Pseudorandom Number Generator Streams

Pseudorandom number generator (PRNG) streams.

Installation

npm install @stdlib/random-streams

Usage

var ns = require( '@stdlib/random-streams' );

ns

Namespace containing pseudorandom number generator (PRNG) streams.

var streams = ns;
// returns {...}

The namespace contains the following functions for creating readable pseudorandom number generator streams:

• arcsine( a, b[, options] ): create a readable stream for generating pseudorandom numbers drawn from an arcsine distribution.
• bernoulli( p[, options] ): create a readable stream for generating pseudorandom numbers drawn from a Bernoulli distribution.
• beta( alpha, beta[, options] ): create a readable stream for generating pseudorandom numbers drawn from a beta distribution.
• betaprime( alpha, beta[, options] ): create a readable stream for generating pseudorandom numbers drawn from a beta prime distribution.
• binomial( n, p[, options] ): create a readable stream for generating pseudorandom numbers drawn from a binomial distribution.
• boxMuller( [options] ): create a readable stream for generating pseudorandom numbers drawn from a standard normal distribution using the Box-Muller transform.
• cauchy( x0, gamma[, options] ): create a readable stream for generating pseudorandom numbers drawn from a Cauchy distribution.
• chi( k[, options] ): create a readable stream for generating pseudorandom numbers drawn from a chi distribution.
• chisquare( k[, options] ): create a readable stream for generating pseudorandom numbers drawn from a chi-square distribution.
• cosine( mu, s[, options] ): create a readable stream for generating pseudorandom numbers drawn from a raised cosine distribution.
• discreteUniform( a, b[, options] ): create a readable stream for generating pseudorandom numbers drawn from a discrete uniform distribution.
• erlang( k, lambda[, options] ): create a readable stream for generating pseudorandom numbers drawn from an Erlang distribution.
• exponential( lambda[, options] ): create a readable stream for generating pseudorandom numbers drawn from an exponential distribution.
• f( d1, d2[, options] ): create a readable stream for generating pseudorandom numbers drawn from an F distribution.
• frechet( alpha, s, m[, options] ): create a readable stream for generating pseudorandom numbers drawn from a Fréchet distribution.
• gamma( alpha, beta[, options] ): create a readable stream for generating pseudorandom numbers drawn from a gamma distribution.
• geometric( p[, options] ): create a readable stream for generating pseudorandom numbers drawn from a geometric distribution.
• gumbel( mu, beta[, options] ): create a readable stream for generating pseudorandom numbers drawn from a Gumbel distribution.
• hypergeometric( N, K, n[, options] ): create a readable stream for generating pseudorandom numbers drawn from a hypergeometric distribution.
• improvedZiggurat( [options] ): create a readable stream for generating pseudorandom numbers drawn from a standard normal distribution using the Improved Ziggurat algorithm.
• invgamma( alpha, beta[, options] ): create a readable stream for generating pseudorandom numbers drawn from an inverse gamma distribution.
• kumaraswamy( a, b[, options] ): create a readable stream for generating pseudorandom numbers drawn from a Kumaraswamy's double bounded distribution.
• laplace( mu, b[, options] ): create a readable stream for generating pseudorandom numbers drawn from a Laplace (double exponential) distribution.
• levy( mu, c[, options] ): create a readable stream for generating pseudorandom numbers drawn from a Lévy distribution.
• logistic( mu, s[, options] ): create a readable stream for generating pseudorandom numbers drawn from a logistic distribution.
• lognormal( mu, sigma[, options] ): create a readable stream for generating pseudorandom numbers drawn from a lognormal distribution.
• minstdShuffle( [options] ): create a readable stream for a linear congruential pseudorandom number generator (LCG) whose output is shuffled.
• minstd( [options] ): create a readable stream for a linear congruential pseudorandom number generator (LCG) based on Park and Miller.
• mt19937( [options] ): create a readable stream for a 32-bit Mersenne Twister pseudorandom number generator.
• negativeBinomial( r, p[, options] ): create a readable stream for generating pseudorandom numbers drawn from a negative binomial distribution.
• normal( mu, sigma[, options] ): create a readable stream for generating pseudorandom numbers drawn from a normal distribution.
• pareto1( alpha, beta[, options] ): create a readable stream for generating pseudorandom numbers drawn from a Pareto (Type I) distribution.
• poisson( lambda[, options] ): create a readable stream for generating pseudorandom numbers drawn from a Poisson distribution.
• randi( [options] ): create a readable stream for generating pseudorandom numbers having integer values.
• randn( [options] ): create a readable stream for generating pseudorandom numbers drawn from a standard normal distribution.
• randu( [options] ): create a readable stream for generating uniformly distributed pseudorandom numbers between 0 and 1.
• rayleigh( sigma[, options] ): create a readable stream for generating pseudorandom numbers drawn from a Rayleigh distribution.
• t( v[, options] ): create a readable stream for generating pseudorandom numbers drawn from a Student's t distribution.
• triangular( a, b, c[, options] ): create a readable stream for generating pseudorandom numbers drawn from a triangular distribution.
• uniform( a, b[, options] ): create a readable stream for generating pseudorandom numbers drawn from a uniform distribution.
• weibull( k, lambda[, options] ): create a readable stream for generating pseudorandom numbers drawn from a Weibull distribution.

Examples

var objectKeys = require( '@stdlib/utils-keys' );
var ns = require( '@stdlib/random-streams' );

console.log( objectKeys( ns ) );

Notice

This package is part of stdlib, a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.

For more information on the project, filing bug reports and feature requests, and guidance on how to develop stdlib, see the main project repository.

Community

License

See LICENSE.

Copyright

Copyright © 2016-2024. The Stdlib Authors.