@sipemu/anofox-statistics

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

Installation

``bash
npm install @sipemu/anofox-statistics
`

Usage

$3

`javascript
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, ... }
`

$3

`javascript
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

$3

| 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 |

$3

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

$3

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

$3

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

$3

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

$3

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

$3

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

$3

| 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 |

$3

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

Enums

`typescript
// 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

$3

`javascript
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)});
`

$3

`javascript
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)});
`

$3

`javascript
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)});
`

$3

`javascript
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:

`typescript
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