (_, j) => (i === j ? 1 : 0)),
+ );
+}
+
+/** Matrix–matrix product A (m×k) · B (k×n) → (m×n). */
+function mmul(A: Mat, B: Mat): MutMat {
+ const m = rows(A);
+ const k = cols(A);
+ const n = cols(B);
+ const C = zeros(m, n);
+ for (let i = 0; i < m; i++) {
+ const ai = A[i]!;
+ const ci = C[i]!;
+ for (let p = 0; p < k; p++) {
+ const aip = ai[p]!;
+ if (aip === 0) {
+ continue;
+ }
+ const bp = B[p]!;
+ for (let j = 0; j < n; j++) {
+ ci[j] = (ci[j] ?? 0) + aip * bp[j]!;
+ }
+ }
+ }
+ return C;
+}
+
+/** Matrix–vector product A (m×k) · x (k) → (m). */
+function mvmul(A: Mat, x: readonly number[]): number[] {
+ const m = rows(A);
+ const k = x.length;
+ const y = new Array(m).fill(0);
+ for (let i = 0; i < m; i++) {
+ const ai = A[i]!;
+ let s = 0;
+ for (let p = 0; p < k; p++) {
+ s += ai[p]! * x[p]!;
+ }
+ y[i] = s;
+ }
+ return y;
+}
+
+/** Transpose of A (m×n) → (n×m). */
+function T(A: Mat): MutMat {
+ const m = rows(A);
+ const n = cols(A);
+ const At = zeros(n, m);
+ for (let i = 0; i < m; i++) {
+ for (let j = 0; j < n; j++) {
+ At[j]![i] = A[i]?.[j]!;
+ }
+ }
+ return At;
+}
+
+/** A + B (element-wise). */
+function madd(A: Mat, B: Mat): MutMat {
+ const m = rows(A);
+ const n = cols(A);
+ return Array.from({ length: m }, (_, i) =>
+ Array.from({ length: n }, (_, j) => A[i]?.[j]! + B[i]?.[j]!),
+ );
+}
+
+/** A − B (element-wise). */
+function msub(A: Mat, B: Mat): MutMat {
+ const m = rows(A);
+ const n = cols(A);
+ return Array.from({ length: m }, (_, i) =>
+ Array.from({ length: n }, (_, j) => A[i]?.[j]! - B[i]?.[j]!),
+ );
+}
+
+/** Vector addition a + b. */
+function vadd(a: readonly number[], b: readonly number[]): number[] {
+ return a.map((ai, i) => ai + b[i]!);
+}
+
+/** Vector subtraction a − b. */
+function vsub(a: readonly number[], b: readonly number[]): number[] {
+ return a.map((ai, i) => ai - b[i]!);
+}
+
+/**
+ * Invert a square matrix via Gaussian elimination with partial pivoting.
+ * Returns null if the matrix is singular (|pivot| < 1e-14).
+ */
+function matInv(A: Mat): MutMat | null {
+ const n = rows(A);
+ // Augmented [A | I]
+ const aug: MutMat = Array.from({ length: n }, (_, i) => [
+ ...A[i]!,
+ ...Array.from({ length: n }, (_, j) => (i === j ? 1 : 0)),
+ ]);
+ for (let col = 0; col < n; col++) {
+ let maxRow = col;
+ let maxVal = Math.abs(aug[col]?.[col]!);
+ for (let row = col + 1; row < n; row++) {
+ const v = Math.abs(aug[row]?.[col]!);
+ if (v > maxVal) {
+ maxVal = v;
+ maxRow = row;
+ }
+ }
+ if (maxVal < 1e-14) {
+ return null;
+ }
+ [aug[col], aug[maxRow]] = [aug[maxRow]!, aug[col]!];
+ const pivot = aug[col]?.[col]!;
+ const pivRow = aug[col]!;
+ for (let j = 0; j < 2 * n; j++) {
+ pivRow[j] = pivRow[j]! / pivot;
+ }
+ for (let row = 0; row < n; row++) {
+ if (row === col) {
+ continue;
+ }
+ const fac = aug[row]?.[col]!;
+ if (fac === 0) {
+ continue;
+ }
+ const r = aug[row]!;
+ for (let j = 0; j < 2 * n; j++) {
+ r[j] = r[j]! - fac * pivRow[j]!;
+ }
+ }
+ }
+ return aug.map((row) => row.slice(n));
+}
+
+/** log-determinant via LU decomposition (for log-likelihood). */
+function logDet(A: Mat): number {
+ const n = rows(A);
+ const L: MutMat = Array.from({ length: n }, (_, i) => [...A[i]!]);
+ let logD = 0;
+ for (let col = 0; col < n; col++) {
+ let maxRow = col;
+ let maxVal = Math.abs(L[col]?.[col]!);
+ for (let row = col + 1; row < n; row++) {
+ const v = Math.abs(L[row]?.[col]!);
+ if (v > maxVal) {
+ maxVal = v;
+ maxRow = row;
+ }
+ }
+ if (maxRow !== col) {
+ [L[col], L[maxRow]] = [L[maxRow]!, L[col]!];
+ logD += Math.log(-1); // sign flip — handled by real part
+ }
+ const pivot = L[col]?.[col]!;
+ if (Math.abs(pivot) < 1e-300) {
+ return Number.NEGATIVE_INFINITY;
+ }
+ logD += Math.log(Math.abs(pivot));
+ for (let row = col + 1; row < n; row++) {
+ const fac = L[row]?.[col]! / pivot;
+ const r = L[row]!;
+ for (let j = col; j < n; j++) {
+ r[j] = r[j]! - fac * L[col]?.[j]!;
+ }
+ }
+ }
+ return logD;
+}
+
+/** Outer product a·bᵀ → matrix. */
+function outer(a: readonly number[], b: readonly number[]): MutMat {
+ return Array.from({ length: a.length }, (_, i) =>
+ Array.from({ length: b.length }, (_, j) => a[i]! * b[j]!),
+ );
+}
+
+/** Scale matrix by scalar. */
+function mscale(A: Mat, s: number): MutMat {
+ return A.map((row) => row.map((v) => v * s));
+}
+
+// ─── Public types ──────────────────────────────────────────────────────────────
+
+/** Constructor options for {@link KalmanFilter}. */
+export interface KalmanFilterOptions {
+ /**
+ * State transition matrix **F** (n_states × n_states).
+ * Defines how the state evolves: x_t = F·x_{t-1} + noise.
+ */
+ readonly transitionMatrix: readonly (readonly number[])[];
+ /**
+ * Observation matrix **H** (n_obs × n_states).
+ * Maps states to observations: y_t = H·x_t + noise.
+ */
+ readonly observationMatrix: readonly (readonly number[])[];
+ /**
+ * Process noise covariance **Q** (n_states × n_states).
+ * Covariance of the state-transition noise.
+ */
+ readonly processNoiseCov: readonly (readonly number[])[];
+ /**
+ * Observation noise covariance **R** (n_obs × n_obs).
+ * Covariance of the observation noise.
+ */
+ readonly observationNoiseCov: readonly (readonly number[])[];
+ /**
+ * Initial state mean **m₀** (n_states vector).
+ * Defaults to the zero vector.
+ */
+ readonly initialStateMean?: readonly number[];
+ /**
+ * Initial state covariance **P₀** (n_states × n_states).
+ * Defaults to the identity matrix.
+ */
+ readonly initialStateCovariance?: readonly (readonly number[])[];
+}
+
+/** Options for {@link KalmanFilter.localLevel}. */
+export interface LocalLevelOptions {
+ /** Process (state) noise variance σ²_q. Default: 1. */
+ readonly processNoise?: number;
+ /** Observation noise variance σ²_r. Default: 1. */
+ readonly observationNoise?: number;
+ /** Initial state mean scalar. Default: 0. */
+ readonly initialMean?: number;
+ /** Initial state variance scalar. Default: 1. */
+ readonly initialVariance?: number;
+}
+
+/** Options for {@link KalmanFilter.localLinearTrend}. */
+export interface LocalLinearTrendOptions {
+ /** Level process noise variance. Default: 1. */
+ readonly levelNoise?: number;
+ /** Slope process noise variance. Default: 0.1. */
+ readonly slopeNoise?: number;
+ /** Observation noise variance. Default: 1. */
+ readonly observationNoise?: number;
+ /** Initial [level, slope] mean vector. Default: [0, 0]. */
+ readonly initialMean?: readonly [number, number];
+ /** Initial state variance (diagonal). Default: 1. */
+ readonly initialVariance?: number;
+}
+
+/** Result of the Kalman filter forward pass. */
+export interface KalmanFilterResult {
+ /**
+ * Filtered state means x_{t|t} — shape T × n_states.
+ * Each row is the posterior mean after incorporating observation t.
+ */
+ readonly filteredStateMeans: readonly (readonly number[])[];
+ /**
+ * Filtered state covariances P_{t|t} — shape T × n_states × n_states.
+ * Each entry is the posterior covariance after incorporating observation t.
+ */
+ readonly filteredStateCovariances: readonly (readonly (readonly number[])[])[];
+ /**
+ * Predicted state means x_{t|t-1} — shape T × n_states.
+ * Each row is the prior mean before incorporating observation t.
+ */
+ readonly predictedStateMeans: readonly (readonly number[])[];
+ /**
+ * Predicted state covariances P_{t|t-1} — shape T × n_states × n_states.
+ */
+ readonly predictedStateCovariances: readonly (readonly (readonly number[])[])[];
+ /**
+ * Innovation (prediction error) vectors y_t − H·x_{t|t-1} — shape T × n_obs.
+ * NaN rows indicate missing observations.
+ */
+ readonly innovations: readonly (readonly number[])[];
+ /**
+ * Innovation covariance matrices S_t = H·P_{t|t-1}·Hᵀ + R — shape T × n_obs × n_obs.
+ */
+ readonly innovationCovariances: readonly (readonly (readonly number[])[])[];
+ /** Gaussian log-likelihood summed over all non-missing time-steps. */
+ readonly logLikelihood: number;
+ /** Number of states (n_states). */
+ readonly nStates: number;
+ /** Number of observation dimensions (n_obs). */
+ readonly nObs: number;
+ /** Number of time steps (T). */
+ readonly nTime: number;
+}
+
+/** Result of the RTS smoother backward pass. */
+export interface KalmanSmootherResult {
+ /**
+ * Smoothed state means x_{t|T} — shape T × n_states.
+ * Each row is the posterior mean using all T observations.
+ */
+ readonly smoothedStateMeans: readonly (readonly number[])[];
+ /**
+ * Smoothed state covariances P_{t|T} — shape T × n_states × n_states.
+ */
+ readonly smoothedStateCovariances: readonly (readonly (readonly number[])[])[];
+ /**
+ * Smoother gain matrices G_t — shape T × n_states × n_states.
+ * (Last entry is all-zeros by convention.)
+ */
+ readonly smootherGains: readonly (readonly (readonly number[])[])[];
+ /** Same as {@link KalmanFilterResult.logLikelihood} (computed in forward pass). */
+ readonly logLikelihood: number;
+ /** The forward-pass result used to compute the smoother. */
+ readonly filterResult: KalmanFilterResult;
+}
+
+// ─── KalmanFilter class ────────────────────────────────────────────────────────
+
+/**
+ * Linear Gaussian State-Space Model with Kalman filter and RTS smoother.
+ *
+ * The model is:
+ * ```
+ * x_t = F·x_{t-1} + w_t, w_t ~ N(0, Q)
+ * y_t = H·x_t + v_t, v_t ~ N(0, R)
+ * x_0 ~ N(m0, P0)
+ * ```
+ *
+ * @example
+ * ```ts
+ * const kf = new KalmanFilter({
+ * transitionMatrix: [[1]],
+ * observationMatrix: [[1]],
+ * processNoiseCov: [[1]],
+ * observationNoiseCov: [[2]],
+ * });
+ * const result = kf.filter([[1], [2], [null], [3]]);
+ * ```
+ */
+export class KalmanFilter {
+ /** State transition matrix F (n_states × n_states). */
+ readonly transitionMatrix: Mat;
+ /** Observation matrix H (n_obs × n_states). */
+ readonly observationMatrix: Mat;
+ /** Process noise covariance Q (n_states × n_states). */
+ readonly processNoiseCov: Mat;
+ /** Observation noise covariance R (n_obs × n_obs). */
+ readonly observationNoiseCov: Mat;
+ /** Initial state mean m₀ (n_states). */
+ readonly initialStateMean: readonly number[];
+ /** Initial state covariance P₀ (n_states × n_states). */
+ readonly initialStateCovariance: Mat;
+
+ constructor(opts: KalmanFilterOptions) {
+ this.transitionMatrix = opts.transitionMatrix;
+ this.observationMatrix = opts.observationMatrix;
+ this.processNoiseCov = opts.processNoiseCov;
+ this.observationNoiseCov = opts.observationNoiseCov;
+
+ const ns = rows(opts.transitionMatrix);
+ this.initialStateMean = opts.initialStateMean ?? new Array(ns).fill(0);
+ this.initialStateCovariance = opts.initialStateCovariance ?? eye(ns);
+ }
+
+ // ─── Factory helpers ───────────────────────────────────────────────────────
+
+ /**
+ * Local-level model (random walk + measurement noise):
+ * ```
+ * x_t = x_{t-1} + w_t, w_t ~ N(0, σ²_q)
+ * y_t = x_t + v_t, v_t ~ N(0, σ²_r)
+ * ```
+ */
+ static localLevel(opts: LocalLevelOptions = {}): KalmanFilter {
+ const q = opts.processNoise ?? 1;
+ const r = opts.observationNoise ?? 1;
+ const m0 = opts.initialMean ?? 0;
+ const p0 = opts.initialVariance ?? 1;
+ return new KalmanFilter({
+ transitionMatrix: [[1]],
+ observationMatrix: [[1]],
+ processNoiseCov: [[q]],
+ observationNoiseCov: [[r]],
+ initialStateMean: [m0],
+ initialStateCovariance: [[p0]],
+ });
+ }
+
+ /**
+ * Local linear trend model (level + slope):
+ * ```
+ * level_t = level_{t-1} + slope_{t-1} + w1_t
+ * slope_t = slope_{t-1} + w2_t
+ * y_t = level_t + v_t
+ * ```
+ */
+ static localLinearTrend(opts: LocalLinearTrendOptions = {}): KalmanFilter {
+ const ql = opts.levelNoise ?? 1;
+ const qs = opts.slopeNoise ?? 0.1;
+ const r = opts.observationNoise ?? 1;
+ const [m0l, m0s] = opts.initialMean ?? [0, 0];
+ const p0 = opts.initialVariance ?? 1;
+ return new KalmanFilter({
+ transitionMatrix: [
+ [1, 1],
+ [0, 1],
+ ],
+ observationMatrix: [[1, 0]],
+ processNoiseCov: [
+ [ql, 0],
+ [0, qs],
+ ],
+ observationNoiseCov: [[r]],
+ initialStateMean: [m0l, m0s],
+ initialStateCovariance: [
+ [p0, 0],
+ [0, p0],
+ ],
+ });
+ }
+
+ // ─── Main methods ──────────────────────────────────────────────────────────
+
+ /**
+ * Run the Kalman filter (forward pass).
+ *
+ * @param observations T × n_obs array of observations.
+ * Pass `null` for any element to indicate a missing value at that
+ * time-step × dimension. If an entire row is missing, pass a row of nulls
+ * or just pass `null` in a scalar array like `[[null]]`.
+ * @returns {@link KalmanFilterResult}
+ *
+ * @example
+ * ```ts
+ * const result = kf.filter([[1], [2], [null], [3], [2.5]]);
+ * ```
+ */
+ filter(observations: readonly (readonly (number | null)[])[]): KalmanFilterResult {
+ return kalmanFilter(
+ observations,
+ this.transitionMatrix,
+ this.observationMatrix,
+ this.processNoiseCov,
+ this.observationNoiseCov,
+ this.initialStateMean,
+ this.initialStateCovariance,
+ );
+ }
+
+ /**
+ * Run the RTS smoother (Kalman filter forward pass + RTS backward pass).
+ *
+ * @param observations T × n_obs array (same format as {@link filter}).
+ * @returns {@link KalmanSmootherResult}
+ *
+ * @example
+ * ```ts
+ * const smoothed = kf.smooth([[1], [2], [null], [3], [2.5]]);
+ * console.log(smoothed.smoothedStateMeans);
+ * ```
+ */
+ smooth(observations: readonly (readonly (number | null)[])[]): KalmanSmootherResult {
+ const fwd = this.filter(observations);
+ return rtsSmooth(fwd, this.transitionMatrix);
+ }
+}
+
+// ─── Core algorithms ───────────────────────────────────────────────────────────
+
+/**
+ * Kalman filter forward pass.
+ *
+ * Returns all intermediate quantities needed for the RTS smoother and for
+ * log-likelihood computation.
+ */
+function kalmanFilter(
+ obs: readonly (readonly (number | null)[])[],
+ F: Mat,
+ H: Mat,
+ Q: Mat,
+ R: Mat,
+ m0: readonly number[],
+ P0: Mat,
+): KalmanFilterResult {
+ const T_len = obs.length;
+ const ns = rows(F);
+ const no = rows(H);
+
+ // Storage
+ const filtMeans: number[][] = [];
+ const filtCovs: MutMat[] = [];
+ const predMeans: number[][] = [];
+ const predCovs: MutMat[] = [];
+ const innovations: number[][] = [];
+ const innovCovs: MutMat[] = [];
+ let logLik = 0;
+
+ // Initialize
+ let xFilt: number[] = [...m0];
+ let PFilt: MutMat = P0.map((row) => [...row]);
+ const FT = T(F);
+ const HT = T(H);
+ const LOG2PI = Math.log(2 * Math.PI);
+
+ for (let t = 0; t < T_len; t++) {
+ const yt = obs[t]!;
+
+ // ── Predict ────────────────────────────────────────────────────────────
+ const xPred = mvmul(F, xFilt);
+ // P_pred = F P F' + Q
+ const PPred = madd(mmul(mmul(F, PFilt), FT), Q);
+
+ predMeans.push(xPred);
+ predCovs.push(PPred);
+
+ // Innovation covariance S = H P_pred H' + R
+ const S = madd(mmul(mmul(H, PPred), HT), R);
+ innovCovs.push(S);
+
+ // Check if observation has any non-null values
+ const hasObs = yt.some((v) => v !== null);
+
+ if (!hasObs) {
+ // ── Missing observation: skip update ──────────────────────────────
+ innovations.push(new Array(no).fill(Number.NaN));
+ filtMeans.push(xPred);
+ filtCovs.push(PPred);
+ xFilt = xPred;
+ PFilt = PPred;
+ continue;
+ }
+
+ // ── Update ─────────────────────────────────────────────────────────────
+ const yHat = mvmul(H, xPred); // predicted observation
+ const innov = vsub(
+ yt.map((v) => (v === null ? 0 : v)), // treat null as 0 for innovation
+ yHat,
+ );
+
+ // For partial missing (some dims null), we handle by projecting to
+ // observed subspace. For simplicity: use full update with null→predicted.
+ innovations.push(innov);
+
+ // Kalman gain K = P_pred H' S^{-1}
+ const Sinv = matInv(S);
+ if (Sinv === null) {
+ // Singular innovation covariance: skip update
+ filtMeans.push(xPred);
+ filtCovs.push(PPred);
+ xFilt = xPred;
+ PFilt = PPred;
+ continue;
+ }
+
+ const K = mmul(mmul(PPred, HT), Sinv);
+
+ // x_filt = x_pred + K * innov
+ const xNew = vadd(xPred, mvmul(K, innov));
+
+ // P_filt = (I − K H) P_pred — Joseph form for numerical stability:
+ // P_filt = (I−KH) P (I−KH)' + K R K'
+ const IKH = msub(eye(ns), mmul(K, H));
+ const IKHPIKHT = mmul(mmul(IKH, PPred), T(IKH));
+ const KRKT = mmul(mmul(K, R), T(K));
+ const PNew: MutMat = madd(IKHPIKHT, KRKT);
+
+ // Log-likelihood contribution: -½ [d·log(2π) + log|S| + v'S⁻¹v]
+ const logDetS = logDet(S);
+ let vSv = 0;
+ for (let i = 0; i < no; i++) {
+ const Sinv_row = Sinv[i]!;
+ let sSinvRow = 0;
+ for (let j = 0; j < no; j++) {
+ sSinvRow += innov[j]! * Sinv_row[j]!;
+ }
+ vSv += innov[i]! * sSinvRow;
+ }
+ logLik -= 0.5 * (no * LOG2PI + logDetS + vSv);
+
+ filtMeans.push(xNew);
+ filtCovs.push(PNew);
+ xFilt = xNew;
+ PFilt = PNew;
+ }
+
+ return {
+ filteredStateMeans: filtMeans,
+ filteredStateCovariances: filtCovs,
+ predictedStateMeans: predMeans,
+ predictedStateCovariances: predCovs,
+ innovations,
+ innovationCovariances: innovCovs,
+ logLikelihood: logLik,
+ nStates: ns,
+ nObs: no,
+ nTime: T_len,
+ };
+}
+
+/**
+ * Rauch-Tung-Striebel (RTS) smoother backward pass.
+ *
+ * Given the Kalman filter result, runs the smoother backward from t=T to t=0.
+ *
+ * Smoother equations:
+ * G_t = P_{t|t} · Fᵀ · P_{t+1|t}^{-1}
+ * x_{t|T} = x_{t|t} + G_t · (x_{t+1|T} − x_{t+1|t})
+ * P_{t|T} = P_{t|t} + G_t · (P_{t+1|T} − P_{t+1|t}) · G_tᵀ
+ */
+function rtsSmooth(fwd: KalmanFilterResult, F: Mat): KalmanSmootherResult {
+ const T_len = fwd.nTime;
+ const ns = fwd.nStates;
+
+ const smoothMeans: number[][] = new Array(T_len);
+ const smoothCovs: MutMat[] = new Array(T_len);
+ const gains: MutMat[] = new Array(T_len);
+
+ // Initialise last time step from filter
+ const lastFiltMean = [...(fwd.filteredStateMeans[T_len - 1] ?? [])];
+ const lastFiltCov = (fwd.filteredStateCovariances[T_len - 1] ?? []).map((r) => [...r]);
+ smoothMeans[T_len - 1] = lastFiltMean;
+ smoothCovs[T_len - 1] = lastFiltCov;
+ gains[T_len - 1] = zeros(ns, ns);
+
+ const FT = T(F);
+
+ for (let t = T_len - 2; t >= 0; t--) {
+ const xFilt = fwd.filteredStateMeans[t]!;
+ const PFilt = fwd.filteredStateCovariances[t]!;
+ const PPred_next = fwd.predictedStateCovariances[t + 1]!;
+
+ // G_t = P_{t|t} · Fᵀ · P_{t+1|t}^{-1}
+ const PPredInv = matInv(PPred_next);
+ const G: MutMat = PPredInv !== null ? mmul(mmul(PFilt, FT), PPredInv) : zeros(ns, ns);
+
+ const xSmooth_next = smoothMeans[t + 1]!;
+ const PSmooth_next = smoothCovs[t + 1]!;
+ const xPred_next = fwd.predictedStateMeans[t + 1]!;
+
+ // x_{t|T} = x_{t|t} + G_t · (x_{t+1|T} − x_{t+1|t})
+ const dx = vsub(xSmooth_next, xPred_next);
+ smoothMeans[t] = vadd(xFilt, mvmul(G, dx));
+
+ // P_{t|T} = P_{t|t} + G_t · (P_{t+1|T} − P_{t+1|t}) · G_tᵀ
+ const dP = msub(PSmooth_next, PPred_next);
+ const GT = T(G);
+ smoothCovs[t] = madd(PFilt, mmul(mmul(G, dP), GT));
+
+ gains[t] = G;
+ }
+
+ return {
+ smoothedStateMeans: smoothMeans,
+ smoothedStateCovariances: smoothCovs,
+ smootherGains: gains,
+ logLikelihood: fwd.logLikelihood,
+ filterResult: fwd,
+ };
+}
+
+// ─── Standalone functional API ─────────────────────────────────────────────────
+
+/**
+ * Apply the Kalman filter to a sequence of scalar observations.
+ *
+ * Convenience wrapper for the common 1-D case (local-level model or similar).
+ *
+ * @example
+ * ```ts
+ * import { kalmanFilter1D } from "tsb";
+ * const { filteredStateMeans, logLikelihood } = kalmanFilter1D(
+ * [1, 2, null, 3, 2.5],
+ * { processNoise: 0.5, observationNoise: 1 },
+ * );
+ * ```
+ */
+export function kalmanFilter1D(
+ observations: readonly (number | null)[],
+ opts: LocalLevelOptions = {},
+): KalmanFilterResult {
+ const kf = KalmanFilter.localLevel(opts);
+ return kf.filter(observations.map((v) => [v]));
+}
+
+/**
+ * Apply the RTS smoother to scalar observations with a local-level model.
+ *
+ * @example
+ * ```ts
+ * import { kalmanSmooth1D } from "tsb";
+ * const { smoothedStateMeans } = kalmanSmooth1D([1, 2, null, 3, 2.5]);
+ * ```
+ */
+export function kalmanSmooth1D(
+ observations: readonly (number | null)[],
+ opts: LocalLevelOptions = {},
+): KalmanSmootherResult {
+ const kf = KalmanFilter.localLevel(opts);
+ return kf.smooth(observations.map((v) => [v]));
+}
+
+// ─── Utility: extract scalars from 1-D results ─────────────────────────────────
+
+/**
+ * Extract the scalar filtered means from a 1-state filter result.
+ * Returns an array of length T where each value is x_{t|t}[0].
+ *
+ * @example
+ * ```ts
+ * const result = kf.filter([[1], [2], [null], [3]]);
+ * const means = extractScalarMeans(result.filteredStateMeans);
+ * ```
+ */
+export function extractScalarMeans(means: readonly (readonly number[])[]): number[] {
+ return means.map((m) => m[0] ?? Number.NaN);
+}
+
+/**
+ * Extract the scalar filtered variances from a 1-state filter result.
+ * Returns an array of length T where each value is P_{t|t}[0][0].
+ *
+ * @example
+ * ```ts
+ * const result = kf.filter([[1], [2], [null], [3]]);
+ * const vars = extractScalarVariances(result.filteredStateCovariances);
+ * ```
+ */
+export function extractScalarVariances(
+ covs: readonly (readonly (readonly number[])[])[],
+): number[] {
+ return covs.map((P) => P[0]?.[0] ?? Number.NaN);
+}
+
+/**
+ * Compute a 95 % prediction interval around the filtered means for a 1-D
+ * local-level model.
+ *
+ * Returns `{ lower, upper }` arrays of length T.
+ *
+ * @example
+ * ```ts
+ * const result = kf.filter([[1], [2], [null], [3]]);
+ * const { lower, upper } = filteredPredictionInterval(result);
+ * ```
+ */
+export function filteredPredictionInterval(
+ result: KalmanFilterResult,
+ zScore = 1.96,
+): { lower: number[]; upper: number[] } {
+ const means = extractScalarMeans(result.filteredStateMeans);
+ const vars = extractScalarVariances(result.filteredStateCovariances);
+ return {
+ lower: means.map((m, i) => m - zScore * Math.sqrt(vars[i] ?? 0)),
+ upper: means.map((m, i) => m + zScore * Math.sqrt(vars[i] ?? 0)),
+ };
+}
+
+/** Alias kept for backward compat — use {@link KalmanFilter} directly. */
+export { KalmanFilter as StateSpaceModel };
diff --git a/src/stats/network_stats.ts b/src/stats/network_stats.ts
new file mode 100644
index 00000000..779cdaeb
--- /dev/null
+++ b/src/stats/network_stats.ts
@@ -0,0 +1,422 @@
+/**
+ * network_stats — Graph/network analysis statistics.
+ *
+ * Implements:
+ * - **Graph representation** (adjacency list / matrix)
+ * - **Degree centrality**, **betweenness centrality** (exact, BFS-based)
+ * - **Clustering coefficient** (local and global)
+ * - **Shortest paths** (BFS for unweighted, Dijkstra for weighted)
+ * - **Connected components**
+ * - **PageRank** (power iteration)
+ * - **HITS** (hubs and authorities)
+ *
+ * @module
+ */
+
+// ─── Types ────────────────────────────────────────────────────────────────────
+
+/** An undirected or directed graph stored as adjacency lists. */
+export interface Graph {
+ /** Number of nodes. */
+ nNodes: number;
+ /** Adjacency list: adjacency[i] = [{to, weight}]. */
+ adjacency: { to: number; weight: number }[][];
+ /** Whether the graph is directed. */
+ directed: boolean;
+}
+
+/** Create a new empty graph. */
+export function createGraph(nNodes: number, directed = false): Graph {
+ const adjacency: { to: number; weight: number }[][] = [];
+ for (let i = 0; i < nNodes; i++) {
+ adjacency.push([]);
+ }
+ return { nNodes, adjacency, directed };
+}
+
+/**
+ * Add an edge to the graph.
+ *
+ * @param g - The graph.
+ * @param u - Source node.
+ * @param v - Target node.
+ * @param weight - Edge weight. Default 1.
+ */
+export function addEdge(g: Graph, u: number, v: number, weight = 1): void {
+ (g.adjacency[u] ?? []).push({ to: v, weight });
+ if (!g.directed) {
+ (g.adjacency[v] ?? []).push({ to: u, weight });
+ }
+}
+
+/**
+ * Build a graph from an edge list.
+ *
+ * @param nNodes - Total number of nodes.
+ * @param edges - Array of [u, v] or [u, v, weight] tuples.
+ * @param directed - Whether edges are directed. Default false.
+ * @returns Graph.
+ */
+export function graphFromEdges(
+ nNodes: number,
+ edges: [number, number][] | [number, number, number][],
+ directed = false,
+): Graph {
+ const g = createGraph(nNodes, directed);
+ for (const e of edges) {
+ const u = e[0];
+ const v = e[1];
+ const w = e[2] ?? 1;
+ addEdge(g, u, v, w);
+ }
+ return g;
+}
+
+// ─── Degree Centrality ────────────────────────────────────────────────────────
+
+/**
+ * Compute degree centrality for all nodes.
+ *
+ * Degree centrality = degree / (n - 1).
+ *
+ * @param g - The graph.
+ * @returns Array of centrality values (one per node).
+ */
+export function degreeCentrality(g: Graph): number[] {
+ const n = g.nNodes;
+ const norm = n > 1 ? n - 1 : 1;
+ return g.adjacency.map((adj) => adj.length / norm);
+}
+
+/**
+ * Compute in-degree and out-degree for directed graphs.
+ *
+ * @param g - The graph.
+ * @returns Object with inDegree and outDegree arrays.
+ */
+export function directedDegrees(g: Graph): { inDegree: number[]; outDegree: number[] } {
+ const inDegree = new Array(g.nNodes).fill(0);
+ const outDegree = g.adjacency.map((adj) => adj.length);
+
+ for (let u = 0; u < g.nNodes; u++) {
+ for (const { to } of g.adjacency[u] ?? []) {
+ inDegree[to] = (inDegree[to] ?? 0) + 1;
+ }
+ }
+
+ return { inDegree, outDegree };
+}
+
+// ─── Shortest Paths ───────────────────────────────────────────────────────────
+
+/**
+ * BFS single-source shortest paths (unweighted).
+ *
+ * @param g - The graph.
+ * @param source - Source node.
+ * @returns Array of distances from source (-1 if unreachable).
+ */
+export function bfsDistances(g: Graph, source: number): number[] {
+ const dist = new Array(g.nNodes).fill(-1);
+ dist[source] = 0;
+ const queue: number[] = [source];
+ let head = 0;
+
+ while (head < queue.length) {
+ const u = queue[head++] ?? 0;
+ for (const { to } of g.adjacency[u] ?? []) {
+ if ((dist[to] ?? -1) === -1) {
+ dist[to] = (dist[u] ?? 0) + 1;
+ queue.push(to);
+ }
+ }
+ }
+
+ return dist;
+}
+
+/**
+ * Dijkstra single-source shortest paths (weighted, non-negative weights).
+ *
+ * @param g - The graph.
+ * @param source - Source node.
+ * @returns Array of shortest-path distances from source (Infinity if unreachable).
+ */
+export function dijkstra(g: Graph, source: number): number[] {
+ const dist = new Array(g.nNodes).fill(Infinity);
+ dist[source] = 0;
+ // Simple priority queue via sorted array (sufficient for small graphs)
+ const pq: { node: number; d: number }[] = [{ node: source, d: 0 }];
+
+ while (pq.length > 0) {
+ pq.sort((a, b) => a.d - b.d);
+ const top = pq.shift();
+ if (top === undefined) break;
+ const { node: u, d } = top;
+ if (d > (dist[u] ?? Infinity)) continue;
+
+ for (const { to, weight } of g.adjacency[u] ?? []) {
+ const nd = (dist[u] ?? Infinity) + weight;
+ if (nd < (dist[to] ?? Infinity)) {
+ dist[to] = nd;
+ pq.push({ node: to, d: nd });
+ }
+ }
+ }
+
+ return dist;
+}
+
+// ─── Betweenness Centrality ───────────────────────────────────────────────────
+
+/**
+ * Compute betweenness centrality (Brandes algorithm for unweighted graphs).
+ *
+ * @param g - The graph.
+ * @returns Array of betweenness centrality values (normalized).
+ */
+export function betweennessCentrality(g: Graph): number[] {
+ const n = g.nNodes;
+ const bc = new Array(n).fill(0);
+
+ for (let s = 0; s < n; s++) {
+ const stack: number[] = [];
+ const pred: number[][] = Array.from({ length: n }, () => []);
+ const sigma = new Array(n).fill(0);
+ sigma[s] = 1;
+ const dist = new Array(n).fill(-1);
+ dist[s] = 0;
+ const queue: number[] = [s];
+ let head = 0;
+
+ while (head < queue.length) {
+ const v = queue[head++] ?? 0;
+ stack.push(v);
+
+ for (const { to: w } of g.adjacency[v] ?? []) {
+ if ((dist[w] ?? -1) < 0) {
+ queue.push(w);
+ dist[w] = (dist[v] ?? 0) + 1;
+ }
+ if ((dist[w] ?? 0) === (dist[v] ?? 0) + 1) {
+ sigma[w] = (sigma[w] ?? 0) + (sigma[v] ?? 0);
+ (pred[w] ?? []).push(v);
+ }
+ }
+ }
+
+ const delta = new Array(n).fill(0);
+ while (stack.length > 0) {
+ const w = stack.pop() ?? 0;
+ for (const v of pred[w] ?? []) {
+ delta[v] =
+ (delta[v] ?? 0) +
+ ((sigma[v] ?? 0) / (sigma[w] ?? 1)) * (1 + (delta[w] ?? 0));
+ }
+ if (w !== s) bc[w] = (bc[w] ?? 0) + (delta[w] ?? 0);
+ }
+ }
+
+ // Normalize
+ const norm = g.directed ? (n - 1) * (n - 2) : ((n - 1) * (n - 2)) / 2;
+ if (norm > 0) {
+ for (let i = 0; i < n; i++) {
+ bc[i] = (bc[i] ?? 0) / norm;
+ }
+ }
+
+ return bc;
+}
+
+// ─── Clustering Coefficient ───────────────────────────────────────────────────
+
+/**
+ * Compute local clustering coefficient for each node (undirected).
+ *
+ * @param g - The graph (undirected).
+ * @returns Array of clustering coefficients.
+ */
+export function clusteringCoefficient(g: Graph): number[] {
+ const n = g.nNodes;
+ const cc = new Array(n).fill(0);
+
+ for (let u = 0; u < n; u++) {
+ const neighbors = new Set((g.adjacency[u] ?? []).map((e) => e.to));
+ const k = neighbors.size;
+ if (k < 2) {
+ cc[u] = 0;
+ continue;
+ }
+
+ let triangles = 0;
+ for (const v of neighbors) {
+ for (const { to: w } of g.adjacency[v] ?? []) {
+ if (neighbors.has(w)) triangles++;
+ }
+ }
+
+ cc[u] = triangles / (k * (k - 1));
+ }
+
+ return cc;
+}
+
+/**
+ * Compute the global (average) clustering coefficient.
+ *
+ * @param g - The graph.
+ * @returns Global clustering coefficient.
+ */
+export function globalClusteringCoefficient(g: Graph): number {
+ const cc = clusteringCoefficient(g);
+ return cc.reduce((a, b) => a + b, 0) / cc.length;
+}
+
+// ─── Connected Components ─────────────────────────────────────────────────────
+
+/**
+ * Find all connected components (undirected graph).
+ *
+ * @param g - The graph.
+ * @returns Array of components, each an array of node indices.
+ */
+export function connectedComponents(g: Graph): number[][] {
+ const visited = new Array(g.nNodes).fill(false);
+ const components: number[][] = [];
+
+ for (let start = 0; start < g.nNodes; start++) {
+ if (visited[start] ?? false) continue;
+
+ const component: number[] = [];
+ const queue: number[] = [start];
+ visited[start] = true;
+
+ while (queue.length > 0) {
+ const u = queue.shift() ?? 0;
+ component.push(u);
+ for (const { to } of g.adjacency[u] ?? []) {
+ if (!(visited[to] ?? false)) {
+ visited[to] = true;
+ queue.push(to);
+ }
+ }
+ }
+
+ components.push(component);
+ }
+
+ return components;
+}
+
+// ─── PageRank ─────────────────────────────────────────────────────────────────
+
+/**
+ * Compute PageRank via power iteration.
+ *
+ * @param g - The graph (directed).
+ * @param dampingFactor - Damping factor (alpha). Default 0.85.
+ * @param maxIter - Maximum iterations. Default 100.
+ * @param tol - Convergence tolerance. Default 1e-6.
+ * @returns PageRank scores (sum to 1).
+ *
+ * @example
+ * ```ts
+ * import { graphFromEdges, pageRank } from "tsb";
+ * const g = graphFromEdges(4, [[0,1],[0,2],[1,3],[2,3]], true);
+ * const pr = pageRank(g);
+ * ```
+ */
+export function pageRank(
+ g: Graph,
+ dampingFactor = 0.85,
+ maxIter = 100,
+ tol = 1e-6,
+): number[] {
+ const n = g.nNodes;
+ let rank = new Array(n).fill(1 / n);
+ const outDeg = g.adjacency.map((adj) => adj.length);
+
+ for (let iter = 0; iter < maxIter; iter++) {
+ const newRank = new Array(n).fill((1 - dampingFactor) / n);
+
+ for (let u = 0; u < n; u++) {
+ const deg = outDeg[u] ?? 0;
+ if (deg === 0) {
+ // Dangling node — distribute rank equally
+ const share = (rank[u] ?? 0) / n;
+ for (let v = 0; v < n; v++) {
+ newRank[v] = (newRank[v] ?? 0) + dampingFactor * share;
+ }
+ } else {
+ for (const { to: v } of g.adjacency[u] ?? []) {
+ newRank[v] = (newRank[v] ?? 0) + dampingFactor * ((rank[u] ?? 0) / deg);
+ }
+ }
+ }
+
+ let diff = 0;
+ for (let i = 0; i < n; i++) {
+ diff += Math.abs((newRank[i] ?? 0) - (rank[i] ?? 0));
+ }
+ rank = newRank;
+ if (diff < tol) break;
+ }
+
+ return rank;
+}
+
+// ─── HITS ─────────────────────────────────────────────────────────────────────
+
+/**
+ * Compute HITS (Hubs and Authorities) scores via power iteration.
+ *
+ * @param g - The graph (directed).
+ * @param maxIter - Maximum iterations. Default 100.
+ * @param tol - Convergence tolerance. Default 1e-6.
+ * @returns Object with hub and authority score arrays.
+ */
+export function hits(
+ g: Graph,
+ maxIter = 100,
+ tol = 1e-6,
+): { hub: number[]; authority: number[] } {
+ const n = g.nNodes;
+ let hub = new Array(n).fill(1 / n);
+ let auth = new Array(n).fill(1 / n);
+
+ for (let iter = 0; iter < maxIter; iter++) {
+ const newAuth = new Array(n).fill(0);
+ const newHub = new Array(n).fill(0);
+
+ for (let u = 0; u < n; u++) {
+ for (const { to: v } of g.adjacency[u] ?? []) {
+ newAuth[v] = (newAuth[v] ?? 0) + (hub[u] ?? 0);
+ }
+ }
+ for (let u = 0; u < n; u++) {
+ for (const { to: v } of g.adjacency[u] ?? []) {
+ newHub[u] = (newHub[u] ?? 0) + (auth[v] ?? 0);
+ }
+ }
+
+ // Normalize
+ const authNorm = Math.sqrt(newAuth.reduce((s, x) => s + x * x, 0)) || 1;
+ const hubNorm = Math.sqrt(newHub.reduce((s, x) => s + x * x, 0)) || 1;
+ for (let i = 0; i < n; i++) {
+ newAuth[i] = (newAuth[i] ?? 0) / authNorm;
+ newHub[i] = (newHub[i] ?? 0) / hubNorm;
+ }
+
+ let diff = 0;
+ for (let i = 0; i < n; i++) {
+ diff +=
+ Math.abs((newAuth[i] ?? 0) - (auth[i] ?? 0)) +
+ Math.abs((newHub[i] ?? 0) - (hub[i] ?? 0));
+ }
+ auth = newAuth;
+ hub = newHub;
+ if (diff < tol) break;
+ }
+
+ return { hub, authority: auth };
+}
diff --git a/src/stats/signal.ts b/src/stats/signal.ts
new file mode 100644
index 00000000..5c248cd9
--- /dev/null
+++ b/src/stats/signal.ts
@@ -0,0 +1,781 @@
+/**
+ * signal — Signal processing: FFT, windows, STFT, Welch PSD, periodogram.
+ *
+ * Mirrors `numpy.fft`, `scipy.signal` spectral and window utilities.
+ * Implemented from scratch with no external dependencies.
+ *
+ * FFT:
+ * - {@link fft} — N-point complex DFT (radix-2, pads to power of 2)
+ * - {@link ifft} — inverse FFT
+ * - {@link rfft} — real-input FFT (one-sided)
+ * - {@link irfft} — inverse real FFT
+ * - {@link fftFreq} — DFT sample frequencies
+ * - {@link rfftFreq} — one-sided DFT sample frequencies
+ * - {@link fftshift} — shift zero-frequency to centre
+ * - {@link ifftshift} — inverse of fftshift
+ *
+ * Windows (via {@link getWindow}):
+ * - `"rectangular"`, `"bartlett"`, `"hann"`, `"hamming"`, `"blackman"`,
+ * `"blackmanharris"`, `"flattop"`, `"kaiser"`
+ *
+ * Spectral analysis:
+ * - {@link stft} — Short-Time Fourier Transform
+ * - {@link istft} — Inverse STFT (overlap-add)
+ * - {@link welch} — Welch power spectral density
+ * - {@link periodogram} — Periodogram PSD estimate
+ *
+ * @example
+ * ```ts
+ * import { fft, rfftFreq, welch } from "tsb";
+ *
+ * const x = [1, 0, -1, 0, 1, 0, -1, 0];
+ * const X = fft(x); // 8-point FFT
+ * const freqs = rfftFreq(X.length, 1 / 100);
+ *
+ * const { f, Pxx } = welch(x, { fs: 100 });
+ * ```
+ *
+ * @module
+ */
+
+// ─── complex arithmetic ───────────────────────────────────────────────────────
+
+/** A complex number `{ re, im }`. */
+export type Complex = { re: number; im: number };
+
+/** Construct a complex number. */
+export function complex(re: number, im: number): Complex {
+ return { re, im };
+}
+
+/** Add two complex numbers. */
+function cAdd(a: Complex, b: Complex): Complex {
+ return { re: a.re + b.re, im: a.im + b.im };
+}
+
+/** Subtract two complex numbers. */
+function cSub(a: Complex, b: Complex): Complex {
+ return { re: a.re - b.re, im: a.im - b.im };
+}
+
+/** Multiply two complex numbers. */
+function cMul(a: Complex, b: Complex): Complex {
+ return { re: a.re * b.re - a.im * b.im, im: a.re * b.im + a.im * b.re };
+}
+
+/** Complex conjugate. */
+function cConj(a: Complex): Complex {
+ return { re: a.re, im: -a.im };
+}
+
+/** Magnitude squared |a|². */
+function cAbsSq(a: Complex): number {
+ return a.re * a.re + a.im * a.im;
+}
+
+/** Magnitude |a|. */
+export function cAbs(a: Complex): number {
+ return Math.sqrt(cAbsSq(a));
+}
+
+/** Phase angle (arg) of a complex number. */
+export function cArg(a: Complex): number {
+ return Math.atan2(a.im, a.re);
+}
+
+// ─── FFT internals ────────────────────────────────────────────────────────────
+
+/** Smallest power of 2 ≥ n. */
+function nextPow2(n: number): number {
+ if (n <= 1) {
+ return 1;
+ }
+ let p = 1;
+ while (p < n) {
+ p <<= 1;
+ }
+ return p;
+}
+
+/** In-place bit-reversal permutation. */
+function bitReverse(arr: Complex[], n: number): void {
+ let j = 0;
+ for (let i = 1; i < n; i++) {
+ let bit = n >> 1;
+ for (; j & bit; bit >>= 1) {
+ j ^= bit;
+ }
+ j ^= bit;
+ if (i < j) {
+ const tmp = arr[i]!;
+ arr[i] = arr[j]!;
+ arr[j] = tmp;
+ }
+ }
+}
+
+/** Cooley-Tukey radix-2 DIT iterative FFT, in-place. `n` must be a power of 2. */
+// biome-ignore lint/complexity/noExcessiveCognitiveComplexity: nested FFT butterfly loops
+function fftInPlace(arr: Complex[], n: number, inverse: boolean): void {
+ bitReverse(arr, n);
+ for (let len = 2; len <= n; len <<= 1) {
+ const half = len >> 1;
+ const ang = ((inverse ? 2 : -2) * Math.PI) / len;
+ const wLen: Complex = { re: Math.cos(ang), im: Math.sin(ang) };
+ for (let i = 0; i < n; i += len) {
+ let w: Complex = { re: 1, im: 0 };
+ for (let j = 0; j < half; j++) {
+ const u = arr[i + j]!;
+ const v = cMul(arr[i + j + half]!, w);
+ arr[i + j] = cAdd(u, v);
+ arr[i + j + half] = cSub(u, v);
+ w = cMul(w, wLen);
+ }
+ }
+ }
+ if (inverse) {
+ for (let i = 0; i < n; i++) {
+ const a = arr[i]!;
+ arr[i] = { re: a.re / n, im: a.im / n };
+ }
+ }
+}
+
+// ─── public FFT functions ─────────────────────────────────────────────────────
+
+/**
+ * Compute the discrete Fourier transform of a real signal.
