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!

Strided

[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url]

> Pseudorandom number generator (PRNG) strided array functions.

Installation

``bash npm install @stdlib/random-strided`

`Usage`

`javascript var ns = require( '@stdlib/random-strided' );`

#### ns

Namespace containing strided array pseudorandom number generator (PRNG) functions.

`javascript var o = ns; // returns {...}`

The namespace contains the following:

- [arcsine( N, a, sa, b, sb, out, so[, options] )][@stdlib/random/strided/arcsine]: fill a strided array with pseudorandom numbers drawn from an arcsine distribution. - [bernoulli( N, p, sp, out, so )][@stdlib/random/strided/bernoulli]: fill a strided array with pseudorandom numbers drawn from a Bernoulli distribution. - [beta( N, alpha, sa, beta, sb, out, so[, options] )][@stdlib/random/strided/beta]: fill a strided array with pseudorandom numbers drawn from a beta distribution. - [betaprime( N, alpha, sa, beta, sb, out, so[, options] )][@stdlib/random/strided/betaprime]: fill a strided array with pseudorandom numbers drawn from a beta prime distribution. - [chi( N, k, sk, out, so )][@stdlib/random/strided/chi]: fill a strided array with pseudorandom numbers drawn from a chi distribution. - [chisquare( N, k, sk, out, so )][@stdlib/random/strided/chisquare]: fill a strided array with pseudorandom numbers drawn from a chi-square distribution. - [cosine( N, mu, sm, s, ss, out, so[, options] )][@stdlib/random/strided/cosine]: fill a strided array with pseudorandom numbers drawn from a raised cosine distribution. - [discreteUniform( N, a, sa, b, sb, out, so[, options] )][@stdlib/random/strided/discrete-uniform]: fill a strided array with pseudorandom numbers drawn from a discrete uniform distribution. - [exponential( N, lambda, sl, out, so )][@stdlib/random/strided/exponential]: fill a strided array with pseudorandom numbers drawn from an exponential distribution. - [gamma( N, alpha, sa, beta, sb, out, so[, options] )][@stdlib/random/strided/gamma]: fill a strided array with pseudorandom numbers drawn from a gamma distribution. - [geometric( N, p, sp, out, so )][@stdlib/random/strided/geometric]: fill a strided array with pseudorandom numbers drawn from a geometric distribution. - [invgamma( N, alpha, sa, beta, sb, out, so[, options] )][@stdlib/random/strided/invgamma]: fill a strided array with pseudorandom numbers drawn from an inverse gamma distribution. - [lognormal( N, mu, sm, sigma, ss, out, so[, options] )][@stdlib/random/strided/lognormal]: fill a strided array with pseudorandom numbers drawn from a lognormal distribution. - [minstdShuffle( N, out, so[, options] )][@stdlib/random/strided/minstd-shuffle]: fill a strided array with pseudorandom numbers generated using a linear congruential pseudorandom number generator (LCG) whose output is shuffled. - [minstd( N, out, so[, options] )][@stdlib/random/strided/minstd]: fill a strided array with pseudorandom numbers generated using a linear congruential pseudorandom number generator (LCG). - [mt19937( N, out, so[, options] )][@stdlib/random/strided/mt19937]: fill a strided array with pseudorandom numbers generated using a 32-bit Mersenne Twister pseudorandom number generator. - [normal( N, mu, sm, sigma, ss, out, so[, options] )][@stdlib/random/strided/normal]: fill a strided array with pseudorandom numbers drawn from a normal distribution. - [poisson( N, lambda, sl, out, so )][@stdlib/random/strided/poisson]: fill a strided array with pseudorandom numbers drawn from a Poisson distribution. - [randu( N, out, so[, options] )][@stdlib/random/strided/randu]: fill a strided array with uniformly distributed pseudorandom numbers between 0 and 1. - [rayleigh( N, sigma, ss, out, so )][@stdlib/random/strided/rayleigh]: fill a strided array with pseudorandom numbers drawn from a Rayleigh distribution. - [t( N, v, sv, out, so )][@stdlib/random/strided/t]: fill a strided array with pseudorandom numbers drawn from a Student's t-distribution. - [uniform( N, a, sa, b, sb, out, so[, options] )][@stdlib/random/strided/uniform]: fill a strided array with pseudorandom numbers drawn from a continuous uniform distribution. - [weibull( N, k, sk, lambda, sl, out, so[, options] )][@stdlib/random/strided/weibull]: fill a strided array with pseudorandom numbers drawn from a Weibull distribution.

