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@stdlib/random-streams

Standard library pseudorandom number generator streams.

https://github.com/stdlib-js/random-streams

Science Score: 44.0%

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Keywords

generator javascript lib library math mathematics node node-js nodejs prng pseudorandom rand random rng seed seedable standard statistics stats stdlib
Last synced: 6 months ago · JSON representation ·

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Standard library pseudorandom number generator streams.

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Topics
generator javascript lib library math mathematics node node-js nodejs prng pseudorandom rand random rng seed seedable standard statistics stats stdlib
Created over 4 years ago · Last pushed 11 months ago
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README.md

About stdlib...

We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.

The library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.

When you use stdlib, you can be absolutely certain that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code out there.

To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!

Pseudorandom Number Generator Streams

NPM version Build Status Coverage Status <!-- dependencies -->

Pseudorandom number generator (PRNG) streams.

## Installation ```bash npm install @stdlib/random-streams ``` Alternatively, - To load the package in a website via a `script` tag without installation and bundlers, use the [ES Module][es-module] available on the [`esm`][esm-url] branch (see [README][esm-readme]). - If you are using Deno, visit the [`deno`][deno-url] branch (see [README][deno-readme] for usage intructions). - For use in Observable, or in browser/node environments, use the [Universal Module Definition (UMD)][umd] build available on the [`umd`][umd-url] branch (see [README][umd-readme]). The [branches.md][branches-url] file summarizes the available branches and displays a diagram illustrating their relationships. To view installation and usage instructions specific to each branch build, be sure to explicitly navigate to the respective README files on each branch, as linked to above.
## Usage ```javascript var ns = require( '@stdlib/random-streams' ); ``` #### ns Namespace containing pseudorandom number generator (PRNG) streams. ```javascript var streams = ns; // returns {...} ``` The namespace contains the following functions for creating readable pseudorandom number generator streams:
- [`arcsine( a, b[, options] )`][@stdlib/random/streams/arcsine]: create a readable stream for generating pseudorandom numbers drawn from an arcsine distribution. - [`bernoulli( p[, options] )`][@stdlib/random/streams/bernoulli]: create a readable stream for generating pseudorandom numbers drawn from a Bernoulli distribution. - [`beta( alpha, beta[, options] )`][@stdlib/random/streams/beta]: create a readable stream for generating pseudorandom numbers drawn from a beta distribution. - [`betaprime( alpha, beta[, options] )`][@stdlib/random/streams/betaprime]: create a readable stream for generating pseudorandom numbers drawn from a beta prime distribution. - [`binomial( n, p[, options] )`][@stdlib/random/streams/binomial]: create a readable stream for generating pseudorandom numbers drawn from a binomial distribution. - [`boxMuller( [options] )`][@stdlib/random/streams/box-muller]: create a readable stream for generating pseudorandom numbers drawn from a standard normal distribution using the Box-Muller transform. - [`cauchy( x0, gamma[, options] )`][@stdlib/random/streams/cauchy]: create a readable stream for generating pseudorandom numbers drawn from a Cauchy distribution. - [`chi( k[, options] )`][@stdlib/random/streams/chi]: create a readable stream for generating pseudorandom numbers drawn from a chi distribution. - [`chisquare( k[, options] )`][@stdlib/random/streams/chisquare]: create a readable stream for generating pseudorandom numbers drawn from a chi-square distribution. - [`cosine( mu, s[, options] )`][@stdlib/random/streams/cosine]: create a readable stream for generating pseudorandom numbers drawn from a raised cosine distribution. - [`discreteUniform( a, b[, options] )`][@stdlib/random/streams/discrete-uniform]: create a readable stream for generating pseudorandom numbers drawn from a discrete uniform distribution. - [`erlang( k, lambda[, options] )`][@stdlib/random/streams/erlang]: create a readable stream for generating pseudorandom numbers drawn from an Erlang distribution. - [`exponential( lambda[, options] )`][@stdlib/random/streams/exponential]: create a readable stream for generating pseudorandom numbers drawn from an exponential distribution. - [`f( d1, d2[, options] )`][@stdlib/random/streams/f]: create a readable stream for generating pseudorandom numbers drawn from an F distribution. - [`frechet( alpha, s, m[, options] )`][@stdlib/random/streams/frechet]: create a readable stream for generating pseudorandom numbers drawn from a Fréchet distribution. - [`gamma( alpha, beta[, options] )`][@stdlib/random/streams/gamma]: create a readable stream for generating pseudorandom numbers drawn from a gamma distribution. - [`geometric( p[, options] )`][@stdlib/random/streams/geometric]: create a readable stream for generating pseudorandom numbers drawn from a geometric distribution. - [`gumbel( mu, beta[, options] )`][@stdlib/random/streams/gumbel]: create a readable stream for generating pseudorandom numbers drawn from a Gumbel distribution. - [`hypergeometric( N, K, n[, options] )`][@stdlib/random/streams/hypergeometric]: create a readable stream for generating pseudorandom numbers drawn from a