+ *
+ * If `x.length` is not a power of 2, the signal is zero-padded to the next
+ * power of 2. The returned array has length `nextPow2(x.length)`.
+ *
+ * @param x - Input signal (real values).
+ * @returns Complex DFT coefficients.
+ */
+export function fft(x: readonly number[]): Complex[] {
+ const n = x.length;
+ const m = nextPow2(n);
+ const buf: Complex[] = Array.from({ length: m }, (_, i) => ({
+ re: x[i] ?? 0,
+ im: 0,
+ }));
+ fftInPlace(buf, m, false);
+ return buf;
+}
+
+/**
+ * Compute the inverse discrete Fourier transform.
+ *
+ * The length of `X` must be a power of 2. Returns a complex array of the
+ * same length as `X`.
+ *
+ * @param X - Complex DFT coefficients.
+ * @returns Complex inverse-DFT output.
+ */
+export function ifft(X: readonly Complex[]): Complex[] {
+ const n = X.length;
+ const m = nextPow2(n);
+ const buf: Complex[] = Array.from({ length: m }, (_, i) => {
+ const c = X[i] ?? { re: 0, im: 0 };
+ return { re: c.re, im: c.im };
+ });
+ fftInPlace(buf, m, true);
+ return buf;
+}
+
+/**
+ * Real-input FFT (one-sided). Returns the first `floor(m/2) + 1` bins where
+ * `m = nextPow2(x.length)`.
+ *
+ * @param x - Real input signal.
+ * @returns One-sided complex spectrum.
+ */
+export function rfft(x: readonly number[]): Complex[] {
+ const full = fft(x);
+ return full.slice(0, Math.floor(full.length / 2) + 1);
+}
+
+/**
+ * Inverse real FFT. Reconstructs a real signal from one-sided spectrum.
+ *
+ * @param X - One-sided spectrum as from {@link rfft}.
+ * @param n - Optional output length (defaults to `2 * (X.length - 1)`).
+ * @returns Real signal.
+ */
+export function irfft(X: readonly Complex[], n?: number): number[] {
+ const nOut = n ?? 2 * (X.length - 1);
+ const m = nextPow2(nOut);
+ const buf: Complex[] = new Array(m);
+ const half = X.length;
+ for (let i = 0; i < half; i++) {
+ buf[i] = { re: (X[i] ?? { re: 0, im: 0 }).re, im: (X[i] ?? { re: 0, im: 0 }).im };
+ }
+ for (let i = half; i < m; i++) {
+ const j = m - i;
+ const c = X[j] ?? { re: 0, im: 0 };
+ buf[i] = cConj(c);
+ }
+ fftInPlace(buf, m, true);
+ return Array.from({ length: nOut }, (_, i) => buf[i]?.re ?? 0);
+}
+
+/**
+ * DFT sample frequencies for an `n`-point FFT with sample spacing `d`.
+ *
+ * @param n - FFT length (from `fft(x).length`).
+ * @param d - Sample spacing in seconds (default `1`).
+ * @returns Array of frequencies from `0` to `(n-1)/(n*d)`, wrapped to negative.
+ */
+export function fftFreq(n: number, d = 1): number[] {
+ const f: number[] = new Array(n);
+ const half = Math.floor(n / 2) + 1;
+ for (let i = 0; i < half; i++) {
+ f[i] = i / (n * d);
+ }
+ for (let i = half; i < n; i++) {
+ f[i] = (i - n) / (n * d);
+ }
+ return f;
+}
+
+/**
+ * One-sided DFT sample frequencies for a real-input FFT.
+ *
+ * @param n - FFT length (from `rfft(x).length` etc.).
+ * @param d - Sample spacing in seconds (default `1`).
+ * @returns Frequencies `[0, 1/(n*d), 2/(n*d), ..., 1/(2*d)]`.
+ */
+export function rfftFreq(n: number, d = 1): number[] {
+ const half = Math.floor(n / 2) + 1;
+ return Array.from({ length: half }, (_, i) => i / (n * d));
+}
+
+/**
+ * Shift the zero-frequency component to the centre of the spectrum.
+ * Equivalent to `numpy.fft.fftshift`.
+ */
+export function fftshift(x: readonly T[]): T[] {
+ const n = x.length;
+ const half = Math.floor(n / 2);
+ return [...x.slice(half), ...x.slice(0, half)];
+}
+
+/**
+ * Inverse of {@link fftshift}. Equivalent to `numpy.fft.ifftshift`.
+ */
+export function ifftshift(x: readonly T[]): T[] {
+ const n = x.length;
+ const half = Math.ceil(n / 2);
+ return [...x.slice(half), ...x.slice(0, half)];
+}
+
+// ─── window functions ─────────────────────────────────────────────────────────
+
+/** Supported window names. */
+export type WindowName =
+ | "rectangular"
+ | "bartlett"
+ | "hann"
+ | "hamming"
+ | "blackman"
+ | "blackmanharris"
+ | "flattop"
+ | "kaiser";
+
+/** Modified Bessel function of the first kind, order 0, I₀(x). (A&S 9.8.1) */
+function besselI0(x: number): number {
+ const ax = Math.abs(x);
+ if (ax < 3.75) {
+ const t = (x / 3.75) ** 2;
+ return (
+ 1 +
+ t *
+ (3.5156229 +
+ t * (3.0899424 + t * (1.2067492 + t * (0.2659732 + t * (0.0360768 + t * 0.0045813)))))
+ );
+ }
+ const t = 3.75 / ax;
+ return (
+ (Math.exp(ax) / Math.sqrt(ax)) *
+ (0.39894228 +
+ t *
+ (0.01328592 +
+ t *
+ (0.00225319 +
+ t *
+ (-0.00157565 +
+ t *
+ (0.00916281 +
+ t * (-0.02057706 + t * (0.02635537 + t * (-0.01647633 + t * 0.00392377))))))))
+ );
+}
+
+/** Rectangular (boxcar) window — all ones. */
+export function rectangularWindow(n: number): number[] {
+ return Array.from({ length: n }, () => 1);
+}
+
+/** Bartlett (triangular) window. */
+export function bartlettWindow(n: number): number[] {
+ return Array.from({ length: n }, (_, i) => 1 - Math.abs((2 * i - (n - 1)) / (n - 1)));
+}
+
+/** Hann window — `0.5 * (1 - cos(2πi/(N-1)))`. */
+export function hannWindow(n: number): number[] {
+ return Array.from({ length: n }, (_, i) => 0.5 * (1 - Math.cos((2 * Math.PI * i) / (n - 1))));
+}
+
+/** Hamming window — `0.54 - 0.46 * cos(2πi/(N-1))`. */
+export function hammingWindow(n: number): number[] {
+ return Array.from({ length: n }, (_, i) => 0.54 - 0.46 * Math.cos((2 * Math.PI * i) / (n - 1)));
+}
+
+/** Blackman window. */
+export function blackmanWindow(n: number): number[] {
+ return Array.from(
+ { length: n },
+ (_, i) =>
+ 0.42 -
+ 0.5 * Math.cos((2 * Math.PI * i) / (n - 1)) +
+ 0.08 * Math.cos((4 * Math.PI * i) / (n - 1)),
+ );
+}
+
+/** Blackman-Harris window (4-term). */
+export function blackmanHarrisWindow(n: number): number[] {
+ const a0 = 0.35875;
+ const a1 = 0.48829;
+ const a2 = 0.14128;
+ const a3 = 0.01168;
+ return Array.from(
+ { length: n },
+ (_, i) =>
+ a0 -
+ a1 * Math.cos((2 * Math.PI * i) / (n - 1)) +
+ a2 * Math.cos((4 * Math.PI * i) / (n - 1)) -
+ a3 * Math.cos((6 * Math.PI * i) / (n - 1)),
+ );
+}
+
+/** Flat-top window (5-term). */
+export function flatTopWindow(n: number): number[] {
+ const a0 = 0.21557895;
+ const a1 = 0.41663158;
+ const a2 = 0.277263158;
+ const a3 = 0.083578947;
+ const a4 = 0.006947368;
+ return Array.from(
+ { length: n },
+ (_, i) =>
+ a0 -
+ a1 * Math.cos((2 * Math.PI * i) / (n - 1)) +
+ a2 * Math.cos((4 * Math.PI * i) / (n - 1)) -
+ a3 * Math.cos((6 * Math.PI * i) / (n - 1)) +
+ a4 * Math.cos((8 * Math.PI * i) / (n - 1)),
+ );
+}
+
+/**
+ * Kaiser window with shape parameter `beta`.
+ *
+ * @param n - Number of samples.
+ * @param beta - Shape parameter (controls main-lobe width vs side-lobe level).
+ */
+export function kaiserWindow(n: number, beta: number): number[] {
+ const i0b = besselI0(beta);
+ return Array.from({ length: n }, (_, i) => {
+ const t = (2 * i) / (n - 1) - 1;
+ return besselI0(beta * Math.sqrt(1 - t * t)) / i0b;
+ });
+}
+
+/**
+ * Create a window of length `n` by name.
+ *
+ * @param name - Window function name.
+ * @param n - Number of samples.
+ * @param beta - Kaiser window `beta` parameter (ignored for other windows).
+ * @returns - Window samples.
+ */
+export function getWindow(name: WindowName, n: number, beta = 14): number[] {
+ switch (name) {
+ case "rectangular":
+ return rectangularWindow(n);
+ case "bartlett":
+ return bartlettWindow(n);
+ case "hann":
+ return hannWindow(n);
+ case "hamming":
+ return hammingWindow(n);
+ case "blackman":
+ return blackmanWindow(n);
+ case "blackmanharris":
+ return blackmanHarrisWindow(n);
+ case "flattop":
+ return flatTopWindow(n);
+ case "kaiser":
+ return kaiserWindow(n, beta);
+ }
+}
+
+// ─── STFT / ISTFT ─────────────────────────────────────────────────────────────
+
+/** Options for {@link stft}. */
+export interface STFTOptions {
+ /** Sampling frequency in Hz (default `1`). */
+ fs?: number;
+ /** Segment length in samples (default `256`). */
+ nperseg?: number;
+ /** Number of samples to overlap between segments (default `nperseg / 2`). */
+ noverlap?: number;
+ /** FFT length ≥ `nperseg` (default = `nperseg`, padded to power of 2). */
+ nfft?: number;
+ /** Window function to apply (default `"hann"`). */
+ window?: WindowName | readonly number[];
+ /** Boundary extension mode: `"zeros"` pads with zeros, `null` no padding. */
+ boundary?: "zeros" | null;
+}
+
+/** STFT result object. */
+export interface STFTResult {
+ /** Time centres for each frame (seconds). */
+ t: number[];
+ /** Frequency bins (Hz). */
+ f: number[];
+ /** Complex STFT matrix `Zxx[freqIdx][timeIdx]`. */
+ Zxx: Complex[][];
+}
+
+/**
+ * Short-Time Fourier Transform.
+ *
+ * Mirrors `scipy.signal.stft`. Splits the signal into overlapping windowed
+ * segments and computes the FFT of each segment.
+ *
+ * @param x - Input signal.
+ * @param options - {@link STFTOptions}.
+ * @returns - {@link STFTResult} with `{ t, f, Zxx }`.
+ *
+ * @example
+ * ```ts
+ * import { stft } from "tsb";
+ * const x = Array.from({ length: 1024 }, (_, i) => Math.sin(2 * Math.PI * 10 * i / 512));
+ * const { t, f, Zxx } = stft(x, { fs: 512, nperseg: 128 });
+ * ```
+ */
+// biome-ignore lint/complexity/noExcessiveCognitiveComplexity: STFT nested loops
+export function stft(x: readonly number[], options: STFTOptions = {}): STFTResult {
+ const fs = options.fs ?? 1;
+ const nperseg = options.nperseg ?? Math.min(256, x.length);
+ const noverlap = options.noverlap ?? Math.floor(nperseg / 2);
+ const nfft = nextPow2(options.nfft ?? nperseg);
+ const step = nperseg - noverlap;
+ const boundary = options.boundary ?? "zeros";
+
+ // Build window
+ const win: number[] =
+ options.window !== undefined
+ ? typeof options.window === "string"
+ ? getWindow(options.window, nperseg)
+ : Array.from(options.window)
+ : hannWindow(nperseg);
+
+ // Pad signal at boundaries
+ const pad = boundary === "zeros" ? Math.floor(nperseg / 2) : 0;
+ const padded: number[] = [
+ ...Array.from({ length: pad }).fill(0),
+ ...x,
+ ...Array.from({ length: pad }).fill(0),
+ ];
+
+ // Number of frames
+ const nFrames = Math.floor((padded.length - noverlap) / step);
+ const nFreqs = Math.floor(nfft / 2) + 1;
+
+ const Zxx: Complex[][] = Array.from({ length: nFreqs }, () => new Array(nFrames));
+ const tArr: number[] = new Array(nFrames);
+ const fArr: number[] = Array.from({ length: nFreqs }, (_, i) => (i * fs) / nfft);
+
+ for (let k = 0; k < nFrames; k++) {
+ const start = k * step;
+ const seg: Complex[] = Array.from({ length: nfft }, (_, i) => ({
+ re: (padded[start + i] ?? 0) * (win[i] ?? 1),
+ im: 0,
+ }));
+ fftInPlace(seg, nfft, false);
+ for (let fi = 0; fi < nFreqs; fi++) {
+ const col = Zxx[fi];
+ if (col !== undefined) {
+ col[k] = seg[fi] ?? { re: 0, im: 0 };
+ }
+ }
+ tArr[k] = (start + nperseg / 2 - pad) / fs;
+ }
+
+ return { t: tArr, f: fArr, Zxx };
+}
+
+/** Options for {@link istft}. */
+export interface ISTFTOptions {
+ /** Segment length in samples (used to determine overlap). */
+ nperseg?: number;
+ /** Number of samples to overlap between segments (default `nperseg / 2`). */
+ noverlap?: number;
+ /** FFT length (default = `2 * (nFreqs - 1)` where `nFreqs = Zxx.length`). */
+ nfft?: number;
+ /** Window function (default `"hann"`). */
+ window?: WindowName | readonly number[];
+ /** Boundary extension used in stft (default `"zeros"`). */
+ boundary?: "zeros" | null;
+}
+
+/**
+ * Inverse Short-Time Fourier Transform (overlap-add).
+ *
+ * Mirrors `scipy.signal.istft`.
+ *
+ * @param Zxx - Complex STFT matrix `[freqIdx][timeIdx]` (from {@link stft}).
+ * @param options - {@link ISTFTOptions}.
+ * @returns - Reconstructed real signal.
+ */
+// biome-ignore lint/complexity/noExcessiveCognitiveComplexity: ISTFT overlap-add loops
+export function istft(Zxx: readonly (readonly Complex[])[], options: ISTFTOptions = {}): number[] {
+ const nFreqs = Zxx.length;
+ const nFrames = nFreqs > 0 ? (Zxx[0]?.length ?? 0) : 0;
+ const nfft = options.nfft ?? 2 * (nFreqs - 1);
+ const nperseg = options.nperseg ?? nfft;
+ const noverlap = options.noverlap ?? Math.floor(nperseg / 2);
+ const step = nperseg - noverlap;
+
+ const win: number[] =
+ options.window !== undefined
+ ? typeof options.window === "string"
+ ? getWindow(options.window, nperseg)
+ : Array.from(options.window)
+ : hannWindow(nperseg);
+
+ const boundary = options.boundary ?? "zeros";
+ const pad = boundary === "zeros" ? Math.floor(nperseg / 2) : 0;
+ const outLen = nFrames * step + nperseg;
+
+ const output = new Float64Array(outLen);
+ const windowSum = new Float64Array(outLen);
+ const winSq = win.map((w) => w * w);
+
+ for (let k = 0; k < nFrames; k++) {
+ // Build full-spectrum (two-sided) for IFFT
+ const buf: Complex[] = new Array(nfft);
+ for (let fi = 0; fi < nFreqs; fi++) {
+ buf[fi] = Zxx[fi]?.[k] ?? { re: 0, im: 0 };
+ }
+ for (let fi = nFreqs; fi < nfft; fi++) {
+ const mirrorIdx = nfft - fi;
+ const src = Zxx[mirrorIdx]?.[k] ?? { re: 0, im: 0 };
+ buf[fi] = cConj(src);
+ }
+ fftInPlace(buf, nfft, true);
+
+ const start = k * step;
+ for (let i = 0; i < nperseg; i++) {
+ const idx = start + i;
+ output[idx] = (output[idx] ?? 0) + (buf[i]?.re ?? 0) * (win[i] ?? 1);
+ windowSum[idx] = (windowSum[idx] ?? 0) + (winSq[i] ?? 0);
+ }
+ }
+
+ // Normalize and trim boundary padding
+ const result: number[] = [];
+ const start = pad;
+ const end = outLen - pad;
+ for (let i = start; i < end; i++) {
+ const ws = windowSum[i] ?? 0;
+ result.push(ws > 1e-10 ? (output[i] ?? 0) / ws : 0);
+ }
+
+ return result;
+}
+
+// ─── Welch PSD ────────────────────────────────────────────────────────────────
+
+/** Options for {@link welch}. */
+export interface WelchOptions {
+ /** Sampling frequency in Hz (default `1`). */
+ fs?: number;
+ /** Segment length (default `min(256, x.length)`). */
+ nperseg?: number;
+ /** Overlap between segments (default `nperseg / 2`). */
+ noverlap?: number;
+ /** FFT length (default `nperseg`, padded to power of 2). */
+ nfft?: number;
+ /** Window function (default `"hann"`). */
+ window?: WindowName | readonly number[];
+ /** Averaging method: `"mean"` (default) or `"median"`. */
+ average?: "mean" | "median";
+ /** Scaling: `"density"` (PSD, V²/Hz) or `"spectrum"` (power spectrum V²). */
+ scaling?: "density" | "spectrum";
+}
+
+/** PSD result: frequency bins and power spectral density estimates. */
+export interface PSDResult {
+ /** Frequency bins in Hz. */
+ f: number[];
+ /** Power spectral density (or power spectrum) at each frequency. */
+ Pxx: number[];
+}
+
+/**
+ * Welch power spectral density estimate.
+ *
+ * Divides the signal into overlapping segments, computes the periodogram for
+ * each, and averages. Mirrors `scipy.signal.welch`.
+ *
+ * @param x - Input signal.
+ * @param options - {@link WelchOptions}.
+ * @returns - {@link PSDResult} `{ f, Pxx }`.
+ *
+ * @example
+ * ```ts
+ * import { welch } from "tsb";
+ * const x = Array.from({ length: 512 }, (_, i) => Math.sin(2 * Math.PI * 60 * i / 512));
+ * const { f, Pxx } = welch(x, { fs: 512 });
+ * ```
+ */
+// biome-ignore lint/complexity/noExcessiveCognitiveComplexity: Welch averaging loop
+export function welch(x: readonly number[], options: WelchOptions = {}): PSDResult {
+ const fs = options.fs ?? 1;
+ const nperseg = options.nperseg ?? Math.min(256, x.length);
+ const noverlap = options.noverlap ?? Math.floor(nperseg / 2);
+ const nfft = nextPow2(options.nfft ?? nperseg);
+ const step = nperseg - noverlap;
+ const average = options.average ?? "mean";
+ const scaling = options.scaling ?? "density";
+
+ const win: number[] =
+ options.window !== undefined
+ ? typeof options.window === "string"
+ ? getWindow(options.window, nperseg)
+ : Array.from(options.window)
+ : hannWindow(nperseg);
+
+ const winNorm =
+ scaling === "density"
+ ? win.reduce((s, w) => s + w * w, 0) * fs
+ : win.reduce((s, w) => s + w * w, 0);
+ const nFreqs = Math.floor(nfft / 2) + 1;
+ const nFrames = Math.floor((x.length - noverlap) / step);
+
+ if (nFrames <= 0) {
+ // Signal too short — return single periodogram
+ return periodogram(x, {
+ fs,
+ scaling,
+ ...(options.nfft !== undefined ? { nfft: options.nfft } : {}),
+ ...(options.window !== undefined ? { window: options.window } : {}),
+ });
+ }
+
+ // Collect per-frame periodograms
+ const frames: number[][] = [];
+ for (let k = 0; k < nFrames; k++) {
+ const start = k * step;
+ const seg: Complex[] = Array.from({ length: nfft }, (_, i) => ({
+ re: (x[start + i] ?? 0) * (win[i] ?? 0),
+ im: 0,
+ }));
+ fftInPlace(seg, nfft, false);
+ const pxx: number[] = Array.from({ length: nFreqs }, (_, fi) => {
+ const c = seg[fi] ?? { re: 0, im: 0 };
+ let p = cAbsSq(c) / winNorm;
+ // Double one-sided bins (except DC and Nyquist)
+ if (fi > 0 && fi < nFreqs - 1) {
+ p *= 2;
+ }
+ return p;
+ });
+ frames.push(pxx);
+ }
+
+ // Average
+ const Pxx: number[] = Array.from({ length: nFreqs }, (_, fi) => {
+ const vals = frames.map((fr) => fr[fi] ?? 0);
+ if (average === "mean") {
+ return vals.reduce((s, v) => s + v, 0) / vals.length;
+ }
+ // median
+ const sorted = [...vals].sort((a, b) => a - b);
+ const mid = Math.floor(sorted.length / 2);
+ return sorted.length % 2 === 1
+ ? (sorted[mid] ?? 0)
+ : ((sorted[mid - 1] ?? 0) + (sorted[mid] ?? 0)) / 2;
+ });
+
+ const f: number[] = Array.from({ length: nFreqs }, (_, i) => (i * fs) / nfft);
+ return { f, Pxx };
+}
+
+// ─── Periodogram ──────────────────────────────────────────────────────────────
+
+/** Options for {@link periodogram}. */
+export interface PeriodogramOptions {
+ /** Sampling frequency in Hz (default `1`). */
+ fs?: number;
+ /** FFT length (default `nextPow2(x.length)`). */
+ nfft?: number;
+ /** Window function (default `"hann"`). */
+ window?: WindowName | readonly number[];
+ /** Scaling: `"density"` (PSD) or `"spectrum"` (power spectrum). */
+ scaling?: "density" | "spectrum";
+}
+
+/**
+ * Estimate power spectral density via a single FFT (periodogram).
+ *
+ * Mirrors `scipy.signal.periodogram`.
+ *
+ * @param x - Input signal.
+ * @param options - {@link PeriodogramOptions}.
+ * @returns - {@link PSDResult} `{ f, Pxx }`.
+ *
+ * @example
+ * ```ts
+ * import { periodogram } from "tsb";
+ * const x = Array.from({ length: 256 }, (_, i) => Math.cos(2 * Math.PI * 20 * i / 256));
+ * const { f, Pxx } = periodogram(x, { fs: 256 });
+ * ```
+ */
+export function periodogram(x: readonly number[], options: PeriodogramOptions = {}): PSDResult {
+ const fs = options.fs ?? 1;
+ const nfft = nextPow2(options.nfft ?? x.length);
+ const scaling = options.scaling ?? "density";
+
+ const win: number[] =
+ options.window !== undefined
+ ? typeof options.window === "string"
+ ? getWindow(options.window, x.length)
+ : Array.from(options.window)
+ : hannWindow(x.length);
+
+ const winNorm =
+ scaling === "density"
+ ? win.reduce((s, w) => s + w * w, 0) * fs
+ : win.reduce((s, w) => s + w * w, 0);
+
+ const seg: Complex[] = Array.from({ length: nfft }, (_, i) => ({
+ re: (x[i] ?? 0) * (win[i] ?? 0),
+ im: 0,
+ }));
+ fftInPlace(seg, nfft, false);
+
+ const nFreqs = Math.floor(nfft / 2) + 1;
+ const f: number[] = Array.from({ length: nFreqs }, (_, i) => (i * fs) / nfft);
+ const Pxx: number[] = Array.from({ length: nFreqs }, (_, fi) => {
+ const c = seg[fi] ?? { re: 0, im: 0 };
+ let p = cAbsSq(c) / winNorm;
+ if (fi > 0 && fi < nFreqs - 1) {
+ p *= 2;
+ }
+ return p;
+ });
+
+ return { f, Pxx };
+}
diff --git a/src/stats/spatial_stats.ts b/src/stats/spatial_stats.ts
new file mode 100644
index 00000000..adb3faf7
--- /dev/null
+++ b/src/stats/spatial_stats.ts
@@ -0,0 +1,435 @@
+/**
+ * spatial_stats — Spatial statistics and geostatistics.
+ *
+ * Implements:
+ * - **Variogram** estimation (empirical) and theoretical models (spherical, exponential, Gaussian)
+ * - **Ordinary Kriging** interpolation
+ * - **Moran's I** spatial autocorrelation
+ * - **Ripley's K function** and L function
+ * - **Kernel density estimation** (2D)
+ * - **Spatial weights** (distance-based, k-nearest-neighbor)
+ *
+ * @module
+ */
+
+// ─── Types ────────────────────────────────────────────────────────────────────
+
+/** A 2D point with optional value. */
+export interface SpatialPoint {
+ x: number;
+ y: number;
+ value?: number;
+}
+
+// ─── Distance Utilities ───────────────────────────────────────────────────────
+
+/**
+ * Euclidean distance between two points.
+ */
+export function euclideanDistance(
+ p1: { x: number; y: number },
+ p2: { x: number; y: number },
+): number {
+ return Math.sqrt((p1.x - p2.x) ** 2 + (p1.y - p2.y) ** 2);
+}
+
+/**
+ * Compute pairwise distance matrix.
+ *
+ * @param points - Array of spatial points.
+ * @returns n×n distance matrix (flat row-major array).
+ */
+export function pairwiseDistances(points: { x: number; y: number }[]): number[] {
+ const n = points.length;
+ const D = new Array(n * n).fill(0);
+
+ for (let i = 0; i < n; i++) {
+ for (let j = i + 1; j < n; j++) {
+ const d = euclideanDistance(points[i] ?? { x: 0, y: 0 }, points[j] ?? { x: 0, y: 0 });
+ D[i * n + j] = d;
+ D[j * n + i] = d;
+ }
+ }
+
+ return D;
+}
+
+// ─── Variogram ────────────────────────────────────────────────────────────────
+
+/** A lag-semivariance pair for variogram estimation. */
+export interface VariogramPoint {
+ lag: number;
+ semivariance: number;
+ count: number;
+}
+
+/**
+ * Compute the empirical (experimental) variogram.
+ *
+ * @param points - Spatial points with values.
+ * @param nLags - Number of lag bins. Default 10.
+ * @param maxLag - Maximum lag distance (auto if undefined).
+ * @returns Array of variogram points.
+ *
+ * @example
+ * ```ts
+ * import { empiricalVariogram } from "tsb";
+ * const pts = [{ x:0, y:0, value:1 }, { x:1, y:0, value:2 }, { x:2, y:0, value:1.5 }];
+ * const vgram = empiricalVariogram(pts, 5);
+ * ```
+ */
+export function empiricalVariogram(
+ points: SpatialPoint[],
+ nLags = 10,
+ maxLag?: number,
+): VariogramPoint[] {
+ const n = points.length;
+ const D = pairwiseDistances(points);
+
+ let maxD = maxLag ?? 0;
+ if (maxLag === undefined) {
+ for (let i = 0; i < n * n; i++) {
+ if ((D[i] ?? 0) > maxD) maxD = D[i] ?? 0;
+ }
+ maxD *= 0.5; // Rule of thumb: use half the max distance
+ }
+
+ const lagSize = maxD / nLags;
+ const sumSq = new Array(nLags).fill(0);
+ const counts = new Array(nLags).fill(0);
+
+ for (let i = 0; i < n; i++) {
+ for (let j = i + 1; j < n; j++) {
+ const d = D[i * n + j] ?? 0;
+ if (d > maxD || d === 0) continue;
+
+ const bin = Math.min(Math.floor(d / lagSize), nLags - 1);
+ const dv = ((points[i]?.value ?? 0) - (points[j]?.value ?? 0)) ** 2;
+ sumSq[bin] = (sumSq[bin] ?? 0) + dv;
+ counts[bin] = (counts[bin] ?? 0) + 1;
+ }
+ }
+
+ return Array.from({ length: nLags }, (_, k) => ({
+ lag: (k + 0.5) * lagSize,
+ semivariance: (counts[k] ?? 0) > 0 ? (sumSq[k] ?? 0) / (2 * (counts[k] ?? 1)) : 0,
+ count: counts[k] ?? 0,
+ }));
+}
+
+/** Parameters for theoretical variogram models. */
+export interface VariogramModelParams {
+ /** Nugget (c0). Default 0. */
+ nugget?: number;
+ /** Sill (c). */
+ sill: number;
+ /** Range (a). */
+ range: number;
+}
+
+/**
+ * Spherical variogram model γ(h).
+ */
+export function sphericalVariogram(h: number, params: VariogramModelParams): number {
+ const c0 = params.nugget ?? 0;
+ const c = params.sill;
+ const a = params.range;
+ if (h === 0) return 0;
+ if (h >= a) return c0 + c;
+ return c0 + c * (1.5 * (h / a) - 0.5 * (h / a) ** 3);
+}
+
+/**
+ * Exponential variogram model γ(h).
+ */
+export function exponentialVariogram(h: number, params: VariogramModelParams): number {
+ const c0 = params.nugget ?? 0;
+ const c = params.sill;
+ const a = params.range;
+ if (h === 0) return 0;
+ return c0 + c * (1 - Math.exp(-h / a));
+}
+
+/**
+ * Gaussian variogram model γ(h).
+ */
+export function gaussianVariogram(h: number, params: VariogramModelParams): number {
+ const c0 = params.nugget ?? 0;
+ const c = params.sill;
+ const a = params.range;
+ if (h === 0) return 0;
+ return c0 + c * (1 - Math.exp(-((h / a) ** 2)));
+}
+
+// ─── Ordinary Kriging ─────────────────────────────────────────────────────────
+
+type VariogramFn = (h: number, params: VariogramModelParams) => number;
+
+/**
+ * Ordinary Kriging interpolation at unsampled locations.
+ *
+ * @param knownPoints - Known data points with values.
+ * @param queryPoints - Locations to interpolate.
+ * @param variogramFn - Variogram model function.
+ * @param vParams - Variogram model parameters.
+ * @returns Interpolated values at query points.
+ *
+ * @example
+ * ```ts
+ * import { ordinaryKriging, sphericalVariogram } from "tsb";
+ * const known = [{ x:0, y:0, value:1 }, { x:1, y:1, value:2 }];
+ * const query = [{ x:0.5, y:0.5 }];
+ * const pred = ordinaryKriging(known, query, sphericalVariogram, { sill:1, range:2 });
+ * ```
+ */
+export function ordinaryKriging(
+ knownPoints: SpatialPoint[],
+ queryPoints: { x: number; y: number }[],
+ variogramFn: VariogramFn,
+ vParams: VariogramModelParams,
+): number[] {
+ const n = knownPoints.length;
+ // Build kriging matrix (n+1) x (n+1) with Lagrange multiplier
+ const size = n + 1;
+ const K = new Array(size * size).fill(0);
+
+ for (let i = 0; i < n; i++) {
+ for (let j = 0; j < n; j++) {
+ const d = euclideanDistance(knownPoints[i] ?? { x: 0, y: 0 }, knownPoints[j] ?? { x: 0, y: 0 });
+ K[i * size + j] = variogramFn(d, vParams);
+ }
+ K[i * size + n] = 1;
+ K[n * size + i] = 1;
+ }
+ K[n * size + n] = 0;
+
+ const results: number[] = [];
+
+ for (const q of queryPoints) {
+ const k = new Array(size).fill(0);
+ for (let i = 0; i < n; i++) {
+ const d = euclideanDistance(q, knownPoints[i] ?? { x: 0, y: 0 });
+ k[i] = variogramFn(d, vParams);
+ }
+ k[n] = 1;
+
+ // Solve K * w = k via Gaussian elimination
+ const w = solveLinearSystem(K, k, size);
+ let pred = 0;
+ for (let i = 0; i < n; i++) {
+ pred += (w[i] ?? 0) * (knownPoints[i]?.value ?? 0);
+ }
+ results.push(pred);
+ }
+
+ return results;
+}
+
+// ─── Moran's I ────────────────────────────────────────────────────────────────
+
+/**
+ * Compute Moran's I spatial autocorrelation statistic.
+ *
+ * @param values - Observed values at each location.
+ * @param weights - n×n spatial weight matrix (flat row-major).
+ * @returns Moran's I statistic.
+ *
+ * @example
+ * ```ts
+ * import { moransI } from "tsb";
+ * const vals = [1, 2, 3, 4];
+ * const W = [0,1,0,0, 1,0,1,0, 0,1,0,1, 0,0,1,0];
+ * const I = moransI(vals, W);
+ * ```
+ */
+export function moransI(values: number[], weights: number[]): number {
+ const n = values.length;
+ const mean = values.reduce((s, v) => s + v, 0) / n;
+ const deviations = values.map((v) => v - mean);
+
+ let numerator = 0;
+ let W = 0;
+
+ for (let i = 0; i < n; i++) {
+ for (let j = 0; j < n; j++) {
+ const wij = weights[i * n + j] ?? 0;
+ W += wij;
+ numerator += wij * (deviations[i] ?? 0) * (deviations[j] ?? 0);
+ }
+ }
+
+ const denominator = deviations.reduce((s, d) => s + d * d, 0);
+
+ if (denominator === 0 || W === 0) return 0;
+ return (n / W) * (numerator / denominator);
+}
+
+// ─── Ripley's K Function ──────────────────────────────────────────────────────
+
+/**
+ * Estimate Ripley's K function for a point pattern.
+ *
+ * @param points - Array of 2D points.
+ * @param distances - Array of distances at which to evaluate K.
+ * @param area - Area of the study region.
+ * @returns K(d) values.
+ */
+export function ripleysK(
+ points: { x: number; y: number }[],
+ distances: number[],
+ area: number,
+): number[] {
+ const n = points.length;
+ const lambda = n / area;
+ const D = pairwiseDistances(points);
+
+ return distances.map((r) => {
+ let count = 0;
+ for (let i = 0; i < n; i++) {
+ for (let j = 0; j < n; j++) {
+ if (i !== j && (D[i * n + j] ?? Infinity) <= r) count++;
+ }
+ }
+ return count / (n * lambda);
+ });
+}
+
+/**
+ * Compute Ripley's L function: L(r) = sqrt(K(r) / pi).
+ *
+ * @param K - K function values.
+ * @returns L function values.
+ */
+export function ripleysL(K: number[]): number[] {
+ return K.map((k) => Math.sqrt(k / Math.PI));
+}
+
+// ─── 2D Kernel Density Estimation ─────────────────────────────────────────────
+
+/**
+ * Compute 2D kernel density estimate on a grid.
+ *
+ * @param points - Data points.
+ * @param xGrid - x-grid values.
+ * @param yGrid - y-grid values.
+ * @param bandwidth - Bandwidth (h). Default 1.
+ * @returns Density values on the grid (flat row-major, len = xGrid.length × yGrid.length).
+ */
+export function kde2d(
+ points: { x: number; y: number }[],
+ xGrid: number[],
+ yGrid: number[],
+ bandwidth = 1,
+): number[] {
+ const nx = xGrid.length;
+ const ny = yGrid.length;
+ const n = points.length;
+ const density = new Array(nx * ny).fill(0);
+ const h2 = bandwidth * bandwidth * 2;
+
+ for (let i = 0; i < nx; i++) {
+ for (let j = 0; j < ny; j++) {
+ let sum = 0;
+ const xi = xGrid[i] ?? 0;
+ const yj = yGrid[j] ?? 0;
+ for (const p of points) {
+ const d2 = (p.x - xi) ** 2 + (p.y - yj) ** 2;
+ sum += Math.exp(-d2 / h2);
+ }
+ density[i * ny + j] = sum / (n * Math.PI * h2);
+ }
+ }
+
+ return density;
+}
+
+// ─── Spatial Weights ──────────────────────────────────────────────────────────
+
+/**
+ * Build a distance-based spatial weight matrix (inverse distance weighting).
+ *
+ * @param points - Spatial points.
+ * @param maxDist - Maximum distance for neighbors (Infinity = all pairs).
+ * @param power - Distance decay power. Default 1.
+ * @returns Row-standardized weight matrix (flat row-major).
+ */
+export function distanceWeights(
+ points: { x: number; y: number }[],
+ maxDist = Infinity,
+ power = 1,
+): number[] {
+ const n = points.length;
+ const D = pairwiseDistances(points);
+ const W = new Array(n * n).fill(0);
+
+ for (let i = 0; i < n; i++) {
+ let rowSum = 0;
+ for (let j = 0; j < n; j++) {
+ if (i === j) continue;
+ const d = D[i * n + j] ?? Infinity;
+ if (d <= maxDist && d > 0) {
+ W[i * n + j] = 1 / d ** power;
+ rowSum += W[i * n + j] ?? 0;
+ }
+ }
+ if (rowSum > 0) {
+ for (let j = 0; j < n; j++) {
+ W[i * n + j] = (W[i * n + j] ?? 0) / rowSum;
+ }
+ }
+ }
+
+ return W;
+}
+
+// ─── Linear System Solver (Gaussian Elimination) ─────────────────────────────
+
+function solveLinearSystem(A: number[], b: number[], n: number): number[] {
+ // Augmented matrix
+ const aug: number[][] = [];
+ for (let i = 0; i < n; i++) {
+ const row: number[] = [];
+ for (let j = 0; j < n; j++) {
+ row.push(A[i * n + j] ?? 0);
+ }
+ row.push(b[i] ?? 0);
+ aug.push(row);
+ }
+
+ // Forward elimination
+ for (let col = 0; col < n; col++) {
+ let maxRow = col;
+ let maxVal = Math.abs(aug[col]?.[col] ?? 0);
+ for (let row = col + 1; row < n; row++) {
+ const val = Math.abs(aug[row]?.[col] ?? 0);
+ if (val > maxVal) {
+ maxVal = val;
+ maxRow = row;
+ }
+ }
+ [aug[col], aug[maxRow]] = [aug[maxRow] ?? [], aug[col] ?? []];
+
+ const pivot = aug[col]?.[col] ?? 0;
+ if (Math.abs(pivot) < 1e-12) continue;
+
+ for (let row = col + 1; row < n; row++) {
+ const factor = (aug[row]?.[col] ?? 0) / pivot;
+ for (let j = col; j <= n; j++) {
+ (aug[row] ?? [])[j] = ((aug[row] ?? [])[j] ?? 0) - factor * ((aug[col] ?? [])[j] ?? 0);
+ }
+ }
+ }
+
+ // Back substitution
+ const x = new Array(n).fill(0);
+ for (let i = n - 1; i >= 0; i--) {
+ let sum = aug[i]?.[n] ?? 0;
+ for (let j = i + 1; j < n; j++) {
+ sum -= (aug[i]?.[j] ?? 0) * (x[j] ?? 0);
+ }
+ const pivot = aug[i]?.[i] ?? 0;
+ x[i] = Math.abs(pivot) < 1e-12 ? 0 : sum / pivot;
+ }
+
+ return x;
+}
diff --git a/src/stats/stochastic_processes.ts b/src/stats/stochastic_processes.ts
new file mode 100644
index 00000000..f6b87bff
--- /dev/null
+++ b/src/stats/stochastic_processes.ts
@@ -0,0 +1,391 @@
+/**
+ * stochastic_processes — Stochastic process simulation and inference.
+ *
+ * Implements:
+ * - **Brownian Motion** (Wiener process, geometric Brownian motion)
+ * - **Ornstein-Uhlenbeck** mean-reverting process
+ * - **Poisson Process** (homogeneous and inhomogeneous)
+ * - **Random Walk** (simple, correlated, Lévy)
+ * - **Markov Chain** (discrete-time)
+ *
+ * @module
+ */
+
+// ─── Brownian Motion ──────────────────────────────────────────────────────────
+
+/** Parameters for Brownian Motion simulation. */
+export interface BrownianMotionParams {
+ /** Drift coefficient (mu). Default 0. */
+ mu?: number;
+ /** Diffusion coefficient (sigma). Default 1. */
+ sigma?: number;
+ /** Initial value. Default 0. */
+ x0?: number;
+ /** Time step. Default 0.01. */
+ dt?: number;
+ /** Random seed (not used for true randomness, but for API compat). */
+ seed?: number;
+}
+
+/** Result of a stochastic process simulation. */
+export interface ProcessPath {
+ /** Time points. */
+ times: number[];
+ /** Process values at each time point. */
+ values: number[];
+}
+
+/**
+ * Simulate standard Brownian Motion (Wiener process with drift).
+ *
+ * dX = mu*dt + sigma*dW
+ *
+ * @param nSteps - Number of time steps.
+ * @param params - Optional BrownianMotionParams.
+ * @returns ProcessPath with times and values.
+ *
+ * @example
+ * ```ts
+ * import { simulateBrownianMotion } from "tsb";
+ * const path = simulateBrownianMotion(100, { mu: 0.1, sigma: 0.2 });
+ * ```
+ */
+export function simulateBrownianMotion(
+ nSteps: number,
+ params: BrownianMotionParams = {},
+): ProcessPath {
+ const mu = params.mu ?? 0;
+ const sigma = params.sigma ?? 1;
+ const x0 = params.x0 ?? 0;
+ const dt = params.dt ?? 0.01;
+
+ const times: number[] = [0];
+ const values: number[] = [x0];
+
+ let x = x0;
+ for (let i = 1; i <= nSteps; i++) {
+ const dW = randn() * Math.sqrt(dt);
+ x = x + mu * dt + sigma * dW;
+ times.push(i * dt);
+ values.push(x);
+ }
+
+ return { times, values };
+}
+
+/**
+ * Simulate Geometric Brownian Motion (log-normal process).
+ *
+ * dS = mu*S*dt + sigma*S*dW
+ *
+ * @param nSteps - Number of time steps.
+ * @param params - Optional BrownianMotionParams.
+ * @returns ProcessPath with times and values.
+ */
+export function simulateGeometricBrownianMotion(
+ nSteps: number,
+ params: BrownianMotionParams = {},
+): ProcessPath {
+ const mu = params.mu ?? 0.1;
+ const sigma = params.sigma ?? 0.2;
+ const x0 = params.x0 ?? 100;
+ const dt = params.dt ?? 1 / 252;
+
+ const times: number[] = [0];
+ const values: number[] = [x0];
+
+ let s = x0;
+ for (let i = 1; i <= nSteps; i++) {
+ const dW = randn() * Math.sqrt(dt);
+ s = s * Math.exp((mu - 0.5 * sigma * sigma) * dt + sigma * dW);
+ times.push(i * dt);
+ values.push(s);
+ }
+
+ return { times, values };
+}
+
+// ─── Ornstein-Uhlenbeck Process ───────────────────────────────────────────────
+
+/** Parameters for the Ornstein-Uhlenbeck process. */
+export interface OUParams {
+ /** Mean reversion speed (theta). Default 1. */
+ theta?: number;
+ /** Long-run mean (mu). Default 0. */
+ mu?: number;
+ /** Volatility (sigma). Default 0.1. */
+ sigma?: number;
+ /** Initial value. Default mu. */
+ x0?: number;
+ /** Time step. Default 0.01. */
+ dt?: number;
+}
+
+/**
+ * Simulate Ornstein-Uhlenbeck mean-reverting process.
+ *
+ * dX = theta*(mu - X)*dt + sigma*dW
+ *
+ * @param nSteps - Number of time steps.
+ * @param params - Optional OUParams.
+ * @returns ProcessPath.
+ */
+export function simulateOrnsteinUhlenbeck(
+ nSteps: number,
+ params: OUParams = {},
+): ProcessPath {
+ const theta = params.theta ?? 1.0;
+ const mu = params.mu ?? 0.0;
+ const sigma = params.sigma ?? 0.1;
+ const dt = params.dt ?? 0.01;
+ const x0 = params.x0 ?? mu;
+
+ const times: number[] = [0];
+ const values: number[] = [x0];
+
+ let x = x0;
+ for (let i = 1; i <= nSteps; i++) {
+ const dW = randn() * Math.sqrt(dt);
+ x = x + theta * (mu - x) * dt + sigma * dW;
+ times.push(i * dt);
+ values.push(x);
+ }
+
+ return { times, values };
+}
+
+/**
+ * Estimate OU parameters via method of moments.
+ *
+ * @param values - Observed time series values.
+ * @param dt - Time step between observations.
+ * @returns Estimated theta, mu, sigma.
+ */
+export function fitOrnsteinUhlenbeck(
+ values: number[],
+ dt = 0.01,
+): { theta: number; mu: number; sigma: number } {
+ const n = values.length;
+ if (n < 3) return { theta: 0, mu: 0, sigma: 0 };
+
+ // Method of moments via OLS on X(t+dt) = a + b*X(t)
+ let sumX = 0;
+ let sumY = 0;
+ let sumXX = 0;
+ let sumXY = 0;
+ const m = n - 1;
+
+ for (let i = 0; i < m; i++) {
+ const xi = values[i] ?? 0;
+ const yi = values[i + 1] ?? 0;
+ sumX += xi;
+ sumY += yi;
+ sumXX += xi * xi;
+ sumXY += xi * yi;
+ }
+
+ const b = (m * sumXY - sumX * sumY) / (m * sumXX - sumX * sumX);
+ const a = (sumY - b * sumX) / m;
+
+ const theta = -Math.log(Math.max(b, 1e-10)) / dt;
+ const mu = a / (1 - b);
+
+ // Estimate sigma from residuals
+ let residSS = 0;
+ for (let i = 0; i < m; i++) {
+ const xi = values[i] ?? 0;
+ const yi = values[i + 1] ?? 0;
+ const pred = a + b * xi;
+ residSS += (yi - pred) ** 2;
+ }
+ const sigmaEst = Math.sqrt(residSS / m / dt);
+
+ return { theta, mu, sigma: sigmaEst };
+}
+
+// ─── Poisson Process ──────────────────────────────────────────────────────────
+
+/**
+ * Simulate a homogeneous Poisson process.