`Examples`

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

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`

[npm-image]: http://img.shields.io/npm/v/@stdlib/random-strided.svg [npm-url]: https://npmjs.org/package/@stdlib/random-strided

[test-image]: https://github.com/stdlib-js/random-strided/actions/workflows/test.yml/badge.svg?branch=v0.3.1 [test-url]: https://github.com/stdlib-js/random-strided/actions/workflows/test.yml?query=branch:v0.3.1

[coverage-image]: https://img.shields.io/codecov/c/github/stdlib-js/random-strided/main.svg [coverage-url]: https://codecov.io/github/stdlib-js/random-strided?branch=main

[chat-image]: https://img.shields.io/badge/zulip-join_chat-brightgreen.svg [chat-url]: https://stdlib.zulipchat.com

[stdlib]: https://github.com/stdlib-js/stdlib

[stdlib-authors]: https://github.com/stdlib-js/stdlib/graphs/contributors

[umd]: https://github.com/umdjs/umd [es-module]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules

[deno-url]: https://github.com/stdlib-js/random-strided/tree/deno [deno-readme]: https://github.com/stdlib-js/random-strided/blob/deno/README.md [umd-url]: https://github.com/stdlib-js/random-strided/tree/umd [umd-readme]: https://github.com/stdlib-js/random-strided/blob/umd/README.md [esm-url]: https://github.com/stdlib-js/random-strided/tree/esm [esm-readme]: https://github.com/stdlib-js/random-strided/blob/esm/README.md [branches-url]: https://github.com/stdlib-js/random-strided/blob/main/branches.md

[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/random-strided/main/LICENSE

[@stdlib/random/strided/arcsine]: https://www.npmjs.com/package/@stdlib/random-strided-arcsine

[@stdlib/random/strided/bernoulli]: https://www.npmjs.com/package/@stdlib/random-strided-bernoulli

[@stdlib/random/strided/beta]: https://www.npmjs.com/package/@stdlib/random-strided-beta

[@stdlib/random/strided/betaprime]: https://www.npmjs.com/package/@stdlib/random-strided-betaprime

[@stdlib/random/strided/chi]: https://www.npmjs.com/package/@stdlib/random-strided-chi

[@stdlib/random/strided/chisquare]: https://www.npmjs.com/package/@stdlib/random-strided-chisquare

[@stdlib/random/strided/cosine]: https://www.npmjs.com/package/@stdlib/random-strided-cosine

[@stdlib/random/strided/discrete-uniform]: https://www.npmjs.com/package/@stdlib/random-strided-discrete-uniform

[@stdlib/random/strided/exponential]: https://www.npmjs.com/package/@stdlib/random-strided-exponential

[@stdlib/random/strided/gamma]: https://www.npmjs.com/package/@stdlib/random-strided-gamma

[@stdlib/random/strided/geometric]: https://www.npmjs.com/package/@stdlib/random-strided-geometric

[@stdlib/random/strided/invgamma]: https://www.npmjs.com/package/@stdlib/random-strided-invgamma

[@stdlib/random/strided/lognormal]: https://www.npmjs.com/package/@stdlib/random-strided-lognormal

[@stdlib/random/strided/minstd-shuffle]: https://www.npmjs.com/package/@stdlib/random-strided-minstd-shuffle

[@stdlib/random/strided/minstd]: https://www.npmjs.com/package/@stdlib/random-strided-minstd

[@stdlib/random/strided/mt19937]: https://www.npmjs.com/package/@stdlib/random-strided-mt19937

[@stdlib/random/strided/normal]: https://www.npmjs.com/package/@stdlib/random-strided-normal

[@stdlib/random/strided/poisson]: https://www.npmjs.com/package/@stdlib/random-strided-poisson

[@stdlib/random/strided/randu]: https://www.npmjs.com/package/@stdlib/random-strided-randu

[@stdlib/random/strided/rayleigh]: https://www.npmjs.com/package/@stdlib/random-strided-rayleigh

[@stdlib/random/strided/t]: https://www.npmjs.com/package/@stdlib/random-strided-t

[@stdlib/random/strided/uniform]: https://www.npmjs.com/package/@stdlib/random-strided-uniform

[@stdlib/random/strided/weibull]: https://www.npmjs.com/package/@stdlib/random-strided-weibull


  
    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!

`Strided`

[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url]

> Pseudorandom number generator (PRNG) strided array functions.

