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@arki/math/distributions

Each factory returns an immutable object with cdf, quantile and pdf or pmf, plus mean and variance (undefined where the moment does not exist, Infinity where it diverges). Parameters are validated up front; nothing returns NaN.

import { binomial, chiSquare, fisherF, noncentralT, normal, poisson, studentT } from '@arki/math/distributions';
 
normal().quantile(0.975); // 1.959963984540054
normal(100, 15).cdf(130); // 0.9772498680518208
studentT(10).quantile(0.975); // 2.2281388519862744
chiSquare(1).quantile(0.95); // 3.841458820694124
fisherF(3, 10).cdf(3.708265); // ≈ 0.95
noncentralT(10, 1).cdf(2); // 0.8076115625 — power analysis without jstat
binomial(10, 0.5).pmf(5); // 0.24609375
poisson(3).cdf(2); // 0.42319008112684353

For p-values, use the survival function instead of 1 - cdf(x) — the subtraction loses the tail: 1 - normal().cdf(9) is 0, while normal().sf(9) is 1.13e-19. Discrete quantile(p) is exactly "the smallest k with cdf(k) ≥ p".

The special functions underneath — erf, erfc, erfInv, gamma, gammaLn, beta, betaLn, regularizedGammaP, regularizedBeta — are exported too. Every one of them, and every distribution method, is measured against mpmath far into the tails; the bounds are in Accuracy.