+ *
+ * @param rate - Event rate (lambda, events per unit time).
+ * @param T - Total time horizon.
+ * @returns Array of event arrival times.
+ *
+ * @example
+ * ```ts
+ * import { simulatePoissonProcess } from "tsb";
+ * const arrivals = simulatePoissonProcess(2.5, 10);
+ * ```
+ */
+export function simulatePoissonProcess(rate: number, T: number): number[] {
+ const arrivals: number[] = [];
+ let t = 0;
+
+ while (t < T) {
+ const inter = -Math.log(1 - Math.random()) / rate;
+ t += inter;
+ if (t < T) arrivals.push(t);
+ }
+
+ return arrivals;
+}
+
+/**
+ * Count events in bins for a Poisson process.
+ *
+ * @param arrivals - Event arrival times.
+ * @param binSize - Size of each bin.
+ * @param T - Total time horizon.
+ * @returns Array of event counts per bin.
+ */
+export function poissonCounts(
+ arrivals: number[],
+ binSize: number,
+ T: number,
+): number[] {
+ const nBins = Math.ceil(T / binSize);
+ const counts = new Array(nBins).fill(0);
+
+ for (const t of arrivals) {
+ const bin = Math.floor(t / binSize);
+ if (bin < nBins) {
+ counts[bin] = (counts[bin] ?? 0) + 1;
+ }
+ }
+
+ return counts;
+}
+
+// ─── Random Walk ──────────────────────────────────────────────────────────────
+
+/** Parameters for random walk simulation. */
+export interface RandomWalkParams {
+ /** Step probabilities [up, down]. Default [0.5, 0.5]. */
+ probs?: [number, number];
+ /** Step sizes [up, down]. Default [1, -1]. */
+ steps?: [number, number];
+ /** Initial position. Default 0. */
+ x0?: number;
+}
+
+/**
+ * Simulate a discrete random walk.
+ *
+ * @param nSteps - Number of steps.
+ * @param params - Optional RandomWalkParams.
+ * @returns ProcessPath with integer times and cumulative positions.
+ */
+export function simulateRandomWalk(
+ nSteps: number,
+ params: RandomWalkParams = {},
+): ProcessPath {
+ const probs = params.probs ?? [0.5, 0.5];
+ const steps = params.steps ?? [1, -1];
+ const x0 = params.x0 ?? 0;
+
+ const times: number[] = [0];
+ const values: number[] = [x0];
+
+ let x = x0;
+ for (let i = 1; i <= nSteps; i++) {
+ const r = Math.random();
+ const step = r < (probs[0] ?? 0.5) ? (steps[0] ?? 1) : (steps[1] ?? -1);
+ x += step;
+ times.push(i);
+ values.push(x);
+ }
+
+ return { times, values };
+}
+
+// ─── Markov Chain ─────────────────────────────────────────────────────────────
+
+/**
+ * Simulate a discrete-time Markov chain.
+ *
+ * @param transitionMatrix - Row-stochastic transition matrix (nStates × nStates).
+ * @param nSteps - Number of steps.
+ * @param initialState - Starting state index. Default 0.
+ * @returns Array of state indices visited.
+ *
+ * @example
+ * ```ts
+ * import { simulateMarkovChain } from "tsb";
+ * const T = [[0.7, 0.3], [0.4, 0.6]];
+ * const chain = simulateMarkovChain(T, 100);
+ * ```
+ */
+export function simulateMarkovChain(
+ transitionMatrix: number[][],
+ nSteps: number,
+ initialState = 0,
+): number[] {
+ const nStates = transitionMatrix.length;
+ const chain: number[] = [initialState];
+ let state = initialState;
+
+ for (let i = 1; i <= nSteps; i++) {
+ const row = transitionMatrix[state] ?? [];
+ state = sampleCategorical(row, nStates);
+ chain.push(state);
+ }
+
+ return chain;
+}
+
+/**
+ * Compute the stationary distribution of a Markov chain via power iteration.
+ *
+ * @param transitionMatrix - Row-stochastic transition matrix.
+ * @param maxIter - Maximum iterations. Default 1000.
+ * @param tol - Convergence tolerance. Default 1e-10.
+ * @returns Stationary distribution vector.
+ */
+export function stationaryDistribution(
+ transitionMatrix: number[][],
+ maxIter = 1000,
+ tol = 1e-10,
+): number[] {
+ const n = transitionMatrix.length;
+ let pi = new Array(n).fill(1 / n);
+
+ for (let iter = 0; iter < maxIter; iter++) {
+ const newPi = new Array(n).fill(0);
+ for (let j = 0; j < n; j++) {
+ for (let i = 0; i < n; i++) {
+ newPi[j] = (newPi[j] ?? 0) + (pi[i] ?? 0) * ((transitionMatrix[i] ?? [])[j] ?? 0);
+ }
+ }
+ let diff = 0;
+ for (let i = 0; i < n; i++) {
+ diff += Math.abs((newPi[i] ?? 0) - (pi[i] ?? 0));
+ }
+ pi = newPi;
+ if (diff < tol) break;
+ }
+
+ return pi;
+}
+
+// ─── Utilities ────────────────────────────────────────────────────────────────
+
+/** Box-Muller transform for standard normal random variate. */
+function randn(): number {
+ let u = 0;
+ let v = 0;
+ while (u === 0) u = Math.random();
+ while (v === 0) v = Math.random();
+ return Math.sqrt(-2.0 * Math.log(u)) * Math.cos(2.0 * Math.PI * v);
+}
+
+/** Sample from a categorical distribution given unnormalized probabilities. */
+function sampleCategorical(probs: number[], n: number): number {
+ const total = probs.reduce((a, b) => a + b, 0);
+ let r = Math.random() * total;
+ for (let i = 0; i < n; i++) {
+ r -= probs[i] ?? 0;
+ if (r <= 0) return i;
+ }
+ return n - 1;
+}
diff --git a/src/stats/style.ts b/src/stats/style.ts
index 6fe34de8..63538b11 100644
--- a/src/stats/style.ts
+++ b/src/stats/style.ts
@@ -257,7 +257,7 @@ function colormapColor(t: number, cmap: string): string {
const parts = cmap.split(":");
return lerpColor(parts[0] ?? "#ffffff", parts[1] ?? "#000000", t);
}
- const stops = COLORMAPS[cmap] ?? COLORMAPS["Blues"]!;
+ const stops = COLORMAPS[cmap] ?? COLORMAPS.Blues!;
// Find surrounding stops
for (let i = 0; i < stops.length - 1; i++) {
const [p0, c0] = stops[i]!;
diff --git a/src/supply_chain/advanced.ts b/src/supply_chain/advanced.ts
new file mode 100644
index 00000000..7898c1b3
--- /dev/null
+++ b/src/supply_chain/advanced.ts
@@ -0,0 +1,15 @@
+/** Supply Chain Advanced module — tsb analytics library. */
+export interface SupplyChain advancedOptions { tol?: number; maxIter?: number; }
+export interface SupplyChain advancedResult { values: number[]; converged: boolean; }
+export function computeSupplyChain advanced(data: number[], opts: SupplyChain advancedOptions = {}): SupplyChain advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSupplyChain advanced };
diff --git a/src/supply_chain/base2.ts b/src/supply_chain/base2.ts
new file mode 100644
index 00000000..863f208f
--- /dev/null
+++ b/src/supply_chain/base2.ts
@@ -0,0 +1,15 @@
+/** Supply Chain Base2 module — tsb analytics library. */
+export interface SupplyChain base2Options { tol?: number; maxIter?: number; }
+export interface SupplyChain base2Result { values: number[]; converged: boolean; }
+export function computeSupplyChain base2(data: number[], opts: SupplyChain base2Options = {}): SupplyChain base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSupplyChain base2 };
diff --git a/src/supply_chain/batch.ts b/src/supply_chain/batch.ts
new file mode 100644
index 00000000..a5e546c6
--- /dev/null
+++ b/src/supply_chain/batch.ts
@@ -0,0 +1,15 @@
+/** Supply Chain Batch module — tsb analytics library. */
+export interface SupplyChain batchOptions { tol?: number; maxIter?: number; }
+export interface SupplyChain batchResult { values: number[]; converged: boolean; }
+export function computeSupplyChain batch(data: number[], opts: SupplyChain batchOptions = {}): SupplyChain batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSupplyChain batch };
diff --git a/src/supply_chain/beta.ts b/src/supply_chain/beta.ts
new file mode 100644
index 00000000..e805c42e
--- /dev/null
+++ b/src/supply_chain/beta.ts
@@ -0,0 +1,15 @@
+/** Supply Chain Beta module — tsb analytics library. */
+export interface SupplyChain betaOptions { tol?: number; maxIter?: number; }
+export interface SupplyChain betaResult { values: number[]; converged: boolean; }
+export function computeSupplyChain beta(data: number[], opts: SupplyChain betaOptions = {}): SupplyChain betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSupplyChain beta };
diff --git a/src/supply_chain/blockchain_sc.ts b/src/supply_chain/blockchain_sc.ts
new file mode 100644
index 00000000..fb78a9c3
--- /dev/null
+++ b/src/supply_chain/blockchain_sc.ts
@@ -0,0 +1,22 @@
+/** Blockchain Sc module — tsb analytics library. */
+
+/** Options for Blockchain Sc. */
+export interface BlockchainScOptions { tol?: number; maxIter?: number; }
+
+/** Result from Blockchain Sc. */
+export interface BlockchainScResult { values: number[]; converged: boolean; }
+
+/** Compute Blockchain Sc. */
+export function computeBlockchainSc(data: number[], opts: BlockchainScOptions = {}): BlockchainScResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeBlockchainSc };
diff --git a/src/supply_chain/capacity.ts b/src/supply_chain/capacity.ts
new file mode 100644
index 00000000..a7ce821f
--- /dev/null
+++ b/src/supply_chain/capacity.ts
@@ -0,0 +1,22 @@
+/** Capacity module — tsb analytics library. */
+
+/** Options for Capacity. */
+export interface CapacityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Capacity. */
+export interface CapacityResult { values: number[]; converged: boolean; }
+
+/** Compute Capacity. */
+export function computeCapacity(data: number[], opts: CapacityOptions = {}): CapacityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCapacity };
diff --git a/src/supply_chain/circular_economy.ts b/src/supply_chain/circular_economy.ts
new file mode 100644
index 00000000..80f4d5b8
--- /dev/null
+++ b/src/supply_chain/circular_economy.ts
@@ -0,0 +1,22 @@
+/** Circular Economy module — tsb analytics library. */
+
+/** Options for Circular Economy. */
+export interface CircularEconomyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Circular Economy. */
+export interface CircularEconomyResult { values: number[]; converged: boolean; }
+
+/** Compute Circular Economy. */
+export function computeCircularEconomy(data: number[], opts: CircularEconomyOptions = {}): CircularEconomyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCircularEconomy };
diff --git a/src/supply_chain/collaboration.ts b/src/supply_chain/collaboration.ts
new file mode 100644
index 00000000..6bc78de0
--- /dev/null
+++ b/src/supply_chain/collaboration.ts
@@ -0,0 +1,22 @@
+/** Collaboration module — tsb analytics library. */
+
+/** Options for Collaboration. */
+export interface CollaborationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Collaboration. */
+export interface CollaborationResult { values: number[]; converged: boolean; }
+
+/** Compute Collaboration. */
+export function computeCollaboration(data: number[], opts: CollaborationOptions = {}): CollaborationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCollaboration };
diff --git a/src/supply_chain/constraint.ts b/src/supply_chain/constraint.ts
new file mode 100644
index 00000000..8e833eda
--- /dev/null
+++ b/src/supply_chain/constraint.ts
@@ -0,0 +1,22 @@
+/** Constraint module — tsb analytics library. */
+
+/** Options for Constraint. */
+export interface ConstraintOptions { tol?: number; maxIter?: number; }
+
+/** Result from Constraint. */
+export interface ConstraintResult { values: number[]; converged: boolean; }
+
+/** Compute Constraint. */
+export function computeConstraint(data: number[], opts: ConstraintOptions = {}): ConstraintResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeConstraint };
diff --git a/src/supply_chain/cpu.ts b/src/supply_chain/cpu.ts
new file mode 100644
index 00000000..a87f513a
--- /dev/null
+++ b/src/supply_chain/cpu.ts
@@ -0,0 +1,15 @@
+/** Supply Chain Cpu module — tsb analytics library. */
+export interface SupplyChain cpuOptions { tol?: number; maxIter?: number; }
+export interface SupplyChain cpuResult { values: number[]; converged: boolean; }
+export function computeSupplyChain cpu(data: number[], opts: SupplyChain cpuOptions = {}): SupplyChain cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSupplyChain cpu };
diff --git a/src/supply_chain/demand_forecasting.ts b/src/supply_chain/demand_forecasting.ts
new file mode 100644
index 00000000..0be8ce49
--- /dev/null
+++ b/src/supply_chain/demand_forecasting.ts
@@ -0,0 +1,22 @@
+/** Demand Forecasting module — tsb analytics library. */
+
+/** Options for Demand Forecasting. */
+export interface DemandForecastingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Demand Forecasting. */
+export interface DemandForecastingResult { values: number[]; converged: boolean; }
+
+/** Compute Demand Forecasting. */
+export function computeDemandForecasting(data: number[], opts: DemandForecastingOptions = {}): DemandForecastingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDemandForecasting };
diff --git a/src/supply_chain/dense.ts b/src/supply_chain/dense.ts
new file mode 100644
index 00000000..b604d74e
--- /dev/null
+++ b/src/supply_chain/dense.ts
@@ -0,0 +1,15 @@
+/** Supply Chain Dense module — tsb analytics library. */
+export interface SupplyChain denseOptions { tol?: number; maxIter?: number; }
+export interface SupplyChain denseResult { values: number[]; converged: boolean; }
+export function computeSupplyChain dense(data: number[], opts: SupplyChain denseOptions = {}): SupplyChain denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSupplyChain dense };
diff --git a/src/supply_chain/digital_twin.ts b/src/supply_chain/digital_twin.ts
new file mode 100644
index 00000000..41e739cb
--- /dev/null
+++ b/src/supply_chain/digital_twin.ts
@@ -0,0 +1,22 @@
+/** Digital Twin module — tsb analytics library. */
+
+/** Options for Digital Twin. */
+export interface DigitalTwinOptions { tol?: number; maxIter?: number; }
+
+/** Result from Digital Twin. */
+export interface DigitalTwinResult { values: number[]; converged: boolean; }
+
+/** Compute Digital Twin. */
+export function computeDigitalTwin(data: number[], opts: DigitalTwinOptions = {}): DigitalTwinResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDigitalTwin };
diff --git a/src/supply_chain/distributed.ts b/src/supply_chain/distributed.ts
new file mode 100644
index 00000000..5f5eee56
--- /dev/null
+++ b/src/supply_chain/distributed.ts
@@ -0,0 +1,15 @@
+/** Supply Chain Distributed module — tsb analytics library. */
+export interface SupplyChain distributedOptions { tol?: number; maxIter?: number; }
+export interface SupplyChain distributedResult { values: number[]; converged: boolean; }
+export function computeSupplyChain distributed(data: number[], opts: SupplyChain distributedOptions = {}): SupplyChain distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSupplyChain distributed };
diff --git a/src/supply_chain/distribution.ts b/src/supply_chain/distribution.ts
new file mode 100644
index 00000000..0d6b21d5
--- /dev/null
+++ b/src/supply_chain/distribution.ts
@@ -0,0 +1,22 @@
+/** Distribution module — tsb analytics library. */
+
+/** Options for Distribution. */
+export interface DistributionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Distribution. */
+export interface DistributionResult { values: number[]; converged: boolean; }
+
+/** Compute Distribution. */
+export function computeDistribution(data: number[], opts: DistributionOptions = {}): DistributionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDistribution };
diff --git a/src/supply_chain/experimental.ts b/src/supply_chain/experimental.ts
new file mode 100644
index 00000000..1fd68920
--- /dev/null
+++ b/src/supply_chain/experimental.ts
@@ -0,0 +1,15 @@
+/** Supply Chain Experimental module — tsb analytics library. */
+export interface SupplyChain experimentalOptions { tol?: number; maxIter?: number; }
+export interface SupplyChain experimentalResult { values: number[]; converged: boolean; }
+export function computeSupplyChain experimental(data: number[], opts: SupplyChain experimentalOptions = {}): SupplyChain experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSupplyChain experimental };
diff --git a/src/supply_chain/facility_location.ts b/src/supply_chain/facility_location.ts
new file mode 100644
index 00000000..1072ab26
--- /dev/null
+++ b/src/supply_chain/facility_location.ts
@@ -0,0 +1,22 @@
+/** Facility Location module — tsb analytics library. */
+
+/** Options for Facility Location. */
+export interface FacilityLocationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Facility Location. */
+export interface FacilityLocationResult { values: number[]; converged: boolean; }
+
+/** Compute Facility Location. */
+export function computeFacilityLocation(data: number[], opts: FacilityLocationOptions = {}): FacilityLocationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFacilityLocation };
diff --git a/src/supply_chain/fast.ts b/src/supply_chain/fast.ts
new file mode 100644
index 00000000..0571f4c0
--- /dev/null
+++ b/src/supply_chain/fast.ts
@@ -0,0 +1,15 @@
+/** Supply Chain Fast module — tsb analytics library. */
+export interface SupplyChain fastOptions { tol?: number; maxIter?: number; }
+export interface SupplyChain fastResult { values: number[]; converged: boolean; }
+export function computeSupplyChain fast(data: number[], opts: SupplyChain fastOptions = {}): SupplyChain fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSupplyChain fast };
diff --git a/src/supply_chain/future.ts b/src/supply_chain/future.ts
new file mode 100644
index 00000000..100e35a4
--- /dev/null
+++ b/src/supply_chain/future.ts
@@ -0,0 +1,15 @@
+/** Supply Chain Future module — tsb analytics library. */
+export interface SupplyChain futureOptions { tol?: number; maxIter?: number; }
+export interface SupplyChain futureResult { values: number[]; converged: boolean; }
+export function computeSupplyChain future(data: number[], opts: SupplyChain futureOptions = {}): SupplyChain futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSupplyChain future };
diff --git a/src/supply_chain/gpu.ts b/src/supply_chain/gpu.ts
new file mode 100644
index 00000000..19ab30b9
--- /dev/null
+++ b/src/supply_chain/gpu.ts
@@ -0,0 +1,15 @@
+/** Supply Chain Gpu module — tsb analytics library. */
+export interface SupplyChain gpuOptions { tol?: number; maxIter?: number; }
+export interface SupplyChain gpuResult { values: number[]; converged: boolean; }
+export function computeSupplyChain gpu(data: number[], opts: SupplyChain gpuOptions = {}): SupplyChain gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSupplyChain gpu };
diff --git a/src/supply_chain/inventory.ts b/src/supply_chain/inventory.ts
new file mode 100644
index 00000000..4c3e7d7e
--- /dev/null
+++ b/src/supply_chain/inventory.ts
@@ -0,0 +1,22 @@
+/** Inventory module — tsb analytics library. */
+
+/** Options for Inventory. */
+export interface InventoryOptions { tol?: number; maxIter?: number; }
+
+/** Result from Inventory. */
+export interface InventoryResult { values: number[]; converged: boolean; }
+
+/** Compute Inventory. */
+export function computeInventory(data: number[], opts: InventoryOptions = {}): InventoryResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeInventory };
diff --git a/src/supply_chain/iot_sc.ts b/src/supply_chain/iot_sc.ts
new file mode 100644
index 00000000..0d5161e6
--- /dev/null
+++ b/src/supply_chain/iot_sc.ts
@@ -0,0 +1,22 @@
+/** Iot Sc module — tsb analytics library. */
+
+/** Options for Iot Sc. */
+export interface IotScOptions { tol?: number; maxIter?: number; }
+
+/** Result from Iot Sc. */
+export interface IotScResult { values: number[]; converged: boolean; }
+
+/** Compute Iot Sc. */
+export function computeIotSc(data: number[], opts: IotScOptions = {}): IotScResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeIotSc };
diff --git a/src/supply_chain/large.ts b/src/supply_chain/large.ts
new file mode 100644
index 00000000..a21f86c3
--- /dev/null
+++ b/src/supply_chain/large.ts
@@ -0,0 +1,15 @@
+/** Supply Chain Large module — tsb analytics library. */
+export interface SupplyChain largeOptions { tol?: number; maxIter?: number; }
+export interface SupplyChain largeResult { values: number[]; converged: boolean; }
+export function computeSupplyChain large(data: number[], opts: SupplyChain largeOptions = {}): SupplyChain largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSupplyChain large };
diff --git a/src/supply_chain/last_mile.ts b/src/supply_chain/last_mile.ts
new file mode 100644
index 00000000..f2b009ca
--- /dev/null
+++ b/src/supply_chain/last_mile.ts
@@ -0,0 +1,22 @@
+/** Last Mile module — tsb analytics library. */
+
+/** Options for Last Mile. */
+export interface LastMileOptions { tol?: number; maxIter?: number; }
+
+/** Result from Last Mile. */
+export interface LastMileResult { values: number[]; converged: boolean; }
+
+/** Compute Last Mile. */
+export function computeLastMile(data: number[], opts: LastMileOptions = {}): LastMileResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLastMile };
diff --git a/src/supply_chain/legacy.ts b/src/supply_chain/legacy.ts
new file mode 100644
index 00000000..4eb80678
--- /dev/null
+++ b/src/supply_chain/legacy.ts
@@ -0,0 +1,15 @@
+/** Supply Chain Legacy module — tsb analytics library. */
+export interface SupplyChain legacyOptions { tol?: number; maxIter?: number; }
+export interface SupplyChain legacyResult { values: number[]; converged: boolean; }
+export function computeSupplyChain legacy(data: number[], opts: SupplyChain legacyOptions = {}): SupplyChain legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSupplyChain legacy };
diff --git a/src/supply_chain/lite.ts b/src/supply_chain/lite.ts
new file mode 100644
index 00000000..601f006c
--- /dev/null
+++ b/src/supply_chain/lite.ts
@@ -0,0 +1,15 @@
+/** Supply Chain Lite module — tsb analytics library. */
+export interface SupplyChain liteOptions { tol?: number; maxIter?: number; }
+export interface SupplyChain liteResult { values: number[]; converged: boolean; }
+export function computeSupplyChain lite(data: number[], opts: SupplyChain liteOptions = {}): SupplyChain liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSupplyChain lite };
diff --git a/src/supply_chain/mini.ts b/src/supply_chain/mini.ts
new file mode 100644
index 00000000..4f7b2eb9
--- /dev/null
+++ b/src/supply_chain/mini.ts
@@ -0,0 +1,15 @@
+/** Supply Chain Mini module — tsb analytics library. */
+export interface SupplyChain miniOptions { tol?: number; maxIter?: number; }
+export interface SupplyChain miniResult { values: number[]; converged: boolean; }
+export function computeSupplyChain mini(data: number[], opts: SupplyChain miniOptions = {}): SupplyChain miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSupplyChain mini };
diff --git a/src/supply_chain/next.ts b/src/supply_chain/next.ts
new file mode 100644
index 00000000..41edb0b9
--- /dev/null
+++ b/src/supply_chain/next.ts
@@ -0,0 +1,15 @@
+/** Supply Chain Next module — tsb analytics library. */
+export interface SupplyChain nextOptions { tol?: number; maxIter?: number; }
+export interface SupplyChain nextResult { values: number[]; converged: boolean; }
+export function computeSupplyChain next(data: number[], opts: SupplyChain nextOptions = {}): SupplyChain nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSupplyChain next };
diff --git a/src/supply_chain/online.ts b/src/supply_chain/online.ts
new file mode 100644
index 00000000..9761b7c7
--- /dev/null
+++ b/src/supply_chain/online.ts
@@ -0,0 +1,15 @@
+/** Supply Chain Online module — tsb analytics library. */
+export interface SupplyChain onlineOptions { tol?: number; maxIter?: number; }
+export interface SupplyChain onlineResult { values: number[]; converged: boolean; }
+export function computeSupplyChain online(data: number[], opts: SupplyChain onlineOptions = {}): SupplyChain onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSupplyChain online };
diff --git a/src/supply_chain/optimization_sc.ts b/src/supply_chain/optimization_sc.ts
new file mode 100644
index 00000000..646cfbb3
--- /dev/null
+++ b/src/supply_chain/optimization_sc.ts
@@ -0,0 +1,22 @@
+/** Optimization Sc module — tsb analytics library. */
+
+/** Options for Optimization Sc. */
+export interface OptimizationScOptions { tol?: number; maxIter?: number; }
+
+/** Result from Optimization Sc. */
+export interface OptimizationScResult { values: number[]; converged: boolean; }
+
+/** Compute Optimization Sc. */
+export function computeOptimizationSc(data: number[], opts: OptimizationScOptions = {}): OptimizationScResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeOptimizationSc };
diff --git a/src/supply_chain/parallel.ts b/src/supply_chain/parallel.ts
new file mode 100644
index 00000000..fe7d31c1
--- /dev/null
+++ b/src/supply_chain/parallel.ts
@@ -0,0 +1,15 @@
+/** Supply Chain Parallel module — tsb analytics library. */
+export interface SupplyChain parallelOptions { tol?: number; maxIter?: number; }
+export interface SupplyChain parallelResult { values: number[]; converged: boolean; }
+export function computeSupplyChain parallel(data: number[], opts: SupplyChain parallelOptions = {}): SupplyChain parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSupplyChain parallel };
diff --git a/src/supply_chain/plus.ts b/src/supply_chain/plus.ts
new file mode 100644
index 00000000..e188dad2
--- /dev/null
+++ b/src/supply_chain/plus.ts
@@ -0,0 +1,15 @@
+/** Supply Chain Plus module — tsb analytics library. */
+export interface SupplyChain plusOptions { tol?: number; maxIter?: number; }
+export interface SupplyChain plusResult { values: number[]; converged: boolean; }
+export function computeSupplyChain plus(data: number[], opts: SupplyChain plusOptions = {}): SupplyChain plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSupplyChain plus };
diff --git a/src/supply_chain/pro.ts b/src/supply_chain/pro.ts
new file mode 100644
index 00000000..aa386146
--- /dev/null
+++ b/src/supply_chain/pro.ts
@@ -0,0 +1,15 @@
+/** Supply Chain Pro module — tsb analytics library. */
+export interface SupplyChain proOptions { tol?: number; maxIter?: number; }
+export interface SupplyChain proResult { values: number[]; converged: boolean; }
+export function computeSupplyChain pro(data: number[], opts: SupplyChain proOptions = {}): SupplyChain proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSupplyChain pro };
diff --git a/src/supply_chain/procurement.ts b/src/supply_chain/procurement.ts
new file mode 100644
index 00000000..7c0e53ff
--- /dev/null
+++ b/src/supply_chain/procurement.ts
@@ -0,0 +1,22 @@
+/** Procurement module — tsb analytics library. */
+
+/** Options for Procurement. */
+export interface ProcurementOptions { tol?: number; maxIter?: number; }
+
+/** Result from Procurement. */
+export interface ProcurementResult { values: number[]; converged: boolean; }
+
+/** Compute Procurement. */
+export function computeProcurement(data: number[], opts: ProcurementOptions = {}): ProcurementResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeProcurement };
diff --git a/src/supply_chain/production_planning.ts b/src/supply_chain/production_planning.ts
new file mode 100644
index 00000000..e545f8b5
--- /dev/null
+++ b/src/supply_chain/production_planning.ts
@@ -0,0 +1,22 @@
+/** Production Planning module — tsb analytics library. */
+
+/** Options for Production Planning. */
+export interface ProductionPlanningOptions { tol?: number; maxIter?: number; }
+
+/** Result from Production Planning. */
+export interface ProductionPlanningResult { values: number[]; converged: boolean; }
+
+/** Compute Production Planning. */
+export function computeProductionPlanning(data: number[], opts: ProductionPlanningOptions = {}): ProductionPlanningResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeProductionPlanning };
diff --git a/src/supply_chain/quality.ts b/src/supply_chain/quality.ts
new file mode 100644
index 00000000..bf7f8c23
--- /dev/null
+++ b/src/supply_chain/quality.ts
@@ -0,0 +1,22 @@
+/** Quality module — tsb analytics library. */
+
+/** Options for Quality. */
+export interface QualityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Quality. */
+export interface QualityResult { values: number[]; converged: boolean; }
+
+/** Compute Quality. */
+export function computeQuality(data: number[], opts: QualityOptions = {}): QualityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeQuality };
diff --git a/src/supply_chain/resilience.ts b/src/supply_chain/resilience.ts
new file mode 100644
index 00000000..e5021990
--- /dev/null
+++ b/src/supply_chain/resilience.ts
@@ -0,0 +1,22 @@
+/** Resilience module — tsb analytics library. */
+
+/** Options for Resilience. */
+export interface ResilienceOptions { tol?: number; maxIter?: number; }
+
+/** Result from Resilience. */
+export interface ResilienceResult { values: number[]; converged: boolean; }
+
+/** Compute Resilience. */
+export function computeResilience(data: number[], opts: ResilienceOptions = {}): ResilienceResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeResilience };
diff --git a/src/supply_chain/reverse_logistics.ts b/src/supply_chain/reverse_logistics.ts
new file mode 100644
index 00000000..82e2415b
--- /dev/null
+++ b/src/supply_chain/reverse_logistics.ts
@@ -0,0 +1,22 @@
+/** Reverse Logistics module — tsb analytics library. */
+
+/** Options for Reverse Logistics. */
+export interface ReverseLogisticsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Reverse Logistics. */
+export interface ReverseLogisticsResult { values: number[]; converged: boolean; }
+
+/** Compute Reverse Logistics. */
+export function computeReverseLogistics(data: number[], opts: ReverseLogisticsOptions = {}): ReverseLogisticsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeReverseLogistics };
diff --git a/src/supply_chain/risk_supply.ts b/src/supply_chain/risk_supply.ts
new file mode 100644
index 00000000..f34fd783
--- /dev/null
+++ b/src/supply_chain/risk_supply.ts
@@ -0,0 +1,22 @@
+/** Risk Supply module — tsb analytics library. */
+
+/** Options for Risk Supply. */
+export interface RiskSupplyOptions { tol?: number; maxIter?: number; }
+
+/** Result from Risk Supply. */
+export interface RiskSupplyResult { values: number[]; converged: boolean; }
+
+/** Compute Risk Supply. */
+export function computeRiskSupply(data: number[], opts: RiskSupplyOptions = {}): RiskSupplyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRiskSupply };
diff --git a/src/supply_chain/robust.ts b/src/supply_chain/robust.ts
new file mode 100644
index 00000000..5fcc4321
--- /dev/null
+++ b/src/supply_chain/robust.ts
@@ -0,0 +1,15 @@
+/** Supply Chain Robust module — tsb analytics library. */
+export interface SupplyChain robustOptions { tol?: number; maxIter?: number; }
+export interface SupplyChain robustResult { values: number[]; converged: boolean; }
+export function computeSupplyChain robust(data: number[], opts: SupplyChain robustOptions = {}): SupplyChain robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSupplyChain robust };
diff --git a/src/supply_chain/routing.ts b/src/supply_chain/routing.ts
new file mode 100644
index 00000000..3de2357e
--- /dev/null
+++ b/src/supply_chain/routing.ts
@@ -0,0 +1,22 @@
+/** Routing module — tsb analytics library. */
+
+/** Options for Routing. */
+export interface RoutingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Routing. */
+export interface RoutingResult { values: number[]; converged: boolean; }
+
+/** Compute Routing. */
+export function computeRouting(data: number[], opts: RoutingOptions = {}): RoutingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRouting };
diff --git a/src/supply_chain/scheduling.ts b/src/supply_chain/scheduling.ts
new file mode 100644
index 00000000..abf9ca6e
--- /dev/null
+++ b/src/supply_chain/scheduling.ts
@@ -0,0 +1,22 @@
+/** Scheduling module — tsb analytics library. */
+
+/** Options for Scheduling. */
+export interface SchedulingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Scheduling. */
+export interface SchedulingResult { values: number[]; converged: boolean; }
+
+/** Compute Scheduling. */
+export function computeScheduling(data: number[], opts: SchedulingOptions = {}): SchedulingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeScheduling };
diff --git a/src/supply_chain/simulation_sc.ts b/src/supply_chain/simulation_sc.ts
new file mode 100644
index 00000000..13b01419
--- /dev/null
+++ b/src/supply_chain/simulation_sc.ts
@@ -0,0 +1,22 @@
+/** Simulation Sc module — tsb analytics library. */
+
+/** Options for Simulation Sc. */
+export interface SimulationScOptions { tol?: number; maxIter?: number; }
+
+/** Result from Simulation Sc. */
+export interface SimulationScResult { values: number[]; converged: boolean; }
+
+/** Compute Simulation Sc. */
+export function computeSimulationSc(data: number[], opts: SimulationScOptions = {}): SimulationScResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSimulationSc };
diff --git a/src/supply_chain/small.ts b/src/supply_chain/small.ts
new file mode 100644
index 00000000..c617f090
--- /dev/null
+++ b/src/supply_chain/small.ts
@@ -0,0 +1,15 @@
+/** Supply Chain Small module — tsb analytics library. */
+export interface SupplyChain smallOptions { tol?: number; maxIter?: number; }
+export interface SupplyChain smallResult { values: number[]; converged: boolean; }
+export function computeSupplyChain small(data: number[], opts: SupplyChain smallOptions = {}): SupplyChain smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSupplyChain small };
diff --git a/src/supply_chain/sourcing.ts b/src/supply_chain/sourcing.ts
new file mode 100644
index 00000000..935b59c3
--- /dev/null
+++ b/src/supply_chain/sourcing.ts
@@ -0,0 +1,22 @@
+/** Sourcing module — tsb analytics library. */
+
+/** Options for Sourcing. */
+export interface SourcingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Sourcing. */
+export interface SourcingResult { values: number[]; converged: boolean; }
+
+/** Compute Sourcing. */
+export function computeSourcing(data: number[], opts: SourcingOptions = {}): SourcingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSourcing };
diff --git a/src/supply_chain/sparse.ts b/src/supply_chain/sparse.ts
new file mode 100644
index 00000000..31c25772
--- /dev/null
+++ b/src/supply_chain/sparse.ts
@@ -0,0 +1,15 @@
+/** Supply Chain Sparse module — tsb analytics library. */
+export interface SupplyChain sparseOptions { tol?: number; maxIter?: number; }
+export interface SupplyChain sparseResult { values: number[]; converged: boolean; }
+export function computeSupplyChain sparse(data: number[], opts: SupplyChain sparseOptions = {}): SupplyChain sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSupplyChain sparse };
diff --git a/src/supply_chain/stable.ts b/src/supply_chain/stable.ts
new file mode 100644
index 00000000..90ef9231
--- /dev/null
+++ b/src/supply_chain/stable.ts
@@ -0,0 +1,15 @@
+/** Supply Chain Stable module — tsb analytics library. */
+export interface SupplyChain stableOptions { tol?: number; maxIter?: number; }
+export interface SupplyChain stableResult { values: number[]; converged: boolean; }
+export function computeSupplyChain stable(data: number[], opts: SupplyChain stableOptions = {}): SupplyChain stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSupplyChain stable };
diff --git a/src/supply_chain/streaming.ts b/src/supply_chain/streaming.ts
new file mode 100644
index 00000000..498740e7
--- /dev/null
+++ b/src/supply_chain/streaming.ts
@@ -0,0 +1,15 @@
+/** Supply Chain Streaming module — tsb analytics library. */
+export interface SupplyChain streamingOptions { tol?: number; maxIter?: number; }
+export interface SupplyChain streamingResult { values: number[]; converged: boolean; }
+export function computeSupplyChain streaming(data: number[], opts: SupplyChain streamingOptions = {}): SupplyChain streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSupplyChain streaming };
diff --git a/src/supply_chain/supplier.ts b/src/supply_chain/supplier.ts
new file mode 100644
index 00000000..41da1100
--- /dev/null
+++ b/src/supply_chain/supplier.ts
@@ -0,0 +1,22 @@
+/** Supplier module — tsb analytics library. */
+
+/** Options for Supplier. */
+export interface SupplierOptions { tol?: number; maxIter?: number; }
+
+/** Result from Supplier. */
+export interface SupplierResult { values: number[]; converged: boolean; }
+
+/** Compute Supplier. */
+export function computeSupplier(data: number[], opts: SupplierOptions = {}): SupplierResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSupplier };
diff --git a/src/supply_chain/sustainability.ts b/src/supply_chain/sustainability.ts
new file mode 100644
index 00000000..77749fa8
--- /dev/null
+++ b/src/supply_chain/sustainability.ts
@@ -0,0 +1,22 @@
+/** Sustainability module — tsb analytics library. */
+
+/** Options for Sustainability. */
+export interface SustainabilityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Sustainability. */
+export interface SustainabilityResult { values: number[]; converged: boolean; }
+
+/** Compute Sustainability. */
+export function computeSustainability(data: number[], opts: SustainabilityOptions = {}): SustainabilityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSustainability };
diff --git a/src/supply_chain/traceability.ts b/src/supply_chain/traceability.ts
new file mode 100644
index 00000000..87d655ba
--- /dev/null
+++ b/src/supply_chain/traceability.ts
@@ -0,0 +1,22 @@
+/** Traceability module — tsb analytics library. */
+
+/** Options for Traceability. */
+export interface TraceabilityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Traceability. */
+export interface TraceabilityResult { values: number[]; converged: boolean; }
+
+/** Compute Traceability. */
+export function computeTraceability(data: number[], opts: TraceabilityOptions = {}): TraceabilityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTraceability };
diff --git a/src/supply_chain/v2.ts b/src/supply_chain/v2.ts
new file mode 100644
index 00000000..c83b5853
--- /dev/null
+++ b/src/supply_chain/v2.ts
@@ -0,0 +1,15 @@
+/** Supply Chain V2 module — tsb analytics library. */
+export interface SupplyChain v2Options { tol?: number; maxIter?: number; }
+export interface SupplyChain v2Result { values: number[]; converged: boolean; }
+export function computeSupplyChain v2(data: number[], opts: SupplyChain v2Options = {}): SupplyChain v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSupplyChain v2 };
diff --git a/src/supply_chain/v3.ts b/src/supply_chain/v3.ts
new file mode 100644
index 00000000..3c8df844
--- /dev/null
+++ b/src/supply_chain/v3.ts
@@ -0,0 +1,15 @@
+/** Supply Chain V3 module — tsb analytics library. */
+export interface SupplyChain v3Options { tol?: number; maxIter?: number; }
+export interface SupplyChain v3Result { values: number[]; converged: boolean; }
+export function computeSupplyChain v3(data: number[], opts: SupplyChain v3Options = {}): SupplyChain v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSupplyChain v3 };
diff --git a/src/supply_chain/vehicle_routing.ts b/src/supply_chain/vehicle_routing.ts
new file mode 100644
index 00000000..5714bd55
--- /dev/null
+++ b/src/supply_chain/vehicle_routing.ts
@@ -0,0 +1,22 @@
+/** Vehicle Routing module — tsb analytics library. */
+
+/** Options for Vehicle Routing. */
+export interface VehicleRoutingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Vehicle Routing. */
+export interface VehicleRoutingResult { values: number[]; converged: boolean; }
+
+/** Compute Vehicle Routing. */
+export function computeVehicleRouting(data: number[], opts: VehicleRoutingOptions = {}): VehicleRoutingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeVehicleRouting };
diff --git a/src/supply_chain/visibility.ts b/src/supply_chain/visibility.ts
new file mode 100644
index 00000000..45f7c93f
--- /dev/null
+++ b/src/supply_chain/visibility.ts
@@ -0,0 +1,22 @@
+/** Visibility module — tsb analytics library. */
+
+/** Options for Visibility. */
+export interface VisibilityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Visibility. */
+export interface VisibilityResult { values: number[]; converged: boolean; }
+
+/** Compute Visibility. */
+export function computeVisibility(data: number[], opts: VisibilityOptions = {}): VisibilityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeVisibility };
diff --git a/src/supply_chain/warehouse.ts b/src/supply_chain/warehouse.ts
new file mode 100644
index 00000000..0a669193
--- /dev/null
+++ b/src/supply_chain/warehouse.ts
@@ -0,0 +1,22 @@
+/** Warehouse module — tsb analytics library. */
+
+/** Options for Warehouse. */
+export interface WarehouseOptions { tol?: number; maxIter?: number; }
+
+/** Result from Warehouse. */
+export interface WarehouseResult { values: number[]; converged: boolean; }
+
+/** Compute Warehouse. */
+export function computeWarehouse(data: number[], opts: WarehouseOptions = {}): WarehouseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeWarehouse };
diff --git a/src/supply_chain/wasm.ts b/src/supply_chain/wasm.ts
new file mode 100644
index 00000000..8d567424
--- /dev/null
+++ b/src/supply_chain/wasm.ts
@@ -0,0 +1,15 @@
+/** Supply Chain Wasm module — tsb analytics library. */
+export interface SupplyChain wasmOptions { tol?: number; maxIter?: number; }
+export interface SupplyChain wasmResult { values: number[]; converged: boolean; }
+export function computeSupplyChain wasm(data: number[], opts: SupplyChain wasmOptions = {}): SupplyChain wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSupplyChain wasm };
diff --git a/src/supply_chain/xlarge.ts b/src/supply_chain/xlarge.ts
new file mode 100644
index 00000000..21d32b2a
--- /dev/null
+++ b/src/supply_chain/xlarge.ts
@@ -0,0 +1,15 @@
+/** Supply Chain Xlarge module — tsb analytics library. */
+export interface SupplyChain xlargeOptions { tol?: number; maxIter?: number; }
+export interface SupplyChain xlargeResult { values: number[]; converged: boolean; }
+export function computeSupplyChain xlarge(data: number[], opts: SupplyChain xlargeOptions = {}): SupplyChain xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSupplyChain xlarge };
diff --git a/src/supply_chain/yield.ts b/src/supply_chain/yield.ts
new file mode 100644
index 00000000..d1e9b227
--- /dev/null
+++ b/src/supply_chain/yield.ts
@@ -0,0 +1,22 @@
+/** Yield module — tsb analytics library. */
+
+/** Options for Yield. */
+export interface YieldOptions { tol?: number; maxIter?: number; }
+
+/** Result from Yield. */
+export interface YieldResult { values: number[]; converged: boolean; }
+
+/** Compute Yield. */
+export function computeYield(data: number[], opts: YieldOptions = {}): YieldResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeYield };
diff --git a/src/survey/adaptive_testing.ts b/src/survey/adaptive_testing.ts
new file mode 100644
index 00000000..756d8852
--- /dev/null
+++ b/src/survey/adaptive_testing.ts
@@ -0,0 +1,22 @@
+/** Adaptive Testing module — tsb analytics library. */
+
+/** Options for Adaptive Testing. */
+export interface AdaptiveTestingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Adaptive Testing. */
+export interface AdaptiveTestingResult { values: number[]; converged: boolean; }
+
+/** Compute Adaptive Testing. */
+export function computeAdaptiveTesting(data: number[], opts: AdaptiveTestingOptions = {}): AdaptiveTestingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAdaptiveTesting };
diff --git a/src/survey/advanced.ts b/src/survey/advanced.ts
new file mode 100644
index 00000000..75cca3c2
--- /dev/null
+++ b/src/survey/advanced.ts
@@ -0,0 +1,15 @@
+/** Survey Advanced module — tsb analytics library. */
+export interface Survey advancedOptions { tol?: number; maxIter?: number; }
+export interface Survey advancedResult { values: number[]; converged: boolean; }
+export function computeSurvey advanced(data: number[], opts: Survey advancedOptions = {}): Survey advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSurvey advanced };
diff --git a/src/survey/anchoring.ts b/src/survey/anchoring.ts
new file mode 100644
index 00000000..278fc28c
--- /dev/null
+++ b/src/survey/anchoring.ts
@@ -0,0 +1,22 @@
+/** Anchoring module — tsb analytics library. */
+
+/** Options for Anchoring. */
+export interface AnchoringOptions { tol?: number; maxIter?: number; }
+
+/** Result from Anchoring. */
+export interface AnchoringResult { values: number[]; converged: boolean; }
+
+/** Compute Anchoring. */
+export function computeAnchoring(data: number[], opts: AnchoringOptions = {}): AnchoringResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAnchoring };
diff --git a/src/survey/attrition.ts b/src/survey/attrition.ts
new file mode 100644
index 00000000..e4ee6228
--- /dev/null
+++ b/src/survey/attrition.ts
@@ -0,0 +1,22 @@
+/** Attrition module — tsb analytics library. */
+
+/** Options for Attrition. */
+export interface AttritionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Attrition. */
+export interface AttritionResult { values: number[]; converged: boolean; }
+
+/** Compute Attrition. */
+export function computeAttrition(data: number[], opts: AttritionOptions = {}): AttritionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAttrition };
diff --git a/src/survey/base2.ts b/src/survey/base2.ts
new file mode 100644
index 00000000..1664b17b
--- /dev/null
+++ b/src/survey/base2.ts
@@ -0,0 +1,15 @@
+/** Survey Base2 module — tsb analytics library. */
+export interface Survey base2Options { tol?: number; maxIter?: number; }
+export interface Survey base2Result { values: number[]; converged: boolean; }
+export function computeSurvey base2(data: number[], opts: Survey base2Options = {}): Survey base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSurvey base2 };
diff --git a/src/survey/batch.ts b/src/survey/batch.ts
new file mode 100644
index 00000000..8f635298
--- /dev/null
+++ b/src/survey/batch.ts
@@ -0,0 +1,15 @@
+/** Survey Batch module — tsb analytics library. */
+export interface Survey batchOptions { tol?: number; maxIter?: number; }
+export interface Survey batchResult { values: number[]; converged: boolean; }
+export function computeSurvey batch(data: number[], opts: Survey batchOptions = {}): Survey batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSurvey batch };
diff --git a/src/survey/beta.ts b/src/survey/beta.ts
new file mode 100644
index 00000000..6db5ebc5
--- /dev/null
+++ b/src/survey/beta.ts
@@ -0,0 +1,15 @@
+/** Survey Beta module — tsb analytics library. */
+export interface Survey betaOptions { tol?: number; maxIter?: number; }
+export interface Survey betaResult { values: number[]; converged: boolean; }
+export function computeSurvey beta(data: number[], opts: Survey betaOptions = {}): Survey betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSurvey beta };
diff --git a/src/survey/calibration.ts b/src/survey/calibration.ts
new file mode 100644
index 00000000..1fb4ff42
--- /dev/null
+++ b/src/survey/calibration.ts
@@ -0,0 +1,22 @@
+/** Calibration module — tsb analytics library. */
+
+/** Options for Calibration. */
+export interface CalibrationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Calibration. */
+export interface CalibrationResult { values: number[]; converged: boolean; }
+
+/** Compute Calibration. */
+export function computeCalibration(data: number[], opts: CalibrationOptions = {}): CalibrationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCalibration };
diff --git a/src/survey/complex_design.ts b/src/survey/complex_design.ts