`Installation`

``bash npm install @stdlib/random-strided `

`Usage`

`javascript var ns = require( '@stdlib/random-strided' ); `

#### ns

Namespace containing strided array pseudorandom number generator (PRNG) functions.

`javascript var o = ns; // returns {...} `

The namespace contains the following:

- [arcsine( N, a, sa, b, sb, out, so[, options] )][@stdlib/random/strided/arcsine]: fill a strided array with pseudorandom numbers drawn from an arcsine distribution. - [bernoulli( N, p, sp, out, so )][@stdlib/random/strided/bernoulli]: fill a strided array with pseudorandom numbers drawn from a Bernoulli distribution. - [beta( N, alpha, sa, beta, sb, out, so[, options] )][@stdlib/random/strided/beta]: fill a strided array with pseudorandom numbers drawn from a beta distribution. - [betaprime( N, alpha, sa, beta, sb, out, so[, options] )][@stdlib/random/strided/betaprime]: fill a strided array with pseudorandom numbers drawn from a beta prime distribution. - [chi( N, k, sk, out, so )][@stdlib/random/strided/chi]: fill a strided array with pseudorandom numbers drawn from a chi distribution. - [chisquare( N, k, sk, out, so )][@stdlib/random/strided/chisquare]: fill a strided array with pseudorandom numbers drawn from a chi-square distribution. - [cosine( N, mu, sm, s, ss, out, so[, options] )][@stdlib/random/strided/cosine]: fill a strided array with pseudorandom numbers drawn from a raised cosine distribution. - [discreteUniform( N, a, sa, b, sb, out, so[, options] )][@stdlib/random/strided/discrete-uniform]: fill a strided array with pseudorandom numbers drawn from a discrete uniform distribution. - [exponential( N, lambda, sl, out, so )][@stdlib/random/strided/exponential]: fill a strided array with pseudorandom numbers drawn from an exponential distribution. - [gamma( N, alpha, sa, beta, sb, out, so[, options] )][@stdlib/random/strided/gamma]: fill a strided array with pseudorandom numbers drawn from a gamma distribution. - [geometric( N, p, sp, out, so )][@stdlib/random/strided/geometric]: fill a strided array with pseudorandom numbers drawn from a geometric distribution. - [invgamma( N, alpha, sa, beta, sb, out, so[, options] )][@stdlib/random/strided/invgamma]: fill a strided array with pseudorandom numbers drawn from an inverse gamma distribution. - [lognormal( N, mu, sm, sigma, ss, out, so[, options] )][@stdlib/random/strided/lognormal]: fill a strided array with pseudorandom numbers drawn from a lognormal distribution. - [minstdShuffle( N, out, so[, options] )][@stdlib/random/strided/minstd-shuffle]: fill a strided array with pseudorandom numbers generated using a linear congruential pseudorandom number generator (LCG) whose output is shuffled. - [minstd( N, out, so[, options] )][@stdlib/random/strided/minstd]: fill a strided array with pseudorandom numbers generated using a linear congruential pseudorandom number generator (LCG). - [mt19937( N, out, so[, options] )][@stdlib/random/strided/mt19937]: fill a strided array with pseudorandom numbers generated using a 32-bit Mersenne Twister pseudorandom number generator. - [normal( N, mu, sm, sigma, ss, out, so[, options] )][@stdlib/random/strided/normal]: fill a strided array with pseudorandom numbers drawn from a normal distribution. - [poisson( N, lambda, sl, out, so )][@stdlib/random/strided/poisson]: fill a strided array with pseudorandom numbers drawn from a Poisson distribution. - [randu( N, out, so[, options] )][@stdlib/random/strided/randu]: fill a strided array with uniformly distributed pseudorandom numbers between 0 and 1. - [rayleigh( N, sigma, ss, out, so )][@stdlib/random/strided/rayleigh]: fill a strided array with pseudorandom numbers drawn from a Rayleigh distribution. - [t( N, v, sv, out, so )][@stdlib/random/strided/t]: fill a strided array with pseudorandom numbers drawn from a Student's t-distribution. - [uniform( N, a, sa, b, sb, out, so[, options] )][@stdlib/random/strided/uniform]: fill a strided array with pseudorandom numbers drawn from a continuous uniform distribution. - [weibull( N, k, sk, lambda, sl, out, so[, options] )][@stdlib/random/strided/weibull]: fill a strided array with pseudorandom numbers drawn from a Weibull distribution.

`Examples`

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

console.log( objectKeys( ns ) ); `