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!

Probability Density Function

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

> [Weibull][weibull-distribution] distribution probability density function (PDF).

The [probability density function][pdf] (PDF) for a [Weibull][weibull-distribution] random variable is

Probability density function (PDF) for a Weibull distribution.

where lambda > 0 and k > 0 are the respective [scale][scale] and [shape][shape] parameters of the distribution.

Installation

``bash npm install @stdlib/stats-base-dists-weibull-pdf`

`Usage`

`javascript var pdf = require( '@stdlib/stats-base-dists-weibull-pdf' );`

#### pdf( x, k, lambda )

Evaluates the [probability density function][pdf] (PDF) for a [Weibull][weibull-distribution] distribution with [shape parameter][shape] k and [scale parameter][scale] lambda.

`javascript var y = pdf( 2.0, 1.0, 0.5 ); // returns ~0.037

y = pdf( -1.0, 4.0, 2.0 ); // returns 0.0`

If provided NaN as any argument, the function returns NaN.

`javascript var y = pdf( NaN, 0.0, 1.0 ); // returns NaN

y = pdf( 0.0, NaN, 1.0 ); // returns NaN

y = pdf( 0.0, 0.0, NaN ); // returns NaN`

If provided k <= 0, the function returns NaN.

`javascript var y = pdf( 2.0, 0.0, 1.0 ); // returns NaN

y = pdf( 2.0, -1.0, 1.0 ); // returns NaN`

If provided lambda <= 0, the function returns NaN.

`javascript var y = pdf( 2.0, 1.0, 0.0 ); // returns NaN

y = pdf( 2.0, 1.0, -1.0 ); // returns NaN`

#### pdf.factory( k, lambda )

Returns a function for evaluating the [PDF][pdf] for a [Weibull][weibull-distribution] distribution with [shape parameter][shape] k and [scale parameter][scale] lambda.

`javascript var mypdf = pdf.factory( 2.0, 10.0 );

var y = mypdf( 12.0 ); // returns ~0.057

y = mypdf( 5.0 ); // returns ~0.078`

`Examples`

`javascript var uniform = require( '@stdlib/random-array-uniform' ); var logEachMap = require( '@stdlib/console-log-each-map' ); var pdf = require( '@stdlib/stats-base-dists-weibull-pdf' );

var opts = { 'dtype': 'float64' }; var lambda = uniform( 10, 0.0, 10.0, opts ); var k = uniform( 10, 0.0, 10.0, opts ); var x = uniform( 10, 0.0, 10.0, opts );

logEachMap( 'x: %0.4f, k: %0.4f, λ: %0.4f, f(x;k,λ): %0.4f', x, k, lambda, pdf );`

*

`C APIs`

`$3`

`c #include "stdlib/stats/base/dists/weibull/pdf.h"`

#### stdlib_base_dists_weibull_pdf( x, k, lambda )

Evaluates the [probability density function][pdf] (PDF) for a [Weibull][weibull-distribution] distribution with [shape parameter][shape] k and [scale parameter][scale] lambda.

`c double out = stdlib_base_dists_weibull_pdf( 2.0, 1.0, 1.0 ); // returns ~0.037`

The function accepts the following arguments:

- x: [in] doubleinput value. - k:[in] doubleshape parameter. - lambda:[in] double scale parameter.

`c double stdlib_base_dists_weibull_pdf( const double x, const double k, const double lambda );`

`$3`

`c #include "stdlib/stats/base/dists/weibull/pdf.h" #include #include

static double random_uniform( const double min, const double max ) { double v = (double)rand() / ( (double)RAND_MAX + 1.0 ); return min + ( v*(max-min) ); }

int main( void ) { double lambda; double x; double k; double y; int i;

for ( i = 0; i < 25; i++ ) { x = random_uniform( 0.0, 10.0 ); lambda = random_uniform( 0.0, 10.0 ); k = random_uniform( 0.0, 10.0 ); y = stdlib_base_dists_weibull_pdf( x, k, lambda ); printf( "x: %lf, k: %lf, λ: %lf, f(x;k,λ): %lf\n", x, k, lambda, y ); } }`