new file mode 100644
index 00000000..baa1e86a
--- /dev/null
+++ b/src/survey/complex_design.ts
@@ -0,0 +1,22 @@
+/** Complex Design module — tsb analytics library. */
+
+/** Options for Complex Design. */
+export interface ComplexDesignOptions { tol?: number; maxIter?: number; }
+
+/** Result from Complex Design. */
+export interface ComplexDesignResult { values: number[]; converged: boolean; }
+
+/** Compute Complex Design. */
+export function computeComplexDesign(data: number[], opts: ComplexDesignOptions = {}): ComplexDesignResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeComplexDesign };
diff --git a/src/survey/cpu.ts b/src/survey/cpu.ts
new file mode 100644
index 00000000..452be4c8
--- /dev/null
+++ b/src/survey/cpu.ts
@@ -0,0 +1,15 @@
+/** Survey Cpu module — tsb analytics library. */
+export interface Survey cpuOptions { tol?: number; maxIter?: number; }
+export interface Survey cpuResult { values: number[]; converged: boolean; }
+export function computeSurvey cpu(data: number[], opts: Survey cpuOptions = {}): Survey cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSurvey cpu };
diff --git a/src/survey/dense.ts b/src/survey/dense.ts
new file mode 100644
index 00000000..6086576a
--- /dev/null
+++ b/src/survey/dense.ts
@@ -0,0 +1,15 @@
+/** Survey Dense module — tsb analytics library. */
+export interface Survey denseOptions { tol?: number; maxIter?: number; }
+export interface Survey denseResult { values: number[]; converged: boolean; }
+export function computeSurvey dense(data: number[], opts: Survey denseOptions = {}): Survey denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSurvey dense };
diff --git a/src/survey/distributed.ts b/src/survey/distributed.ts
new file mode 100644
index 00000000..934effab
--- /dev/null
+++ b/src/survey/distributed.ts
@@ -0,0 +1,15 @@
+/** Survey Distributed module — tsb analytics library. */
+export interface Survey distributedOptions { tol?: number; maxIter?: number; }
+export interface Survey distributedResult { values: number[]; converged: boolean; }
+export function computeSurvey distributed(data: number[], opts: Survey distributedOptions = {}): Survey distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSurvey distributed };
diff --git a/src/survey/experimental.ts b/src/survey/experimental.ts
new file mode 100644
index 00000000..14667370
--- /dev/null
+++ b/src/survey/experimental.ts
@@ -0,0 +1,15 @@
+/** Survey Experimental module — tsb analytics library. */
+export interface Survey experimentalOptions { tol?: number; maxIter?: number; }
+export interface Survey experimentalResult { values: number[]; converged: boolean; }
+export function computeSurvey experimental(data: number[], opts: Survey experimentalOptions = {}): Survey experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSurvey experimental };
diff --git a/src/survey/factor.ts b/src/survey/factor.ts
new file mode 100644
index 00000000..fc4bc52b
--- /dev/null
+++ b/src/survey/factor.ts
@@ -0,0 +1,22 @@
+/** Factor module — tsb analytics library. */
+
+/** Options for Factor. */
+export interface FactorOptions { tol?: number; maxIter?: number; }
+
+/** Result from Factor. */
+export interface FactorResult { values: number[]; converged: boolean; }
+
+/** Compute Factor. */
+export function computeFactor(data: number[], opts: FactorOptions = {}): FactorResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFactor };
diff --git a/src/survey/fast.ts b/src/survey/fast.ts
new file mode 100644
index 00000000..3461bc5f
--- /dev/null
+++ b/src/survey/fast.ts
@@ -0,0 +1,15 @@
+/** Survey Fast module — tsb analytics library. */
+export interface Survey fastOptions { tol?: number; maxIter?: number; }
+export interface Survey fastResult { values: number[]; converged: boolean; }
+export function computeSurvey fast(data: number[], opts: Survey fastOptions = {}): Survey fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSurvey fast };
diff --git a/src/survey/framing.ts b/src/survey/framing.ts
new file mode 100644
index 00000000..c0901463
--- /dev/null
+++ b/src/survey/framing.ts
@@ -0,0 +1,22 @@
+/** Framing module — tsb analytics library. */
+
+/** Options for Framing. */
+export interface FramingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Framing. */
+export interface FramingResult { values: number[]; converged: boolean; }
+
+/** Compute Framing. */
+export function computeFraming(data: number[], opts: FramingOptions = {}): FramingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFraming };
diff --git a/src/survey/future.ts b/src/survey/future.ts
new file mode 100644
index 00000000..87c0c205
--- /dev/null
+++ b/src/survey/future.ts
@@ -0,0 +1,15 @@
+/** Survey Future module — tsb analytics library. */
+export interface Survey futureOptions { tol?: number; maxIter?: number; }
+export interface Survey futureResult { values: number[]; converged: boolean; }
+export function computeSurvey future(data: number[], opts: Survey futureOptions = {}): Survey futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSurvey future };
diff --git a/src/survey/gpu.ts b/src/survey/gpu.ts
new file mode 100644
index 00000000..15947bc6
--- /dev/null
+++ b/src/survey/gpu.ts
@@ -0,0 +1,15 @@
+/** Survey Gpu module — tsb analytics library. */
+export interface Survey gpuOptions { tol?: number; maxIter?: number; }
+export interface Survey gpuResult { values: number[]; converged: boolean; }
+export function computeSurvey gpu(data: number[], opts: Survey gpuOptions = {}): Survey gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSurvey gpu };
diff --git a/src/survey/imputation.ts b/src/survey/imputation.ts
new file mode 100644
index 00000000..41014255
--- /dev/null
+++ b/src/survey/imputation.ts
@@ -0,0 +1,22 @@
+/** Imputation module — tsb analytics library. */
+
+/** Options for Imputation. */
+export interface ImputationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Imputation. */
+export interface ImputationResult { values: number[]; converged: boolean; }
+
+/** Compute Imputation. */
+export function computeImputation(data: number[], opts: ImputationOptions = {}): ImputationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeImputation };
diff --git a/src/survey/interviewer.ts b/src/survey/interviewer.ts
new file mode 100644
index 00000000..054dde92
--- /dev/null
+++ b/src/survey/interviewer.ts
@@ -0,0 +1,22 @@
+/** Interviewer module — tsb analytics library. */
+
+/** Options for Interviewer. */
+export interface InterviewerOptions { tol?: number; maxIter?: number; }
+
+/** Result from Interviewer. */
+export interface InterviewerResult { values: number[]; converged: boolean; }
+
+/** Compute Interviewer. */
+export function computeInterviewer(data: number[], opts: InterviewerOptions = {}): InterviewerResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeInterviewer };
diff --git a/src/survey/irt.ts b/src/survey/irt.ts
new file mode 100644
index 00000000..c402cf12
--- /dev/null
+++ b/src/survey/irt.ts
@@ -0,0 +1,22 @@
+/** Irt module — tsb analytics library. */
+
+/** Options for Irt. */
+export interface IrtOptions { tol?: number; maxIter?: number; }
+
+/** Result from Irt. */
+export interface IrtResult { values: number[]; converged: boolean; }
+
+/** Compute Irt. */
+export function computeIrt(data: number[], opts: IrtOptions = {}): IrtResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeIrt };
diff --git a/src/survey/item_analysis.ts b/src/survey/item_analysis.ts
new file mode 100644
index 00000000..0a9605c2
--- /dev/null
+++ b/src/survey/item_analysis.ts
@@ -0,0 +1,22 @@
+/** Item Analysis module — tsb analytics library. */
+
+/** Options for Item Analysis. */
+export interface ItemAnalysisOptions { tol?: number; maxIter?: number; }
+
+/** Result from Item Analysis. */
+export interface ItemAnalysisResult { values: number[]; converged: boolean; }
+
+/** Compute Item Analysis. */
+export function computeItemAnalysis(data: number[], opts: ItemAnalysisOptions = {}): ItemAnalysisResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeItemAnalysis };
diff --git a/src/survey/large.ts b/src/survey/large.ts
new file mode 100644
index 00000000..d87c4bb1
--- /dev/null
+++ b/src/survey/large.ts
@@ -0,0 +1,15 @@
+/** Survey Large module — tsb analytics library. */
+export interface Survey largeOptions { tol?: number; maxIter?: number; }
+export interface Survey largeResult { values: number[]; converged: boolean; }
+export function computeSurvey large(data: number[], opts: Survey largeOptions = {}): Survey largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSurvey large };
diff --git a/src/survey/legacy.ts b/src/survey/legacy.ts
new file mode 100644
index 00000000..e17136dc
--- /dev/null
+++ b/src/survey/legacy.ts
@@ -0,0 +1,15 @@
+/** Survey Legacy module — tsb analytics library. */
+export interface Survey legacyOptions { tol?: number; maxIter?: number; }
+export interface Survey legacyResult { values: number[]; converged: boolean; }
+export function computeSurvey legacy(data: number[], opts: Survey legacyOptions = {}): Survey legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSurvey legacy };
diff --git a/src/survey/lite.ts b/src/survey/lite.ts
new file mode 100644
index 00000000..e0d01c8c
--- /dev/null
+++ b/src/survey/lite.ts
@@ -0,0 +1,15 @@
+/** Survey Lite module — tsb analytics library. */
+export interface Survey liteOptions { tol?: number; maxIter?: number; }
+export interface Survey liteResult { values: number[]; converged: boolean; }
+export function computeSurvey lite(data: number[], opts: Survey liteOptions = {}): Survey liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSurvey lite };
diff --git a/src/survey/longitudinal.ts b/src/survey/longitudinal.ts
new file mode 100644
index 00000000..3530933e
--- /dev/null
+++ b/src/survey/longitudinal.ts
@@ -0,0 +1,22 @@
+/** Longitudinal module — tsb analytics library. */
+
+/** Options for Longitudinal. */
+export interface LongitudinalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Longitudinal. */
+export interface LongitudinalResult { values: number[]; converged: boolean; }
+
+/** Compute Longitudinal. */
+export function computeLongitudinal(data: number[], opts: LongitudinalOptions = {}): LongitudinalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeLongitudinal };
diff --git a/src/survey/measurement.ts b/src/survey/measurement.ts
new file mode 100644
index 00000000..4eabb944
--- /dev/null
+++ b/src/survey/measurement.ts
@@ -0,0 +1,22 @@
+/** Measurement module — tsb analytics library. */
+
+/** Options for Measurement. */
+export interface MeasurementOptions { tol?: number; maxIter?: number; }
+
+/** Result from Measurement. */
+export interface MeasurementResult { values: number[]; converged: boolean; }
+
+/** Compute Measurement. */
+export function computeMeasurement(data: number[], opts: MeasurementOptions = {}): MeasurementResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMeasurement };
diff --git a/src/survey/mini.ts b/src/survey/mini.ts
new file mode 100644
index 00000000..4ef1232c
--- /dev/null
+++ b/src/survey/mini.ts
@@ -0,0 +1,15 @@
+/** Survey Mini module — tsb analytics library. */
+export interface Survey miniOptions { tol?: number; maxIter?: number; }
+export interface Survey miniResult { values: number[]; converged: boolean; }
+export function computeSurvey mini(data: number[], opts: Survey miniOptions = {}): Survey miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSurvey mini };
diff --git a/src/survey/mode.ts b/src/survey/mode.ts
new file mode 100644
index 00000000..1fb2b4d7
--- /dev/null
+++ b/src/survey/mode.ts
@@ -0,0 +1,22 @@
+/** Mode module — tsb analytics library. */
+
+/** Options for Mode. */
+export interface ModeOptions { tol?: number; maxIter?: number; }
+
+/** Result from Mode. */
+export interface ModeResult { values: number[]; converged: boolean; }
+
+/** Compute Mode. */
+export function computeMode(data: number[], opts: ModeOptions = {}): ModeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMode };
diff --git a/src/survey/multistage.ts b/src/survey/multistage.ts
new file mode 100644
index 00000000..7b2d4db8
--- /dev/null
+++ b/src/survey/multistage.ts
@@ -0,0 +1,22 @@
+/** Multistage module — tsb analytics library. */
+
+/** Options for Multistage. */
+export interface MultistageOptions { tol?: number; maxIter?: number; }
+
+/** Result from Multistage. */
+export interface MultistageResult { values: number[]; converged: boolean; }
+
+/** Compute Multistage. */
+export function computeMultistage(data: number[], opts: MultistageOptions = {}): MultistageResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeMultistage };
diff --git a/src/survey/next.ts b/src/survey/next.ts
new file mode 100644
index 00000000..7d844a64
--- /dev/null
+++ b/src/survey/next.ts
@@ -0,0 +1,15 @@
+/** Survey Next module — tsb analytics library. */
+export interface Survey nextOptions { tol?: number; maxIter?: number; }
+export interface Survey nextResult { values: number[]; converged: boolean; }
+export function computeSurvey next(data: number[], opts: Survey nextOptions = {}): Survey nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSurvey next };
diff --git a/src/survey/nonresponse.ts b/src/survey/nonresponse.ts
new file mode 100644
index 00000000..7f6b0aca
--- /dev/null
+++ b/src/survey/nonresponse.ts
@@ -0,0 +1,22 @@
+/** Nonresponse module — tsb analytics library. */
+
+/** Options for Nonresponse. */
+export interface NonresponseOptions { tol?: number; maxIter?: number; }
+
+/** Result from Nonresponse. */
+export interface NonresponseResult { values: number[]; converged: boolean; }
+
+/** Compute Nonresponse. */
+export function computeNonresponse(data: number[], opts: NonresponseOptions = {}): NonresponseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNonresponse };
diff --git a/src/survey/online.ts b/src/survey/online.ts
new file mode 100644
index 00000000..331b12da
--- /dev/null
+++ b/src/survey/online.ts
@@ -0,0 +1,15 @@
+/** Survey Online module — tsb analytics library. */
+export interface Survey onlineOptions { tol?: number; maxIter?: number; }
+export interface Survey onlineResult { values: number[]; converged: boolean; }
+export function computeSurvey online(data: number[], opts: Survey onlineOptions = {}): Survey onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSurvey online };
diff --git a/src/survey/panel.ts b/src/survey/panel.ts
new file mode 100644
index 00000000..f6e6a900
--- /dev/null
+++ b/src/survey/panel.ts
@@ -0,0 +1,22 @@
+/** Panel module — tsb analytics library. */
+
+/** Options for Panel. */
+export interface PanelOptions { tol?: number; maxIter?: number; }
+
+/** Result from Panel. */
+export interface PanelResult { values: number[]; converged: boolean; }
+
+/** Compute Panel. */
+export function computePanel(data: number[], opts: PanelOptions = {}): PanelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePanel };
diff --git a/src/survey/parallel.ts b/src/survey/parallel.ts
new file mode 100644
index 00000000..efbc5ee3
--- /dev/null
+++ b/src/survey/parallel.ts
@@ -0,0 +1,15 @@
+/** Survey Parallel module — tsb analytics library. */
+export interface Survey parallelOptions { tol?: number; maxIter?: number; }
+export interface Survey parallelResult { values: number[]; converged: boolean; }
+export function computeSurvey parallel(data: number[], opts: Survey parallelOptions = {}): Survey parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSurvey parallel };
diff --git a/src/survey/plus.ts b/src/survey/plus.ts
new file mode 100644
index 00000000..41254ed8
--- /dev/null
+++ b/src/survey/plus.ts
@@ -0,0 +1,15 @@
+/** Survey Plus module — tsb analytics library. */
+export interface Survey plusOptions { tol?: number; maxIter?: number; }
+export interface Survey plusResult { values: number[]; converged: boolean; }
+export function computeSurvey plus(data: number[], opts: Survey plusOptions = {}): Survey plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSurvey plus };
diff --git a/src/survey/pooling.ts b/src/survey/pooling.ts
new file mode 100644
index 00000000..99a42360
--- /dev/null
+++ b/src/survey/pooling.ts
@@ -0,0 +1,22 @@
+/** Pooling module — tsb analytics library. */
+
+/** Options for Pooling. */
+export interface PoolingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Pooling. */
+export interface PoolingResult { values: number[]; converged: boolean; }
+
+/** Compute Pooling. */
+export function computePooling(data: number[], opts: PoolingOptions = {}): PoolingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePooling };
diff --git a/src/survey/pro.ts b/src/survey/pro.ts
new file mode 100644
index 00000000..ed9671fa
--- /dev/null
+++ b/src/survey/pro.ts
@@ -0,0 +1,15 @@
+/** Survey Pro module — tsb analytics library. */
+export interface Survey proOptions { tol?: number; maxIter?: number; }
+export interface Survey proResult { values: number[]; converged: boolean; }
+export function computeSurvey pro(data: number[], opts: Survey proOptions = {}): Survey proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSurvey pro };
diff --git a/src/survey/recall.ts b/src/survey/recall.ts
new file mode 100644
index 00000000..7a9251cd
--- /dev/null
+++ b/src/survey/recall.ts
@@ -0,0 +1,22 @@
+/** Recall module — tsb analytics library. */
+
+/** Options for Recall. */
+export interface RecallOptions { tol?: number; maxIter?: number; }
+
+/** Result from Recall. */
+export interface RecallResult { values: number[]; converged: boolean; }
+
+/** Compute Recall. */
+export function computeRecall(data: number[], opts: RecallOptions = {}): RecallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeRecall };
diff --git a/src/survey/reliability.ts b/src/survey/reliability.ts
new file mode 100644
index 00000000..1f53c73e
--- /dev/null
+++ b/src/survey/reliability.ts
@@ -0,0 +1,22 @@
+/** Reliability module — tsb analytics library. */
+
+/** Options for Reliability. */
+export interface ReliabilityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Reliability. */
+export interface ReliabilityResult { values: number[]; converged: boolean; }
+
+/** Compute Reliability. */
+export function computeReliability(data: number[], opts: ReliabilityOptions = {}): ReliabilityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeReliability };
diff --git a/src/survey/robust.ts b/src/survey/robust.ts
new file mode 100644
index 00000000..9b3b999b
--- /dev/null
+++ b/src/survey/robust.ts
@@ -0,0 +1,15 @@
+/** Survey Robust module — tsb analytics library. */
+export interface Survey robustOptions { tol?: number; maxIter?: number; }
+export interface Survey robustResult { values: number[]; converged: boolean; }
+export function computeSurvey robust(data: number[], opts: Survey robustOptions = {}): Survey robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSurvey robust };
diff --git a/src/survey/sampling.ts b/src/survey/sampling.ts
new file mode 100644
index 00000000..656bc191
--- /dev/null
+++ b/src/survey/sampling.ts
@@ -0,0 +1,22 @@
+/** Sampling module — tsb analytics library. */
+
+/** Options for Sampling. */
+export interface SamplingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Sampling. */
+export interface SamplingResult { values: number[]; converged: boolean; }
+
+/** Compute Sampling. */
+export function computeSampling(data: number[], opts: SamplingOptions = {}): SamplingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSampling };
diff --git a/src/survey/sem.ts b/src/survey/sem.ts
new file mode 100644
index 00000000..2fd6b2a7
--- /dev/null
+++ b/src/survey/sem.ts
@@ -0,0 +1,22 @@
+/** Sem module — tsb analytics library. */
+
+/** Options for Sem. */
+export interface SemOptions { tol?: number; maxIter?: number; }
+
+/** Result from Sem. */
+export interface SemResult { values: number[]; converged: boolean; }
+
+/** Compute Sem. */
+export function computeSem(data: number[], opts: SemOptions = {}): SemResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSem };
diff --git a/src/survey/small.ts b/src/survey/small.ts
new file mode 100644
index 00000000..f6178d35
--- /dev/null
+++ b/src/survey/small.ts
@@ -0,0 +1,15 @@
+/** Survey Small module — tsb analytics library. */
+export interface Survey smallOptions { tol?: number; maxIter?: number; }
+export interface Survey smallResult { values: number[]; converged: boolean; }
+export function computeSurvey small(data: number[], opts: Survey smallOptions = {}): Survey smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSurvey small };
diff --git a/src/survey/small_area.ts b/src/survey/small_area.ts
new file mode 100644
index 00000000..a40919c3
--- /dev/null
+++ b/src/survey/small_area.ts
@@ -0,0 +1,22 @@
+/** Small Area module — tsb analytics library. */
+
+/** Options for Small Area. */
+export interface SmallAreaOptions { tol?: number; maxIter?: number; }
+
+/** Result from Small Area. */
+export interface SmallAreaResult { values: number[]; converged: boolean; }
+
+/** Compute Small Area. */
+export function computeSmallArea(data: number[], opts: SmallAreaOptions = {}): SmallAreaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSmallArea };
diff --git a/src/survey/social_desirability.ts b/src/survey/social_desirability.ts
new file mode 100644
index 00000000..98ff23eb
--- /dev/null
+++ b/src/survey/social_desirability.ts
@@ -0,0 +1,22 @@
+/** Social Desirability module — tsb analytics library. */
+
+/** Options for Social Desirability. */
+export interface SocialDesirabilityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Social Desirability. */
+export interface SocialDesirabilityResult { values: number[]; converged: boolean; }
+
+/** Compute Social Desirability. */
+export function computeSocialDesirability(data: number[], opts: SocialDesirabilityOptions = {}): SocialDesirabilityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSocialDesirability };
diff --git a/src/survey/sparse.ts b/src/survey/sparse.ts
new file mode 100644
index 00000000..75a7d06a
--- /dev/null
+++ b/src/survey/sparse.ts
@@ -0,0 +1,15 @@
+/** Survey Sparse module — tsb analytics library. */
+export interface Survey sparseOptions { tol?: number; maxIter?: number; }
+export interface Survey sparseResult { values: number[]; converged: boolean; }
+export function computeSurvey sparse(data: number[], opts: Survey sparseOptions = {}): Survey sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSurvey sparse };
diff --git a/src/survey/stable.ts b/src/survey/stable.ts
new file mode 100644
index 00000000..01705eab
--- /dev/null
+++ b/src/survey/stable.ts
@@ -0,0 +1,15 @@
+/** Survey Stable module — tsb analytics library. */
+export interface Survey stableOptions { tol?: number; maxIter?: number; }
+export interface Survey stableResult { values: number[]; converged: boolean; }
+export function computeSurvey stable(data: number[], opts: Survey stableOptions = {}): Survey stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSurvey stable };
diff --git a/src/survey/stratification.ts b/src/survey/stratification.ts
new file mode 100644
index 00000000..2d2e88af
--- /dev/null
+++ b/src/survey/stratification.ts
@@ -0,0 +1,22 @@
+/** Stratification module — tsb analytics library. */
+
+/** Options for Stratification. */
+export interface StratificationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Stratification. */
+export interface StratificationResult { values: number[]; converged: boolean; }
+
+/** Compute Stratification. */
+export function computeStratification(data: number[], opts: StratificationOptions = {}): StratificationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeStratification };
diff --git a/src/survey/streaming.ts b/src/survey/streaming.ts
new file mode 100644
index 00000000..ac98649a
--- /dev/null
+++ b/src/survey/streaming.ts
@@ -0,0 +1,15 @@
+/** Survey Streaming module — tsb analytics library. */
+export interface Survey streamingOptions { tol?: number; maxIter?: number; }
+export interface Survey streamingResult { values: number[]; converged: boolean; }
+export function computeSurvey streaming(data: number[], opts: Survey streamingOptions = {}): Survey streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSurvey streaming };
diff --git a/src/survey/test_equating.ts b/src/survey/test_equating.ts
new file mode 100644
index 00000000..67c2eb8a
--- /dev/null
+++ b/src/survey/test_equating.ts
@@ -0,0 +1,22 @@
+/** Test Equating module — tsb analytics library. */
+
+/** Options for Test Equating. */
+export interface TestEquatingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Test Equating. */
+export interface TestEquatingResult { values: number[]; converged: boolean; }
+
+/** Compute Test Equating. */
+export function computeTestEquating(data: number[], opts: TestEquatingOptions = {}): TestEquatingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTestEquating };
diff --git a/src/survey/translation.ts b/src/survey/translation.ts
new file mode 100644
index 00000000..95026cb1
--- /dev/null
+++ b/src/survey/translation.ts
@@ -0,0 +1,22 @@
+/** Translation module — tsb analytics library. */
+
+/** Options for Translation. */
+export interface TranslationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Translation. */
+export interface TranslationResult { values: number[]; converged: boolean; }
+
+/** Compute Translation. */
+export function computeTranslation(data: number[], opts: TranslationOptions = {}): TranslationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeTranslation };
diff --git a/src/survey/v2.ts b/src/survey/v2.ts
new file mode 100644
index 00000000..f64bd913
--- /dev/null
+++ b/src/survey/v2.ts
@@ -0,0 +1,15 @@
+/** Survey V2 module — tsb analytics library. */
+export interface Survey v2Options { tol?: number; maxIter?: number; }
+export interface Survey v2Result { values: number[]; converged: boolean; }
+export function computeSurvey v2(data: number[], opts: Survey v2Options = {}): Survey v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSurvey v2 };
diff --git a/src/survey/v3.ts b/src/survey/v3.ts
new file mode 100644
index 00000000..d5e5f969
--- /dev/null
+++ b/src/survey/v3.ts
@@ -0,0 +1,15 @@
+/** Survey V3 module — tsb analytics library. */
+export interface Survey v3Options { tol?: number; maxIter?: number; }
+export interface Survey v3Result { values: number[]; converged: boolean; }
+export function computeSurvey v3(data: number[], opts: Survey v3Options = {}): Survey v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSurvey v3 };
diff --git a/src/survey/validity.ts b/src/survey/validity.ts
new file mode 100644
index 00000000..8ce7c271
--- /dev/null
+++ b/src/survey/validity.ts
@@ -0,0 +1,22 @@
+/** Validity module — tsb analytics library. */
+
+/** Options for Validity. */
+export interface ValidityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Validity. */
+export interface ValidityResult { values: number[]; converged: boolean; }
+
+/** Compute Validity. */
+export function computeValidity(data: number[], opts: ValidityOptions = {}): ValidityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeValidity };
diff --git a/src/survey/vignette.ts b/src/survey/vignette.ts
new file mode 100644
index 00000000..3e8cc029
--- /dev/null
+++ b/src/survey/vignette.ts
@@ -0,0 +1,22 @@
+/** Vignette module — tsb analytics library. */
+
+/** Options for Vignette. */
+export interface VignetteOptions { tol?: number; maxIter?: number; }
+
+/** Result from Vignette. */
+export interface VignetteResult { values: number[]; converged: boolean; }
+
+/** Compute Vignette. */
+export function computeVignette(data: number[], opts: VignetteOptions = {}): VignetteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeVignette };
diff --git a/src/survey/wasm.ts b/src/survey/wasm.ts
new file mode 100644
index 00000000..bf918ac3
--- /dev/null
+++ b/src/survey/wasm.ts
@@ -0,0 +1,15 @@
+/** Survey Wasm module — tsb analytics library. */
+export interface Survey wasmOptions { tol?: number; maxIter?: number; }
+export interface Survey wasmResult { values: number[]; converged: boolean; }
+export function computeSurvey wasm(data: number[], opts: Survey wasmOptions = {}): Survey wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSurvey wasm };
diff --git a/src/survey/weighting.ts b/src/survey/weighting.ts
new file mode 100644
index 00000000..da555106
--- /dev/null
+++ b/src/survey/weighting.ts
@@ -0,0 +1,22 @@
+/** Weighting module — tsb analytics library. */
+
+/** Options for Weighting. */
+export interface WeightingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Weighting. */
+export interface WeightingResult { values: number[]; converged: boolean; }
+
+/** Compute Weighting. */
+export function computeWeighting(data: number[], opts: WeightingOptions = {}): WeightingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeWeighting };
diff --git a/src/survey/xlarge.ts b/src/survey/xlarge.ts
new file mode 100644
index 00000000..4910998b
--- /dev/null
+++ b/src/survey/xlarge.ts
@@ -0,0 +1,15 @@
+/** Survey Xlarge module — tsb analytics library. */
+export interface Survey xlargeOptions { tol?: number; maxIter?: number; }
+export interface Survey xlargeResult { values: number[]; converged: boolean; }
+export function computeSurvey xlarge(data: number[], opts: Survey xlargeOptions = {}): Survey xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeSurvey xlarge };
diff --git a/src/timeseries/advanced.ts b/src/timeseries/advanced.ts
new file mode 100644
index 00000000..0a2f92f2
--- /dev/null
+++ b/src/timeseries/advanced.ts
@@ -0,0 +1,15 @@
+/** Timeseries Advanced module — tsb analytics library. */
+export interface Timeseries advancedOptions { tol?: number; maxIter?: number; }
+export interface Timeseries advancedResult { values: number[]; converged: boolean; }
+export function computeTimeseries advanced(data: number[], opts: Timeseries advancedOptions = {}): Timeseries advancedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeTimeseries advanced };
diff --git a/src/timeseries/alignment.ts b/src/timeseries/alignment.ts
new file mode 100644
index 00000000..d9072c48
--- /dev/null
+++ b/src/timeseries/alignment.ts
@@ -0,0 +1,22 @@
+/** Alignment module — tsb analytics library. */
+
+/** Options for Alignment. */
+export interface AlignmentOptions { tol?: number; maxIter?: number; }
+
+/** Result from Alignment. */
+export interface AlignmentResult { values: number[]; converged: boolean; }
+
+/** Compute Alignment. */
+export function computeAlignment(data: number[], opts: AlignmentOptions = {}): AlignmentResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAlignment };
diff --git a/src/timeseries/arima.ts b/src/timeseries/arima.ts
new file mode 100644
index 00000000..8554623b
--- /dev/null
+++ b/src/timeseries/arima.ts
@@ -0,0 +1,22 @@
+/** Arima module — tsb analytics library. */
+
+/** Options for Arima. */
+export interface ArimaOptions { tol?: number; maxIter?: number; }
+
+/** Result from Arima. */
+export interface ArimaResult { values: number[]; converged: boolean; }
+
+/** Compute Arima. */
+export function computeArima(data: number[], opts: ArimaOptions = {}): ArimaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeArima };
diff --git a/src/timeseries/autocorrelation.ts b/src/timeseries/autocorrelation.ts
new file mode 100644
index 00000000..d4140561
--- /dev/null
+++ b/src/timeseries/autocorrelation.ts
@@ -0,0 +1,22 @@
+/** Autocorrelation module — tsb analytics library. */
+
+/** Options for Autocorrelation. */
+export interface AutocorrelationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Autocorrelation. */
+export interface AutocorrelationResult { values: number[]; converged: boolean; }
+
+/** Compute Autocorrelation. */
+export function computeAutocorrelation(data: number[], opts: AutocorrelationOptions = {}): AutocorrelationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeAutocorrelation };
diff --git a/src/timeseries/base2.ts b/src/timeseries/base2.ts
new file mode 100644
index 00000000..98217bd0
--- /dev/null
+++ b/src/timeseries/base2.ts
@@ -0,0 +1,15 @@
+/** Timeseries Base2 module — tsb analytics library. */
+export interface Timeseries base2Options { tol?: number; maxIter?: number; }
+export interface Timeseries base2Result { values: number[]; converged: boolean; }
+export function computeTimeseries base2(data: number[], opts: Timeseries base2Options = {}): Timeseries base2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeTimeseries base2 };
diff --git a/src/timeseries/batch.ts b/src/timeseries/batch.ts
new file mode 100644
index 00000000..b1bcb48a
--- /dev/null
+++ b/src/timeseries/batch.ts
@@ -0,0 +1,15 @@
+/** Timeseries Batch module — tsb analytics library. */
+export interface Timeseries batchOptions { tol?: number; maxIter?: number; }
+export interface Timeseries batchResult { values: number[]; converged: boolean; }
+export function computeTimeseries batch(data: number[], opts: Timeseries batchOptions = {}): Timeseries batchResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeTimeseries batch };
diff --git a/src/timeseries/beta.ts b/src/timeseries/beta.ts
new file mode 100644
index 00000000..04ff99e3
--- /dev/null
+++ b/src/timeseries/beta.ts
@@ -0,0 +1,15 @@
+/** Timeseries Beta module — tsb analytics library. */
+export interface Timeseries betaOptions { tol?: number; maxIter?: number; }
+export interface Timeseries betaResult { values: number[]; converged: boolean; }
+export function computeTimeseries beta(data: number[], opts: Timeseries betaOptions = {}): Timeseries betaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeTimeseries beta };
diff --git a/src/timeseries/change_point.ts b/src/timeseries/change_point.ts
new file mode 100644
index 00000000..14b9698a
--- /dev/null
+++ b/src/timeseries/change_point.ts
@@ -0,0 +1,22 @@
+/** Change Point module — tsb analytics library. */
+
+/** Options for Change Point. */
+export interface ChangePointOptions { tol?: number; maxIter?: number; }
+
+/** Result from Change Point. */
+export interface ChangePointResult { values: number[]; converged: boolean; }
+
+/** Compute Change Point. */
+export function computeChangePoint(data: number[], opts: ChangePointOptions = {}): ChangePointResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeChangePoint };
diff --git a/src/timeseries/cointegration.ts b/src/timeseries/cointegration.ts
new file mode 100644
index 00000000..1b657386
--- /dev/null
+++ b/src/timeseries/cointegration.ts
@@ -0,0 +1,22 @@
+/** Cointegration module — tsb analytics library. */
+
+/** Options for Cointegration. */
+export interface CointegrationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Cointegration. */
+export interface CointegrationResult { values: number[]; converged: boolean; }
+
+/** Compute Cointegration. */
+export function computeCointegration(data: number[], opts: CointegrationOptions = {}): CointegrationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeCointegration };
diff --git a/src/timeseries/cpu.ts b/src/timeseries/cpu.ts
new file mode 100644
index 00000000..df7cf176
--- /dev/null
+++ b/src/timeseries/cpu.ts
@@ -0,0 +1,15 @@
+/** Timeseries Cpu module — tsb analytics library. */
+export interface Timeseries cpuOptions { tol?: number; maxIter?: number; }
+export interface Timeseries cpuResult { values: number[]; converged: boolean; }
+export function computeTimeseries cpu(data: number[], opts: Timeseries cpuOptions = {}): Timeseries cpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeTimeseries cpu };
diff --git a/src/timeseries/decomposition.ts b/src/timeseries/decomposition.ts
new file mode 100644
index 00000000..320eef69
--- /dev/null
+++ b/src/timeseries/decomposition.ts
@@ -0,0 +1,22 @@
+/** Decomposition module — tsb analytics library. */
+
+/** Options for Decomposition. */
+export interface DecompositionOptions { tol?: number; maxIter?: number; }
+
+/** Result from Decomposition. */
+export interface DecompositionResult { values: number[]; converged: boolean; }
+
+/** Compute Decomposition. */
+export function computeDecomposition(data: number[], opts: DecompositionOptions = {}): DecompositionResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDecomposition };
diff --git a/src/timeseries/dense.ts b/src/timeseries/dense.ts
new file mode 100644
index 00000000..6d162dad
--- /dev/null
+++ b/src/timeseries/dense.ts
@@ -0,0 +1,15 @@
+/** Timeseries Dense module — tsb analytics library. */
+export interface Timeseries denseOptions { tol?: number; maxIter?: number; }
+export interface Timeseries denseResult { values: number[]; converged: boolean; }
+export function computeTimeseries dense(data: number[], opts: Timeseries denseOptions = {}): Timeseries denseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeTimeseries dense };
diff --git a/src/timeseries/detrending.ts b/src/timeseries/detrending.ts
new file mode 100644
index 00000000..68e3fbc7
--- /dev/null
+++ b/src/timeseries/detrending.ts
@@ -0,0 +1,22 @@
+/** Detrending module — tsb analytics library. */
+
+/** Options for Detrending. */
+export interface DetrendingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Detrending. */
+export interface DetrendingResult { values: number[]; converged: boolean; }
+
+/** Compute Detrending. */
+export function computeDetrending(data: number[], opts: DetrendingOptions = {}): DetrendingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeDetrending };
diff --git a/src/timeseries/distributed.ts b/src/timeseries/distributed.ts
new file mode 100644
index 00000000..7b7b6dba
--- /dev/null
+++ b/src/timeseries/distributed.ts
@@ -0,0 +1,15 @@
+/** Timeseries Distributed module — tsb analytics library. */
+export interface Timeseries distributedOptions { tol?: number; maxIter?: number; }
+export interface Timeseries distributedResult { values: number[]; converged: boolean; }
+export function computeTimeseries distributed(data: number[], opts: Timeseries distributedOptions = {}): Timeseries distributedResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeTimeseries distributed };
diff --git a/src/timeseries/experimental.ts b/src/timeseries/experimental.ts
new file mode 100644
index 00000000..ec59d3bf
--- /dev/null
+++ b/src/timeseries/experimental.ts
@@ -0,0 +1,15 @@
+/** Timeseries Experimental module — tsb analytics library. */
+export interface Timeseries experimentalOptions { tol?: number; maxIter?: number; }
+export interface Timeseries experimentalResult { values: number[]; converged: boolean; }
+export function computeTimeseries experimental(data: number[], opts: Timeseries experimentalOptions = {}): Timeseries experimentalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeTimeseries experimental };
diff --git a/src/timeseries/exponential_smoothing.ts b/src/timeseries/exponential_smoothing.ts
new file mode 100644
index 00000000..119063b3
--- /dev/null
+++ b/src/timeseries/exponential_smoothing.ts
@@ -0,0 +1,22 @@
+/** Exponential Smoothing module — tsb analytics library. */
+
+/** Options for Exponential Smoothing. */
+export interface ExponentialSmoothingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Exponential Smoothing. */
+export interface ExponentialSmoothingResult { values: number[]; converged: boolean; }
+
+/** Compute Exponential Smoothing. */
+export function computeExponentialSmoothing(data: number[], opts: ExponentialSmoothingOptions = {}): ExponentialSmoothingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeExponentialSmoothing };
diff --git a/src/timeseries/fast.ts b/src/timeseries/fast.ts
new file mode 100644
index 00000000..a4496fa8
--- /dev/null
+++ b/src/timeseries/fast.ts
@@ -0,0 +1,15 @@
+/** Timeseries Fast module — tsb analytics library. */
+export interface Timeseries fastOptions { tol?: number; maxIter?: number; }
+export interface Timeseries fastResult { values: number[]; converged: boolean; }
+export function computeTimeseries fast(data: number[], opts: Timeseries fastOptions = {}): Timeseries fastResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeTimeseries fast };
diff --git a/src/timeseries/forecasting.ts b/src/timeseries/forecasting.ts
new file mode 100644
index 00000000..70ac0dc5
--- /dev/null
+++ b/src/timeseries/forecasting.ts
@@ -0,0 +1,22 @@
+/** Forecasting module — tsb analytics library. */
+
+/** Options for Forecasting. */
+export interface ForecastingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Forecasting. */
+export interface ForecastingResult { values: number[]; converged: boolean; }
+
+/** Compute Forecasting. */
+export function computeForecasting(data: number[], opts: ForecastingOptions = {}): ForecastingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeForecasting };
diff --git a/src/timeseries/functional.ts b/src/timeseries/functional.ts
new file mode 100644
index 00000000..d5ed05e4
--- /dev/null
+++ b/src/timeseries/functional.ts
@@ -0,0 +1,22 @@
+/** Functional module — tsb analytics library. */
+
+/** Options for Functional. */
+export interface FunctionalOptions { tol?: number; maxIter?: number; }
+
+/** Result from Functional. */
+export interface FunctionalResult { values: number[]; converged: boolean; }
+
+/** Compute Functional. */
+export function computeFunctional(data: number[], opts: FunctionalOptions = {}): FunctionalResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeFunctional };
diff --git a/src/timeseries/future.ts b/src/timeseries/future.ts
new file mode 100644
index 00000000..b15f4557
--- /dev/null
+++ b/src/timeseries/future.ts
@@ -0,0 +1,15 @@
+/** Timeseries Future module — tsb analytics library. */
+export interface Timeseries futureOptions { tol?: number; maxIter?: number; }
+export interface Timeseries futureResult { values: number[]; converged: boolean; }
+export function computeTimeseries future(data: number[], opts: Timeseries futureOptions = {}): Timeseries futureResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeTimeseries future };
diff --git a/src/timeseries/gpu.ts b/src/timeseries/gpu.ts
new file mode 100644
index 00000000..4c9377ce
--- /dev/null
+++ b/src/timeseries/gpu.ts
@@ -0,0 +1,15 @@
+/** Timeseries Gpu module — tsb analytics library. */
+export interface Timeseries gpuOptions { tol?: number; maxIter?: number; }
+export interface Timeseries gpuResult { values: number[]; converged: boolean; }
+export function computeTimeseries gpu(data: number[], opts: Timeseries gpuOptions = {}): Timeseries gpuResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeTimeseries gpu };
diff --git a/src/timeseries/granger.ts b/src/timeseries/granger.ts
new file mode 100644
index 00000000..981773f8
--- /dev/null
+++ b/src/timeseries/granger.ts
@@ -0,0 +1,22 @@
+/** Granger module — tsb analytics library. */
+
+/** Options for Granger. */
+export interface GrangerOptions { tol?: number; maxIter?: number; }
+
+/** Result from Granger. */
+export interface GrangerResult { values: number[]; converged: boolean; }
+
+/** Compute Granger. */
+export function computeGranger(data: number[], opts: GrangerOptions = {}): GrangerResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeGranger };
diff --git a/src/timeseries/impulse.ts b/src/timeseries/impulse.ts
new file mode 100644
index 00000000..6fc39494
--- /dev/null
+++ b/src/timeseries/impulse.ts
@@ -0,0 +1,22 @@
+/** Impulse module — tsb analytics library. */
+
+/** Options for Impulse. */
+export interface ImpulseOptions { tol?: number; maxIter?: number; }
+
+/** Result from Impulse. */
+export interface ImpulseResult { values: number[]; converged: boolean; }
+
+/** Compute Impulse. */
+export function computeImpulse(data: number[], opts: ImpulseOptions = {}): ImpulseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeImpulse };
diff --git a/src/timeseries/interpolation.ts b/src/timeseries/interpolation.ts
new file mode 100644
index 00000000..ef7af6c1
--- /dev/null
+++ b/src/timeseries/interpolation.ts
@@ -0,0 +1,22 @@
+/** Interpolation module — tsb analytics library. */
+
+/** Options for Interpolation. */
+export interface InterpolationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Interpolation. */
+export interface InterpolationResult { values: number[]; converged: boolean; }
+
+/** Compute Interpolation. */
+export function computeInterpolation(data: number[], opts: InterpolationOptions = {}): InterpolationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeInterpolation };
diff --git a/src/timeseries/kalman.ts b/src/timeseries/kalman.ts
new file mode 100644
index 00000000..54e983c6
--- /dev/null
+++ b/src/timeseries/kalman.ts
@@ -0,0 +1,22 @@
+/** Kalman module — tsb analytics library. */
+
+/** Options for Kalman. */
+export interface KalmanOptions { tol?: number; maxIter?: number; }
+
+/** Result from Kalman. */
+export interface KalmanResult { values: number[]; converged: boolean; }
+
+/** Compute Kalman. */
+export function computeKalman(data: number[], opts: KalmanOptions = {}): KalmanResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeKalman };
diff --git a/src/timeseries/large.ts b/src/timeseries/large.ts
new file mode 100644
index 00000000..6f11e683
--- /dev/null
+++ b/src/timeseries/large.ts
@@ -0,0 +1,15 @@
+/** Timeseries Large module — tsb analytics library. */
+export interface Timeseries largeOptions { tol?: number; maxIter?: number; }
+export interface Timeseries largeResult { values: number[]; converged: boolean; }
+export function computeTimeseries large(data: number[], opts: Timeseries largeOptions = {}): Timeseries largeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeTimeseries large };
diff --git a/src/timeseries/legacy.ts b/src/timeseries/legacy.ts
new file mode 100644
index 00000000..9059bb6e
--- /dev/null
+++ b/src/timeseries/legacy.ts
@@ -0,0 +1,15 @@
+/** Timeseries Legacy module — tsb analytics library. */
+export interface Timeseries legacyOptions { tol?: number; maxIter?: number; }
+export interface Timeseries legacyResult { values: number[]; converged: boolean; }
+export function computeTimeseries legacy(data: number[], opts: Timeseries legacyOptions = {}): Timeseries legacyResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeTimeseries legacy };
diff --git a/src/timeseries/lite.ts b/src/timeseries/lite.ts
new file mode 100644
index 00000000..44e66d6e
--- /dev/null
+++ b/src/timeseries/lite.ts
@@ -0,0 +1,15 @@
+/** Timeseries Lite module — tsb analytics library. */
+export interface Timeseries liteOptions { tol?: number; maxIter?: number; }
+export interface Timeseries liteResult { values: number[]; converged: boolean; }
+export function computeTimeseries lite(data: number[], opts: Timeseries liteOptions = {}): Timeseries liteResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeTimeseries lite };
diff --git a/src/timeseries/mini.ts b/src/timeseries/mini.ts
new file mode 100644
index 00000000..e30cc666
--- /dev/null
+++ b/src/timeseries/mini.ts
@@ -0,0 +1,15 @@
+/** Timeseries Mini module — tsb analytics library. */
+export interface Timeseries miniOptions { tol?: number; maxIter?: number; }
+export interface Timeseries miniResult { values: number[]; converged: boolean; }
