@acausal/sampler

Statistical distribution sampling with deterministic seeded RNG. Normal, exponential, Poisson, beta, gamma, binomial, geometric, Weibull, Cauchy, logistic, and more.

Install

npm install @acausal/sampler

Usage

import { RandomSampler } from '@acausal/sampler';

const sampler = new RandomSampler({ seed: 42 });

sampler.normal(170, 7);        // height ~ N(170, 7)
sampler.uniform(1, 10);        // uniform in [1, 10)
sampler.poisson(5);            // count ~ Poisson(5)
sampler.beta(2, 5);            // proportion ~ Beta(2, 5)
sampler.weightedChoice({ red: 55, auburn: 25, blonde: 20 });

// Data-driven sampling from config
sampler.sampleDistribution({ type: 'normal', mu: 170, sigma: 7 });

All methods are deterministic given the same seed. Built on @acausal/random.

Part of the acausal procedural generation toolkit.