@sipemu/anofox-statistics

A comprehensive statistical hypothesis testing library compiled to WebAssembly for JavaScript/TypeScript applications. All tests are validated against R.

Installation

npm install @sipemu/anofox-statistics

Usage

Web/Browser

import init, {
  tTest,
  shapiroWilk,
  mannWhitneyU,
  oneWayAnova,
  JsTTestKind,
  JsAlternative,
  JsAnovaKind
} from '@sipemu/anofox-statistics';

// Initialize the WASM module
await init();

// Two-sample t-test
const result = tTest(
  new Float64Array([1.2, 2.3, 3.1, 4.5, 5.2]),
  new Float64Array([2.1, 3.4, 4.2, 5.6, 6.1]),
  JsTTestKind.Welch,
  JsAlternative.TwoSided,
  0.0,  // null hypothesis value
  0.95  // confidence level
);

console.log(result);
// { statistic: -2.34, df: 7.89, p_value: 0.047, mean_x: 3.26, mean_y: 4.28, ... }

Node.js

const { tTest, JsTTestKind, JsAlternative } = require('@sipemu/anofox-statistics');

// Use directly (WASM auto-initializes)
const result = tTest(
  new Float64Array([1.2, 2.3, 3.1]),
  new Float64Array([2.1, 3.4, 4.2]),
  JsTTestKind.Welch,
  JsAlternative.TwoSided
);

Available Tests

Parametric Tests

| Function | Description | |----------|-------------| | tTest | Student's, Welch's, or paired t-test | | yuenTest | Yuen's test for trimmed means (robust) | | oneWayAnova | One-way ANOVA (Fisher or Welch) | | brownForsythe | Brown-Forsythe test for homogeneity of variances |

Nonparametric Tests

| Function | Description | |----------|-------------| | mannWhitneyU | Mann-Whitney U test (Wilcoxon rank-sum) | | wilcoxonSignedRank | Wilcoxon signed-rank test | | kruskalWallis | Kruskal-Wallis H test | | brunnerMunzel | Brunner-Munzel test |

Distributional Tests

| Function | Description | |----------|-------------| | shapiroWilk | Shapiro-Wilk normality test | | kolmogorovSmirnov | Kolmogorov-Smirnov test | | andersonDarling | Anderson-Darling normality test | | jarqueBera | Jarque-Bera normality test |

Categorical Tests

| Function | Description | |----------|-------------| | chiSquaredTest | Chi-squared test for independence | | fisherExact | Fisher's exact test | | binomialTest | Exact binomial test | | mcnemarTest | McNemar's test for paired proportions |

Correlation Tests

| Function | Description | |----------|-------------| | pearsonCorrelation | Pearson correlation with significance | | spearmanCorrelation | Spearman rank correlation | | kendallCorrelation | Kendall's tau correlation | | icc | Intraclass correlation coefficient |

Equivalence Tests (TOST)

| Function | Description | |----------|-------------| | tostTwoSample | TOST for two independent samples | | tostPaired | TOST for paired samples | | tostCorrelation | TOST for correlation equivalence | | tostBootstrap | Bootstrap TOST |

Modern/Kernel Tests

| Function | Description | |----------|-------------| | energyDistanceTest1d | Energy distance test (1D) | | energyDistanceTest | Energy distance test (multivariate) | | mmdTest1d | Maximum Mean Discrepancy test (1D) | | mmdTest | MMD test (multivariate) |

Forecast Comparison Tests

| Function | Description | |----------|-------------| | dieboldMariano | Diebold-Mariano test | | clarkWest | Clark-West test for nested models | | spaTest | Superior Predictive Ability test | | mspeAdjustedSpa | MSPE-adjusted SPA test | | modelConfidenceSet | Model Confidence Set procedure |

Resampling Tests

| Function | Description | |----------|-------------| | permutationTTest | Permutation t-test | | permutationCorrelation | Permutation correlation test | | bootstrapCI | Bootstrap confidence intervals |