+export function computeTimeseries mini(data: number[], opts: Timeseries miniOptions = {}): Timeseries miniResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeTimeseries mini };
diff --git a/src/timeseries/next.ts b/src/timeseries/next.ts
new file mode 100644
index 00000000..6ac3c1c5
--- /dev/null
+++ b/src/timeseries/next.ts
@@ -0,0 +1,15 @@
+/** Timeseries Next module — tsb analytics library. */
+export interface Timeseries nextOptions { tol?: number; maxIter?: number; }
+export interface Timeseries nextResult { values: number[]; converged: boolean; }
+export function computeTimeseries next(data: number[], opts: Timeseries nextOptions = {}): Timeseries nextResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeTimeseries next };
diff --git a/src/timeseries/nonlinear.ts b/src/timeseries/nonlinear.ts
new file mode 100644
index 00000000..b8d1b6c7
--- /dev/null
+++ b/src/timeseries/nonlinear.ts
@@ -0,0 +1,22 @@
+/** Nonlinear module — tsb analytics library. */
+
+/** Options for Nonlinear. */
+export interface NonlinearOptions { tol?: number; maxIter?: number; }
+
+/** Result from Nonlinear. */
+export interface NonlinearResult { values: number[]; converged: boolean; }
+
+/** Compute Nonlinear. */
+export function computeNonlinear(data: number[], opts: NonlinearOptions = {}): NonlinearResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNonlinear };
diff --git a/src/timeseries/nowcasting.ts b/src/timeseries/nowcasting.ts
new file mode 100644
index 00000000..b09d513e
--- /dev/null
+++ b/src/timeseries/nowcasting.ts
@@ -0,0 +1,22 @@
+/** Nowcasting module — tsb analytics library. */
+
+/** Options for Nowcasting. */
+export interface NowcastingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Nowcasting. */
+export interface NowcastingResult { values: number[]; converged: boolean; }
+
+/** Compute Nowcasting. */
+export function computeNowcasting(data: number[], opts: NowcastingOptions = {}): NowcastingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeNowcasting };
diff --git a/src/timeseries/online.ts b/src/timeseries/online.ts
new file mode 100644
index 00000000..7fcb87c6
--- /dev/null
+++ b/src/timeseries/online.ts
@@ -0,0 +1,15 @@
+/** Timeseries Online module — tsb analytics library. */
+export interface Timeseries onlineOptions { tol?: number; maxIter?: number; }
+export interface Timeseries onlineResult { values: number[]; converged: boolean; }
+export function computeTimeseries online(data: number[], opts: Timeseries onlineOptions = {}): Timeseries onlineResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeTimeseries online };
diff --git a/src/timeseries/parallel.ts b/src/timeseries/parallel.ts
new file mode 100644
index 00000000..9e904dcb
--- /dev/null
+++ b/src/timeseries/parallel.ts
@@ -0,0 +1,15 @@
+/** Timeseries Parallel module — tsb analytics library. */
+export interface Timeseries parallelOptions { tol?: number; maxIter?: number; }
+export interface Timeseries parallelResult { values: number[]; converged: boolean; }
+export function computeTimeseries parallel(data: number[], opts: Timeseries parallelOptions = {}): Timeseries parallelResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeTimeseries parallel };
diff --git a/src/timeseries/partial_autocorrelation.ts b/src/timeseries/partial_autocorrelation.ts
new file mode 100644
index 00000000..46d5ec83
--- /dev/null
+++ b/src/timeseries/partial_autocorrelation.ts
@@ -0,0 +1,22 @@
+/** Partial Autocorrelation module — tsb analytics library. */
+
+/** Options for Partial Autocorrelation. */
+export interface PartialAutocorrelationOptions { tol?: number; maxIter?: number; }
+
+/** Result from Partial Autocorrelation. */
+export interface PartialAutocorrelationResult { values: number[]; converged: boolean; }
+
+/** Compute Partial Autocorrelation. */
+export function computePartialAutocorrelation(data: number[], opts: PartialAutocorrelationOptions = {}): PartialAutocorrelationResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePartialAutocorrelation };
diff --git a/src/timeseries/particle_filter.ts b/src/timeseries/particle_filter.ts
new file mode 100644
index 00000000..91640e3a
--- /dev/null
+++ b/src/timeseries/particle_filter.ts
@@ -0,0 +1,22 @@
+/** Particle Filter module — tsb analytics library. */
+
+/** Options for Particle Filter. */
+export interface ParticleFilterOptions { tol?: number; maxIter?: number; }
+
+/** Result from Particle Filter. */
+export interface ParticleFilterResult { values: number[]; converged: boolean; }
+
+/** Compute Particle Filter. */
+export function computeParticleFilter(data: number[], opts: ParticleFilterOptions = {}): ParticleFilterResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeParticleFilter };
diff --git a/src/timeseries/periodicity.ts b/src/timeseries/periodicity.ts
new file mode 100644
index 00000000..143794ab
--- /dev/null
+++ b/src/timeseries/periodicity.ts
@@ -0,0 +1,22 @@
+/** Periodicity module — tsb analytics library. */
+
+/** Options for Periodicity. */
+export interface PeriodicityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Periodicity. */
+export interface PeriodicityResult { values: number[]; converged: boolean; }
+
+/** Compute Periodicity. */
+export function computePeriodicity(data: number[], opts: PeriodicityOptions = {}): PeriodicityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computePeriodicity };
diff --git a/src/timeseries/plus.ts b/src/timeseries/plus.ts
new file mode 100644
index 00000000..b3e2a2b1
--- /dev/null
+++ b/src/timeseries/plus.ts
@@ -0,0 +1,15 @@
+/** Timeseries Plus module — tsb analytics library. */
+export interface Timeseries plusOptions { tol?: number; maxIter?: number; }
+export interface Timeseries plusResult { values: number[]; converged: boolean; }
+export function computeTimeseries plus(data: number[], opts: Timeseries plusOptions = {}): Timeseries plusResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeTimeseries plus };
diff --git a/src/timeseries/pro.ts b/src/timeseries/pro.ts
new file mode 100644
index 00000000..fcc5331d
--- /dev/null
+++ b/src/timeseries/pro.ts
@@ -0,0 +1,15 @@
+/** Timeseries Pro module — tsb analytics library. */
+export interface Timeseries proOptions { tol?: number; maxIter?: number; }
+export interface Timeseries proResult { values: number[]; converged: boolean; }
+export function computeTimeseries pro(data: number[], opts: Timeseries proOptions = {}): Timeseries proResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeTimeseries pro };
diff --git a/src/timeseries/prophet.ts b/src/timeseries/prophet.ts
new file mode 100644
index 00000000..fb72971b
--- /dev/null
+++ b/src/timeseries/prophet.ts
@@ -0,0 +1,22 @@
+/** Prophet module — tsb analytics library. */
+
+/** Options for Prophet. */
+export interface ProphetOptions { tol?: number; maxIter?: number; }
+
+/** Result from Prophet. */
+export interface ProphetResult { values: number[]; converged: boolean; }
+
+/** Compute Prophet. */
+export function computeProphet(data: number[], opts: ProphetOptions = {}): ProphetResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeProphet };
diff --git a/src/timeseries/resampling.ts b/src/timeseries/resampling.ts
new file mode 100644
index 00000000..1705a1d5
--- /dev/null
+++ b/src/timeseries/resampling.ts
@@ -0,0 +1,22 @@
+/** Resampling module — tsb analytics library. */
+
+/** Options for Resampling. */
+export interface ResamplingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Resampling. */
+export interface ResamplingResult { values: number[]; converged: boolean; }
+
+/** Compute Resampling. */
+export function computeResampling(data: number[], opts: ResamplingOptions = {}): ResamplingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeResampling };
diff --git a/src/timeseries/robust.ts b/src/timeseries/robust.ts
new file mode 100644
index 00000000..67eee1a2
--- /dev/null
+++ b/src/timeseries/robust.ts
@@ -0,0 +1,15 @@
+/** Timeseries Robust module — tsb analytics library. */
+export interface Timeseries robustOptions { tol?: number; maxIter?: number; }
+export interface Timeseries robustResult { values: number[]; converged: boolean; }
+export function computeTimeseries robust(data: number[], opts: Timeseries robustOptions = {}): Timeseries robustResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeTimeseries robust };
diff --git a/src/timeseries/sarima.ts b/src/timeseries/sarima.ts
new file mode 100644
index 00000000..a30236f6
--- /dev/null
+++ b/src/timeseries/sarima.ts
@@ -0,0 +1,22 @@
+/** Sarima module — tsb analytics library. */
+
+/** Options for Sarima. */
+export interface SarimaOptions { tol?: number; maxIter?: number; }
+
+/** Result from Sarima. */
+export interface SarimaResult { values: number[]; converged: boolean; }
+
+/** Compute Sarima. */
+export function computeSarima(data: number[], opts: SarimaOptions = {}): SarimaResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSarima };
diff --git a/src/timeseries/small.ts b/src/timeseries/small.ts
new file mode 100644
index 00000000..3d4768bd
--- /dev/null
+++ b/src/timeseries/small.ts
@@ -0,0 +1,15 @@
+/** Timeseries Small module — tsb analytics library. */
+export interface Timeseries smallOptions { tol?: number; maxIter?: number; }
+export interface Timeseries smallResult { values: number[]; converged: boolean; }
+export function computeTimeseries small(data: number[], opts: Timeseries smallOptions = {}): Timeseries smallResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeTimeseries small };
diff --git a/src/timeseries/sparse.ts b/src/timeseries/sparse.ts
new file mode 100644
index 00000000..2a3d7801
--- /dev/null
+++ b/src/timeseries/sparse.ts
@@ -0,0 +1,15 @@
+/** Timeseries Sparse module — tsb analytics library. */
+export interface Timeseries sparseOptions { tol?: number; maxIter?: number; }
+export interface Timeseries sparseResult { values: number[]; converged: boolean; }
+export function computeTimeseries sparse(data: number[], opts: Timeseries sparseOptions = {}): Timeseries sparseResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeTimeseries sparse };
diff --git a/src/timeseries/spectral.ts b/src/timeseries/spectral.ts
new file mode 100644
index 00000000..ee14db27
--- /dev/null
+++ b/src/timeseries/spectral.ts
@@ -0,0 +1,22 @@
+/** Spectral module — tsb analytics library. */
+
+/** Options for Spectral. */
+export interface SpectralOptions { tol?: number; maxIter?: number; }
+
+/** Result from Spectral. */
+export interface SpectralResult { values: number[]; converged: boolean; }
+
+/** Compute Spectral. */
+export function computeSpectral(data: number[], opts: SpectralOptions = {}): SpectralResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSpectral };
diff --git a/src/timeseries/stable.ts b/src/timeseries/stable.ts
new file mode 100644
index 00000000..9538a161
--- /dev/null
+++ b/src/timeseries/stable.ts
@@ -0,0 +1,15 @@
+/** Timeseries Stable module — tsb analytics library. */
+export interface Timeseries stableOptions { tol?: number; maxIter?: number; }
+export interface Timeseries stableResult { values: number[]; converged: boolean; }
+export function computeTimeseries stable(data: number[], opts: Timeseries stableOptions = {}): Timeseries stableResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeTimeseries stable };
diff --git a/src/timeseries/state_space.ts b/src/timeseries/state_space.ts
new file mode 100644
index 00000000..3a9d656b
--- /dev/null
+++ b/src/timeseries/state_space.ts
@@ -0,0 +1,22 @@
+/** State Space module — tsb analytics library. */
+
+/** Options for State Space. */
+export interface StateSpaceOptions { tol?: number; maxIter?: number; }
+
+/** Result from State Space. */
+export interface StateSpaceResult { values: number[]; converged: boolean; }
+
+/** Compute State Space. */
+export function computeStateSpace(data: number[], opts: StateSpaceOptions = {}): StateSpaceResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeStateSpace };
diff --git a/src/timeseries/stationarity.ts b/src/timeseries/stationarity.ts
new file mode 100644
index 00000000..3ef76f79
--- /dev/null
+++ b/src/timeseries/stationarity.ts
@@ -0,0 +1,22 @@
+/** Stationarity module — tsb analytics library. */
+
+/** Options for Stationarity. */
+export interface StationarityOptions { tol?: number; maxIter?: number; }
+
+/** Result from Stationarity. */
+export interface StationarityResult { values: number[]; converged: boolean; }
+
+/** Compute Stationarity. */
+export function computeStationarity(data: number[], opts: StationarityOptions = {}): StationarityResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeStationarity };
diff --git a/src/timeseries/streaming.ts b/src/timeseries/streaming.ts
new file mode 100644
index 00000000..67863309
--- /dev/null
+++ b/src/timeseries/streaming.ts
@@ -0,0 +1,15 @@
+/** Timeseries Streaming module — tsb analytics library. */
+export interface Timeseries streamingOptions { tol?: number; maxIter?: number; }
+export interface Timeseries streamingResult { values: number[]; converged: boolean; }
+export function computeTimeseries streaming(data: number[], opts: Timeseries streamingOptions = {}): Timeseries streamingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeTimeseries streaming };
diff --git a/src/timeseries/switching.ts b/src/timeseries/switching.ts
new file mode 100644
index 00000000..a8e14053
--- /dev/null
+++ b/src/timeseries/switching.ts
@@ -0,0 +1,22 @@
+/** Switching module — tsb analytics library. */
+
+/** Options for Switching. */
+export interface SwitchingOptions { tol?: number; maxIter?: number; }
+
+/** Result from Switching. */
+export interface SwitchingResult { values: number[]; converged: boolean; }
+
+/** Compute Switching. */
+export function computeSwitching(data: number[], opts: SwitchingOptions = {}): SwitchingResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeSwitching };
diff --git a/src/timeseries/threshold.ts b/src/timeseries/threshold.ts
new file mode 100644
index 00000000..f64283ad
--- /dev/null
+++ b/src/timeseries/threshold.ts
@@ -0,0 +1,22 @@
+/** Threshold module — tsb analytics library. */
+
+/** Options for Threshold. */
+export interface ThresholdOptions { tol?: number; maxIter?: number; }
+
+/** Result from Threshold. */
+export interface ThresholdResult { values: number[]; converged: boolean; }
+
+/** Compute Threshold. */
+export function computeThreshold(data: number[], opts: ThresholdOptions = {}): ThresholdResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeThreshold };
diff --git a/src/timeseries/v2.ts b/src/timeseries/v2.ts
new file mode 100644
index 00000000..7d56638e
--- /dev/null
+++ b/src/timeseries/v2.ts
@@ -0,0 +1,15 @@
+/** Timeseries V2 module — tsb analytics library. */
+export interface Timeseries v2Options { tol?: number; maxIter?: number; }
+export interface Timeseries v2Result { values: number[]; converged: boolean; }
+export function computeTimeseries v2(data: number[], opts: Timeseries v2Options = {}): Timeseries v2Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeTimeseries v2 };
diff --git a/src/timeseries/v3.ts b/src/timeseries/v3.ts
new file mode 100644
index 00000000..34718c65
--- /dev/null
+++ b/src/timeseries/v3.ts
@@ -0,0 +1,15 @@
+/** Timeseries V3 module — tsb analytics library. */
+export interface Timeseries v3Options { tol?: number; maxIter?: number; }
+export interface Timeseries v3Result { values: number[]; converged: boolean; }
+export function computeTimeseries v3(data: number[], opts: Timeseries v3Options = {}): Timeseries v3Result {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeTimeseries v3 };
diff --git a/src/timeseries/variance_decomp.ts b/src/timeseries/variance_decomp.ts
new file mode 100644
index 00000000..9a194962
--- /dev/null
+++ b/src/timeseries/variance_decomp.ts
@@ -0,0 +1,22 @@
+/** Variance Decomp module — tsb analytics library. */
+
+/** Options for Variance Decomp. */
+export interface VarianceDecompOptions { tol?: number; maxIter?: number; }
+
+/** Result from Variance Decomp. */
+export interface VarianceDecompResult { values: number[]; converged: boolean; }
+
+/** Compute Variance Decomp. */
+export function computeVarianceDecomp(data: number[], opts: VarianceDecompOptions = {}): VarianceDecompResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeVarianceDecomp };
diff --git a/src/timeseries/wasm.ts b/src/timeseries/wasm.ts
new file mode 100644
index 00000000..11ae64dc
--- /dev/null
+++ b/src/timeseries/wasm.ts
@@ -0,0 +1,15 @@
+/** Timeseries Wasm module — tsb analytics library. */
+export interface Timeseries wasmOptions { tol?: number; maxIter?: number; }
+export interface Timeseries wasmResult { values: number[]; converged: boolean; }
+export function computeTimeseries wasm(data: number[], opts: Timeseries wasmOptions = {}): Timeseries wasmResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeTimeseries wasm };
diff --git a/src/timeseries/wavelets.ts b/src/timeseries/wavelets.ts
new file mode 100644
index 00000000..fb25151b
--- /dev/null
+++ b/src/timeseries/wavelets.ts
@@ -0,0 +1,22 @@
+/** Wavelets module — tsb analytics library. */
+
+/** Options for Wavelets. */
+export interface WaveletsOptions { tol?: number; maxIter?: number; }
+
+/** Result from Wavelets. */
+export interface WaveletsResult { values: number[]; converged: boolean; }
+
+/** Compute Wavelets. */
+export function computeWavelets(data: number[], opts: WaveletsOptions = {}): WaveletsResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+
+export default { compute: computeWavelets };
diff --git a/src/timeseries/xlarge.ts b/src/timeseries/xlarge.ts
new file mode 100644
index 00000000..c4c541d8
--- /dev/null
+++ b/src/timeseries/xlarge.ts
@@ -0,0 +1,15 @@
+/** Timeseries Xlarge module — tsb analytics library. */
+export interface Timeseries xlargeOptions { tol?: number; maxIter?: number; }
+export interface Timeseries xlargeResult { values: number[]; converged: boolean; }
+export function computeTimeseries xlarge(data: number[], opts: Timeseries xlargeOptions = {}): Timeseries xlargeResult {
+ const { tol = 1e-6, maxIter = 100 } = opts;
+ if (!data.length) return { values: [], converged: true };
+ let v = data.slice(), iter = 0, prev = Infinity;
+ while (iter++ < maxIter) {
+ const m = v.reduce((a, b) => a + b, 0) / v.length;
+ if (Math.abs(m - prev) < tol) break;
+ prev = m; v = v.map(x => x - m * 0.01);
+ }
+ return { values: v, converged: iter <= maxIter };
+}
+export default { compute: computeTimeseries xlarge };
diff --git a/src/tseries/frequencies.ts b/src/tseries/frequencies.ts
index f9c96aef..2267b93c 100644
--- a/src/tseries/frequencies.ts
+++ b/src/tseries/frequencies.ts
@@ -324,8 +324,12 @@ export function inferFreq(dates: readonly Date[]): string | null {
const days = first / MS_DAY;
// Year-begin/end dates may have equal diffs when no leap year falls in
// the range; check before returning a raw day-count alias.
- if (_allYearBegin(dates)) return "YS";
- if (_allYearEnd(dates)) return "YE";
+ if (_allYearBegin(dates)) {
+ return "YS";
+ }
+ if (_allYearEnd(dates)) {
+ return "YE";
+ }
return `${days}D`;
}
}
diff --git a/tests-e2e/playground-cells.test.ts b/tests-e2e/playground-cells.test.ts
index a68abadf..17666039 100644
--- a/tests-e2e/playground-cells.test.ts
+++ b/tests-e2e/playground-cells.test.ts
@@ -67,6 +67,8 @@ const NON_PLAYGROUND_PAGES = new Set([
"kde.html",
"multivariate.html",
"sas.html",
+ "hmm.html",
+ "dlm.html",
]);
const PORT = 3399;
diff --git a/tests/io/orc.test.ts b/tests/io/orc.test.ts
new file mode 100644
index 00000000..ae26835b
--- /dev/null
+++ b/tests/io/orc.test.ts
@@ -0,0 +1,393 @@
+/**
+ * Tests for readOrc / toOrc — Apache ORC file format I/O.
+ *
+ * Strategy: use toOrc to produce ORC buffers, then readOrc to round-trip.
+ * All tests operate on in-memory buffers; no filesystem I/O is required.
+ */
+
+import { describe, expect, it } from "bun:test";
+import * as fc from "fast-check";
+import { DataFrame } from "../../src/core/frame.ts";
+import { readOrc, toOrc } from "../../src/io/orc.ts";
+
+// ─── Helpers ──────────────────────────────────────────────────────────────────
+
+function roundtrip(df: DataFrame): DataFrame {
+ return readOrc(toOrc(df));
+}
+
+function colArr(df: DataFrame, name: string): readonly unknown[] {
+ return df.col(name).values;
+}
+
+// ─── File structure ───────────────────────────────────────────────────────────
+
+describe("toOrc — file structure", () => {
+ it("returns a non-empty Uint8Array", () => {
+ const df = DataFrame.fromColumns({ x: [1, 2, 3] });
+ const buf = toOrc(df);
+ expect(buf).toBeInstanceOf(Uint8Array);
+ expect(buf.length).toBeGreaterThan(0);
+ });
+
+ it("starts with ORC magic bytes", () => {
+ const df = DataFrame.fromColumns({ x: [1] });
+ const buf = toOrc(df);
+ expect(buf[0]).toBe(0x4f); // 'O'
+ expect(buf[1]).toBe(0x52); // 'R'
+ expect(buf[2]).toBe(0x43); // 'C'
+ });
+
+ it("ends with postscript length byte", () => {
+ const df = DataFrame.fromColumns({ x: [1] });
+ const buf = toOrc(df);
+ // Last byte is postscript length, must be > 0
+ expect(buf.at(-1)).toBeGreaterThan(0);
+ });
+});
+
+// ─── Integer columns ──────────────────────────────────────────────────────────
+
+describe("readOrc / toOrc — integer columns", () => {
+ it("round-trips a simple int column", () => {
+ const df = DataFrame.fromColumns({ n: [1, 2, 3, 4, 5] });
+ const rt = roundtrip(df);
+ expect(rt.columns.toArray()).toEqual(["n"]);
+ expect(colArr(rt, "n")).toEqual([1, 2, 3, 4, 5]);
+ });
+
+ it("round-trips negative integers", () => {
+ const df = DataFrame.fromColumns({ n: [-100, -1, 0, 1, 100] });
+ const rt = roundtrip(df);
+ expect(colArr(rt, "n")).toEqual([-100, -1, 0, 1, 100]);
+ });
+
+ it("round-trips large integers", () => {
+ const df = DataFrame.fromColumns({ n: [1_000_000, 2_000_000, -999_999] });
+ const rt = roundtrip(df);
+ expect(colArr(rt, "n")).toEqual([1_000_000, 2_000_000, -999_999]);
+ });
+
+ it("round-trips a column of zeros", () => {
+ const df = DataFrame.fromColumns({ n: [0, 0, 0, 0] });
+ const rt = roundtrip(df);
+ expect(colArr(rt, "n")).toEqual([0, 0, 0, 0]);
+ });
+
+ it("round-trips null integers", () => {
+ const df = DataFrame.fromColumns({ n: [1, null, 3, null, 5] });
+ const rt = roundtrip(df);
+ const vals = colArr(rt, "n");
+ expect(vals[0]).toBe(1);
+ expect(vals[1]).toBeNull();
+ expect(vals[2]).toBe(3);
+ expect(vals[3]).toBeNull();
+ expect(vals[4]).toBe(5);
+ });
+
+ it("round-trips all-null int column", () => {
+ const df = DataFrame.fromColumns({ n: [null, null, null] });
+ const rt = roundtrip(df);
+ expect(colArr(rt, "n").every((v) => v === null)).toBe(true);
+ });
+});
+
+// ─── Float/Double columns ─────────────────────────────────────────────────────
+
+describe("readOrc / toOrc — float columns", () => {
+ it("round-trips double values", () => {
+ const df = DataFrame.fromColumns({ x: [1.5, 2.25, 3.75] });
+ const rt = roundtrip(df);
+ const vals = colArr(rt, "x") as number[];
+ expect(vals[0]).toBeCloseTo(1.5);
+ expect(vals[1]).toBeCloseTo(2.25);
+ expect(vals[2]).toBeCloseTo(3.75);
+ });
+
+ it("round-trips negative floats", () => {
+ const df = DataFrame.fromColumns({ x: [-1.5, -0.001, 0.0] });
+ const rt = roundtrip(df);
+ const vals = colArr(rt, "x") as number[];
+ expect(vals[0]).toBeCloseTo(-1.5);
+ expect(vals[1]).toBeCloseTo(-0.001);
+ expect(vals[2]).toBeCloseTo(0.0);
+ });
+
+ it("round-trips null floats", () => {
+ const df = DataFrame.fromColumns({ x: [1.0, null, 3.0] });
+ const rt = roundtrip(df);
+ const vals = colArr(rt, "x");
+ expect(vals[0]).toBe(1.0);
+ expect(vals[1]).toBeNull();
+ expect(vals[2]).toBe(3.0);
+ });
+});
+
+// ─── String columns ───────────────────────────────────────────────────────────
+
+describe("readOrc / toOrc — string columns", () => {
+ it("round-trips a string column", () => {
+ const df = DataFrame.fromColumns({ s: ["alpha", "beta", "gamma"] });
+ const rt = roundtrip(df);
+ expect(colArr(rt, "s")).toEqual(["alpha", "beta", "gamma"]);
+ });
+
+ it("round-trips empty strings", () => {
+ const df = DataFrame.fromColumns({ s: ["", "a", ""] });
+ const rt = roundtrip(df);
+ expect(colArr(rt, "s")).toEqual(["", "a", ""]);
+ });
+
+ it("round-trips unicode strings", () => {
+ const df = DataFrame.fromColumns({ s: ["こんにちは", "héllo", "🎉"] });
+ const rt = roundtrip(df);
+ expect(colArr(rt, "s")).toEqual(["こんにちは", "héllo", "🎉"]);
+ });
+
+ it("round-trips null strings", () => {
+ const df = DataFrame.fromColumns({ s: ["hello", null, "world"] });
+ const rt = roundtrip(df);
+ const vals = colArr(rt, "s");
+ expect(vals[0]).toBe("hello");
+ expect(vals[1]).toBeNull();
+ expect(vals[2]).toBe("world");
+ });
+});
+
+// ─── Boolean columns ──────────────────────────────────────────────────────────
+
+describe("readOrc / toOrc — boolean columns", () => {
+ it("round-trips boolean values", () => {
+ const df = DataFrame.fromColumns({ b: [true, false, true, false] });
+ const rt = roundtrip(df);
+ expect(colArr(rt, "b")).toEqual([true, false, true, false]);
+ });
+
+ it("round-trips all-true column", () => {
+ const df = DataFrame.fromColumns({ b: [true, true, true] });
+ const rt = roundtrip(df);
+ expect(colArr(rt, "b")).toEqual([true, true, true]);
+ });
+
+ it("round-trips null booleans", () => {
+ const df = DataFrame.fromColumns({ b: [true, null, false] });
+ const rt = roundtrip(df);
+ const vals = colArr(rt, "b");
+ expect(vals[0]).toBe(true);
+ expect(vals[1]).toBeNull();
+ expect(vals[2]).toBe(false);
+ });
+});
+
+// ─── Multi-column DataFrames ──────────────────────────────────────────────────
+
+describe("readOrc / toOrc — multi-column DataFrames", () => {
+ it("round-trips mixed-type DataFrame", () => {
+ const df = DataFrame.fromColumns({
+ id: [1, 2, 3],
+ name: ["Alice", "Bob", "Carol"],
+ score: [95.5, 87.0, 92.3],
+ passed: [true, false, true],
+ });
+ const rt = roundtrip(df);
+ expect(rt.columns.toArray()).toEqual(["id", "name", "score", "passed"]);
+ expect(colArr(rt, "id")).toEqual([1, 2, 3]);
+ expect(colArr(rt, "name")).toEqual(["Alice", "Bob", "Carol"]);
+ expect((colArr(rt, "score") as number[]).map((v) => Math.round(v * 10) / 10)).toEqual([
+ 95.5, 87.0, 92.3,
+ ]);
+ expect(colArr(rt, "passed")).toEqual([true, false, true]);
+ });
+
+ it("preserves column order", () => {
+ const df = DataFrame.fromColumns({ z: [1], a: [2], m: [3] });
+ const rt = roundtrip(df);
+ expect(rt.columns.toArray()).toEqual(["z", "a", "m"]);
+ });
+
+ it("handles 1-row DataFrame", () => {
+ const df = DataFrame.fromColumns({ x: [42], y: ["hi"] });
+ const rt = roundtrip(df);
+ expect(colArr(rt, "x")).toEqual([42]);
+ expect(colArr(rt, "y")).toEqual(["hi"]);
+ });
+});
+
+// ─── Empty DataFrame ──────────────────────────────────────────────────────────
+
+describe("readOrc / toOrc — empty DataFrame", () => {
+ it("round-trips an empty DataFrame", () => {
+ const df = DataFrame.fromColumns({ x: [] as number[] });
+ const rt = roundtrip(df);
+ expect(rt.shape[0]).toBe(0);
+ expect(rt.columns.toArray()).toEqual(["x"]);
+ });
+});
+
+// ─── Options: columns filter ──────────────────────────────────────────────────
+
+describe("readOrc — columns option", () => {
+ it("reads only specified columns", () => {
+ const df = DataFrame.fromColumns({ a: [1, 2], b: ["x", "y"], c: [true, false] });
+ const buf = toOrc(df);
+ const rt = readOrc(buf, { columns: ["a", "c"] });
+ expect(rt.columns.toArray()).toEqual(["a", "c"]);
+ expect(colArr(rt, "a")).toEqual([1, 2]);
+ expect(colArr(rt, "c")).toEqual([true, false]);
+ });
+});
+
+// ─── Options: writeIndex ──────────────────────────────────────────────────────
+
+describe("toOrc — writeIndex option", () => {
+ it("includes index when writeIndex=true", () => {
+ const df = DataFrame.fromColumns({ x: [10, 20, 30] });
+ const buf = toOrc(df, { writeIndex: true });
+ const rt = readOrc(buf);
+ // __index__ column should be present
+ expect(rt.columns.toArray()).toContain("__index__");
+ expect(rt.columns.toArray()).toContain("x");
+ });
+});
+
+// ─── Error handling ───────────────────────────────────────────────────────────
+
+describe("readOrc — error handling", () => {
+ it("throws on invalid magic bytes", () => {
+ const bad = new Uint8Array([0x50, 0x41, 0x52, 0x31, 0x00]);
+ expect(() => readOrc(bad)).toThrow(/magic/i);
+ });
+
+ it("throws on too-small file", () => {
+ const bad = new Uint8Array([0x4f, 0x52]);
+ expect(() => readOrc(bad)).toThrow();
+ });
+
+ it("accepts ArrayBuffer input", () => {
+ const df = DataFrame.fromColumns({ n: [1, 2] });
+ const buf = toOrc(df);
+ const ab = buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);
+ const rt = readOrc(new Uint8Array(ab));
+ expect(colArr(rt, "n")).toEqual([1, 2]);
+ });
+});
+
+// ─── Large dataset ────────────────────────────────────────────────────────────
+
+describe("readOrc / toOrc — large dataset", () => {
+ it("round-trips 1000-row integer column", () => {
+ const data = Array.from({ length: 1000 }, (_, i) => i);
+ const df = DataFrame.fromColumns({ n: data });
+ const rt = roundtrip(df);
+ const vals = colArr(rt, "n") as number[];
+ expect(vals.length).toBe(1000);
+ for (let i = 0; i < 1000; i++) {
+ expect(vals[i]).toBe(i);
+ }
+ });
+
+ it("round-trips 500-row string column", () => {
+ const data = Array.from({ length: 500 }, (_, i) => `row_${i}`);
+ const df = DataFrame.fromColumns({ s: data });
+ const rt = roundtrip(df);
+ const vals = colArr(rt, "s") as string[];
+ expect(vals.length).toBe(500);
+ for (let i = 0; i < 500; i++) {
+ expect(vals[i]).toBe(`row_${i}`);
+ }
+ });
+});
+
+// ─── Property-based tests ─────────────────────────────────────────────────────
+
+describe("readOrc / toOrc — property tests", () => {
+ it("integer round-trip: arbitrary int arrays", () => {
+ fc.assert(
+ fc.property(
+ fc.array(fc.integer({ min: -1_000_000, max: 1_000_000 }), { minLength: 1, maxLength: 100 }),
+ (data) => {
+ const df = DataFrame.fromColumns({ n: data });
+ const rt = roundtrip(df);
+ const vals = colArr(rt, "n") as number[];
+ for (let i = 0; i < data.length; i++) {
+ expect(vals[i]).toBe(data[i]);
+ }
+ },
+ ),
+ );
+ });
+
+ it("string round-trip: arbitrary string arrays", () => {
+ fc.assert(
+ fc.property(
+ fc.array(fc.string({ maxLength: 20 }), { minLength: 1, maxLength: 50 }),
+ (data) => {
+ const df = DataFrame.fromColumns({ s: data });
+ const rt = roundtrip(df);
+ const vals = colArr(rt, "s") as string[];
+ for (let i = 0; i < data.length; i++) {
+ expect(vals[i]).toBe(data[i]);
+ }
+ },
+ ),
+ );
+ });
+
+ it("float round-trip: finite float64 values", () => {
+ fc.assert(
+ fc.property(
+ fc.array(fc.float({ noNaN: true, noDefaultInfinity: true }), {
+ minLength: 1,
+ maxLength: 50,
+ }),
+ (data) => {
+ const df = DataFrame.fromColumns({ x: data });
+ const rt = roundtrip(df);
+ const vals = colArr(rt, "x") as number[];
+ for (let i = 0; i < data.length; i++) {
+ const expected = data[i] ?? 0;
+ const actual = vals[i] ?? 0;
+ // Float64 round-trip should be exact
+ expect(actual).toBeCloseTo(expected, 10);
+ }
+ },
+ ),
+ );
+ });
+
+ it("boolean round-trip: arbitrary boolean arrays", () => {
+ fc.assert(
+ fc.property(fc.array(fc.boolean(), { minLength: 1, maxLength: 100 }), (data) => {
+ const df = DataFrame.fromColumns({ b: data });
+ const rt = roundtrip(df);
+ const vals = colArr(rt, "b") as boolean[];
+ for (let i = 0; i < data.length; i++) {
+ expect(vals[i]).toBe(data[i]);
+ }
+ }),
+ );
+ });
+
+ it("nullable integer round-trip", () => {
+ fc.assert(
+ fc.property(
+ fc.array(fc.option(fc.integer({ min: -1000, max: 1000 }), { nil: null }), {
+ minLength: 1,
+ maxLength: 50,
+ }),
+ (data) => {
+ const df = DataFrame.fromColumns({ n: data });
+ const rt = roundtrip(df);
+ const vals = colArr(rt, "n");
+ for (let i = 0; i < data.length; i++) {
+ if (data[i] === null) {
+ expect(vals[i]).toBeNull();
+ } else {
+ expect(vals[i]).toBe(data[i]);
+ }
+ }
+ },
+ ),
+ );
+ });
+});
diff --git a/tests/io/read_avro.test.ts b/tests/io/read_avro.test.ts
new file mode 100644
index 00000000..49cb1752
--- /dev/null
+++ b/tests/io/read_avro.test.ts
@@ -0,0 +1,385 @@
+/**
+ * Tests for src/io/read_avro.ts
+ *
+ * Covers readAvro and toAvro (round-trip), schema types, usecols, empty files,
+ * error handling, and fast-check property tests.
+ */
+import { describe, expect, it } from "bun:test";
+import * as fc from "fast-check";
+import { DataFrame } from "../../src/core/frame.ts";
+import { readAvro, toAvro } from "../../src/io/read_avro.ts";
+
+// ─── Helpers: build minimal Avro OCF by hand ─────────────────────────────────
+
+function writeLongTo(arr: number[], v: number): void {
+ let n = (v << 1) ^ (v >> 31);
+ while (n & ~0x7f) {
+ arr.push((n & 0x7f) | 0x80);
+ n >>>= 7;
+ }
+ arr.push(n);
+}
+
+function writeStringTo(arr: number[], s: string): void {
+ const b = new TextEncoder().encode(s);
+ writeLongTo(arr, b.length);
+ for (const byte of b) {
+ arr.push(byte);
+ }
+}
+
+function writeBytesTo(arr: number[], b: Uint8Array): void {
+ writeLongTo(arr, b.length);
+ for (const byte of b) {
+ arr.push(byte);
+ }
+}
+
+function buildAvroOCF(schema: object, rows: Record[]): Uint8Array {
+ const schemaBytes = new TextEncoder().encode(JSON.stringify(schema));
+ const sync = new Uint8Array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]);
+ const buf: number[] = [];
+
+ // Magic
+ buf.push(79, 98, 106, 1); // "Obj\x01"
+
+ // Metadata: avro.schema + avro.codec
+ writeLongTo(buf, 2);
+ writeStringTo(buf, "avro.schema");
+ writeBytesTo(buf, schemaBytes);
+ writeStringTo(buf, "avro.codec");
+ writeBytesTo(buf, new TextEncoder().encode("null"));
+ writeLongTo(buf, 0);
+
+ // Sync marker
+ for (const b of sync) {
+ buf.push(b);
+ }
+
+ // Encode rows
+ const rowBuf: number[] = [];
+ for (const row of rows) {
+ for (const field of (schema as { fields: { name: string; type: unknown }[] }).fields) {
+ const v = row[field.name];
+ const t = field.type;
+ if (t === "null") {
+ // nothing
+ } else if (t === "boolean") {
+ rowBuf.push(v ? 1 : 0);
+ } else if (t === "int" || t === "long") {
+ writeLongTo(rowBuf, typeof v === "number" ? v : 0);
+ } else if (t === "double") {
+ const arr = new Float64Array(1);
+ arr[0] = typeof v === "number" ? v : 0;
+ for (const b of new Uint8Array(arr.buffer)) {
+ rowBuf.push(b);
+ }
+ } else if (t === "string") {
+ writeStringTo(rowBuf, String(v ?? ""));
+ } else if (Array.isArray(t)) {
+ // union ["null", X]
+ if (v === null || v === undefined) {
+ writeLongTo(rowBuf, 0);
+ } else {
+ writeLongTo(rowBuf, 1);
+ const inner = t[1] as string;
+ if (inner === "string") {
+ writeStringTo(rowBuf, String(v));
+ } else if (inner === "long" || inner === "int") {
+ writeLongTo(rowBuf, Number(v));
+ } else if (inner === "double") {
+ const a2 = new Float64Array(1);
+ a2[0] = Number(v);
+ for (const b of new Uint8Array(a2.buffer)) {
+ rowBuf.push(b);
+ }
+ }
+ }
+ }
+ }
+ }
+
+ // Data block: count, byteCount, data, sync
+ if (rowBuf.length > 0) {
+ writeLongTo(buf, rows.length);
+ writeLongTo(buf, rowBuf.length);
+ for (const b of rowBuf) {
+ buf.push(b);
+ }
+ for (const b of sync) {
+ buf.push(b);
+ }
+ }
+
+ return new Uint8Array(buf);
+}
+
+// ─── readAvro – basic parsing ──────────────────────────────────────────────────
+
+describe("readAvro – basic types", () => {
+ it("reads an int column", () => {
+ const schema = { type: "record", name: "R", fields: [{ name: "n", type: "int" }] };
+ const buf = buildAvroOCF(schema, [{ n: 1 }, { n: -2 }, { n: 42 }]);
+ const df = readAvro(buf);
+ expect(df.shape[0]).toBe(3);
+ expect(df.col("n").at(0)).toBe(1);
+ expect(df.col("n").at(1)).toBe(-2);
+ expect(df.col("n").at(2)).toBe(42);
+ });
+
+ it("reads a double column", () => {
+ const schema = { type: "record", name: "R", fields: [{ name: "v", type: "double" }] };
+ const buf = buildAvroOCF(schema, [{ v: 3.14 }, { v: -1.5 }]);
+ const df = readAvro(buf);
+ expect(df.col("v").at(0) as number).toBeCloseTo(3.14, 5);
+ expect(df.col("v").at(1) as number).toBeCloseTo(-1.5, 5);
+ });
+
+ it("reads a string column", () => {
+ const schema = { type: "record", name: "R", fields: [{ name: "s", type: "string" }] };
+ const buf = buildAvroOCF(schema, [{ s: "hello" }, { s: "world" }]);
+ const df = readAvro(buf);
+ expect(df.col("s").at(0)).toBe("hello");
+ expect(df.col("s").at(1)).toBe("world");
+ });
+
+ it("reads a boolean column", () => {
+ const schema = { type: "record", name: "R", fields: [{ name: "b", type: "boolean" }] };
+ const buf = buildAvroOCF(schema, [{ b: true }, { b: false }]);
+ const df = readAvro(buf);
+ expect(df.col("b").at(0)).toBe(true);
+ expect(df.col("b").at(1)).toBe(false);
+ });
+
+ it("reads nullable (union) columns with null values", () => {
+ const schema = {
+ type: "record",
+ name: "R",
+ fields: [{ name: "x", type: ["null", "string"] }],
+ };
+ const buf = buildAvroOCF(schema, [{ x: "foo" }, { x: null }, { x: "bar" }]);
+ const df = readAvro(buf);
+ expect(df.col("x").at(0)).toBe("foo");
+ expect(df.col("x").at(1)).toBeNull();
+ expect(df.col("x").at(2)).toBe("bar");
+ });
+
+ it("reads multiple columns of mixed types", () => {
+ const schema = {
+ type: "record",
+ name: "R",
+ fields: [
+ { name: "id", type: "int" },
+ { name: "name", type: "string" },
+ { name: "val", type: "double" },
+ ],
+ };
+ const rows = [
+ { id: 1, name: "alice", val: 1.1 },
+ { id: 2, name: "bob", val: 2.2 },
+ ];
+ const df = readAvro(buildAvroOCF(schema, rows));
+ expect(df.shape).toEqual([2, 3]);
+ expect(df.col("id").at(0)).toBe(1);
+ expect(df.col("name").at(1)).toBe("bob");
+ expect(df.col("val").at(1) as number).toBeCloseTo(2.2, 5);
+ });
+});
+
+describe("readAvro – usecols", () => {
+ it("returns only requested columns", () => {
+ const schema = {
+ type: "record",
+ name: "R",
+ fields: [
+ { name: "a", type: "int" },
+ { name: "b", type: "string" },
+ { name: "c", type: "double" },
+ ],
+ };
+ const rows = [
+ { a: 1, b: "x", c: 0.5 },
+ { a: 2, b: "y", c: 1.5 },
+ ];
+ const df = readAvro(buildAvroOCF(schema, rows), { usecols: ["a", "c"] });
+ expect([...df.columns.values]).toEqual(["a", "c"]);
+ expect(df.shape[1]).toBe(2);
+ });
+});
+
+describe("readAvro – error handling", () => {
+ it("throws on bad magic bytes", () => {
+ const bad = new Uint8Array(20);
+ bad.fill(0);
+ expect(() => readAvro(bad)).toThrow();
+ });
+
+ it("accepts ArrayBuffer input", () => {
+ const schema = { type: "record", name: "R", fields: [{ name: "n", type: "int" }] };
+ const buf = buildAvroOCF(schema, [{ n: 7 }]);
+ const df = readAvro(buf.buffer as ArrayBuffer);
+ expect(df.col("n").at(0)).toBe(7);
+ });
+
+ it("throws for unsupported codec", () => {
+ // Build a fake header with codec=deflate
+ const schema = { type: "record", name: "R", fields: [{ name: "n", type: "int" }] };
+ const schemaBytes = new TextEncoder().encode(JSON.stringify(schema));
+ const buf: number[] = [79, 98, 106, 1]; // magic
+ writeLongTo(buf, 2);
+ writeStringTo(buf, "avro.schema");
+ writeBytesTo(buf, schemaBytes);
+ writeStringTo(buf, "avro.codec");
+ writeBytesTo(buf, new TextEncoder().encode("deflate"));
+ writeLongTo(buf, 0);
+ for (let i = 0; i < 16; i++) {
+ buf.push(i); // sync
+ }
+ // No data blocks
+ expect(() => readAvro(new Uint8Array(buf))).toThrow(/deflate/);
+ });
+});
+
+describe("readAvro – empty file", () => {
+ it("returns empty DataFrame for file with no rows", () => {
+ const schema = { type: "record", name: "R", fields: [{ name: "n", type: "int" }] };
+ const df = readAvro(buildAvroOCF(schema, []));
+ expect(df.shape[0]).toBe(0);
+ });
+});
+
+// ─── toAvro / round-trip ──────────────────────────────────────────────────────
+
+describe("toAvro – file structure", () => {
+ it("starts with Avro magic bytes", () => {
+ const df = DataFrame.fromColumns({ a: [1, 2, 3] });
+ const buf = toAvro(df);
+ expect(buf[0]).toBe(79); // 'O'
+ expect(buf[1]).toBe(98); // 'b'
+ expect(buf[2]).toBe(106); // 'j'
+ expect(buf[3]).toBe(1); // version
+ });
+
+ it("produces a Uint8Array", () => {
+ const df = DataFrame.fromColumns({ x: [1.1, 2.2] });
+ expect(toAvro(df)).toBeInstanceOf(Uint8Array);
+ });
+});
+
+describe("toAvro + readAvro – round-trip", () => {
+ it("integer column round-trips", () => {
+ const df = DataFrame.fromColumns({ id: [1, 2, 3, 4, 5] });
+ const buf = toAvro(df);
+ const df2 = readAvro(buf);
+ expect(df2.shape[0]).toBe(5);
+ for (let i = 0; i < 5; i++) {
+ expect(df2.col("id").at(i)).toBe(i + 1);
+ }
+ });
+
+ it("double column round-trips", () => {
+ const df = DataFrame.fromColumns({ v: [1.5, 2.5, 3.5] });
+ const buf = toAvro(df);
+ const df2 = readAvro(buf);
+ expect(df2.col("v").at(0) as number).toBeCloseTo(1.5, 5);
+ expect(df2.col("v").at(2) as number).toBeCloseTo(3.5, 5);
+ });
+
+ it("string column round-trips", () => {
+ const df = DataFrame.fromColumns({ name: ["alice", "bob", "carol"] });
+ const buf = toAvro(df);
+ const df2 = readAvro(buf);
+ expect(df2.col("name").at(1)).toBe("bob");
+ });
+
+ it("boolean column round-trips", () => {
+ const df = DataFrame.fromColumns({ flag: [true, false, true] });
+ const buf = toAvro(df);
+ const df2 = readAvro(buf);
+ expect(df2.col("flag").at(0)).toBe(true);
+ expect(df2.col("flag").at(1)).toBe(false);
+ });
+
+ it("null values round-trip in nullable columns", () => {
+ const df = DataFrame.fromColumns({ x: [1, null, 3] });
+ const buf = toAvro(df);
+ const df2 = readAvro(buf);
+ expect(df2.col("x").at(0)).toBe(1);
+ expect(df2.col("x").at(1)).toBeNull();
+ expect(df2.col("x").at(2)).toBe(3);
+ });
+
+ it("multi-column mixed-type round-trip", () => {
+ const df = DataFrame.fromColumns({
+ id: [1, 2, 3],
+ name: ["a", "b", "c"],
+ score: [0.1, 0.2, 0.3],
+ active: [true, false, true],
+ });
+ const buf = toAvro(df);
+ const df2 = readAvro(buf);
+ expect(df2.shape).toEqual([3, 4]);
+ expect(df2.col("name").at(1)).toBe("b");
+ expect(df2.col("score").at(2) as number).toBeCloseTo(0.3, 5);
+ });
+
+ it("empty DataFrame round-trips", () => {
+ const df = DataFrame.fromColumns({ a: [] as number[] });
+ const buf = toAvro(df);
+ const df2 = readAvro(buf);
+ expect(df2.shape[0]).toBe(0);
+ });
+});
+
+// ─── Property-based tests ──────────────────────────────────────────────────────
+
+describe("property tests", () => {
+ it("integer round-trip: toAvro → readAvro preserves integer values", () => {
+ fc.assert(
+ fc.property(
+ fc.array(fc.integer({ min: -1000, max: 1000 }), { minLength: 1, maxLength: 20 }),
+ (vals) => {
+ const df = DataFrame.fromColumns({ n: vals });
+ const df2 = readAvro(toAvro(df));
+ for (let i = 0; i < vals.length; i++) {
+ if (df2.col("n").at(i) !== vals[i]) {
+ return false;
+ }
+ }
+ return true;
+ },
+ ),
+ );
+ });
+
+ it("string round-trip preserves values", () => {
+ fc.assert(
+ fc.property(
+ fc.array(fc.string({ minLength: 0, maxLength: 20 }), { minLength: 1, maxLength: 15 }),
+ (vals) => {
+ const df = DataFrame.fromColumns({ s: vals });
+ const df2 = readAvro(toAvro(df));
+ for (let i = 0; i < vals.length; i++) {
+ if (df2.col("s").at(i) !== vals[i]) {
+ return false;
+ }
+ }
+ return true;
+ },
+ ),
+ );
+ });
+
+ it("row count is preserved in round-trip", () => {
+ fc.assert(
+ fc.property(
+ fc.array(fc.integer({ min: 0, max: 100 }), { minLength: 0, maxLength: 30 }),
+ (vals) => {
+ const df = DataFrame.fromColumns({ n: vals });
+ const df2 = readAvro(toAvro(df));
+ return df2.shape[0] === vals.length;
+ },
+ ),
+ );
+ });
+});
diff --git a/tests/io/read_html.test.ts b/tests/io/read_html.test.ts
index dfc41d7f..8a65f076 100644
--- a/tests/io/read_html.test.ts
+++ b/tests/io/read_html.test.ts
@@ -55,7 +55,7 @@ describe("readHtml – basic", () => {
test("values are numeric by default", () => {
const html = simpleTable(["n"], [["42"], ["3.14"]]);
const [df] = readHtml(html);
- const vals = df!.col("n").toArray();
+ const vals = df?.col("n").toArray();
expect(vals[0]).toBe(42);
expect(vals[1]).toBeCloseTo(3.14);
});
@@ -87,7 +87,7 @@ describe("readHtml – header", () => {
1 2 3 hello world ({ data: [1, 2, 3, 4, 3, 2, 1, 2, 3, 4, 3, 2, 1, 2, 3, 4] });
+ const model4 = fitArima(s, { p: 1, d: 0, q: 0 });
+ expect(model4.p).toBe(1);
+ expect(model4.forecast(2).forecast.length).toBe(2);
+ });
+});
+
+// ─── Property-based tests ───────────────────────────────────────────────────────
+
+describe("property tests", () => {
+ it("fitted value count always equals input length", () => {
+ fc.assert(
+ fc.property(
+ fc.array(fc.float({ noNaN: true, noDefaultInfinity: true, min: -100, max: 100 }), {
+ minLength: 20,
+ maxLength: 60,
+ }),
+ fc.integer({ min: 0, max: 2 }),
+ fc.integer({ min: 0, max: 2 }),
+ (y, p, q) => {
+ const model5 = new ARIMAModel({ p, d: 0, q });
+ try {
+ const res = model5.fit(y);
+ return res.fittedValues.length === y.length;
+ } catch {
+ return true; // short series allowed to throw
+ }
+ },
+ ),
+ );
+ });
+
+ it("forecast intervals always satisfy lower <= forecast <= upper", () => {
+ fc.assert(
+ fc.property(
+ fc.array(fc.float({ noNaN: true, noDefaultInfinity: true, min: -50, max: 50 }), {
+ minLength: 20,
+ maxLength: 50,
+ }),
+ (y) => {
+ const model6 = new ARIMAModel({ p: 1, d: 0, q: 0 });
+ try {
+ model6.fit(y);
+ const fc2 = model6.forecast(3);
+ for (let h = 0; h < 3; h++) {
+ if ((fc2.lower[h] ?? 0) > (fc2.forecast[h] ?? 0) + 1e-6) {
+ return false;
+ }
+ if ((fc2.upper[h] ?? 0) < (fc2.forecast[h] ?? 0) - 1e-6) {
+ return false;
+ }
+ }
+ return true;
+ } catch {
+ return true;
+ }
+ },
+ ),
+ );
+ });
+
+ it("sigma2 is always positive after fit", () => {
+ fc.assert(
+ fc.property(
+ fc.array(fc.float({ noNaN: true, noDefaultInfinity: true, min: -100, max: 100 }), {
+ minLength: 15,
+ maxLength: 40,
+ }),
+ (y) => {
+ const model7 = new ARIMAModel({ p: 1, d: 0, q: 0 });
+ try {
+ const res = model7.fit(y);
+ return res.sigma2 > 0;
+ } catch {
+ return true;
+ }
+ },
+ ),
+ );
+ });
+});
+
+// ─── AIC / BIC ordering ─────────────────────────────────────────────────────────
+
+describe("information criteria", () => {
+ it("higher-order model has smaller AIC on a long series with signal", () => {
+ const y6 = ar1Series(0.8, 300, 0.1);
+ const m1 = new ARIMAModel({ p: 1, d: 0, q: 0 }).fit(y6);
+ const m2 = new ARIMAModel({ p: 2, d: 0, q: 0 }).fit(y6);
+ // AR(2) should not have dramatically worse AIC on an AR(1) series
+ expect(m2.aic).not.toBeNaN();
+ expect(m1.aic).not.toBeNaN();
+ });
+
+ it("fitted values MAE on AR(1) is less than naive mean", () => {
+ const y7 = ar1Series(0.85, 200, 0.05);
+ const result = new ARIMAModel({ p: 1, d: 0, q: 0 }).fit(y7);
+ const mean = y7.reduce((s, v) => s + v, 0) / y7.length;
+ const naiveMae = mae(y7, new Array(y7.length).fill(mean));
+ const modelMae = mae(y7, result.fittedValues);
+ expect(modelMae).toBeLessThan(naiveMae);
+ });
+});
diff --git a/tests/stats/dlm.test.ts b/tests/stats/dlm.test.ts
new file mode 100644
index 00000000..dd859bab
--- /dev/null
+++ b/tests/stats/dlm.test.ts
@@ -0,0 +1,446 @@
+/**
+ * Tests for dlm.ts — Dynamic Linear Model (State-Space).
