Randlibjs

A lightweight JavaScript library for generating random numbers with various probability distributions.

Features

• Multiple Distributions: Generate random numbers from various probability distributions including normal, uniform, exponential, and more
• Reproducible Results: Set seeds to generate reproducible sequences of random numbers for testing and validation
• Lightweight: Minimal dependencies and optimized code for fast performance in both browser and Node.js environments
• Easy to Use: Simple and intuitive API with comprehensive documentation and examples

Installation

npm

npm install randlibjs

Quick Start

ES Modules

// Import specific functions
import { normal, uniform, seed } from 'randlibjs';

// Set a seed for reproducibility
seed(12345);

// Generate 10 numbers from normal distribution
const normalSample = normal(0, 1, 10);
console.log(normalSample);

// Generate 5 uniformly distributed numbers
const uniformSample = uniform(0, 10, 5);
console.log(uniformSample);

Browser

<script src="https://cdn.jsdelivr.net/npm/randlibjs@latest/dist/randlib.min.js"></script>
<script>
  // Set a seed
  randlib.seed(12345);
  
  // Generate random numbers
  const normalSample = randlib.normal(0, 1, 10);
  console.log(normalSample);
</script>

Available Distributions

Continuous Distributions

| Function | Description | Parameters | |----------|-------------|------------| | uniform(low, high, size) | Uniform distribution | low: Lower bound (default: 0)high: Upper bound (default: 1)size: Sample size (default: 1) | | normal(mean, std, size) | Normal (Gaussian) distribution | mean: Mean (default: 0)std: Standard deviation (default: 1)size: Sample size (default: 1) | | exponential(lambda, size) | Exponential distribution | lambda: Rate parameter (default: 1)size: Sample size (default: 1) | | cauchy(loc, scale, size) | Cauchy distribution | loc: Location parameter (default: 0)scale: Scale parameter (default: 1)size: Sample size (default: 1) | | lognormal(mean, sigma, size) | Lognormal distribution | mean: Mean of logarithm (default: 0)sigma: Standard deviation of logarithm (default: 1)size: Sample size (default: 1) | | pareto(alpha, size) | Pareto distribution | alpha: Shape parameter (default: 1)size: Sample size (default: 1) | | triangular(low, high, mode, size) | Triangular distribution | low: Lower bound (default: 0)high: Upper bound (default: 1)mode: Mode (default: 0.5)size: Sample size (default: 1) |

Discrete Distributions

| Function | Description | Parameters | |----------|-------------|------------| | randint(low, high, size) | Discrete uniform distribution | low: Lower bound (included, default: 0)high: Upper bound (excluded)size: Sample size (default: 1) | | poisson(lambda, size) | Poisson distribution | lambda: Rate parameter (default: 1)size: Sample size (default: 1) | | binomial(n, p, size) | Binomial distribution | n: Number of trialsp: Success probabilitysize: Sample size (default: 1) | | geometric(p, size) | Geometric distribution | p: Success probabilitysize: Sample size (default: 1) | | chisquare(df, size) | Chi-square distribution | df: Degrees of freedomsize: Sample size (default: 1) |

Multivariate Distributions

| Function | Description | Parameters | |----------|-------------|------------| | multivariateNormal(mean, cov, size) | Multivariate normal distribution | mean: Mean vectorcov: Covariance matrixsize: Sample size (default: 1) |

Utilities

| Function | Description | Parameters | |----------|-------------|------------| | seed(value) | Set random seed | value: Seed value | | shuffle(array) | Randomly shuffle array | array: Array to shuffle | | permutation(n) | Random permutation of integers | n: Number of elements | | randString(length) | Random string | length: Length of the string | | mixture(distributions, weights, size) | Mixture of distributions | distributions: Array of distribution functionsweights: Array of weightssize: Sample size (default: 1) |

Examples

Monte Carlo Simulation

import { uniform } from 'randlibjs';

function estimatePi(samples = 1000) {
  let insideCircle = 0;
  
  for (let i = 0; i < samples; i++) {
    const x = uniform(-1, 1);
    const y = uniform(-1, 1);
    
    if (x*x + y*y <= 1) {
      insideCircle++;
    }
  }
  
  return 4 * insideCircle / samples;
}

// Estimate π with 10,000 samples
console.log(estimatePi(10000));

Random Walk Simulation

import { randint } from 'randlibjs';

function randomWalk(steps = 100, p = 0.5) {
  let position = 0;
  const positions = [position];
  
  for (let i = 0; i < steps; i++) {
    // Movement: +1 with probability p, -1 with probability 1-p
    const step = Math.random() < p ? 1 : -1;
    position += step;
    positions.push(position);
  }
  
  return positions;
}

// Perform a random walk of 100 steps
const walk = randomWalk(100);
console.log(walk);

Documentation

For detailed documentation and more examples, visit:

• Official Website
• API Documentation
• GitHub Repository

License

BSD-3-Clause License

Author

Adrián Muñoz Perera