hypergeometric distribution. - [`improvedZiggurat( [options] )`][@stdlib/random/streams/improved-ziggurat]: create a readable stream for generating pseudorandom numbers drawn from a standard normal distribution using the Improved Ziggurat algorithm. - [`invgamma( alpha, beta[, options] )`][@stdlib/random/streams/invgamma]: create a readable stream for generating pseudorandom numbers drawn from an inverse gamma distribution. - [`kumaraswamy( a, b[, options] )`][@stdlib/random/streams/kumaraswamy]: create a readable stream for generating pseudorandom numbers drawn from a Kumaraswamy's double bounded distribution. - [`laplace( mu, b[, options] )`][@stdlib/random/streams/laplace]: create a readable stream for generating pseudorandom numbers drawn from a Laplace (double exponential) distribution. - [`levy( mu, c[, options] )`][@stdlib/random/streams/levy]: create a readable stream for generating pseudorandom numbers drawn from a Lévy distribution. - [`logistic( mu, s[, options] )`][@stdlib/random/streams/logistic]: create a readable stream for generating pseudorandom numbers drawn from a logistic distribution. - [`lognormal( mu, sigma[, options] )`][@stdlib/random/streams/lognormal]: create a readable stream for generating pseudorandom numbers drawn from a lognormal distribution. - [`minstdShuffle( [options] )`][@stdlib/random/streams/minstd-shuffle]: create a readable stream for a linear congruential pseudorandom number generator (LCG) whose output is shuffled. - [`minstd( [options] )`][@stdlib/random/streams/minstd]: create a readable stream for a linear congruential pseudorandom number generator (LCG) based on Park and Miller. - [`mt19937( [options] )`][@stdlib/random/streams/mt19937]: create a readable stream for a 32-bit Mersenne Twister pseudorandom number generator. - [`negativeBinomial( r, p[, options] )`][@stdlib/random/streams/negative-binomial]: create a readable stream for generating pseudorandom numbers drawn from a negative binomial distribution. - [`normal( mu, sigma[, options] )`][@stdlib/random/streams/normal]: create a readable stream for generating pseudorandom numbers drawn from a normal distribution. - [`pareto1( alpha, beta[, options] )`][@stdlib/random/streams/pareto-type1]: create a readable stream for generating pseudorandom numbers drawn from a Pareto (Type I) distribution. - [`poisson( lambda[, options] )`][@stdlib/random/streams/poisson]: create a readable stream for generating pseudorandom numbers drawn from a Poisson distribution. - [`randi( [options] )`][@stdlib/random/streams/randi]: create a readable stream for generating pseudorandom numbers having integer values. - [`randn( [options] )`][@stdlib/random/streams/randn]: create a readable stream for generating pseudorandom numbers drawn from a standard normal distribution. - [`randu( [options] )`][@stdlib/random/streams/randu]: create a readable stream for generating uniformly distributed pseudorandom numbers between `0` and `1`. - [`rayleigh( sigma[, options] )`][@stdlib/random/streams/rayleigh]: create a readable stream for generating pseudorandom numbers drawn from a Rayleigh distribution. - [`t( v[, options] )`][@stdlib/random/streams/t]: create a readable stream for generating pseudorandom numbers drawn from a Student's t distribution. - [`triangular( a, b, c[, options] )`][@stdlib/random/streams/triangular]: create a readable stream for generating pseudorandom numbers drawn from a triangular distribution. - [`uniform( a, b[, options] )`][@stdlib/random/streams/uniform]: create a readable stream for generating pseudorandom numbers drawn from a uniform distribution. - [`weibull( k, lambda[, options] )`][@stdlib/random/streams/weibull]: create a readable stream for generating pseudorandom numbers drawn from a Weibull distribution.
## Examples ```javascript var objectKeys = require( '@stdlib/utils-keys' ); var ns = require( '@stdlib/random-streams' ); console.log( objectKeys( ns ) ); ```
* * * ## Notice This package is part of [stdlib][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][stdlib], see the main project [repository][stdlib]. #### Community [![Chat][chat-image]][chat-url] --- ## License See [LICENSE][stdlib-license]. ## Copyright Copyright © 2016-2025. The Stdlib [Authors][stdlib-authors].

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  • Name: stdlib
  • Login: stdlib-js
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Standard library for JavaScript.

Citation (CITATION.cff) 

cff-version: 1.2.0
title: stdlib
message: >-
  If you use this software, please cite it using the
  metadata from this file.

type: software

authors:
  - name: The Stdlib Authors
    url: https://github.com/stdlib-js/stdlib/graphs/contributors

repository-code: https://github.com/stdlib-js/stdlib
url: https://stdlib.io

abstract: |
  Standard library for JavaScript and Node.js.

keywords:
  - JavaScript
  - Node.js
  - TypeScript
  - standard library
  - scientific computing
  - numerical computing
  - statistical computing

license: Apache-2.0 AND BSL-1.0

date-released: 2016

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npmjs.org: @stdlib/random-streams

Pseudorandom number generator streams.

  • Homepage: https://stdlib.io
  • License: Apache-2.0
  • Latest release: 0.2.1
    published almost 2 years ago
  • Versions: 9
  • Dependent Packages: 2
  • Dependent Repositories: 0
  • Downloads: 3 Last month
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Dependent repos count: 25.3%
Downloads: 30.3%
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planeshifter rreusser stdlib-bot kgryte
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Last synced: 6 months ago

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