+ */
+import { describe, expect, test } from "bun:test";
+import * as fc from "fast-check";
+import {
+ DLM,
+ buildFourier,
+ buildLocalLevel,
+ buildLocalLinearTrend,
+ buildPolynomial,
+ buildRegression,
+ combineDLMs,
+} from "../../src/stats/dlm.ts";
+
+// ─── helpers ───────────────────────────────────────────────────────────────────
+function approx(a: number, b: number, tol = 1e-6): boolean {
+ return Math.abs(a - b) <= tol * (1 + Math.abs(b));
+}
+function approxVec(a: readonly number[], b: readonly number[], tol = 1e-4): boolean {
+ return a.length === b.length && a.every((v, i) => approx(v, b[i]!, tol));
+}
+
+// ─── Local-level model ─────────────────────────────────────────────────────────
+describe("DLM — local-level", () => {
+ const y = [1, 2, 3, 2, 3, 4, 3, 4, 5, 4];
+
+ test("filter returns correct dimensions", () => {
+ const dlm = DLM.localLevel({ sigmaObs: 1, sigmaLevel: 0.5 });
+ const res = dlm.filter(y);
+ expect(res.steps.length).toBe(y.length);
+ expect(res.filteredMeans.length).toBe(y.length);
+ expect(res.filteredMeans[0]?.length).toBe(1);
+ expect(res.forecastMeans.length).toBe(y.length);
+ });
+
+ test("log-likelihood is finite and negative", () => {
+ const dlm = DLM.localLevel({ sigmaObs: 1, sigmaLevel: 0.5 });
+ const res = dlm.filter(y);
+ expect(Number.isFinite(res.logLikelihood)).toBe(true);
+ expect(res.logLikelihood).toBeLessThan(0);
+ });
+
+ test("filtered means track the signal", () => {
+ const dlm = DLM.localLevel({ sigmaObs: 1, sigmaLevel: 0.5 });
+ const res = dlm.filter(y);
+ // Filtered means should be between 0 and 6 for this data
+ for (const m of res.filteredMeans) {
+ expect(m[0]).toBeGreaterThan(0);
+ expect(m[0]).toBeLessThan(6);
+ }
+ });
+
+ test("filter with missing observations", () => {
+ const dlm = DLM.localLevel({ sigmaObs: 1, sigmaLevel: 0.5 });
+ const yMissing = [1, null, 3, null, 5];
+ const res = dlm.filter(yMissing);
+ expect(res.steps.length).toBe(5);
+ // Steps with null observations have null innovation
+ expect(res.steps[1]?.innovation).toBeNull();
+ expect(res.steps[3]?.innovation).toBeNull();
+ // Steps with observations have non-null innovation
+ expect(res.steps[0]?.innovation).not.toBeNull();
+ });
+
+ test("higher observation noise → filtered closer to prior", () => {
+ const dlmLow = DLM.localLevel({ sigmaObs: 0.01, sigmaLevel: 1 });
+ const dlmHigh = DLM.localLevel({ sigmaObs: 100, sigmaLevel: 1 });
+ const resLow = dlmLow.filter(y);
+ const resHigh = dlmHigh.filter(y);
+ // With low obs noise, filtered mean ≈ observation
+ expect(Math.abs(resLow.filteredMeans[0]?.[0]! - y[0]!)).toBeLessThan(0.1);
+ // With high obs noise, filtered mean stays near prior (zero init, drifts slowly)
+ // Just check the values are different
+ expect(resLow.filteredMeans[5]?.[0]).not.toBeCloseTo(resHigh.filteredMeans[5]?.[0]!, 1);
+ });
+});
+
+// ─── Local-linear-trend model ──────────────────────────────────────────────────
+describe("DLM — local-linear-trend", () => {
+ // Linearly increasing data with noise
+ const y = Array.from({ length: 20 }, (_, t) => t + 0.5 * (Math.sin(t) * 0.1));
+
+ test("filter dimensions", () => {
+ const dlm = DLM.localLinearTrend({ sigmaObs: 0.5, sigmaLevel: 0.1, sigmaSlope: 0.01 });
+ const res = dlm.filter(y);
+ expect(res.filteredMeans.length).toBe(y.length);
+ expect(res.filteredMeans[0]?.length).toBe(2); // [level, slope]
+ });
+
+ test("slope converges near 1 for linear data", () => {
+ const dlm = DLM.localLinearTrend({ sigmaObs: 0.1, sigmaLevel: 0.01, sigmaSlope: 0.01 });
+ const res = dlm.filter(y);
+ const lastSlope = res.filteredMeans[y.length - 1]?.[1]!;
+ expect(lastSlope).toBeGreaterThan(0.5);
+ expect(lastSlope).toBeLessThan(2);
+ });
+
+ test("log-likelihood improves with better sigma", () => {
+ const dlmGood = DLM.localLinearTrend({ sigmaObs: 0.5, sigmaLevel: 0.1, sigmaSlope: 0.01 });
+ const dlmBad = DLM.localLinearTrend({ sigmaObs: 10, sigmaLevel: 10, sigmaSlope: 10 });
+ expect(dlmGood.filter(y).logLikelihood).toBeGreaterThan(dlmBad.filter(y).logLikelihood);
+ });
+});
+
+// ─── Smoother ──────────────────────────────────────────────────────────────────
+describe("DLM — RTS smoother", () => {
+ const y = [1, 2, 1.5, 3, 2.5, 4, 3.5, 5, 4.5, 6];
+
+ test("smoothed means have same length as data", () => {
+ const dlm = DLM.localLevel({ sigmaObs: 1, sigmaLevel: 0.5 });
+ const res = dlm.smooth(y);
+ expect(res.smoothedMeans.length).toBe(y.length);
+ expect(res.smoothedCovs.length).toBe(y.length);
+ });
+
+ test("smoother log-likelihood equals filter log-likelihood", () => {
+ const dlm = DLM.localLevel({ sigmaObs: 1, sigmaLevel: 0.5 });
+ const filterRes = dlm.filter(y);
+ const smoothRes = dlm.smooth(y);
+ expect(smoothRes.logLikelihood).toBeCloseTo(filterRes.logLikelihood, 6);
+ });
+
+ test("smoothed covariances ≤ filtered covariances (trace)", () => {
+ const dlm = DLM.localLinearTrend({ sigmaObs: 1, sigmaLevel: 0.5, sigmaSlope: 0.1 });
+ const res = dlm.smooth(y);
+ for (let t = 0; t < y.length - 1; t++) {
+ const filtTrace = res.filteredCovs[t]?.[0]?.[0]! + (res.filteredCovs[t]?.[1]?.[1] ?? 0);
+ const smTrace = res.smoothedCovs[t]?.[0]?.[0]! + (res.smoothedCovs[t]?.[1]?.[1] ?? 0);
+ // Smoother should not increase uncertainty
+ expect(smTrace).toBeLessThanOrEqual(filtTrace + 1e-6);
+ }
+ });
+
+ test("last smoothed = last filtered", () => {
+ const dlm = DLM.localLevel({ sigmaObs: 1, sigmaLevel: 0.5 });
+ const res = dlm.smooth(y);
+ const T = y.length;
+ expect(res.smoothedMeans[T - 1]?.[0]).toBeCloseTo(res.filteredMeans[T - 1]?.[0]!, 6);
+ });
+});
+
+// ─── Forecasting ───────────────────────────────────────────────────────────────
+describe("DLM — forecasting", () => {
+ const y = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10];
+
+ test("forecast returns h steps", () => {
+ const dlm = DLM.localLevel({ sigmaObs: 0.5, sigmaLevel: 0.2 });
+ const res = dlm.filter(y);
+ const fc = dlm.forecast(res, 5);
+ expect(fc.mean.length).toBe(5);
+ expect(fc.lower.length).toBe(5);
+ expect(fc.upper.length).toBe(5);
+ });
+
+ test("forecast mean > lower, < upper", () => {
+ const dlm = DLM.localLevel({ sigmaObs: 0.5, sigmaLevel: 0.2 });
+ const res = dlm.filter(y);
+ const fc = dlm.forecast(res, 5);
+ for (let i = 0; i < 5; i++) {
+ expect(fc.mean[i]?.[0]).toBeGreaterThan(fc.lower[i]!);
+ expect(fc.mean[i]?.[0]).toBeLessThan(fc.upper[i]!);
+ }
+ });
+
+ test("prediction intervals widen over forecast horizon", () => {
+ const dlm = DLM.localLevel({ sigmaObs: 0.5, sigmaLevel: 0.2 });
+ const res = dlm.filter(y);
+ const fc = dlm.forecast(res, 5);
+ // Intervals should widen (or stay same) as h increases
+ for (let i = 1; i < 5; i++) {
+ const widthPrev = fc.upper[i - 1]! - fc.lower[i - 1]!;
+ const widthCurr = fc.upper[i]! - fc.lower[i]!;
+ expect(widthCurr).toBeGreaterThanOrEqual(widthPrev - 1e-10);
+ }
+ });
+
+ test("local-linear-trend forecast extrapolates trend", () => {
+ const dlm = DLM.localLinearTrend({ sigmaObs: 0.1, sigmaLevel: 0.01, sigmaSlope: 0.001 });
+ const res = dlm.filter(y);
+ const fc = dlm.forecast(res, 3);
+ // Should forecast above 10
+ expect(fc.mean[0]?.[0]).toBeGreaterThan(9);
+ expect(fc.mean[2]?.[0]).toBeGreaterThan(fc.mean[0]?.[0]!);
+ });
+});
+
+// ─── Polynomial DLM ────────────────────────────────────────────────────────────
+describe("buildPolynomial", () => {
+ test("order=1 is equivalent to local-level", () => {
+ const spec = buildPolynomial(1, { sigmaObs: 1, sigmaState: 0.5 });
+ expect(spec.G).toEqual([[1]]);
+ expect(spec.F).toEqual([[1]]);
+ });
+
+ test("order=2 has 2×2 G with upper-triangular 1", () => {
+ const spec = buildPolynomial(2);
+ expect(spec.G[0]).toEqual([1, 1]);
+ expect(spec.G[1]).toEqual([0, 1]);
+ });
+
+ test("filter runs for order=3", () => {
+ const spec = buildPolynomial(3, { sigmaObs: 1, sigmaState: 0.1 });
+ const dlm = new DLM(spec);
+ const res = dlm.filter([1, 2, 3, 4, 5]);
+ expect(res.filteredMeans.length).toBe(5);
+ expect(res.filteredMeans[0]?.length).toBe(3);
+ });
+});
+
+// ─── Fourier seasonal DLM ──────────────────────────────────────────────────────
+describe("buildFourier", () => {
+ test("state dimension is 2*harmonics", () => {
+ const spec = buildFourier(12, 3);
+ expect(spec.G.length).toBe(6);
+ expect(spec.F[0]?.length).toBe(6);
+ });
+
+ test("rotation matrix property: G G' = I (for each 2×2 block)", () => {
+ const spec = buildFourier(12, 2);
+ const G = spec.G;
+ // Check first block is rotation
+ const c = G[0]?.[0]!;
+ const s = G[1]?.[0]!;
+ expect(c * c + s * s).toBeCloseTo(1, 6);
+ });
+
+ test("filter runs without error on seasonal data", () => {
+ // Synthetic seasonal data: period=4, 2 cycles
+ const y = [1, 2, 3, 2, 1, 2, 3, 2];
+ const spec = buildFourier(4, 2, { sigmaObs: 0.5, sigmaState: 0.1 });
+ const dlm = new DLM(spec);
+ const res = dlm.filter(y);
+ expect(res.steps.length).toBe(8);
+ expect(Number.isFinite(res.logLikelihood)).toBe(true);
+ });
+});
+
+// ─── buildRegression ───────────────────────────────────────────────────────────
+describe("buildRegression", () => {
+ test("spec has correct dimensions", () => {
+ const spec = buildRegression(3, { sigmaObs: 1, sigmaState: 0.001 });
+ expect(spec.G.length).toBe(3);
+ expect(spec.G[0]?.length).toBe(3);
+ expect(spec.F[0]?.length).toBe(3);
+ });
+});
+
+// ─── combineDLMs ───────────────────────────────────────────────────────────────
+describe("combineDLMs", () => {
+ test("combines state dimensions", () => {
+ const ll = buildLocalLevel({ sigmaObs: 1, sigmaLevel: 0.5 });
+ const llt = buildLocalLinearTrend({ sigmaObs: 1, sigmaLevel: 0.5, sigmaSlope: 0.1 });
+ const combined = combineDLMs(ll, llt);
+ // ll has 1 state, llt has 2 → combined has 3
+ expect(combined.G.length).toBe(3);
+ expect(combined.F[0]?.length).toBe(3);
+ expect(combined.W.length).toBe(3);
+ });
+
+ test("single spec is identity", () => {
+ const ll = buildLocalLevel({ sigmaObs: 1, sigmaLevel: 0.5 });
+ const combined = combineDLMs(ll);
+ expect(combined.G).toEqual(ll.G);
+ });
+
+ test("combined DLM can be filtered", () => {
+ const ll = buildLocalLevel({ sigmaObs: 1, sigmaLevel: 0.5 });
+ const fourier = buildFourier(4, 1, { sigmaObs: 1, sigmaState: 0.1 });
+ const combined = combineDLMs(ll, fourier);
+ const dlm = new DLM(combined);
+ const y = [1, 2, 3, 2, 1, 2, 3, 2];
+ const res = dlm.filter(y);
+ expect(res.steps.length).toBe(8);
+ expect(Number.isFinite(res.logLikelihood)).toBe(true);
+ });
+
+ test("throws on empty input", () => {
+ expect(() => combineDLMs()).toThrow();
+ });
+});
+
+// ─── MLE fitting ──────────────────────────────────────────────────────────────
+describe("DLM.fitMLE", () => {
+ test("returns a DLM instance", () => {
+ const dlm = DLM.localLevel({ sigmaObs: 1, sigmaLevel: 1 });
+ const y = [1, 2, 1.5, 2.5, 3, 2, 3.5, 3, 4, 3.5];
+ const fitted = dlm.fitMLE(y);
+ expect(fitted).toBeInstanceOf(DLM);
+ });
+
+ test("fitted log-likelihood ≥ initial log-likelihood", () => {
+ const dlm = DLM.localLevel({ sigmaObs: 5, sigmaLevel: 5 });
+ const y = [1, 2, 1.5, 2.5, 3, 2, 3.5, 3, 4, 3.5];
+ const fitted = dlm.fitMLE(y);
+ const llInitial = dlm.filter(y).logLikelihood;
+ const llFitted = fitted.filter(y).logLikelihood;
+ expect(llFitted).toBeGreaterThanOrEqual(llInitial - 0.1);
+ });
+});
+
+// ─── Discount factor filter ───────────────────────────────────────────────────
+describe("DLM.filterDiscount", () => {
+ test("discount=1 matches standard filter closely", () => {
+ const dlm = DLM.localLevel({ sigmaObs: 1, sigmaLevel: 0.5 });
+ const y = [1, 2, 3, 4, 5];
+ const _stdRes = dlm.filter(y);
+ // With discount=1, R_t = G C G' (no extra growth) — different from W-augmented
+ const discRes = dlm.filterDiscount(y, 1.0);
+ expect(discRes.steps.length).toBe(y.length);
+ expect(Number.isFinite(discRes.logLikelihood)).toBe(true);
+ });
+
+ test("lower discount factor → wider prediction intervals", () => {
+ const dlm = DLM.localLevel({ sigmaObs: 1, sigmaLevel: 0 });
+ const y = [1, 2, 3, 4, 5, 6, 7, 8];
+ const res095 = dlm.filterDiscount(y, 0.95);
+ const res099 = dlm.filterDiscount(y, 0.99);
+ // delta=0.95 has more uncertainty → larger Q
+ const q095 = res095.steps[3]?.forecastCov[0]?.[0]!;
+ const q099 = res099.steps[3]?.forecastCov[0]?.[0]!;
+ expect(q095).toBeGreaterThanOrEqual(q099 - 1e-6);
+ });
+});
+
+// ─── Factory static methods ────────────────────────────────────────────────────
+describe("DLM factory methods", () => {
+ test("DLM.localLevel matches buildLocalLevel dimensions", () => {
+ const dlm1 = DLM.localLevel({ sigmaObs: 1, sigmaLevel: 0.5 });
+ const spec = buildLocalLevel({ sigmaObs: 1, sigmaLevel: 0.5 });
+ // Both have G=[[1]], F=[[1]]
+ const dlm2 = new DLM(spec);
+ const y = [1, 2, 3];
+ const r1 = dlm1.filter(y);
+ const r2 = dlm2.filter(y);
+ expect(r1.filteredMeans[0]?.[0]).toBeCloseTo(r2.filteredMeans[0]?.[0]!, 4);
+ });
+
+ test("DLM.fourier factory", () => {
+ const dlm = DLM.fourier(12, 3, { sigmaObs: 1, sigmaState: 0.01 });
+ expect(dlm).toBeInstanceOf(DLM);
+ const res = dlm.filter([1, 2, 3, 2, 1, 2, 3, 2, 1, 2, 3, 2]);
+ expect(res.steps.length).toBe(12);
+ });
+
+ test("DLM.polynomial(1) is same as localLevel", () => {
+ const dlm = DLM.polynomial(1, { sigmaObs: 1, sigmaState: 0.5 });
+ expect(dlm).toBeInstanceOf(DLM);
+ });
+});
+
+// ─── Edge cases ────────────────────────────────────────────────────────────────
+describe("DLM edge cases", () => {
+ test("single observation", () => {
+ const dlm = DLM.localLevel({ sigmaObs: 1, sigmaLevel: 0.5 });
+ const res = dlm.filter([3.14]);
+ expect(res.filteredMeans.length).toBe(1);
+ expect(Number.isFinite(res.logLikelihood)).toBe(true);
+ });
+
+ test("all missing observations", () => {
+ const dlm = DLM.localLevel({ sigmaObs: 1, sigmaLevel: 0.5 });
+ const res = dlm.filter([null, null, null]);
+ expect(res.steps.length).toBe(3);
+ // No log-likelihood contributions from missing obs
+ expect(res.logLikelihood).toBe(0);
+ });
+
+ test("constant series", () => {
+ const dlm = DLM.localLevel({ sigmaObs: 0.5, sigmaLevel: 0.1 });
+ const res = dlm.filter([5, 5, 5, 5, 5]);
+ // Filtered means should converge toward 5
+ const last = res.filteredMeans[4]?.[0]!;
+ expect(last).toBeGreaterThan(4);
+ expect(last).toBeLessThan(6);
+ });
+
+ test("custom prior m0 and C0", () => {
+ const dlm = DLM.localLevel({ sigmaObs: 1, sigmaLevel: 0.5 });
+ const _res1 = dlm.filter([1, 2, 3], { m0: [0], C0: [[1000]] });
+ const res2 = dlm.filter([1, 2, 3], { m0: [5], C0: [[0.01]] });
+ // Strong prior on m0=5 should keep filtered mean near 5 initially
+ expect(res2.filteredMeans[0]?.[0]).toBeGreaterThan(3);
+ });
+});
+
+// ─── Property-based tests ──────────────────────────────────────────────────────
+describe("DLM property tests", () => {
+ test("logLikelihood is finite for any finite observations", () => {
+ fc.assert(
+ fc.property(
+ fc.array(fc.float({ min: -100, max: 100, noNaN: true }), { minLength: 2, maxLength: 20 }),
+ fc.float({ min: 0.01, max: 10, noNaN: true }),
+ fc.float({ min: 0.01, max: 10, noNaN: true }),
+ (y, sv, sw) => {
+ const dlm = DLM.localLevel({ sigmaObs: sv, sigmaLevel: sw });
+ const res = dlm.filter(y);
+ return Number.isFinite(res.logLikelihood);
+ },
+ ),
+ );
+ });
+
+ test("filtered means length = input length", () => {
+ fc.assert(
+ fc.property(
+ fc.array(fc.float({ min: -50, max: 50, noNaN: true }), { minLength: 1, maxLength: 30 }),
+ (y) => {
+ const dlm = DLM.localLevel();
+ const res = dlm.filter(y);
+ return res.filteredMeans.length === y.length;
+ },
+ ),
+ );
+ });
+
+ test("smoother length = filter length", () => {
+ fc.assert(
+ fc.property(
+ fc.array(fc.float({ min: -50, max: 50, noNaN: true }), { minLength: 2, maxLength: 20 }),
+ (y) => {
+ const dlm = DLM.localLevel();
+ const res = dlm.smooth(y);
+ return res.smoothedMeans.length === y.length && res.smoothedCovs.length === y.length;
+ },
+ ),
+ );
+ });
+
+ test("forecast lower ≤ mean[0] ≤ upper (scalar model)", () => {
+ fc.assert(
+ fc.property(
+ fc.array(fc.float({ min: -50, max: 50, noNaN: true }), { minLength: 3, maxLength: 15 }),
+ fc.integer({ min: 1, max: 10 }),
+ (y, h) => {
+ const dlm = DLM.localLevel();
+ const res = dlm.filter(y);
+ const fc2 = dlm.forecast(res, h);
+ return fc2.lower.every(
+ (lo, i) => lo <= fc2.mean[i]?.[0]! + 1e-8 && fc2.mean[i]?.[0]! <= fc2.upper[i]! + 1e-8,
+ );
+ },
+ ),
+ );
+ });
+});
diff --git a/tests/stats/ets.test.ts b/tests/stats/ets.test.ts
new file mode 100644
index 00000000..c3d64270
--- /dev/null
+++ b/tests/stats/ets.test.ts
@@ -0,0 +1,824 @@
+/**
+ * Tests for src/stats/ets.ts
+ *
+ * Covers Simple Exponential Smoothing (SES), Holt linear trend, and
+ * Holt-Winters (full ETS) with additive and multiplicative seasonality.
+ * Mirrors statsmodels.tsa.holtwinters behaviour.
+ */
+import { describe, expect, it } from "bun:test";
+import fc from "fast-check";
+import {
+ ExponentialSmoothing,
+ Holt,
+ Series,
+ SimpleExpSmoothing,
+ fitEts,
+ holt,
+ simpleExpSmoothing,
+} from "../../src/index.ts";
+
+// ─── Test fixtures ─────────────────────────────────────────────────────────────
+
+/** Passenger data (Box & Jenkins airline data first 12 months). */
+const AIRLINE = [
+ 112, 118, 132, 129, 121, 135, 148, 148, 136, 119, 104, 118, 115, 126, 141, 135, 125, 149, 170,
+ 170, 158, 133, 114, 140, 145, 150, 178, 163, 172, 178, 199, 199, 184, 162, 146, 166,
+];
+
+/** Simple upward trend series. */
+const TREND = [10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30];
+
+/** Seasonal additive series (no trend). */
+const SEASONAL_ADD = [5, 8, 7, 4, 5, 8, 7, 4, 5, 8, 7, 4, 5, 8, 7, 4, 5, 8, 7, 4, 6, 9, 8, 5];
+
+/** Deterministic LCG for reproducible pseudo-noise. */
+function lcg(seed: number): () => number {
+ let s = seed;
+ return () => {
+ s = (s * 1664525 + 1013904223) & 0x7fffffff;
+ return s / 0x7fffffff;
+ };
+}
+
+/** Build a noisy sinusoidal series with additive seasonal component. */
+function buildSeasonal(n: number, amplitude: number, period: number, noiseAmp = 0.5): number[] {
+ const rand = lcg(1234);
+ return Array.from({ length: n }, (_, t) => {
+ const trend = 10 + 0.2 * t;
+ const seasonal = amplitude * Math.sin((2 * Math.PI * t) / period);
+ const noise = (rand() - 0.5) * noiseAmp;
+ return trend + seasonal + noise;
+ });
+}
+
+/** Mean absolute error between two arrays. */
+function mae(a: readonly number[], b: readonly number[]): number {
+ let s = 0;
+ for (let i = 0; i < a.length; i++) {
+ s += Math.abs((a[i] ?? 0) - (b[i] ?? 0));
+ }
+ return s / a.length;
+}
+
+/** Root mean squared error. */
+function rmse(a: readonly number[], b: readonly number[]): number {
+ let s = 0;
+ for (let i = 0; i < a.length; i++) {
+ s += ((a[i] ?? 0) - (b[i] ?? 0)) ** 2;
+ }
+ return Math.sqrt(s / a.length);
+}
+
+// ─── SimpleExpSmoothing ────────────────────────────────────────────────────────
+
+describe("SimpleExpSmoothing", () => {
+ describe("construction", () => {
+ it("creates instance without arguments", () => {
+ expect(() => new SimpleExpSmoothing()).not.toThrow();
+ });
+ });
+
+ describe("fit()", () => {
+ it("requires at least 2 observations", () => {
+ expect(() => new SimpleExpSmoothing().fit([1])).toThrow(RangeError);
+ });
+
+ it("returns alpha in (0, 1)", () => {
+ const fit = new SimpleExpSmoothing().fit(TREND);
+ expect(fit.alpha).toBeGreaterThan(0);
+ expect(fit.alpha).toBeLessThan(1);
+ });
+
+ it("returns fittedValues of same length as input", () => {
+ const fit = new SimpleExpSmoothing().fit(TREND);
+ expect(fit.fittedValues.length).toBe(TREND.length);
+ });
+
+ it("residuals + fittedValues = y", () => {
+ const fit = new SimpleExpSmoothing().fit(TREND);
+ for (let i = 0; i < TREND.length; i++) {
+ expect((fit.fittedValues[i] ?? 0) + (fit.residuals[i] ?? 0)).toBeCloseTo(TREND[i] ?? 0, 8);
+ }
+ });
+
+ it("sse equals sum of squared residuals", () => {
+ const fit = new SimpleExpSmoothing().fit(TREND);
+ let sse = 0;
+ for (const e of fit.residuals) {
+ sse += e * e;
+ }
+ expect(fit.sse).toBeCloseTo(sse, 6);
+ });
+
+ it("fixed alpha is respected", () => {
+ const alpha = 0.4;
+ const fit = new SimpleExpSmoothing().fit(TREND, { alpha });
+ expect(fit.alpha).toBeCloseTo(alpha, 10);
+ });
+
+ it("optimised alpha produces lower SSE than α=0.5 for trending data", () => {
+ const fit1 = new SimpleExpSmoothing().fit(TREND);
+ const fit2 = new SimpleExpSmoothing().fit(TREND, { alpha: 0.5 });
+ expect(fit1.sse).toBeLessThanOrEqual(fit2.sse + 1e-6);
+ });
+
+ it("AIC > 0", () => {
+ const fit = new SimpleExpSmoothing().fit(TREND);
+ expect(Number.isFinite(fit.aic)).toBe(true);
+ });
+
+ it("BIC ≥ AIC for k > 0, n > e", () => {
+ const fit = new SimpleExpSmoothing().fit(AIRLINE.slice(0, 24));
+ // With k=2, n=24: BIC = AIC + k*(ln(n) - 2)
+ // ln(24) ≈ 3.18 > 2, so BIC ≥ AIC
+ expect(fit.bic).toBeGreaterThanOrEqual(fit.aic - 1e-6);
+ });
+
+ it("accepts Series input", () => {
+ const s = new Series({ data: TREND });
+ const fit = new SimpleExpSmoothing().fit(s);
+ expect(fit.fittedValues.length).toBe(TREND.length);
+ });
+
+ it("initialLevel option overrides default", () => {
+ const fit = new SimpleExpSmoothing().fit(TREND, { initialLevel: 5 });
+ expect(fit.initialLevel).toBeCloseTo(5, 10);
+ });
+ });
+
+ describe("forecast()", () => {
+ it("throws if called before fit()", () => {
+ expect(() => new SimpleExpSmoothing().forecast(3)).toThrow();
+ });
+
+ it("returns flat forecast (all equal) of correct length", () => {
+ const model = new SimpleExpSmoothing();
+ model.fit(TREND);
+ const fc = model.forecast(4);
+ expect(fc.length).toBe(4);
+ for (const v of fc) {
+ expect(v).toBeCloseTo(fc[0] ?? 0, 8);
+ }
+ });
+
+ it("forecast ≈ last observed value for α ≈ 1", () => {
+ const model = new SimpleExpSmoothing();
+ model.fit(TREND, { alpha: 0.9999 });
+ const fc = model.forecast(1);
+ expect(fc[0]).toBeCloseTo(TREND.at(-1) ?? 0, 0);
+ });
+
+ it("functional API simpleExpSmoothing returns same result", () => {
+ const fit1 = simpleExpSmoothing(TREND);
+ const fit2 = new SimpleExpSmoothing().fit(TREND);
+ expect(fit1.alpha).toBeCloseTo(fit2.alpha, 8);
+ expect(fit1.sse).toBeCloseTo(fit2.sse, 8);
+ });
+ });
+
+ describe("accuracy", () => {
+ it("fitted values within 20% of actuals on AIRLINE data", () => {
+ const fit = new SimpleExpSmoothing().fit(AIRLINE);
+ const err = mae(fit.fittedValues, AIRLINE);
+ expect(err / (AIRLINE.reduce((a, b) => a + b, 0) / AIRLINE.length)).toBeLessThan(0.2);
+ });
+
+ it("forecast stays in reasonable range for constant series", () => {
+ const y = new Array(20).fill(5);
+ const model = new SimpleExpSmoothing();
+ model.fit(y);
+ const fc = model.forecast(5);
+ for (const v of fc) {
+ expect(Math.abs(v - 5)).toBeLessThan(1);
+ }
+ });
+ });
+});
+
+// ─── Holt ─────────────────────────────────────────────────────────────────────
+
+describe("Holt", () => {
+ describe("construction", () => {
+ it("creates instance without arguments", () => {
+ expect(() => new Holt()).not.toThrow();
+ });
+
+ it("stores options from constructor", () => {
+ const model = new Holt({ damped: true });
+ const fit = model.fit(TREND);
+ expect(fit.phi).toBeLessThan(1); // damped
+ });
+ });
+
+ describe("fit()", () => {
+ it("requires at least 3 observations", () => {
+ expect(() => new Holt().fit([1, 2])).toThrow(RangeError);
+ });
+
+ it("returns alpha and beta in (0, 1)", () => {
+ const fit = new Holt().fit(TREND);
+ expect(fit.alpha).toBeGreaterThan(0);
+ expect(fit.alpha).toBeLessThan(1);
+ expect(fit.beta).toBeGreaterThan(0);
+ expect(fit.beta).toBeLessThan(1);
+ });
+
+ it("phi = 1 when damped = false (default)", () => {
+ const fit = new Holt().fit(TREND);
+ expect(fit.phi).toBe(1.0);
+ });
+
+ it("phi < 1 when damped = true", () => {
+ const fit = new Holt({ damped: true }).fit(TREND);
+ expect(fit.phi).toBeGreaterThan(0.8);
+ expect(fit.phi).toBeLessThan(1);
+ });
+
+ it("fittedValues length = n", () => {
+ const fit = new Holt().fit(TREND);
+ expect(fit.fittedValues.length).toBe(TREND.length);
+ });
+
+ it("fitted + residuals = y", () => {
+ const fit = new Holt().fit(AIRLINE.slice(0, 12));
+ for (let i = 0; i < 12; i++) {
+ expect((fit.fittedValues[i] ?? 0) + (fit.residuals[i] ?? 0)).toBeCloseTo(
+ AIRLINE[i] ?? 0,
+ 6,
+ );
+ }
+ });
+
+ it("sse = sum of squared residuals", () => {
+ const fit = new Holt().fit(TREND);
+ let sse = 0;
+ for (const e of fit.residuals) {
+ sse += e * e;
+ }
+ expect(fit.sse).toBeCloseTo(sse, 6);
+ });
+
+ it("fixed alpha and beta are respected", () => {
+ const fit = new Holt().fit(TREND, { alpha: 0.5, beta: 0.2 });
+ expect(fit.alpha).toBeCloseTo(0.5, 10);
+ expect(fit.beta).toBeCloseTo(0.2, 10);
+ });
+
+ it("optimised Holt SSE ≤ SES SSE for trending data", () => {
+ const sesSSE = simpleExpSmoothing(TREND).sse;
+ const holtSSE = holt(TREND).sse;
+ expect(holtSSE).toBeLessThanOrEqual(sesSSE + 1e-3);
+ });
+
+ it("functional holt() returns same result as class", () => {
+ const fit1 = holt(TREND);
+ const fit2 = new Holt().fit(TREND);
+ expect(fit1.alpha).toBeCloseTo(fit2.alpha, 6);
+ expect(fit1.sse).toBeCloseTo(fit2.sse, 6);
+ });
+ });
+
+ describe("forecast()", () => {
+ it("throws if called before fit()", () => {
+ expect(() => new Holt().forecast(3)).toThrow();
+ });
+
+ it("returns correct number of steps", () => {
+ const model = new Holt();
+ model.fit(TREND);
+ expect(model.forecast(5).length).toBe(5);
+ });
+
+ it("trend forecasts increase for upward trend data", () => {
+ const model = new Holt();
+ model.fit(TREND);
+ const fc = model.forecast(3);
+ expect(fc[1] ?? 0).toBeGreaterThan(fc[0] ?? 0);
+ expect(fc[2] ?? 0).toBeGreaterThan(fc[1] ?? 0);
+ });
+
+ it("damped forecasts converge to a limit", () => {
+ const model = new Holt({ damped: true });
+ model.fit(TREND);
+ const fc = model.forecast(20);
+ // Differences should shrink
+ const diffs = fc.slice(1).map((v, i) => v - (fc[i] ?? 0));
+ for (let i = 1; i < diffs.length; i++) {
+ expect(Math.abs(diffs[i] ?? 0)).toBeLessThanOrEqual(Math.abs(diffs[i - 1] ?? 0) + 1e-6);
+ }
+ });
+
+ it("forecast closely follows linear trend", () => {
+ // Perfect linear data: y = 2t + 10
+ const y = Array.from({ length: 20 }, (_, t) => 2 * t + 10);
+ const model = new Holt();
+ model.fit(y);
+ const fc = model.forecast(3);
+ // Should forecast ≈ [50, 52, 54]
+ expect(fc[0]).toBeCloseTo(50, 0);
+ expect(fc[2]).toBeCloseTo(54, 0);
+ });
+ });
+
+ describe("accuracy", () => {
+ it("RMSE on AIRLINE (first 24) < 20", () => {
+ const y = AIRLINE.slice(0, 24);
+ const fit = new Holt().fit(y);
+ const err = rmse(fit.fittedValues.slice(1), y.slice(1));
+ expect(err).toBeLessThan(20);
+ });
+ });
+});
+
+// ─── ExponentialSmoothing (Holt-Winters) ─────────────────────────────────────
+
+describe("ExponentialSmoothing", () => {
+ describe("construction and validation", () => {
+ it("creates with no options (SES mode)", () => {
+ const model = new ExponentialSmoothing();
+ const fit = model.fit(TREND);
+ expect(fit.beta).toBeNull();
+ expect(fit.gamma).toBeNull();
+ });
+
+ it("requires at least 3 observations", () => {
+ expect(() => new ExponentialSmoothing().fit([1, 2])).toThrow(RangeError);
+ });
+
+ it("requires 2 full seasonal periods when seasonal is set", () => {
+ expect(() =>
+ new ExponentialSmoothing({ seasonal: "add", seasonalPeriods: 4 }).fit([1, 2, 3, 4, 5]),
+ ).toThrow(RangeError);
+ });
+ });
+
+ describe("SES mode (no trend, no seasonal)", () => {
+ it("result matches SimpleExpSmoothing", () => {
+ const r1 = new ExponentialSmoothing().fit(TREND);
+ const r2 = new SimpleExpSmoothing().fit(TREND);
+ expect(r1.alpha).toBeCloseTo(r2.alpha, 4);
+ expect(r1.sse).toBeCloseTo(r2.sse, 4);
+ });
+
+ it("beta and gamma are null", () => {
+ const fit = new ExponentialSmoothing().fit(TREND);
+ expect(fit.beta).toBeNull();
+ expect(fit.gamma).toBeNull();
+ });
+ });
+
+ describe("additive trend, no seasonal (= Holt)", () => {
+ it("alpha and beta are in (0,1)", () => {
+ const fit = new ExponentialSmoothing({ trend: "add" }).fit(TREND);
+ expect(fit.alpha).toBeGreaterThan(0);
+ expect(fit.beta).not.toBeNull();
+ expect(fit.beta ?? 0).toBeGreaterThan(0);
+ });
+
+ it("gamma is null", () => {
+ const fit = new ExponentialSmoothing({ trend: "add" }).fit(TREND);
+ expect(fit.gamma).toBeNull();
+ });
+
+ it("SSE roughly matches Holt class", () => {
+ const r1 = new ExponentialSmoothing({ trend: "add" }).fit(TREND);
+ const r2 = new Holt().fit(TREND);
+ // They may find slightly different optima; SSE should be comparable
+ expect(Math.abs(r1.sse - r2.sse) / (r2.sse + 1e-8)).toBeLessThan(0.1);
+ });
+ });
+
+ describe("additive trend + additive seasonal (classic Holt-Winters)", () => {
+ const y = SEASONAL_ADD;
+ const m = 4;
+
+ it("all parameters estimated", () => {
+ const fit = new ExponentialSmoothing({
+ trend: "add",
+ seasonal: "add",
+ seasonalPeriods: m,
+ }).fit(y);
+ expect(fit.alpha).toBeGreaterThan(0);
+ expect(fit.beta).not.toBeNull();
+ expect(fit.gamma).not.toBeNull();
+ expect(fit.initialSeasons).not.toBeNull();
+ expect(fit.initialSeasons?.length).toBe(m);
+ });
+
+ it("additive seasonal indices sum close to 0", () => {
+ const fit = new ExponentialSmoothing({
+ trend: "add",
+ seasonal: "add",
+ seasonalPeriods: m,
+ }).fit(y);
+ const sum = (fit.initialSeasons ?? []).reduce((a, b) => a + b, 0);
+ expect(Math.abs(sum)).toBeLessThan(2);
+ });
+
+ it("fitted + residuals = y", () => {
+ const fit = new ExponentialSmoothing({
+ trend: "add",
+ seasonal: "add",
+ seasonalPeriods: m,
+ }).fit(y);
+ for (let i = 0; i < y.length; i++) {
+ expect((fit.fittedValues[i] ?? 0) + (fit.residuals[i] ?? 0)).toBeCloseTo(y[i] ?? 0, 6);
+ }
+ });
+
+ it("sse = sum of squared residuals", () => {
+ const fit = new ExponentialSmoothing({
+ trend: "add",
+ seasonal: "add",
+ seasonalPeriods: m,
+ }).fit(y);
+ let sse = 0;
+ for (const e of fit.residuals) {
+ sse += e * e;
+ }
+ expect(fit.sse).toBeCloseTo(sse, 6);
+ });
+
+ it("AIC < BIC for n > e", () => {
+ const fit = new ExponentialSmoothing({
+ trend: "add",
+ seasonal: "add",
+ seasonalPeriods: m,
+ }).fit(y);
+ // For n = 24, k = 1+1+1+1+4=8: BIC - AIC = k*(ln(n)-2) ≈ 8*(3.18-2)=9.4 > 0
+ expect(fit.bic).toBeGreaterThan(fit.aic - 1e-3);
+ });
+
+ it("forecast returns correct number of steps", () => {
+ const model = new ExponentialSmoothing({
+ trend: "add",
+ seasonal: "add",
+ seasonalPeriods: m,
+ });
+ model.fit(y);
+ const fc = model.forecast(8);
+ expect(fc.length).toBe(8);
+ });
+
+ it("all forecast values are finite", () => {
+ const model = new ExponentialSmoothing({
+ trend: "add",
+ seasonal: "add",
+ seasonalPeriods: m,
+ });
+ model.fit(y);
+ const fc = model.forecast(8);
+ for (const v of fc) {
+ expect(Number.isFinite(v)).toBe(true);
+ }
+ });
+
+ it("seasonal forecast repeats seasonal pattern (low noise data)", () => {
+ // Perfect seasonal data with no noise
+ const perfect: number[] = [];
+ for (let i = 0; i < 24; i++) {
+ const base = [5, 8, 7, 4];
+ perfect.push(base[i % 4] ?? 5);
+ }
+ const model = new ExponentialSmoothing({
+ trend: "add",
+ seasonal: "add",
+ seasonalPeriods: 4,
+ });
+ model.fit(perfect);
+ const fc = model.forecast(8);
+ const pattern = [fc[0] ?? 0, fc[1] ?? 0, fc[2] ?? 0, fc[3] ?? 0];
+ // Next 4 should roughly repeat the pattern
+ for (let i = 0; i < 4; i++) {
+ expect(Math.abs((fc[i + 4] ?? 0) - (pattern[i] ?? 0))).toBeLessThan(2);
+ }
+ });
+ });
+
+ describe("additive trend + multiplicative seasonal", () => {
+ it("estimates all parameters", () => {
+ const fit = new ExponentialSmoothing({
+ trend: "add",
+ seasonal: "mul",
+ seasonalPeriods: 4,
+ }).fit(SEASONAL_ADD);
+ expect(fit.alpha).toBeGreaterThan(0);
+ expect(fit.beta).not.toBeNull();
+ expect(fit.gamma).not.toBeNull();
+ });
+
+ it("fitted + residuals = y", () => {
+ const fit = new ExponentialSmoothing({
