tessl/npm-d3-random

Generate random numbers from various statistical distributions for data visualization and scientific computing.

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d3-random

Name: tessl/npm-d3-random
Author: tessl

d3-random is a JavaScript library that provides functions for generating random numbers from various statistical distributions. Part of the D3.js ecosystem, it offers high-quality pseudorandom number generation with support for seeded random number generators, making it suitable for reproducible statistical simulations, data visualization, Monte Carlo methods, and scientific computing applications.

Package Information

Package Name: d3-random
Package Type: npm
Language: JavaScript (ES6 modules)
Installation: npm install d3-random

Core Imports

ES6 modules:

import { randomUniform, randomNormal, randomLcg } from "d3-random";

Specific imports:

import { randomUniform } from "d3-random";
import { randomNormal } from "d3-random";

CommonJS (legacy):

const { randomUniform, randomNormal } = require("d3-random");

UMD global (legacy browsers):

<script src="https://cdn.jsdelivr.net/npm/d3-random@3"></script>
<script>
  const random = d3.randomUniform(1, 10);
</script>

Basic Usage

import { randomUniform, randomNormal, randomLcg } from "d3-random";

// Basic uniform random numbers
const uniform = randomUniform(0, 100);
console.log(uniform()); // Random number between 0 and 100

// Normal distribution
const normal = randomNormal(50, 10); // mean=50, stddev=10
console.log(normal()); // Normally distributed random number

// Using custom seeded generator for reproducible results
const seed = 0.44871573888282423;
const seededRandom = randomNormal.source(randomLcg(seed))(0, 1);
console.log(seededRandom()); // Always produces the same sequence

Architecture

d3-random is built around several key patterns:

Distribution Functions: Each statistical distribution is implemented as a factory function that returns a generator
Generator Pattern: All functions return generator functions that can be called repeatedly to produce random samples
Source Pattern: Every distribution function exposes a .source(source) method for custom random number generators
Seeded Generation: randomLcg provides deterministic pseudorandom sequences for reproducible results
Functional Design: Stateless API with no global state or side effects

Capabilities

Basic Distributions

Common statistical distributions for general-purpose random number generation, including uniform, normal, and integer distributions.

function randomUniform(min?: number, max?: number): () => number;
function randomInt(min?: number, max?: number): () => number;
function randomNormal(mu?: number, sigma?: number): () => number;

Basic Distributions

Advanced Distributions

Specialized statistical distributions for scientific computing and advanced simulations, including gamma, beta, Weibull, and other continuous and discrete distributions.

function randomGamma(k: number, theta?: number): () => number;
function randomBeta(alpha: number, beta: number): () => number;
function randomWeibull(k: number, a?: number, b?: number): () => number;
function randomPoisson(lambda: number): () => number;

Advanced Distributions

Seeded Random Generation

Utilities for creating reproducible random number sequences using linear congruential generators and custom random sources.

function randomLcg(seed?: number): () => number;

// Source method pattern (available on all distribution functions)
interface RandomFunction {
  source(source: () => number): RandomFunction;
}

Seeded Generation

Types

// Generator function returned by all distribution functions
type RandomGenerator = () => number;

// Random source function (compatible with Math.random)
type RandomSource = () => number;

// Distribution function with source method
interface DistributionFunction extends Function {
  source(source: RandomSource): DistributionFunction;
}

Install with Tessl CLI