Enums

// T-test variants
enum JsTTestKind { Welch, Student, Paired }

// Alternative hypotheses
enum JsAlternative { TwoSided, Less, Greater }

// ANOVA variants
enum JsAnovaKind { Fisher, Welch }

// Kernel types for MMD
enum JsKernel { Gaussian, Laplacian, Linear }

// Loss functions for forecast comparison
enum JsLossFunction { SquaredError, AbsoluteError }

// Variance estimators
enum JsVarEstimator { Acf, Bartlett }

// MCS statistics
enum JsMCSStatistic { Max, Range }

Examples

Normality Testing

import init, { shapiroWilk, andersonDarling } from '@sipemu/anofox-statistics';

await init();

const data = new Float64Array([2.3, 3.1, 2.8, 3.5, 2.9, 3.2, 2.7]);

const sw = shapiroWilk(data);
console.log(`Shapiro-Wilk: W=${sw.statistic.toFixed(4)}, p=${sw.p_value.toFixed(4)}`);

const ad = andersonDarling(data);
console.log(`Anderson-Darling: A=${ad.statistic.toFixed(4)}, p=${ad.p_value.toFixed(4)}`);

ANOVA with Post-hoc

import init, { oneWayAnova, JsAnovaKind } from '@sipemu/anofox-statistics';

await init();

const groups = [
  new Float64Array([23, 25, 28, 31, 27]),
  new Float64Array([31, 33, 35, 37, 34]),
  new Float64Array([41, 43, 45, 47, 44])
];

const result = oneWayAnova(groups, JsAnovaKind.Fisher);
console.log(`F(${result.df_between}, ${result.df_within}) = ${result.statistic.toFixed(2)}`);
console.log(`p-value: ${result.p_value.toFixed(6)}`);

Equivalence Testing (TOST)

import init, { tostTwoSample, JsAlternative } from '@sipemu/anofox-statistics';

await init();

const treatment = new Float64Array([10.2, 11.1, 9.8, 10.5, 10.9]);
const control = new Float64Array([10.0, 10.8, 9.9, 10.3, 10.7]);

// Test equivalence within bounds of -1 to +1
const result = tostTwoSample(treatment, control, -1.0, 1.0, 0.95);
console.log(`Equivalent: ${result.equivalent}`);
console.log(`TOST p-value: ${result.p_value.toFixed(4)}`);

Forecast Model Comparison

import init, {
  dieboldMariano,
  JsLossFunction,
  JsAlternative,
  JsVarEstimator
} from '@sipemu/anofox-statistics';

await init();

const errors1 = new Float64Array([0.5, -0.3, 0.2, -0.1, 0.4]);
const errors2 = new Float64Array([0.3, -0.5, 0.1, 0.2, -0.2]);

const dm = dieboldMariano(
  errors1,
  errors2,
  JsLossFunction.SquaredError,
  1,  // horizon
  JsAlternative.TwoSided,
  JsVarEstimator.Bartlett
);
console.log(`DM statistic: ${dm.statistic.toFixed(4)}, p-value: ${dm.p_value.toFixed(4)}`);

TypeScript Support

Full TypeScript definitions are included. All functions and enums are properly typed:

import init, {
  tTest,
  JsTTestKind,
  JsAlternative
} from '@sipemu/anofox-statistics';

await init();

const result = tTest(
  new Float64Array([1, 2, 3]),
  new Float64Array([4, 5, 6]),
  JsTTestKind.Welch,
  JsAlternative.TwoSided,
  0.0,
  0.95
);

// TypeScript knows result has: statistic, df, p_value, mean_x, mean_y, conf_int, etc.
console.log(result.p_value);

Validation

All statistical tests are validated against R's implementation using extensive test suites. Results match R's output within numerical precision (typically 10^-10).

License

MIT License - see LICENSE for details.

Links

• GitHub Repository
• Rust crate on crates.io
• API Documentation