+ trend: "add",
+ seasonal: "mul",
+ seasonalPeriods: 4,
+ }).fit(SEASONAL_ADD);
+ for (let i = 0; i < SEASONAL_ADD.length; i++) {
+ expect((fit.fittedValues[i] ?? 0) + (fit.residuals[i] ?? 0)).toBeCloseTo(
+ SEASONAL_ADD[i] ?? 0,
+ 6,
+ );
+ }
+ });
+ });
+
+ describe("no trend + additive seasonal", () => {
+ it("beta is null", () => {
+ const fit = new ExponentialSmoothing({
+ seasonal: "add",
+ seasonalPeriods: 4,
+ }).fit(SEASONAL_ADD);
+ expect(fit.beta).toBeNull();
+ expect(fit.gamma).not.toBeNull();
+ });
+ });
+
+ describe("no trend + multiplicative seasonal", () => {
+ it("fits without error", () => {
+ const fit = new ExponentialSmoothing({
+ seasonal: "mul",
+ seasonalPeriods: 4,
+ }).fit(SEASONAL_ADD);
+ expect(fit.gamma).not.toBeNull();
+ expect(fit.beta).toBeNull();
+ });
+ });
+
+ describe("damped trend", () => {
+ it("phi < 1 when damped = true", () => {
+ const fit = new ExponentialSmoothing({
+ trend: "add",
+ damped: true,
+ }).fit(TREND);
+ expect(fit.phi).toBeLessThan(1);
+ expect(fit.phi).toBeGreaterThan(0.8);
+ });
+
+ it("phi = 1 when damped = false", () => {
+ const fit = new ExponentialSmoothing({ trend: "add" }).fit(TREND);
+ expect(fit.phi).toBe(1.0);
+ });
+ });
+
+ describe("known initialisation", () => {
+ it("uses provided initial values", () => {
+ const fit = new ExponentialSmoothing({
+ trend: "add",
+ seasonal: "add",
+ seasonalPeriods: 4,
+ initializationMethod: "known",
+ initialLevel: 6.0,
+ initialTrend: 0.1,
+ initialSeasons: [1, 2, 0, -1],
+ }).fit(SEASONAL_ADD);
+ expect(fit.initialLevel).toBeCloseTo(6.0, 8);
+ expect(fit.initialTrend).toBeCloseTo(0.1, 8);
+ });
+ });
+
+ describe("fixed parameters", () => {
+ it("fixed alpha is respected", () => {
+ const fit = new ExponentialSmoothing({ alpha: 0.4 }).fit(TREND);
+ expect(fit.alpha).toBeCloseTo(0.4, 8);
+ });
+
+ it("fixed beta is respected", () => {
+ const fit = new ExponentialSmoothing({ trend: "add", alpha: 0.3, beta: 0.15 }).fit(TREND);
+ expect(fit.beta).toBeCloseTo(0.15, 8);
+ });
+
+ it("fixed gamma is respected", () => {
+ const fit = new ExponentialSmoothing({
+ seasonal: "add",
+ seasonalPeriods: 4,
+ gamma: 0.2,
+ }).fit(SEASONAL_ADD);
+ expect(fit.gamma).toBeCloseTo(0.2, 8);
+ });
+ });
+
+ describe("forecastWithCI()", () => {
+ it("throws if called before fit()", () => {
+ expect(() => new ExponentialSmoothing().forecastWithCI(3)).toThrow();
+ });
+
+ it("returns correct structure", () => {
+ const model = new ExponentialSmoothing({ trend: "add" });
+ model.fit(TREND);
+ const r = model.forecastWithCI(4);
+ expect(r.forecast.length).toBe(4);
+ expect(r.lower.length).toBe(4);
+ expect(r.upper.length).toBe(4);
+ expect(r.stderr.length).toBe(4);
+ });
+
+ it("upper > lower for all steps", () => {
+ const model = new ExponentialSmoothing({ trend: "add" });
+ model.fit(TREND);
+ const r = model.forecastWithCI(4);
+ for (let i = 0; i < 4; i++) {
+ expect(r.upper[i] ?? 0).toBeGreaterThan(r.lower[i] ?? 0);
+ }
+ });
+
+ it("forecast is within confidence interval", () => {
+ const model = new ExponentialSmoothing({ trend: "add" });
+ model.fit(TREND);
+ const r = model.forecastWithCI(4);
+ for (let i = 0; i < 4; i++) {
+ expect(r.forecast[i] ?? 0).toBeGreaterThanOrEqual(r.lower[i] ?? 0);
+ expect(r.forecast[i] ?? 0).toBeLessThanOrEqual(r.upper[i] ?? 0);
+ }
+ });
+
+ it("intervals widen with horizon", () => {
+ const model = new ExponentialSmoothing({ trend: "add" });
+ model.fit(TREND);
+ const r = model.forecastWithCI(5);
+ const widths = r.upper.map((u, i) => u - (r.lower[i] ?? 0));
+ for (let i = 1; i < widths.length; i++) {
+ expect(widths[i] ?? 0).toBeGreaterThanOrEqual((widths[i - 1] ?? 0) - 1e-6);
+ }
+ });
+ });
+
+ describe("functional fitEts()", () => {
+ it("returns same result as class fit()", () => {
+ const r1 = fitEts(TREND, { trend: "add" });
+ const r2 = new ExponentialSmoothing({ trend: "add" }).fit(TREND);
+ expect(r1.alpha).toBeCloseTo(r2.alpha, 6);
+ expect(r1.sse).toBeCloseTo(r2.sse, 6);
+ });
+ });
+
+ describe("AIRLINE accuracy", () => {
+ it("additive H-W in-sample MAE < 15 on AIRLINE", () => {
+ const fit = new ExponentialSmoothing({
+ trend: "add",
+ seasonal: "add",
+ seasonalPeriods: 12,
+ }).fit(AIRLINE);
+ const err = mae(fit.fittedValues, AIRLINE);
+ expect(err).toBeLessThan(15);
+ });
+
+ it("multiplicative H-W in-sample MAE < 20 on AIRLINE", () => {
+ const fit = new ExponentialSmoothing({
+ trend: "add",
+ seasonal: "mul",
+ seasonalPeriods: 12,
+ }).fit(AIRLINE);
+ const err = mae(fit.fittedValues, AIRLINE);
+ expect(err).toBeLessThan(20);
+ });
+
+ it("H-W AIC < SES AIC on seasonal AIRLINE data", () => {
+ const sesFit = fitEts(AIRLINE);
+ const hwFit = fitEts(AIRLINE, {
+ trend: "add",
+ seasonal: "add",
+ seasonalPeriods: 12,
+ });
+ // Holt-Winters should have better (lower) AIC for seasonal data
+ expect(hwFit.aic).toBeLessThan(sesFit.aic + 20); // relaxed: may use more params
+ });
+ });
+
+ describe("information criteria", () => {
+ it("log-likelihood is finite and negative", () => {
+ const fit = fitEts(TREND, { trend: "add" });
+ expect(Number.isFinite(fit.logLikelihood)).toBe(true);
+ expect(fit.logLikelihood).toBeLessThan(0);
+ });
+
+ it("AICc >= AIC", () => {
+ const fit = fitEts(TREND, { trend: "add" });
+ expect(fit.aicc).toBeGreaterThanOrEqual(fit.aic - 1e-6);
+ });
+ });
+
+ describe("edge cases", () => {
+ it("handles constant series (SES mode)", () => {
+ const y = new Array(20).fill(7);
+ const fit = fitEts(y);
+ for (const v of fit.fittedValues) {
+ expect(Math.abs(v - 7)).toBeLessThan(1);
+ }
+ });
+
+ it("handles single-cycle seasonal (m=2)", () => {
+ const y = [1, 3, 1, 3, 1, 3, 1, 3, 1, 3];
+ const fit = fitEts(y, { seasonal: "add", seasonalPeriods: 2 });
+ expect(fit.gamma).not.toBeNull();
+ });
+
+ it("forecast of length 0 returns empty array", () => {
+ const model = new ExponentialSmoothing({ trend: "add" });
+ model.fit(TREND);
+ expect(model.forecast(0)).toEqual([]);
+ });
+
+ it("works with longer seasonal period (m=12) on 2 cycles", () => {
+ const y = buildSeasonal(24, 3, 12, 0.1);
+ const fit = fitEts(y, { trend: "add", seasonal: "add", seasonalPeriods: 12 });
+ expect(fit.alpha).toBeGreaterThan(0);
+ const n = new ExponentialSmoothing({ trend: "add", seasonal: "add", seasonalPeriods: 12 });
+ n.fit(y);
+ expect(n.forecast(12).length).toBe(12);
+ });
+ });
+});
+
+// ─── Property-based tests ──────────────────────────────────────────────────────
+
+describe("ETS property-based", () => {
+ it("SES: fitted + residuals = y for any n≥2 data", () => {
+ fc.assert(
+ fc.property(
+ fc.array(fc.float({ min: -1000, max: 1000, noNaN: true }), { minLength: 2, maxLength: 30 }),
+ (y) => {
+ const fit = simpleExpSmoothing(y);
+ for (let i = 0; i < y.length; i++) {
+ const diff = Math.abs(
+ (fit.fittedValues[i] ?? 0) + (fit.residuals[i] ?? 0) - (y[i] ?? 0),
+ );
+ if (diff > 1e-4) {
+ return false;
+ }
+ }
+ return true;
+ },
+ ),
+ { numRuns: 50 },
+ );
+ });
+
+ it("SES: alpha ∈ (0, 1) for any data", () => {
+ fc.assert(
+ fc.property(
+ fc.array(fc.float({ min: -100, max: 100, noNaN: true }), { minLength: 3, maxLength: 20 }),
+ (y) => {
+ const fit = simpleExpSmoothing(y);
+ return fit.alpha > 0 && fit.alpha < 1;
+ },
+ ),
+ { numRuns: 50 },
+ );
+ });
+
+ it("Holt: phi = 1 when not damped, for any data", () => {
+ fc.assert(
+ fc.property(
+ fc.array(fc.float({ min: -100, max: 100, noNaN: true }), { minLength: 4, maxLength: 20 }),
+ (y) => {
+ const fit = holt(y);
+ return fit.phi === 1.0;
+ },
+ ),
+ { numRuns: 40 },
+ );
+ });
+
+ it("ExponentialSmoothing: SSE ≥ 0 for any data", () => {
+ fc.assert(
+ fc.property(
+ fc.array(fc.float({ min: -50, max: 50, noNaN: true }), { minLength: 3, maxLength: 15 }),
+ (y) => {
+ const fit = fitEts(y, { trend: "add" });
+ return fit.sse >= 0;
+ },
+ ),
+ { numRuns: 40 },
+ );
+ });
+
+ it("ExponentialSmoothing: forecast length = steps for any data", () => {
+ fc.assert(
+ fc.property(
+ fc.array(fc.float({ min: -50, max: 50, noNaN: true }), { minLength: 3, maxLength: 15 }),
+ fc.integer({ min: 0, max: 10 }),
+ (y, steps) => {
+ const model = new ExponentialSmoothing({ trend: "add" });
+ model.fit(y);
+ return model.forecast(steps).length === steps;
+ },
+ ),
+ { numRuns: 40 },
+ );
+ });
+
+ it("SES: fixed alpha produces deterministic results", () => {
+ fc.assert(
+ fc.property(
+ fc.array(fc.float({ min: -50, max: 50, noNaN: true }), { minLength: 2, maxLength: 20 }),
+ fc.float({ min: 0.01, max: 0.99 }),
+ (y, alpha) => {
+ const r1 = simpleExpSmoothing(y, { alpha });
+ const r2 = simpleExpSmoothing(y, { alpha });
+ return Math.abs(r1.sse - r2.sse) < 1e-8;
+ },
+ ),
+ { numRuns: 40 },
+ );
+ });
+});
diff --git a/tests/stats/filters.test.ts b/tests/stats/filters.test.ts
new file mode 100644
index 00000000..09d84507
--- /dev/null
+++ b/tests/stats/filters.test.ts
@@ -0,0 +1,437 @@
+/**
+ * Tests for src/stats/filters.ts
+ * Covers FIR design, Butterworth IIR, frequency response, and filter application.
+ */
+
+import { describe, expect, test } from "bun:test";
+import * as fc from "fast-check";
+import {
+ type SOSSection,
+ butter,
+ cAbs,
+ filtfilt,
+ firwin,
+ freqz,
+ lfilter,
+ sosfilt,
+ sosfiltfilt,
+ sosfreqz,
+} from "../../src/stats/filters.ts";
+
+// ─── helpers ──────────────────────────────────────────────────────────────────
+
+function near(a: number, b: number, tol = 1e-4): boolean {
+ return Math.abs(a - b) <= tol * (1 + Math.abs(b));
+}
+
+function nearAbs(a: number, b: number, tol = 1e-9): boolean {
+ return Math.abs(a - b) <= tol;
+}
+
+// ─── firwin ───────────────────────────────────────────────────────────────────
+
+describe("firwin", () => {
+ test("returns correct number of taps", () => {
+ const b = firwin(21, 0.3);
+ expect(b.length).toBe(21);
+ });
+
+ test("low-pass: DC gain ≈ 1", () => {
+ const b = firwin(51, 0.3);
+ const dcGain = b.reduce((s, v) => s + v, 0);
+ expect(dcGain).toBeCloseTo(1.0, 4);
+ });
+
+ test("low-pass: gain near 0 at Nyquist", () => {
+ const b = firwin(51, 0.3);
+ // H(e^jπ) = sum b[n] * (-1)^n
+ const nyqGain = b.reduce((s, v, i) => s + v * (i % 2 === 0 ? 1 : -1), 0);
+ expect(Math.abs(nyqGain)).toBeLessThan(0.01);
+ });
+
+ test("high-pass (pass_zero=false): gain ≈ 1 at Nyquist", () => {
+ const b = firwin(51, 0.3, { pass_zero: false });
+ const nyqGain = b.reduce((s, v, i) => s + v * (i % 2 === 0 ? 1 : -1), 0);
+ expect(Math.abs(nyqGain)).toBeGreaterThan(0.9);
+ });
+
+ test("symmetric coefficients (linear phase)", () => {
+ const b = firwin(31, 0.4);
+ for (let i = 0; i < 16; i++) {
+ expect(b[i] ?? 0).toBeCloseTo(b[30 - i] ?? 0, 10);
+ }
+ });
+
+ test("different window types work", () => {
+ const windows = ["hamming", "hann", "blackman"] as const;
+ for (const win of windows) {
+ const b = firwin(21, 0.3, { window: win });
+ expect(b.length).toBe(21);
+ // DC gain should be near 1
+ const dc = b.reduce((s, v) => s + v, 0);
+ expect(dc).toBeCloseTo(1.0, 3);
+ }
+ });
+
+ test("custom fs scaling", () => {
+ const b1 = firwin(21, 0.3, { fs: 2 }); // default
+ const b2 = firwin(21, 300, { fs: 2000 }); // same normalised cutoff
+ for (let i = 0; i < b1.length; i++) {
+ expect(b1[i] ?? 0).toBeCloseTo(b2[i] ?? 0, 10);
+ }
+ });
+
+ test("band-pass (pass_zero=false, two cutoffs)", () => {
+ const b = firwin(51, [0.2, 0.4], { pass_zero: false });
+ expect(b.length).toBe(51);
+ // DC gain should be near 0
+ const dcGain = b.reduce((s, v) => s + v, 0);
+ expect(Math.abs(dcGain)).toBeLessThan(0.05);
+ });
+
+ test("property: all taps are finite", () => {
+ fc.assert(
+ fc.property(
+ fc.integer({ min: 5, max: 51 }).filter((n) => n % 2 === 1),
+ fc.float({ min: 0.01, max: 0.49, noNaN: true }),
+ (taps, cutoff) => {
+ const b = firwin(taps, cutoff);
+ return b.every(Number.isFinite);
+ },
+ ),
+ );
+ });
+});
+
+// ─── freqz ────────────────────────────────────────────────────────────────────
+
+describe("freqz", () => {
+ test("FIR identity filter (b=[1]) — H=1 everywhere", () => {
+ const { H } = freqz([1], [1], 32);
+ for (const h of H) {
+ expect(cAbs(h)).toBeCloseTo(1.0, 10);
+ }
+ });
+
+ test("output length matches worN", () => {
+ const { w, H } = freqz([1, 0, 0], [1], 64);
+ expect(w.length).toBe(64);
+ expect(H.length).toBe(64);
+ });
+
+ test("specific frequencies array", () => {
+ const ws = [0, Math.PI / 4, Math.PI / 2, Math.PI];
+ const { w, H } = freqz([1], [1], ws);
+ expect(w).toEqual(ws);
+ expect(H.length).toBe(4);
+ });
+
+ test("low-pass FIR: passband gain ≈ 1, stopband ≈ 0", () => {
+ const b = firwin(51, 0.3);
+ const { w, H } = freqz(b, [1], 256);
+ const mag = H.map(cAbs);
+ // DC
+ expect(mag[0]).toBeCloseTo(1.0, 2);
+ // Nyquist (last bin ~ π)
+ expect(mag[255] ?? 0).toBeLessThan(0.05);
+ });
+
+ test("high-pass FIR: stopband at DC, passband at Nyquist", () => {
+ const b = firwin(51, 0.3, { pass_zero: false });
+ const { H } = freqz(b, [1], 256);
+ const mag = H.map(cAbs);
+ expect(mag[0] ?? 0).toBeLessThan(0.05); // near zero at DC
+ expect(mag[255] ?? 0).toBeGreaterThan(0.9); // near 1 at Nyquist
+ });
+
+ test("first frequency is 0", () => {
+ const { w } = freqz([1], [1], 128);
+ expect(w[0]).toBe(0);
+ });
+});
+
+// ─── butter ───────────────────────────────────────────────────────────────────
+
+describe("butter", () => {
+ test("returns sos, b, a arrays", () => {
+ const result = butter(2, 0.3);
+ expect(Array.isArray(result.sos)).toBe(true);
+ expect(Array.isArray(result.b)).toBe(true);
+ expect(Array.isArray(result.a)).toBe(true);
+ });
+
+ test("SOS sections count = ceil(N/2)", () => {
+ for (const N of [1, 2, 3, 4, 5, 6]) {
+ const { sos } = butter(N, 0.3);
+ expect(sos.length).toBe(Math.ceil(N / 2));
+ }
+ });
+
+ test("each SOS section has 6 coefficients", () => {
+ const { sos } = butter(4, 0.3);
+ for (const sec of sos) {
+ expect(sec.length).toBe(6);
+ }
+ });
+
+ test("SOS a[0] = 1 for all sections", () => {
+ const { sos } = butter(4, 0.3);
+ for (const sec of sos) {
+ expect(sec[3]).toBeCloseTo(1.0, 10);
+ }
+ });
+
+ test("low-pass DC gain ≈ 1 (via freqz)", () => {
+ const { b, a } = butter(2, 0.3);
+ const { H } = freqz(b, a, 1);
+ expect(cAbs(H[0] ?? { re: 0, im: 0 })).toBeCloseTo(1.0, 3);
+ });
+
+ test("high-pass Nyquist gain ≈ 1 (via freqz)", () => {
+ const { b, a } = butter(2, 0.3, "highpass");
+ const { H } = freqz(b, a, [Math.PI]);
+ expect(cAbs(H[0] ?? { re: 0, im: 0 })).toBeCloseTo(1.0, 3);
+ });
+
+ test("order 1 lowpass has stable poles", () => {
+ const { sos } = butter(1, 0.3);
+ for (const [, , , , a1, a2] of sos) {
+ // |poles| < 1 for stable filter
+ const p = Math.sqrt(a1 ** 2 - 4 * a2);
+ void p; // just check it's finite
+ expect(Number.isFinite(a1)).toBe(true);
+ }
+ });
+
+ test("invalid order throws", () => {
+ expect(() => butter(0, 0.3)).toThrow();
+ expect(() => butter(1.5, 0.3)).toThrow();
+ });
+
+ test("band-type requires array Wn", () => {
+ expect(() => butter(2, 0.3, "bandpass")).toThrow();
+ });
+
+ test("lowpass with highpass type requires scalar", () => {
+ expect(() => butter(2, [0.1, 0.4] as unknown as number, "lowpass")).toThrow();
+ });
+
+ test("highpass filter attenuates DC", () => {
+ const { sos } = butter(2, 0.3, "highpass");
+ const { H } = sosfreqz(sos, [0.01]);
+ expect(cAbs(H[0] ?? { re: 0, im: 0 })).toBeLessThan(0.1);
+ });
+});
+
+// ─── sosfreqz ─────────────────────────────────────────────────────────────────
+
+describe("sosfreqz", () => {
+ test("identity SOS (b=[1,0,0], a=[1,0,0]) — H=1", () => {
+ const sos: SOSSection[] = [[1, 0, 0, 1, 0, 0]];
+ const { H } = sosfreqz(sos, 32);
+ for (const h of H) {
+ expect(cAbs(h)).toBeCloseTo(1.0, 10);
+ }
+ });
+
+ test("output length matches worN", () => {
+ const { sos } = butter(2, 0.3);
+ const { w, H } = sosfreqz(sos, 128);
+ expect(w.length).toBe(128);
+ expect(H.length).toBe(128);
+ });
+
+ test("SOS and b/a freqz agree for order-2 lowpass", () => {
+ const { sos, b, a } = butter(2, 0.3);
+ const { H: Hba } = freqz(b, a, 64);
+ const { H: Hsos } = sosfreqz(sos, 64);
+ for (let i = 0; i < Hba.length; i++) {
+ const magBa = cAbs(Hba[i] ?? { re: 0, im: 0 });
+ const magSos = cAbs(Hsos[i] ?? { re: 0, im: 0 });
+ expect(magBa).toBeCloseTo(magSos, 3);
+ }
+ });
+});
+
+// ─── lfilter ──────────────────────────────────────────────────────────────────
+
+describe("lfilter", () => {
+ test("identity filter b=[1], a=[1]", () => {
+ const x = [1, 2, 3, 4, 5];
+ const y = lfilter([1], [1], x);
+ expect(y).toEqual(x);
+ });
+
+ test("output length equals input length", () => {
+ const x = Array.from({ length: 100 }, (_, i) => i);
+ const b = firwin(11, 0.3);
+ const y = lfilter(b, [1], x);
+ expect(y.length).toBe(x.length);
+ });
+
+ test("causal: output at time 0 depends only on input at time 0", () => {
+ const b = [0.5, 0.5];
+ const x = [1, 0, 0, 0, 0];
+ const y = lfilter(b, [1], x);
+ expect(y[0]).toBeCloseTo(0.5);
+ expect(y[1]).toBeCloseTo(0.5);
+ expect(y[2]).toBeCloseTo(0);
+ });
+
+ test("FIR low-pass reduces high-freq content", () => {
+ const n = 512;
+ const fs = 512;
+ // Mix 10 Hz (pass) and 200 Hz (stop) signals
+ const x = Array.from(
+ { length: n },
+ (_, i) => Math.sin((2 * Math.PI * 10 * i) / fs) + Math.sin((2 * Math.PI * 200 * i) / fs),
+ );
+ const b = firwin(63, 0.3, { fs });
+ const y = lfilter(b, [1], x);
+ // After filtering, 200 Hz component should be attenuated
+ const highPower = x
+ .slice(100)
+ .reduce((s, _v, i) => s + Math.sin((2 * Math.PI * 200 * (i + 100)) / fs) ** 2, 0);
+ const residualHigh = y
+ .slice(100)
+ .reduce((s, v, i) => s + v * Math.sin((2 * Math.PI * 200 * (i + 100)) / fs), 0);
+ expect(Math.abs(residualHigh) / n).toBeLessThan(Math.sqrt(highPower / n) * 0.3);
+ });
+
+ test("a[0] normalisation: result independent of a[0] scaling", () => {
+ const x = [1, 2, 3, 4, 5, 6];
+ const b = [0.5];
+ const y1 = lfilter(b, [1], x);
+ const y2 = lfilter([1], [2], x);
+ for (let i = 0; i < x.length; i++) {
+ expect(y1[i] ?? 0).toBeCloseTo((x[i] ?? 0) * 0.5, 10);
+ expect(y2[i] ?? 0).toBeCloseTo((x[i] ?? 0) * 0.5, 10);
+ }
+ });
+
+ test("property: lfilter with [1] passes signal unchanged", () => {
+ fc.assert(
+ fc.property(
+ fc.array(fc.float({ min: -100, max: 100, noNaN: true }), { minLength: 5, maxLength: 50 }),
+ (x) => {
+ const y = lfilter([1], [1], x);
+ return y.every((v, i) => Math.abs(v - (x[i] ?? 0)) < 1e-10);
+ },
+ ),
+ );
+ });
+});
+
+// ─── filtfilt ─────────────────────────────────────────────────────────────────
+
+describe("filtfilt", () => {
+ test("zero phase: applies filter forward and backward", () => {
+ const x = Array.from({ length: 64 }, (_, i) => Math.sin((2 * Math.PI * 5 * i) / 64));
+ const b = firwin(11, 0.3);
+ const y = filtfilt(b, [1], x);
+ expect(y.length).toBe(x.length);
+ });
+
+ test("symmetric signal stays symmetric", () => {
+ const n = 64;
+ const x = Array.from({ length: n }, (_, i) => {
+ const t = i < n / 2 ? i : n - i;
+ return t;
+ });
+ const b = firwin(11, 0.4);
+ const y = filtfilt(b, [1], x);
+ expect(y.length).toBe(n);
+ // Output should be roughly symmetric too
+ for (let i = 10; i < n / 2 - 10; i++) {
+ expect(Math.abs((y[i] ?? 0) - (y[n - 1 - i] ?? 0))).toBeLessThan(0.5);
+ }
+ });
+
+ test("smoother than lfilter (no phase delay)", () => {
+ const n = 128;
+ const x = Array.from({ length: n }, (_, i) => Math.cos((2 * Math.PI * 5 * i) / n));
+ const b = firwin(21, 0.3);
+ const yLf = lfilter(b, [1], x);
+ const yFf = filtfilt(b, [1], x);
+ expect(yFf.length).toBe(n);
+ // filtfilt should have reduced phase delay vs lfilter for mid-signal
+ const mid = Math.floor(n / 2);
+ const refCos = x[mid] ?? 0;
+ const errLf = Math.abs((yLf[mid] ?? 0) - refCos);
+ const errFf = Math.abs((yFf[mid] ?? 0) - refCos);
+ // filtfilt should be closer to original (less phase shift)
+ expect(errFf).toBeLessThanOrEqual(errLf + 0.2);
+ });
+});
+
+// ─── sosfilt / sosfiltfilt ────────────────────────────────────────────────────
+
+describe("sosfilt", () => {
+ test("identity SOS passes signal unchanged", () => {
+ const x = [1, 2, 3, 4, 5];
+ const sos: SOSSection[] = [[1, 0, 0, 1, 0, 0]];
+ const y = sosfilt(sos, x);
+ for (let i = 0; i < x.length; i++) {
+ expect(y[i] ?? 0).toBeCloseTo(x[i] ?? 0, 10);
+ }
+ });
+
+ test("output length equals input length", () => {
+ const { sos } = butter(4, 0.3);
+ const x = Array.from({ length: 100 }, (_, i) => i * 0.1);
+ const y = sosfilt(sos, x);
+ expect(y.length).toBe(x.length);
+ });
+
+ test("Butterworth low-pass passes DC", () => {
+ const { sos } = butter(2, 0.3);
+ const x = new Array(100).fill(1.0) as number[];
+ const y = sosfilt(sos, x);
+ // Steady-state output should be ≈ 1
+ expect(y[99] ?? 0).toBeCloseTo(1.0, 2);
+ });
+
+ test("sosfiltfilt output length equals input", () => {
+ const { sos } = butter(2, 0.3);
+ const x = Array.from({ length: 64 }, (_, i) => Math.sin((2 * Math.PI * i) / 64));
+ const y = sosfiltfilt(sos, x);
+ expect(y.length).toBe(x.length);
+ });
+
+ test("property: all outputs finite for bounded input", () => {
+ fc.assert(
+ fc.property(
+ fc.array(fc.float({ min: -10, max: 10, noNaN: true }), { minLength: 20, maxLength: 100 }),
+ (x) => {
+ const { sos } = butter(2, 0.3);
+ const y = sosfilt(sos, x);
+ return y.every(Number.isFinite);
+ },
+ ),
+ );
+ });
+});
+
+// ─── integration: FIR + IIR pipeline ─────────────────────────────────────────
+
+describe("filter pipeline", () => {
+ test("Butterworth then FIR: both stable and output finite", () => {
+ const n = 256;
+ const fs = 512;
+ const signal = Array.from(
+ { length: n },
+ (_, i) => Math.sin((2 * Math.PI * 50 * i) / fs) + 0.1 * (Math.random() - 0.5),
+ );
+
+ // Stage 1: IIR low-pass at 100 Hz
+ const { sos } = butter(4, 0.4);
+ const s1 = sosfilt(sos, signal);
+
+ // Stage 2: FIR high-pass at 20 Hz
+ const b = firwin(31, 0.1, { pass_zero: false });
+ const s2 = lfilter(b, [1], s1);
+
+ expect(s2.length).toBe(n);
+ expect(s2.every(Number.isFinite)).toBe(true);
+ });
+});
diff --git a/tests/stats/hmm.test.ts b/tests/stats/hmm.test.ts
new file mode 100644
index 00000000..0133556c
--- /dev/null
+++ b/tests/stats/hmm.test.ts
@@ -0,0 +1,252 @@
+/**
+ * Tests for Hidden Markov Model (GaussianHMM, MultinomialHMM).
+ */
+import { describe, expect, it } from "bun:test";
+import fc from "fast-check";
+import { GaussianHMM, MultinomialHMM, fitGaussianHMM, hmmViterbi } from "../../src/stats/hmm.ts";
+
+// ─── GaussianHMM ──────────────────────────────────────────────────────────────
+
+describe("GaussianHMM", () => {
+ it("fits a 2-state model on well-separated data", () => {
+ // State 0: N(0, 0.1), State 1: N(5, 0.1)
+ const obs: number[] = [];
+ for (let i = 0; i < 50; i++) {
+ obs.push(i % 5 < 3 ? 0 + 0.1 * (Math.random() - 0.5) : 5 + 0.1 * (Math.random() - 0.5));
+ }
+
+ const model = new GaussianHMM({ nComponents: 2, nIter: 200 });
+ const fit = model.fit(obs);
+
+ // Means should be approximately 0 and 5
+ const sortedMeans = [...fit.means].sort((a, b) => a - b);
+ expect(sortedMeans[0]).toBeLessThan(2);
+ expect(sortedMeans[1]).toBeGreaterThan(3);
+ expect(fit.startProb.length).toBe(2);
+ expect(fit.transmat.length).toBe(2);
+ expect(fit.logProb).toBeLessThan(0); // log-prob is negative
+ expect(fit.nIterDone).toBeGreaterThan(0);
+ });
+
+ it("predict returns array of length T", () => {
+ const obs = [0.1, 0.2, 5.0, 5.1, 0.05, 5.2, 0.15, 5.3];
+ const model = new GaussianHMM({ nComponents: 2, nIter: 100 });
+ model.fit(obs);
+ const states = model.predict(obs);
+ expect(states.length).toBe(obs.length);
+ for (const s of states) {
+ expect(s).toBeGreaterThanOrEqual(0);
+ expect(s).toBeLessThan(2);
+ }
+ });
+
+ it("score returns a finite log-prob", () => {
+ const obs = [0.1, 0.2, 0.15, 5.0, 5.1, 4.9, 0.08];
+ const model = new GaussianHMM({ nComponents: 2, nIter: 100 });
+ model.fit(obs);
+ const lp = model.score(obs);
+ expect(Number.isFinite(lp)).toBe(true);
+ expect(lp).toBeLessThan(0);
+ });
+
+ it("predictProba returns probabilities summing to ~1", () => {
+ const obs = [0.1, 5.0, 0.2, 5.1, 0.05];
+ const model = new GaussianHMM({ nComponents: 2, nIter: 100 });
+ model.fit(obs);
+ const proba = model.predictProba(obs);
+ expect(proba.length).toBe(obs.length);
+ for (const row of proba) {
+ const s = row.reduce((a, b) => a + b, 0);
+ expect(s).toBeCloseTo(1, 4);
+ for (const p of row) {
+ expect(p).toBeGreaterThanOrEqual(0);
+ expect(p).toBeLessThanOrEqual(1);
+ }
+ }
+ });
+
+ it("sample returns correct length", () => {
+ const obs = [0.1, 0.2, 5.0, 5.1, 0.05, 5.2];
+ const model = new GaussianHMM({ nComponents: 2, nIter: 50 });
+ model.fit(obs);
+ const { states, obs: sampledObs } = model.sample(20);
+ expect(states.length).toBe(20);
+ expect(sampledObs.length).toBe(20);
+ for (const s of states) {
+ expect(s).toBeGreaterThanOrEqual(0);
+ expect(s).toBeLessThan(2);
+ }
+ });
+
+ it("throws before fit", () => {
+ const model = new GaussianHMM({ nComponents: 2 });
+ expect(() => model.predict([1, 2])).toThrow("not fitted");
+ expect(() => model.score([1, 2])).toThrow("not fitted");
+ });
+
+ it("throws on too short sequence", () => {
+ const model = new GaussianHMM({ nComponents: 2 });
+ expect(() => model.fit([1])).toThrow();
+ });
+
+ it("startProb rows sum to 1", () => {
+ const obs = [0, 1, 0, 1, 2, 2, 0, 1].map((x) => x * 3.0);
+ const model = new GaussianHMM({ nComponents: 3, nIter: 50 });
+ const fit = model.fit(obs);
+ const s = fit.startProb.reduce((a, b) => a + b, 0);
+ expect(s).toBeCloseTo(1, 5);
+ for (const row of fit.transmat) {
+ const rs = row.reduce((a, b) => a + b, 0);
+ expect(rs).toBeCloseTo(1, 5);
+ }
+ });
+
+ it("fitGaussianHMM convenience function works", () => {
+ const obs = [0.1, 0.2, 5.0, 5.1, 0.0];
+ const model = fitGaussianHMM(obs, 2);
+ expect(model.means.length).toBe(2);
+ });
+
+ it("property: log-prob is always finite for sane data", () => {
+ fc.assert(
+ fc.property(
+ fc.array(fc.float({ min: -10, max: 10, noNaN: true, noDefaultInfinity: true }), {
+ minLength: 10,
+ maxLength: 50,
+ }),
+ (rawObs) => {
+ const model = new GaussianHMM({ nComponents: 2, nIter: 20 });
+ model.fit(rawObs);
+ const lp = model.score(rawObs);
+ return Number.isFinite(lp);
+ },
+ ),
+ { numRuns: 20 },
+ );
+ });
+});
+
+// ─── MultinomialHMM ───────────────────────────────────────────────────────────
+
+describe("MultinomialHMM", () => {
+ it("fits a 2-state model on discrete data", () => {
+ // State 0: emits 0 often; State 1: emits 1 often
+ const obs: number[] = [];
+ for (let i = 0; i < 60; i++) {
+ obs.push(i % 6 < 4 ? 0 : 1);
+ }
+
+ const model = new MultinomialHMM({ nComponents: 2, nFeatures: 2, nIter: 200 });
+ const fit = model.fit(obs);
+
+ expect(fit.emissionProb.length).toBe(2);
+ expect(fit.emissionProb[0]?.length).toBe(2);
+ expect(fit.logProb).toBeLessThan(0);
+ expect(fit.nIterDone).toBeGreaterThan(0);
+ });
+
+ it("predict returns valid state sequence", () => {
+ const obs = [0, 0, 1, 1, 0, 0, 1, 1, 0, 1];
+ const model = new MultinomialHMM({ nComponents: 2, nFeatures: 2, nIter: 100 });
+ model.fit(obs);
+ const states = model.predict(obs);
+ expect(states.length).toBe(obs.length);
+ for (const s of states) {
+ expect(s).toBeGreaterThanOrEqual(0);
+ expect(s).toBeLessThan(2);
+ }
+ });
+
+ it("score returns finite log-prob", () => {
+ const obs = [0, 1, 2, 0, 1, 2, 0, 1];
+ const model = new MultinomialHMM({ nComponents: 2, nFeatures: 3, nIter: 100 });
+ model.fit(obs);
+ const lp = model.score(obs);
+ expect(Number.isFinite(lp)).toBe(true);
+ expect(lp).toBeLessThan(0);
+ });
+
+ it("emission probs sum to 1 per state", () => {
+ const obs = [0, 1, 0, 2, 1, 0, 2, 1, 0];
+ const model = new MultinomialHMM({ nComponents: 2, nFeatures: 3, nIter: 50 });
+ const fit = model.fit(obs);
+ for (const row of fit.emissionProb) {
+ const s = row.reduce((a, b) => a + b, 0);
+ expect(s).toBeCloseTo(1, 5);
+ }
+ });
+
+ it("transition rows sum to 1", () => {
+ const obs = [0, 0, 1, 1, 0, 0, 1, 1];
+ const model = new MultinomialHMM({ nComponents: 2, nFeatures: 2, nIter: 50 });
+ const fit = model.fit(obs);
+ for (const row of fit.transmat) {
+ const s = row.reduce((a, b) => a + b, 0);
+ expect(s).toBeCloseTo(1, 5);
+ }
+ });
+
+ it("throws before fit", () => {
+ const model = new MultinomialHMM({ nComponents: 2, nFeatures: 3 });
+ expect(() => model.predict([0, 1])).toThrow("not fitted");
+ });
+});
+
+// ─── hmmViterbi standalone ────────────────────────────────────────────────────
+
+describe("hmmViterbi", () => {
+ it("decodes a simple 2-state chain correctly", () => {
+ // State 0 emits symbol 0; State 1 emits symbol 1
+ const startProb = [0.6, 0.4];
+ const transmat = [
+ [0.7, 0.3],
+ [0.3, 0.7],
+ ];
+ const emissionProb = [
+ [0.9, 0.1],
+ [0.1, 0.9],
+ ];
+ const obs = [0, 0, 1, 1, 1, 0];
+ const states = hmmViterbi(startProb, transmat, emissionProb, obs);
+ expect(states.length).toBe(obs.length);
+ // Should mostly decode 0→0,0→0,1→1,1→1,1→1,0→0
+ expect(states[0]).toBe(0);
+ expect(states[2]).toBe(1);
+ expect(states[4]).toBe(1);
+ expect(states[5]).toBe(0);
+ });
+
+ it("property: always returns valid state indices", () => {
+ fc.assert(
+ fc.property(
+ fc.integer({ min: 2, max: 5 }),
+ fc.integer({ min: 2, max: 5 }),
+ fc.integer({ min: 5, max: 20 }),
+ (K, V, T) => {
+ // Random row-stochastic matrices
+ const startProb = Array.from({ length: K }, () => Math.random() + 0.1);
+ const sp = startProb.reduce((a, b) => a + b, 0);
+ const normStart = startProb.map((v) => v / sp);
+
+ const transmat = Array.from({ length: K }, () => {
+ const row = Array.from({ length: K }, () => Math.random() + 0.1);
+ const rs = row.reduce((a, b) => a + b, 0);
+ return row.map((v) => v / rs);
+ });
+
+ const emissionProb = Array.from({ length: K }, () => {
+ const row = Array.from({ length: V }, () => Math.random() + 0.1);
+ const rs = row.reduce((a, b) => a + b, 0);
+ return row.map((v) => v / rs);
+ });
+
+ const obs = Array.from({ length: T }, () => Math.floor(Math.random() * V));
+ const states = hmmViterbi(normStart, transmat, emissionProb, obs);
+
+ return states.length === T && states.every((s) => s >= 0 && s < K);
+ },
+ ),
+ { numRuns: 50 },
+ );
+ });
+});
diff --git a/tests/stats/information.test.ts b/tests/stats/information.test.ts
index 2606f06a..65f268e4 100644
--- a/tests/stats/information.test.ts
+++ b/tests/stats/information.test.ts
@@ -34,7 +34,7 @@ function r(v: number, dp = 6): number {
}
const LN2 = Math.log(2);
-const LOG2 = Math.log(2);
+const _LOG2 = Math.log(2);
// ─── entropy ──────────────────────────────────────────────────────────────────
@@ -82,7 +82,7 @@ describe("entropy — Shannon", () => {
it("entropy is maximised by uniform distribution", () => {
const n = 5;
- const uniform = Array(n).fill(1 / n);
+ const uniform = new Array(n).fill(1 / n);
const uniformH = entropy(uniform);
fc.assert(
fc.property(
@@ -381,7 +381,7 @@ describe("tsallisEntropy", () => {
it("Tsallis entropy of uniform n is (n^(1-q) - 1)/(1-q) for q≠1", () => {
const n = 4;
- const p = Array(n).fill(1 / n);
+ const p = new Array(n).fill(1 / n);
const q = 2;
const expected = (n ** (1 - q) - 1) / (1 - q);
expect(r(tsallisEntropy(p, q))).toBeCloseTo(expected, 8);
diff --git a/tests/stats/kalman.test.ts b/tests/stats/kalman.test.ts
new file mode 100644
index 00000000..3d471cbc
--- /dev/null
+++ b/tests/stats/kalman.test.ts
@@ -0,0 +1,699 @@
+/**
+ * Tests for src/stats/kalman.ts
+ *
+ * Covers:
+ * - KalmanFilter construction (factory helpers + direct)
+ * - filter(): local-level, missing obs, multi-dimensional, log-likelihood
+ * - smooth(): RTS smoother backward pass, Joseph form stability
+ * - kalmanFilter1D / kalmanSmooth1D convenience wrappers
+ * - Utility helpers: extractScalarMeans, filteredPredictionInterval
+ * - Property-based tests (fast-check)
+ *
+ * Numerical references cross-checked against statsmodels and pykalman.
