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@fimbul-works/random

v1.0.0

Published

A comprehensive collection of high-performance 32-bit and 64-bit pseudo-random number generators (PRNG), statistical distributions, and procedural generation utilities for TypeScript and JavaScript

Readme

@fimbul-works/random

license npm version code style bundle size

An ultra-lightweight, ESM-first mathematical toolkit for pseudo-random number generation (PRNG), statistical distributions, and high-performance composable procedural generation utilities.


Key Differentiators

  • High-Entropy Seed Expansion: Fully resolves the common "zero-entropy seed collapse" found in traditional JavaScript PRNGs. Uses @fimbul-works/hash (fastMix) to expand seeds (numbers or strings) into high-quality starting state vectors.
  • Native 32-bit and 64-bit Integer Engines: Dedicated support for both 32-bit uint generators and 64-bit BigInt algorithms (e.g., Lehmer64, Wyrand, xoshiro256**). Inspectable via the .bits property.
  • Stand-Alone & Tree-Shakeable: Each generator and utility is modularly bundled. Unused imports are entirely tree-shaken, keeping individual algorithm footprints under 1 KB.
  • Decoupled State Management: Full serialization and restoration via .getState() and .setState(), enabling seamless save systems, deterministic replays, and procedural chunk streaming.
  • Multi-Precision Decorators: Uniform callable random() returning [0.0, 1.0), alongside native .int(), .int64(), and .double() helper methods.
  • Rich Procedural Generation Utilities: In-place Fisher-Yates shuffling, reservoir sampling, Gaussian/exponential/Poisson distributions, 2D/3D geometry sampling, and curried functional combinators.

Installation

pnpm add @fimbul-works/random
# or
npm install @fimbul-works/random
# or
yarn add @fimbul-works/random

Algorithms

@fimbul-works/random includes 33 optimized PRNG implementations categorized by bit-width:

32-Bit Integer & Float Algorithms (bits === 32)

  • xoshiro / xoroshiro Family: xoshiro128++, xoshiro128+, xoroshiro64++, xoroshiro64**.
  • Romu Family: RomuDuoJr, RomuTrio, RomuQuad (Mark A. Overton's nonlinear fast PRNGs).
  • JSF / Bob Jenkins: JSF32, JSF32B (Small Fast Counting PRNG).
  • Speed & Procgen Favorites: SplitMix32, Mulberry32, SFC32, GJRand32, Tychei.
  • Xorshift Variants: Xorshift32, Xorshift32AMX, Xorshift32M, Xorshift7, Xorshift128, XorShiftMash, Xorwow, Xor4096.
  • Classics: Alea (Johannes Baagøe's high-entropy float PRNG), MersenneTwister, ParkMiller (MINSTD).

64-Bit Integer Algorithms (bits === 64)

  • Lehmer64: Ultra-fast, minimal multiplicative 64-bit congruential generator.
  • Wyrand: State-of-the-art fast 64-bit PRNG by Wang Yi with excellent statistical properties.
  • SplitMix64: Fast 64-bit generator with period $2^{64}$, ideal for generating initial states.
  • MiddleSquareWeyl: Bernard Widynski's Middle Square Weyl Sequence PRNG.
  • xoshiro256++ / xoshiro256**: David Blackman and Sebastiano Vigna's flagship 256-bit state generators.
  • xoroshiro128**: 128-bit state 64-bit output generator from the xoroshiro family.

High-Performance Utilities

  • Array Operations: Pure shuffleArray(), allocation-free shuffleInPlace(), pure weighted picking pickWeightedRandom(), and $O(k)$ unique sampling without replacement sampleRandom().
  • Proportional Mappings: Weighted key extraction from object configurations (randomWeightedKey()).
  • Statistical Distributions: Uniform randomRange(), statistical randomGaussian(), randomExp(), randomLogistic(), and discrete randomPoisson().
  • Geometry Samplers: Uniform area coordinates inside a 2D circle (randomPointInCircle()) and uniform coordinates on a 3D sphere surface (randomPointOnSphere()).
  • Strings & Ranges: Alphanumeric / custom alphabet strings (randomString()), random booleans with custom bias (randomBool()), and random signs (randomSign()).

Usage

1. 32-Bit PRNG Usage

import { createRandomXoshiro128PlusPlus } from "@fimbul-works/random";

// Seeded stateful generator
const rng = createRandomXoshiro128PlusPlus("seed-value-or-number");

// 1. Get raw float in [0.0, 1.0)
const val = rng();

// 2. High-precision integer and double float boundaries
const int32 = rng.int();      // Unsigned 32-bit integer [0, 2^32 - 1]
const int64 = rng.int64();    // Unsigned 64-bit bigint [0n, 2^64 - 1n]
const double = rng.double();  // Double-precision float in [0.0, 1.0)

// 3. Inspect bit-width
console.log(rng.bits); // 32

// 4. Save and restore internal state
const state = rng.getState();
const next1 = rng();
rng.setState(state);
const next2 = rng(); // next1 === next2

2. 64-Bit Native PRNG Usage

import { createRandomLehmer64, createRandomWyrand } from "@fimbul-works/random";

const rng64 = createRandomLehmer64(42n);

console.log(rng64.bits); // 64
const bigVal = rng64.int64(); // Native 64-bit BigInt
const floatVal = rng64();     // Standard [0.0, 1.0) float

3. Composable Utilities & In-Place Shuffling

import {
  createRandomAlea,
  shuffleInPlace,
  sampleRandom,
  randomPointInCircle,
  randomGaussian,
} from "@fimbul-works/random";

const random = createRandomAlea(42);

// Non-allocating in-place Fisher-Yates shuffle
const deck = [1, 2, 3, 4, 5];
shuffleInPlace(deck, random);

// Sample 3 unique items without replacement
const hand = sampleRandom(deck, 3, random);

// Uniform 2D circle sampling
const [x, y] = randomPointInCircle(10.0, random);

// Gaussian normal distribution (mean: 0, stdev: 1.5)
const sample = randomGaussian(0, 1.5, random);

4. Decorator Extensions

import {
  createRandomSplitMix32,
  decorateRandomWithArray,
  decorateRandomWithDistribution,
} from "@fimbul-works/random";

// Compose domain-specific helper methods directly onto the PRNG function
const rng = decorateRandomWithDistribution(
  decorateRandomWithArray(createRandomSplitMix32(12345))
);

const item = rng.pick(["apple", "banana", "cherry"]);
const normalVal = rng.gaussian(100, 15);

Documentation

For full type signatures and module documentation, refer to the co-located /docs folder.

License

MIT License - See LICENSE file for details.


Built with ⚡ by FimbulWorks