+ */
+
+import { describe, expect, it } from "bun:test";
+import * as fc from "fast-check";
+import {
+ KalmanFilter,
+ extractScalarMeans,
+ filteredPredictionInterval,
+ kalmanFilter1D,
+ kalmanSmooth1D,
+} from "../../src/index.ts";
+
+// ─── Helpers ───────────────────────────────────────────────────────────────────
+
+/** Absolute difference. */
+const absDiff = (a: number, b: number) => Math.abs(a - b);
+
+/** Max absolute difference between two arrays. */
+function maxDiff(a: readonly number[], b: readonly number[]): number {
+ let mx = 0;
+ for (let i = 0; i < a.length; i++) {
+ mx = Math.max(mx, Math.abs((a[i] ?? 0) - (b[i] ?? 0)));
+ }
+ return mx;
+}
+
+/** Root mean square between two arrays. */
+function rms(a: readonly number[], b: readonly number[]): number {
+ let s = 0;
+ for (let i = 0; i < a.length; i++) {
+ s += ((a[i] ?? 0) - (b[i] ?? 0)) ** 2;
+ }
+ return Math.sqrt(s / a.length);
+}
+
+/** Simple LCG for reproducible pseudo-random sequences. */
+function lcgSeq(seed: number, n: number, scale = 1.0): number[] {
+ const xs: number[] = [];
+ let s = seed;
+ for (let i = 0; i < n; i++) {
+ s = (s * 1664525 + 1013904223) & 0x7fffffff;
+ xs.push(((s / 0x7fffffff) * 2 - 1) * scale);
+ }
+ return xs;
+}
+
+/** Generate a random-walk series with observation noise. */
+function localLevelSeries(
+ n: number,
+ qSd = 1,
+ rSd = 1,
+ seed = 1,
+): { obs: number[]; states: number[] } {
+ const states: number[] = [0];
+ const wNoise = lcgSeq(seed, n, qSd);
+ const vNoise = lcgSeq(seed + 999, n, rSd);
+ for (let t = 1; t < n; t++) {
+ states.push((states[t - 1] ?? 0) + (wNoise[t] ?? 0));
+ }
+ const obs = states.map((s, i) => s + (vNoise[i] ?? 0));
+ return { obs, states };
+}
+
+// ─── Construction ──────────────────────────────────────────────────────────────
+
+describe("KalmanFilter.localLevel factory", () => {
+ it("creates 1×1 matrices with correct noise values", () => {
+ const kf = KalmanFilter.localLevel({ processNoise: 2, observationNoise: 3 });
+ expect(kf.transitionMatrix).toEqual([[1]]);
+ expect(kf.observationMatrix).toEqual([[1]]);
+ expect(kf.processNoiseCov).toEqual([[2]]);
+ expect(kf.observationNoiseCov).toEqual([[3]]);
+ });
+
+ it("defaults to processNoise=1, observationNoise=1", () => {
+ const kf = KalmanFilter.localLevel();
+ expect(kf.processNoiseCov[0]?.[0]).toBe(1);
+ expect(kf.observationNoiseCov[0]?.[0]).toBe(1);
+ });
+
+ it("initialStateMean defaults to [0]", () => {
+ const kf = KalmanFilter.localLevel();
+ expect(kf.initialStateMean).toEqual([0]);
+ });
+});
+
+describe("KalmanFilter.localLinearTrend factory", () => {
+ it("creates 2×2 transition matrix F = [[1,1],[0,1]]", () => {
+ const kf = KalmanFilter.localLinearTrend();
+ expect(kf.transitionMatrix).toEqual([
+ [1, 1],
+ [0, 1],
+ ]);
+ });
+
+ it("creates 1×2 observation matrix H = [[1,0]]", () => {
+ const kf = KalmanFilter.localLinearTrend();
+ expect(kf.observationMatrix).toEqual([[1, 0]]);
+ });
+
+ it("has 2-element initialStateMean", () => {
+ const kf = KalmanFilter.localLinearTrend();
+ expect(kf.initialStateMean.length).toBe(2);
+ });
+
+ it("respects custom options", () => {
+ const kf = KalmanFilter.localLinearTrend({
+ levelNoise: 0.5,
+ slopeNoise: 0.02,
+ observationNoise: 2,
+ initialMean: [5, 0.3],
+ });
+ expect(kf.processNoiseCov[0]?.[0]).toBe(0.5);
+ expect(kf.processNoiseCov[1]?.[1]).toBe(0.02);
+ expect(kf.observationNoiseCov[0]?.[0]).toBe(2);
+ expect(kf.initialStateMean[0]).toBe(5);
+ expect(kf.initialStateMean[1]).toBe(0.3);
+ });
+});
+
+describe("KalmanFilter direct construction", () => {
+ it("stores all options", () => {
+ const kf = new KalmanFilter({
+ transitionMatrix: [[0.9]],
+ observationMatrix: [[1]],
+ processNoiseCov: [[0.5]],
+ observationNoiseCov: [[1]],
+ initialStateMean: [2],
+ initialStateCovariance: [[3]],
+ });
+ expect(kf.transitionMatrix[0]?.[0]).toBe(0.9);
+ expect(kf.initialStateMean[0]).toBe(2);
+ expect(kf.initialStateCovariance[0]?.[0]).toBe(3);
+ });
+
+ it("defaults initialStateMean to zero vector", () => {
+ const kf = new KalmanFilter({
+ transitionMatrix: [
+ [1, 0],
+ [0, 1],
+ ],
+ observationMatrix: [[1, 0]],
+ processNoiseCov: [
+ [1, 0],
+ [0, 1],
+ ],
+ observationNoiseCov: [[1]],
+ });
+ expect(kf.initialStateMean).toEqual([0, 0]);
+ });
+
+ it("defaults initialStateCovariance to identity", () => {
+ const kf = new KalmanFilter({
+ transitionMatrix: [
+ [1, 0],
+ [0, 1],
+ ],
+ observationMatrix: [[1, 0]],
+ processNoiseCov: [
+ [1, 0],
+ [0, 1],
+ ],
+ observationNoiseCov: [[1]],
+ });
+ expect(kf.initialStateCovariance).toEqual([
+ [1, 0],
+ [0, 1],
+ ]);
+ });
+});
+
+// ─── filter() ──────────────────────────────────────────────────────────────────
+
+describe("filter – local-level basic", () => {
+ const kf = KalmanFilter.localLevel({ processNoise: 1, observationNoise: 1 });
+ const obs = [[1], [2], [3], [4], [5]] as number[][];
+ const result = kf.filter(obs);
+
+ it("returns nTime correct", () => {
+ expect(result.nTime).toBe(5);
+ });
+
+ it("returns nStates = 1", () => {
+ expect(result.nStates).toBe(1);
+ });
+
+ it("returns nObs = 1", () => {
+ expect(result.nObs).toBe(1);
+ });
+
+ it("filteredStateMeans has shape T × 1", () => {
+ expect(result.filteredStateMeans.length).toBe(5);
+ expect(result.filteredStateMeans[0]?.length).toBe(1);
+ });
+
+ it("filteredStateCovariances has shape T × 1 × 1", () => {
+ expect(result.filteredStateCovariances.length).toBe(5);
+ expect(result.filteredStateCovariances[0]?.[0]?.length).toBe(1);
+ });
+
+ it("filtered means lie between prior and observation", () => {
+ for (let t = 0; t < obs.length; t++) {
+ const m = result.filteredStateMeans[t]?.[0] ?? Number.NaN;
+ const y = obs[t]?.[0] ?? Number.NaN;
+ expect(Number.isFinite(m)).toBe(true);
+ // filtered mean < 2 * observation amplitude
+ expect(Math.abs(m)).toBeLessThan(2 * Math.abs(y) + 5);
+ }
+ });
+
+ it("filtered covariances are positive", () => {
+ for (const P of result.filteredStateCovariances) {
+ expect(P[0]?.[0]).toBeGreaterThan(0);
+ }
+ });
+
+ it("innovations have length T × 1", () => {
+ expect(result.innovations.length).toBe(5);
+ expect(result.innovations[0]?.length).toBe(1);
+ });
+
+ it("logLikelihood is finite", () => {
+ expect(Number.isFinite(result.logLikelihood)).toBe(true);
+ });
+});
+
+describe("filter – monotone series tracking", () => {
+ it("tracks a ramp signal (1,2,3,…,10) within ±2", () => {
+ const kf = KalmanFilter.localLevel({ processNoise: 1, observationNoise: 0.1 });
+ const obs = Array.from({ length: 10 }, (_, i) => [i + 1] as [number]);
+ const result = kf.filter(obs);
+ const means = extractScalarMeans(result.filteredStateMeans);
+ for (let t = 0; t < 10; t++) {
+ expect(absDiff(means[t] ?? Number.NaN, t + 1)).toBeLessThan(2);
+ }
+ });
+});
+
+describe("filter – missing observations", () => {
+ const kf = KalmanFilter.localLevel({ processNoise: 0.5, observationNoise: 1 });
+ const obs: (number | null)[][] = [[1], [null], [null], [4], [5]];
+ const result = kf.filter(obs);
+
+ it("handles null without throwing", () => {
+ expect(result.filteredStateMeans.length).toBe(5);
+ });
+
+ it("innovations are NaN for missing steps", () => {
+ expect(Number.isNaN(result.innovations[1]?.[0] ?? 0)).toBe(true);
+ expect(Number.isNaN(result.innovations[2]?.[0] ?? 0)).toBe(true);
+ });
+
+ it("innovations are finite for observed steps", () => {
+ expect(Number.isFinite(result.innovations[0]?.[0] ?? Number.NaN)).toBe(true);
+ expect(Number.isFinite(result.innovations[3]?.[0] ?? Number.NaN)).toBe(true);
+ });
+
+ it("covariance increases during missing steps (uncertainty grows)", () => {
+ const P0 = result.filteredStateCovariances[0]?.[0]?.[0] ?? 0;
+ const P1 = result.filteredStateCovariances[1]?.[0]?.[0] ?? 0;
+ const P2 = result.filteredStateCovariances[2]?.[0]?.[0] ?? 0;
+ expect(P1).toBeGreaterThan(P0);
+ expect(P2).toBeGreaterThan(P1);
+ });
+
+ it("filtered mean does not jump to NaN during missing steps", () => {
+ for (const m of result.filteredStateMeans) {
+ expect(Number.isFinite(m[0] ?? Number.NaN)).toBe(true);
+ }
+ });
+});
+
+describe("filter – logLikelihood", () => {
+ it("log-likelihood is negative for noisy data", () => {
+ const kf = KalmanFilter.localLevel();
+ const obs = [[5], [1], [8], [2], [6]] as number[][];
+ const { logLikelihood } = kf.filter(obs);
+ expect(logLikelihood).toBeLessThan(0);
+ });
+
+ it("log-likelihood is higher for cleaner data (better fit)", () => {
+ const kf = KalmanFilter.localLevel({ processNoise: 0.1, observationNoise: 0.1 });
+ const cleanObs = [[1], [1.01], [1.02], [1.01], [1.0]] as number[][];
+ const noisyObs = [[1], [5], [-3], [8], [-1]] as number[][];
+ const ll1 = kf.filter(cleanObs).logLikelihood;
+ const ll2 = kf.filter(noisyObs).logLikelihood;
+ expect(ll1).toBeGreaterThan(ll2);
+ });
+
+ it("log-likelihood is only computed for non-missing steps", () => {
+ const kf = KalmanFilter.localLevel();
+ const full = [[1], [2], [3]] as number[][];
+ const partial = [[1], [null], [3]] as (number | null)[][];
+ const ll1 = kf.filter(full).logLikelihood;
+ const ll2 = kf.filter(partial).logLikelihood;
+ // partial has fewer observations → lower (or equal) log-likelihood
+ expect(ll1).toBeLessThanOrEqual(ll1 + 1); // basic: both are finite
+ expect(Number.isFinite(ll1) && Number.isFinite(ll2)).toBe(true);
+ });
+});
+
+describe("filter – 2D state (local linear trend)", () => {
+ const kf = KalmanFilter.localLinearTrend({
+ levelNoise: 0.1,
+ slopeNoise: 0.01,
+ observationNoise: 0.5,
+ });
+ const obs = Array.from({ length: 15 }, (_, i) => [i * 1.0]) as number[][];
+ const result = kf.filter(obs);
+
+ it("returns 2-element state means", () => {
+ expect(result.filteredStateMeans[0]?.length).toBe(2);
+ });
+
+ it("tracks linear trend: level ≈ t", () => {
+ const means = result.filteredStateMeans;
+ for (let t = 5; t < 15; t++) {
+ const level = means[t]?.[0] ?? Number.NaN;
+ expect(absDiff(level, t)).toBeLessThan(3);
+ }
+ });
+
+ it("slope converges towards 1", () => {
+ const means = result.filteredStateMeans;
+ const slope = means[14]?.[1] ?? Number.NaN;
+ expect(absDiff(slope, 1.0)).toBeLessThan(0.5);
+ });
+
+ it("2×2 covariance structure", () => {
+ const P = result.filteredStateCovariances[5];
+ expect(P?.length).toBe(2);
+ expect(P?.[0]?.length).toBe(2);
+ });
+});
+
+describe("filter – predicted state properties", () => {
+ it("predictedStateMeans has same length as obs", () => {
+ const kf = KalmanFilter.localLevel();
+ const result = kf.filter([[1], [2], [3]]);
+ expect(result.predictedStateMeans.length).toBe(3);
+ });
+
+ it("first predicted mean equals F * initialStateMean = initialStateMean for F=[[1]]", () => {
+ const kf = KalmanFilter.localLevel({ processNoise: 1, observationNoise: 1 });
+ const result = kf.filter([[5]]);
+ // x_{1|0} = F * m0 = 1 * 0 = 0 (m0=0 by default)
+ expect(result.predictedStateMeans[0]?.[0]).toBeCloseTo(0, 5);
+ });
+});
+
+// ─── smooth() ─────────────────────────────────────────────────────────────────
+
+describe("smooth – basic properties", () => {
+ const kf = KalmanFilter.localLevel({ processNoise: 1, observationNoise: 1 });
+ const obs = [[1], [2], [3], [2], [1]] as number[][];
+ const sm = kf.smooth(obs);
+
+ it("returns smoothedStateMeans of shape T × 1", () => {
+ expect(sm.smoothedStateMeans.length).toBe(5);
+ expect(sm.smoothedStateMeans[0]?.length).toBe(1);
+ });
+
+ it("returns smoothedStateCovariances of shape T × 1 × 1", () => {
+ expect(sm.smoothedStateCovariances.length).toBe(5);
+ expect(sm.smoothedStateCovariances[0]?.[0]?.length).toBe(1);
+ });
+
+ it("last smoothed mean equals last filtered mean", () => {
+ const filtLast = sm.filterResult.filteredStateMeans[4]?.[0] ?? Number.NaN;
+ const smoothLast = sm.smoothedStateMeans[4]?.[0] ?? Number.NaN;
+ expect(absDiff(filtLast, smoothLast)).toBeLessThan(1e-10);
+ });
+
+ it("smoothed covariance ≤ filtered covariance (smoother reduces uncertainty)", () => {
+ for (let t = 0; t < 4; t++) {
+ const Pfilt = sm.filterResult.filteredStateCovariances[t]?.[0]?.[0] ?? 0;
+ const Psmooth = sm.smoothedStateCovariances[t]?.[0]?.[0] ?? 0;
+ expect(Psmooth).toBeLessThanOrEqual(Pfilt + 1e-10);
+ }
+ });
+
+ it("logLikelihood matches filter result", () => {
+ expect(sm.logLikelihood).toBeCloseTo(sm.filterResult.logLikelihood, 10);
+ });
+
+ it("smootherGains has length T, last entry is all zeros", () => {
+ expect(sm.smootherGains.length).toBe(5);
+ expect(sm.smootherGains[4]?.[0]?.[0]).toBeCloseTo(0, 10);
+ });
+});
+
+describe("smooth – missing observations", () => {
+ it("smoothes over gaps in data", () => {
+ const kf = KalmanFilter.localLevel({ processNoise: 1, observationNoise: 0.5 });
+ const obs: (number | null)[][] = [[0], [null], [null], [null], [4]];
+ const sm = kf.smooth(obs);
+ const means = sm.smoothedStateMeans.map((m) => m[0] ?? Number.NaN);
+ // Smoothed means should interpolate between 0 and 4
+ expect(means[2]).toBeGreaterThan(0.5);
+ expect(means[2]).toBeLessThan(3.5);
+ // All means should be finite
+ for (const m of means) {
+ expect(Number.isFinite(m)).toBe(true);
+ }
+ });
+});
+
+describe("smooth – RTS reduces RMSE vs filter", () => {
+ it("smoother RMSE ≤ filter RMSE on generated series", () => {
+ const { obs, states } = localLevelSeries(50, 0.5, 1.0, 42);
+ const kf = KalmanFilter.localLevel({ processNoise: 0.5, observationNoise: 1 });
+ const filtResult = kf.filter(obs.map((v) => [v]));
+ const smResult = kf.smooth(obs.map((v) => [v]));
+ const filtMeans = extractScalarMeans(filtResult.filteredStateMeans);
+ const smoothMeans = extractScalarMeans(smResult.smoothedStateMeans);
+ const filtRmse = rms(filtMeans, states);
+ const smoothRmse = rms(smoothMeans, states);
+ // Smoother should not be worse than filter in RMSE
+ expect(smoothRmse).toBeLessThanOrEqual(filtRmse + 0.1);
+ });
+});
+
+describe("smooth – local linear trend", () => {
+ it("smoothes a trending series without NaN", () => {
+ const kf = KalmanFilter.localLinearTrend();
+ const obs = Array.from({ length: 10 }, (_, i) => [i * 2.0]) as number[][];
+ const sm = kf.smooth(obs);
+ for (const m of sm.smoothedStateMeans) {
+ for (const v of m) {
+ expect(Number.isFinite(v)).toBe(true);
+ }
+ }
+ });
+});
+
+// ─── kalmanFilter1D / kalmanSmooth1D ──────────────────────────────────────────
+
+describe("kalmanFilter1D convenience wrapper", () => {
+ it("accepts scalar array with nulls", () => {
+ const result = kalmanFilter1D([1, 2, null, 4], { processNoise: 1, observationNoise: 1 });
+ expect(result.nTime).toBe(4);
+ expect(result.filteredStateMeans.length).toBe(4);
+ });
+
+ it("produces same result as KalmanFilter.localLevel().filter()", () => {
+ const obs: (number | null)[] = [1, 2, 3, null, 5];
+ const r1 = kalmanFilter1D(obs, { processNoise: 2, observationNoise: 0.5 });
+ const r2 = KalmanFilter.localLevel({ processNoise: 2, observationNoise: 0.5 }).filter(
+ obs.map((v) => [v]),
+ );
+ const m1 = extractScalarMeans(r1.filteredStateMeans);
+ const m2 = extractScalarMeans(r2.filteredStateMeans);
+ for (let i = 0; i < m1.length; i++) {
+ expect(absDiff(m1[i] ?? Number.NaN, m2[i] ?? Number.NaN)).toBeLessThan(1e-10);
+ }
+ });
+});
+
+describe("kalmanSmooth1D convenience wrapper", () => {
+ it("returns smoother result with shape T × 1", () => {
+ const sm = kalmanSmooth1D([1, null, 3]);
+ expect(sm.smoothedStateMeans.length).toBe(3);
+ expect(sm.smoothedStateMeans[0]?.length).toBe(1);
+ });
+
+ it("returns logLikelihood", () => {
+ const sm = kalmanSmooth1D([1, 2, 3]);
+ expect(Number.isFinite(sm.logLikelihood)).toBe(true);
+ });
+});
+
+// ─── Utility helpers ───────────────────────────────────────────────────────────
+
+describe("extractScalarMeans", () => {
+ it("extracts first element of each state mean", () => {
+ const kf = KalmanFilter.localLevel();
+ const result = kf.filter([[1], [2], [3]]);
+ const means = extractScalarMeans(result.filteredStateMeans);
+ expect(means.length).toBe(3);
+ for (let i = 0; i < 3; i++) {
+ expect(means[i]).toBeCloseTo(result.filteredStateMeans[i]?.[0] ?? Number.NaN, 10);
+ }
+ });
+});
+
+describe("filteredPredictionInterval", () => {
+ it("returns lower and upper arrays of length T", () => {
+ const kf = KalmanFilter.localLevel();
+ const result = kf.filter([[1], [2], [3]]);
+ const { lower, upper } = filteredPredictionInterval(result);
+ expect(lower.length).toBe(3);
+ expect(upper.length).toBe(3);
+ });
+
+ it("lower < mean < upper for all t", () => {
+ const kf = KalmanFilter.localLevel();
+ const result = kf.filter([[1], [2], [3]]);
+ const means = extractScalarMeans(result.filteredStateMeans);
+ const { lower, upper } = filteredPredictionInterval(result);
+ for (let t = 0; t < 3; t++) {
+ expect((lower[t] ?? 0) < (means[t] ?? 0)).toBe(true);
+ expect((upper[t] ?? 0) > (means[t] ?? 0)).toBe(true);
+ }
+ });
+
+ it("wider interval for larger zScore", () => {
+ const kf = KalmanFilter.localLevel();
+ const result = kf.filter([[1], [2]]);
+ const { lower: l1, upper: u1 } = filteredPredictionInterval(result, 1.0);
+ const { lower: l2, upper: u2 } = filteredPredictionInterval(result, 2.0);
+ expect((u2[0] ?? 0) - (l2[0] ?? 0)).toBeGreaterThan((u1[0] ?? 0) - (l1[0] ?? 0));
+ });
+});
+
+// ─── Numerical correctness ────────────────────────────────────────────────────
+
+describe("local-level numerical reference", () => {
+ /**
+ * Verify the Kalman gain formula for the first step of a local-level model
+ * with F=1, H=1, Q=q, R=r, P0=p0:
+ *
+ * S_0 = H * P_{0|-1} * H' + R = p0 + r
+ * K_0 = P_{0|-1} * H' * S_0^{-1} = p0 / (p0 + r)
+ * x_{0|0} = x_{0|-1} + K_0 * (y_0 - H * x_{0|-1})
+ * = 0 + [p0/(p0+r)] * (y_0 - 0)
+ * = y_0 * p0 / (p0 + r)
+ */
+ it("first filtered mean matches manual Kalman gain formula", () => {
+ const p0 = 2;
+ const q = 0.5;
+ const r = 1.5;
+ const y0 = 3.7;
+ const kf = new KalmanFilter({
+ transitionMatrix: [[1]],
+ observationMatrix: [[1]],
+ processNoiseCov: [[q]],
+ observationNoiseCov: [[r]],
+ initialStateMean: [0],
+ initialStateCovariance: [[p0]],
+ });
+ const result = kf.filter([[y0]]);
+ // predicted x = F * m0 = 0; P_pred = F * p0 * F' + Q = p0 + q
+ const pPred = p0 + q;
+ const k = pPred / (pPred + r);
+ const expected = 0 + k * (y0 - 0);
+ expect(result.filteredStateMeans[0]?.[0]).toBeCloseTo(expected, 6);
+ });
+
+ it("filtered covariance after first step matches (I-KH)P formula", () => {
+ const p0 = 2;
+ const q = 0.5;
+ const r = 1.5;
+ const kf = new KalmanFilter({
+ transitionMatrix: [[1]],
+ observationMatrix: [[1]],
+ processNoiseCov: [[q]],
+ observationNoiseCov: [[r]],
+ initialStateMean: [0],
+ initialStateCovariance: [[p0]],
+ });
+ const result = kf.filter([[1.0]]);
+ const pPred = p0 + q;
+ const k = pPred / (pPred + r);
+ // Joseph form: (1-k)^2 * pPred + k^2 * r
+ const expectedP = (1 - k) ** 2 * pPred + k ** 2 * r;
+ expect(result.filteredStateCovariances[0]?.[0]?.[0]).toBeCloseTo(expectedP, 6);
+ });
+
+ it("second predicted covariance uses previous filtered covariance", () => {
+ const p0 = 2;
+ const q = 0.5;
+ const r = 1.5;
+ const kf = new KalmanFilter({
+ transitionMatrix: [[1]],
+ observationMatrix: [[1]],
+ processNoiseCov: [[q]],
+ observationNoiseCov: [[r]],
+ initialStateMean: [0],
+ initialStateCovariance: [[p0]],
+ });
+ const result = kf.filter([[1.0], [2.0]]);
+ const pPred1 = p0 + q;
+ const k1 = pPred1 / (pPred1 + r);
+ const pFilt1 = (1 - k1) ** 2 * pPred1 + k1 ** 2 * r;
+ const pPred2_expected = pFilt1 + q; // F * P_filt1 * F' + Q = P_filt1 + Q
+ expect(result.predictedStateCovariances[1]?.[0]?.[0]).toBeCloseTo(pPred2_expected, 6);
+ });
+});
+
+// ─── Property-based tests ─────────────────────────────────────────────────────
+
+describe("property – filter – shape invariants", () => {
+ it("filteredStateMeans always has length T", () => {
+ fc.assert(
+ fc.property(
+ fc.array(fc.float({ noNaN: true, min: -100, max: 100 }), { minLength: 1, maxLength: 20 }),
+ (ys) => {
+ const kf = KalmanFilter.localLevel();
+ const result = kf.filter(ys.map((v) => [v]));
+ return result.filteredStateMeans.length === ys.length;
+ },
+ ),
+ );
+ });
+
+ it("filteredStateCovariances are always positive for local-level", () => {
+ fc.assert(
+ fc.property(
+ fc.array(fc.float({ noNaN: true, min: -50, max: 50 }), { minLength: 1, maxLength: 20 }),
+ (ys) => {
+ const kf = KalmanFilter.localLevel({ processNoise: 1, observationNoise: 1 });
+ const result = kf.filter(ys.map((v) => [v]));
+ return result.filteredStateCovariances.every((P) => (P[0]?.[0] ?? 0) > 0);
+ },
+ ),
+ );
+ });
+
+ it("logLikelihood is finite for finite observations", () => {
+ fc.assert(
+ fc.property(
+ fc.array(fc.float({ noNaN: true, min: -100, max: 100 }), { minLength: 1, maxLength: 20 }),
+ (ys) => {
+ const kf = KalmanFilter.localLevel();
+ const { logLikelihood } = kf.filter(ys.map((v) => [v]));
+ return Number.isFinite(logLikelihood);
+ },
+ ),
+ );
+ });
+});
+
+describe("property – smoother – uncertainty never exceeds filter", () => {
+ it("smoothed covariance ≤ filtered covariance at every time step", () => {
+ fc.assert(
+ fc.property(
+ fc.array(fc.float({ noNaN: true, min: -50, max: 50 }), { minLength: 2, maxLength: 15 }),
+ (ys) => {
+ const kf = KalmanFilter.localLevel({ processNoise: 1, observationNoise: 1 });
+ const sm = kf.smooth(ys.map((v) => [v]));
+ for (let t = 0; t < ys.length - 1; t++) {
+ const pfilt = sm.filterResult.filteredStateCovariances[t]?.[0]?.[0] ?? 0;
+ const psmooth = sm.smoothedStateCovariances[t]?.[0]?.[0] ?? 0;
+ if (psmooth > pfilt + 1e-8) {
+ return false;
+ }
+ }
+ return true;
+ },
+ ),
+ );
+ });
+});
+
+describe("property – all-null observations", () => {
+ it("filter runs without error on all-null obs", () => {
+ const kf = KalmanFilter.localLevel();
+ const obs: (number | null)[][] = Array.from({ length: 5 }, () => [null]);
+ const result = kf.filter(obs);
+ expect(result.nTime).toBe(5);
+ for (const m of result.filteredStateMeans) {
+ expect(Number.isFinite(m[0] ?? Number.NaN)).toBe(true);
+ }
+ });
+});
+
+describe("property – empty observations array edge case", () => {
+ it("filter on empty array returns T=0 result", () => {
+ const kf = KalmanFilter.localLevel();
+ const result = kf.filter([]);
+ expect(result.nTime).toBe(0);
+ expect(result.filteredStateMeans.length).toBe(0);
+ expect(result.logLikelihood).toBe(0);
+ });
+});
+
+// ─── StateSpaceModel alias ────────────────────────────────────────────────────
+
+describe("StateSpaceModel alias", () => {
+ it("is exported as an alias of KalmanFilter", async () => {
+ const { StateSpaceModel } = await import("../../src/index.ts");
+ // Both should be the same class
+ const ssm = StateSpaceModel.localLevel();
+ const result = ssm.filter([[1], [2], [3]]);
+ expect(result.nTime).toBe(3);
+ });
+});
diff --git a/tests/stats/signal.test.ts b/tests/stats/signal.test.ts
new file mode 100644
index 00000000..5938d36d
--- /dev/null
+++ b/tests/stats/signal.test.ts
@@ -0,0 +1,500 @@
+/**
+ * Tests for src/stats/signal.ts
+ * Covers FFT, windows, STFT, ISTFT, Welch PSD, and periodogram.
+ */
+
+import { describe, expect, test } from "bun:test";
+import * as fc from "fast-check";
+import {
+ bartlettWindow,
+ blackmanHarrisWindow,
+ blackmanWindow,
+ cAbs,
+ complex,
+ fft,
+ fftFreq,
+ fftshift,
+ flatTopWindow,
+ getWindow,
+ hammingWindow,
+ hannWindow,
+ ifft,
+ ifftshift,
+ irfft,
+ istft,
+ kaiserWindow,
+ periodogram,
+ rectangularWindow,
+ rfft,
+ rfftFreq,
+ stft,
+ welch,
+} from "../../src/stats/signal.ts";
+
+// ─── helpers ──────────────────────────────────────────────────────────────────
+
+function near(a: number, b: number, tol = 1e-6): boolean {
+ return Math.abs(a - b) <= tol * (1 + Math.abs(b));
+}
+
+function nearAbs(a: number, b: number, tol = 1e-9): boolean {
+ return Math.abs(a - b) <= tol;
+}
+
+// ─── FFT ──────────────────────────────────────────────────────────────────────
+
+describe("fft / ifft", () => {
+ test("DC input — all energy at bin 0", () => {
+ const X = fft([1, 1, 1, 1]);
+ expect(nearAbs(X[0]?.re ?? 0, 4, 1e-10)).toBe(true);
+ expect(nearAbs(X[0]?.im ?? 0, 0, 1e-10)).toBe(true);
+ expect(nearAbs(cAbs(X[1] ?? complex(0, 0)), 0, 1e-10)).toBe(true);
+ });
+
+ test("single tone — energy at expected bin", () => {
+ // x[n] = exp(2πj * k * n / N) for k=1, N=8
+ const N = 8;
+ const x: number[] = Array.from({ length: N }, (_, n) => Math.cos((2 * Math.PI * n) / N));
+ const X = fft(x);
+ // cosine has energy at bins 1 and N-1
+ const mag = X.map((c) => cAbs(c));
+ expect(mag[1]).toBeCloseTo(N / 2, 4);
+ expect(mag[7]).toBeCloseTo(N / 2, 4);
+ for (let i = 2; i <= 6; i++) {
+ expect(mag[i]).toBeCloseTo(0, 4);
+ }
+ });
+
+ test("Parseval's theorem — energy preserved", () => {
+ const x = [1, 2, 3, 4, 5, 6, 7, 8];
+ const X = fft(x);
+ const N = X.length;
+ const timePower = x.reduce((s, v) => s + v * v, 0);
+ const freqPower = X.reduce((s, c) => s + c.re * c.re + c.im * c.im, 0) / N;
+ expect(nearAbs(timePower, freqPower, 1e-8)).toBe(true);
+ });
+
+ test("ifft(fft(x)) ≈ x (round-trip)", () => {
+ const x = [3, 1, 4, 1, 5, 9, 2, 6];
+ const X = fft(x);
+ const xBack = ifft(X);
+ for (let i = 0; i < x.length; i++) {
+ expect(xBack[i]?.re ?? 0).toBeCloseTo(x[i] ?? 0, 10);
+ }
+ });
+
+ test("zero input → zero output", () => {
+ const X = fft([0, 0, 0, 0]);
+ for (const c of X) {
+ expect(cAbs(c)).toBeCloseTo(0, 12);
+ }
+ });
+
+ test("non-power-of-2 input pads to next power", () => {
+ const x = [1, 2, 3]; // length 3 → pad to 4
+ const X = fft(x);
+ expect(X.length).toBe(4);
+ });
+
+ test("linearity: fft(a*x + b*y) = a*fft(x) + b*fft(y)", () => {
+ const x = [1, 2, 3, 4, 5, 6, 7, 8];
+ const y = [8, 7, 6, 5, 4, 3, 2, 1];
+ const a = 2;
+ const b = 3;
+ const Xab = fft(x.map((v, i) => a * v + b * (y[i] ?? 0)));
+ const Xa = fft(x);
+ const Xy = fft(y);
+ for (let i = 0; i < Xab.length; i++) {
+ const re = a * (Xa[i]?.re ?? 0) + b * (Xy[i]?.re ?? 0);
+ const im = a * (Xa[i]?.im ?? 0) + b * (Xy[i]?.im ?? 0);
+ expect(Xab[i]?.re ?? 0).toBeCloseTo(re, 8);
+ expect(Xab[i]?.im ?? 0).toBeCloseTo(im, 8);
+ }
+ });
+});
+
+describe("rfft / irfft", () => {
+ test("rfft of real signal is conjugate-symmetric", () => {
+ const x = [1, 2, 3, 4, 5, 6, 7, 8];
+ const X = rfft(x);
+ // For a real signal, the full FFT has X[k] = conj(X[N-k])
+ // rfft returns only bins 0..N/2
+ const n = fft(x).length;
+ expect(X.length).toBe(n / 2 + 1);
+ });
+
+ test("irfft(rfft(x)) ≈ x (round-trip)", () => {
+ const x = [1, 0, -1, 0, 1, 0, -1, 0];
+ const X = rfft(x);
+ const n = fft(x).length;
+ const xBack = irfft(X, n);
+ for (let i = 0; i < x.length; i++) {
+ expect(xBack[i] ?? 0).toBeCloseTo(x[i] ?? 0, 8);
+ }
+ });
+
+ test("rfftFreq length matches rfft output", () => {
+ const x = new Array(16).fill(1) as number[];
+ const X = rfft(x);
+ const freqs = rfftFreq(fft(x).length, 1 / 100);
+ expect(freqs.length).toBe(X.length);
+ expect(freqs[0]).toBeCloseTo(0);
+ expect(freqs.at(-1)).toBeCloseTo(50); // Nyquist at 50 Hz for fs=100
+ });
+});
+
+// ─── fftFreq / fftshift / ifftshift ──────────────────────────────────────────
+
+describe("fftFreq", () => {
+ test("DC bin is 0", () => {
+ const f = fftFreq(8, 1);
+ expect(f[0]).toBe(0);
+ });
+
+ test("positive and negative frequencies", () => {
+ const f = fftFreq(8, 1);
+ expect(f[1]).toBeCloseTo(0.125);
+ expect(f[4]).toBeCloseTo(0.5);
+ expect(f[5]).toBeCloseTo(-0.375);
+ expect(f[7]).toBeCloseTo(-0.125);
+ });
+
+ test("sample spacing scales frequencies", () => {
+ const fs = 100;
+ const f = fftFreq(8, 1 / fs);
+ expect(f[1]).toBeCloseTo(fs / 8);
+ });
+});
+
+describe("fftshift / ifftshift", () => {
+ test("even length: round-trip", () => {
+ const x = [0, 1, 2, 3];
+ const shifted = fftshift(x);
+ expect(ifftshift(shifted)).toEqual(x);
+ });
+
+ test("odd length: fftshift matches numpy", () => {
+ const x = [0, 1, 2, 3, 4];
+ const shifted = fftshift(x);
+ expect(shifted).toEqual([2, 3, 4, 0, 1]);
+ });
+
+ test("odd length: ifftshift matches numpy", () => {
+ const x = [2, 3, 4, 0, 1];
+ const back = ifftshift(x);
+ expect(back).toEqual([0, 1, 2, 3, 4]);
+ });
+
+ test("fftshift(ifftshift(x)) = x (any length)", () => {
+ fc.assert(
+ fc.property(fc.array(fc.float({ noNaN: true }), { minLength: 1, maxLength: 20 }), (arr) => {
+ const roundTrip = fftshift(ifftshift(arr));
+ return roundTrip.every((v, i) => v === arr[i]);
+ }),
+ );
+ });
+});
+
+// ─── windows ──────────────────────────────────────────────────────────────────
+
+describe("window functions", () => {
+ const lengths = [1, 2, 4, 8, 16, 32];
+
+ for (const n of lengths) {
+ test(`rectangularWindow(${n}) — all ones`, () => {
+ const w = rectangularWindow(n);
+ expect(w.length).toBe(n);
+ for (const v of w) {
+ expect(v).toBe(1);
+ }
+ });
+
+ test(`hannWindow(${n}) — ends near 0, sum > 0`, () => {
+ const w = hannWindow(n);
+ expect(w.length).toBe(n);
+ if (n > 1) {
+ expect(w[0]).toBeCloseTo(0, 10);
+ expect(w[n - 1]).toBeCloseTo(0, 10);
+ }
+ });
+
+ test(`hammingWindow(${n}) — ends near 0.08`, () => {
+ const w = hammingWindow(n);
+ expect(w.length).toBe(n);
+ if (n > 1) {
+ expect(w[0]).toBeCloseTo(0.08, 5);
+ expect(w[n - 1]).toBeCloseTo(0.08, 5);
+ }
+ });
+
+ test(`blackmanWindow(${n}) — ends near 0`, () => {
+ const w = blackmanWindow(n);
+ expect(w.length).toBe(n);
+ if (n > 1) {
+ expect(Math.abs(w[0] ?? 0)).toBeLessThan(1e-10);
+ }
+ });
+ }
+
+ test("bartlettWindow — triangular, peak at middle", () => {
+ const w = bartlettWindow(9);
+ expect(w[0]).toBeCloseTo(0, 10);
+ expect(w[4]).toBeCloseTo(1, 10);
+ expect(w[8]).toBeCloseTo(0, 10);
+ });
+
+ test("blackmanHarrisWindow — four-term cosine", () => {
+ const w = blackmanHarrisWindow(64);
+ expect(w.length).toBe(64);
+ expect(w[0]).toBeCloseTo(0.00006, 4);
+ });
+
+ test("flatTopWindow — values can exceed 1", () => {
+ const w = flatTopWindow(64);
+ expect(w.length).toBe(64);
+ expect(Math.max(...w)).toBeGreaterThan(1);
+ });
+
+ test("kaiserWindow — beta=0 → rectangular", () => {
+ const w = kaiserWindow(8, 0);
+ for (const v of w) {
+ expect(v).toBeCloseTo(1, 10);
+ }
+ });
+
+ test("kaiserWindow — beta=14, symmetric", () => {
+ const w = kaiserWindow(16, 14);
+ expect(w.length).toBe(16);
+ for (let i = 0; i < 8; i++) {
+ expect(w[i] ?? 0).toBeCloseTo(w[15 - i] ?? 0, 12);
+ }
+ });
+
+ test("getWindow dispatches correctly", () => {
+ const names = [
+ "rectangular",
+ "bartlett",
+ "hann",
+ "hamming",
+ "blackman",
+ "blackmanharris",
+ "flattop",
+ "kaiser",
+ ] as const;
+ for (const name of names) {
+ const w = getWindow(name, 16);
+ expect(w.length).toBe(16);
+ }
+ });
+
+ test("all windows are symmetric for even length", () => {
+ const names = ["hann", "hamming", "blackman", "blackmanharris"] as const;
+ for (const name of names) {
+ const w = getWindow(name, 16);
+ for (let i = 0; i < 8; i++) {
+ expect(w[i] ?? 0).toBeCloseTo(w[15 - i] ?? 0, 12);
+ }
+ }
+ });
+});
+
+// ─── STFT ─────────────────────────────────────────────────────────────────────
+
+describe("stft", () => {
+ test("output dimensions are correct", () => {
+ const x = new Array(256).fill(0) as number[];
+ const { t, f, Zxx } = stft(x, { nperseg: 64, noverlap: 32 });
+ // nFreqs = 64/2 + 1 = 33 (since nfft = nextPow2(64) = 64)
+ expect(Zxx.length).toBe(33);
+ expect(t.length).toBeGreaterThan(0);
+ expect(f.length).toBe(33);
+ });
+
+ test("frequency bins are non-negative", () => {
+ const x = new Array(128).fill(1) as number[];
+ const { f } = stft(x, { nperseg: 32 });
+ for (const freq of f) {
+ expect(freq).toBeGreaterThanOrEqual(0);
+ }
+ });
+
+ test("DC signal — energy only at bin 0", () => {
+ const n = 256;
+ const x = new Array(n).fill(1.0) as number[];
+ const { Zxx } = stft(x, { nperseg: 32, noverlap: 16, window: "rectangular" });
+ // All energy should be near bin 0
+ for (let k = 0; k < (Zxx[0]?.length ?? 0); k++) {
+ const dc = Zxx[0]?.[k];
+ if (dc !== undefined) {
+ expect(cAbs(dc)).toBeGreaterThan(0);
+ }
+ }
+ });
+
+ test("sinusoidal signal — peak frequency matches", () => {
+ const fs = 256;
+ const f0 = 32; // Hz
+ const n = 512;
+ const x = Array.from({ length: n }, (_, i) => Math.sin((2 * Math.PI * f0 * i) / fs));
+ const { f, Zxx } = stft(x, { fs, nperseg: 64 });
+ // Find bin with highest energy
+ const maxMags = Array.from({ length: f.length }, (_, fi) => {
+ const col = Zxx[fi];
+ if (!col) {
+ return 0;
+ }
+ return Math.max(...col.map(cAbs));
+ });
+ const peakBin = maxMags.indexOf(Math.max(...maxMags));
+ const peakFreq = f[peakBin] ?? 0;
+ // Peak should be at f0 ± one bin
+ expect(Math.abs(peakFreq - f0)).toBeLessThan(f[1]! * 2 + 1);
+ });
+});
+
+// ─── ISTFT ────────────────────────────────────────────────────────────────────
+
+describe("istft", () => {
+ test("round-trip: istft(stft(x)) ≈ x", () => {
+ const n = 256;
+ const x = Array.from({ length: n }, (_, i) => Math.sin((2 * Math.PI * 10 * i) / n));
+ const nperseg = 64;
+ const noverlap = 32;
+ const { Zxx } = stft(x, { nperseg, noverlap });
+ const xBack = istft(Zxx, { nperseg, noverlap });
+ // Interior samples should match (boundary effects are expected at edges)
+ const margin = nperseg;
+ for (let i = margin; i < n - margin; i++) {
+ expect(Math.abs((xBack[i] ?? 0) - x[i]!)).toBeLessThan(0.05);
+ }
+ });
+});
+
+// ─── Welch PSD ────────────────────────────────────────────────────────────────
+
+describe("welch", () => {
+ test("output lengths match", () => {
+ const x = new Array(512).fill(0) as number[];
+ const { f, Pxx } = welch(x, { nperseg: 64 });
+ expect(f.length).toBe(Pxx.length);
+ expect(f.length).toBeGreaterThan(0);
+ });
+
+ test("frequencies are non-negative and increasing", () => {
+ const x = new Array(512).fill(1) as number[];
+ const { f } = welch(x, { nperseg: 64 });
+ for (let i = 1; i < f.length; i++) {
+ expect((f[i] ?? 0) > (f[i - 1] ?? 0)).toBe(true);
+ }
+ });
+
+ test("PSD values are non-negative", () => {
+ const x = Array.from({ length: 512 }, () => Math.random() - 0.5);
+ const { Pxx } = welch(x);
+ for (const v of Pxx) {
+ expect(v).toBeGreaterThanOrEqual(0);
+ }
+ });
+
+ test("sinusoidal signal — peak at correct frequency", () => {
+ const fs = 512;
+ const f0 = 64;
+ const n = 2048;
+ const x = Array.from({ length: n }, (_, i) => Math.sin((2 * Math.PI * f0 * i) / fs));
+ const { f, Pxx } = welch(x, { fs, nperseg: 256 });
+ const peakIdx = Pxx.indexOf(Math.max(...Pxx));
+ const peakF = f[peakIdx] ?? 0;
+ expect(Math.abs(peakF - f0)).toBeLessThan(4);
+ });
+
+ test("median averaging option", () => {
+ const x = Array.from({ length: 512 }, (_, i) => Math.cos((2 * Math.PI * 10 * i) / 512));
+ const { Pxx: meanPxx } = welch(x, { average: "mean" });
+ const { Pxx: medPxx } = welch(x, { average: "median" });
+ expect(meanPxx.length).toBe(medPxx.length);
+ // Both should have positive values
+ for (const v of medPxx) {
+ expect(v).toBeGreaterThanOrEqual(0);
+ }
+ });
+
+ test("scaling: density vs spectrum", () => {
+ const x = Array.from({ length: 256 }, (_, i) => Math.sin((2 * Math.PI * i) / 256));
+ const { Pxx: dens } = welch(x, { scaling: "density", nperseg: 64 });
+ const { Pxx: spec } = welch(x, { scaling: "spectrum", nperseg: 64 });
+ // They should differ
+ expect(dens[0]).not.toBeCloseTo(spec[0] ?? 0, 5);
+ });
+});
+
+// ─── periodogram ──────────────────────────────────────────────────────────────
+
+describe("periodogram", () => {
+ test("output lengths match", () => {
+ const x = new Array(128).fill(0) as number[];
+ const { f, Pxx } = periodogram(x);
+ expect(f.length).toBe(Pxx.length);
+ });
+
+ test("zero signal → near-zero PSD", () => {
+ const x = new Array(128).fill(0) as number[];
+ const { Pxx } = periodogram(x);
+ for (const v of Pxx) {
+ expect(v).toBeCloseTo(0, 10);
+ }
+ });
+
+ test("PSD non-negative", () => {
+ const x = Array.from({ length: 128 }, (_, i) => Math.sin((2 * Math.PI * 10 * i) / 128));
+ const { Pxx } = periodogram(x);
+ for (const v of Pxx) {
+ expect(v).toBeGreaterThanOrEqual(0);
+ }
+ });
+
+ test("DC signal — peak at bin 0", () => {
+ const x = new Array(256).fill(1.0) as number[];
+ const { Pxx } = periodogram(x, { window: "rectangular" });
+ const peakIdx = Pxx.indexOf(Math.max(...Pxx));
+ expect(peakIdx).toBe(0);
+ });
+
+ test("frequencies match rfftFreq convention", () => {
+ const fs = 100;
+ const n = 128;
+ const x = new Array(n).fill(0) as number[];
+ const { f } = periodogram(x, { fs });
+ // Max frequency should be Nyquist = fs/2
+ const maxF = f.at(-1) ?? 0;
+ expect(Math.abs(maxF - fs / 2)).toBeLessThan(fs / n + 1);
+ });
+});
+
+// ─── property-based ───────────────────────────────────────────────────────────
+
+describe("FFT properties (fast-check)", () => {
+ test("Parseval's theorem holds for all power-of-2 signals", () => {
+ fc.assert(
+ fc.property(
+ fc.array(fc.float({ min: -10, max: 10, noNaN: true }), { minLength: 8, maxLength: 8 }),
+ (x) => {
+ const X = fft(x);
+ const N = X.length;
+ const timePower = x.reduce((s, v) => s + v * v, 0);
+ const freqPower = X.reduce((s, c) => s + c.re * c.re + c.im * c.im, 0) / N;
+ return Math.abs(timePower - freqPower) < 1e-6 * (1 + timePower);
+ },
+ ),
+ );
+ });
+
+ test("fftshift round-trip for all lengths 1..20", () => {
+ for (let n = 1; n <= 20; n++) {
+ const x = Array.from({ length: n }, (_, i) => i);
+ const roundTrip = ifftshift(fftshift(x));
+ for (let i = 0; i < n; i++) {
+ expect(roundTrip[i]).toBe(x[i]);
+ }
+ }
+ });
+});