*

`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/stats-base-dists-weibull-pdf.svg [npm-url]: https://npmjs.org/package/@stdlib/stats-base-dists-weibull-pdf

[test-image]: https://github.com/stdlib-js/stats-base-dists-weibull-pdf/actions/workflows/test.yml/badge.svg?branch=v0.3.1 [test-url]: https://github.com/stdlib-js/stats-base-dists-weibull-pdf/actions/workflows/test.yml?query=branch:v0.3.1

[coverage-image]: https://img.shields.io/codecov/c/github/stdlib-js/stats-base-dists-weibull-pdf/main.svg [coverage-url]: https://codecov.io/github/stdlib-js/stats-base-dists-weibull-pdf?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/stats-base-dists-weibull-pdf/tree/deno [deno-readme]: https://github.com/stdlib-js/stats-base-dists-weibull-pdf/blob/deno/README.md [umd-url]: https://github.com/stdlib-js/stats-base-dists-weibull-pdf/tree/umd [umd-readme]: https://github.com/stdlib-js/stats-base-dists-weibull-pdf/blob/umd/README.md [esm-url]: https://github.com/stdlib-js/stats-base-dists-weibull-pdf/tree/esm [esm-readme]: https://github.com/stdlib-js/stats-base-dists-weibull-pdf/blob/esm/README.md [branches-url]: https://github.com/stdlib-js/stats-base-dists-weibull-pdf/blob/main/branches.md

[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/stats-base-dists-weibull-pdf/main/LICENSE

[pdf]: https://en.wikipedia.org/wiki/Probability_density_function

[weibull-distribution]: https://en.wikipedia.org/wiki/Weibull_distribution

[shape]: https://en.wikipedia.org/wiki/Shape_parameter

[scale]: https://en.wikipedia.org/wiki/Scale_parameter


  
    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!

`Probability Density Function`

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

> [Weibull][weibull-distribution] distribution probability density function (PDF).

The [probability density function][pdf] (PDF) for a [Weibull][weibull-distribution] random variable is

where lambda > 0 and k > 0 are the respective [scale][scale] and [shape][shape] parameters of the distribution.

`Installation`

``bash npm install @stdlib/stats-base-dists-weibull-pdf `

`Usage`

`javascript var pdf = require( '@stdlib/stats-base-dists-weibull-pdf' ); `

#### pdf( x, k, lambda )

Evaluates the [probability density function][pdf] (PDF) for a [Weibull][weibull-distribution] distribution with [shape parameter][shape] k and [scale parameter][scale] lambda.

`javascript var y = pdf( 2.0, 1.0, 0.5 ); // returns ~0.037

y = pdf( -1.0, 4.0, 2.0 ); // returns 0.0 `

If provided NaN as any argument, the function returns NaN.

`javascript var y = pdf( NaN, 0.0, 1.0 ); // returns NaN

y = pdf( 0.0, NaN, 1.0 ); // returns NaN

y = pdf( 0.0, 0.0, NaN ); // returns NaN `

If provided k <= 0, the function returns NaN.

`javascript var y = pdf( 2.0, 0.0, 1.0 ); // returns NaN

y = pdf( 2.0, -1.0, 1.0 ); // returns NaN `

If provided lambda <= 0, the function returns NaN.

`javascript var y = pdf( 2.0, 1.0, 0.0 ); // returns NaN

y = pdf( 2.0, 1.0, -1.0 ); // returns NaN `

#### pdf.factory( k, lambda )

Returns a function for evaluating the [PDF][pdf] for a [Weibull][weibull-distribution] distribution with [shape parameter][shape] k and [scale parameter][scale] lambda.

`javascript var mypdf = pdf.factory( 2.0, 10.0 );

var y = mypdf( 12.0 ); // returns ~0.057

y = mypdf( 5.0 ); // returns ~0.078 `

`Examples`

`javascript var uniform = require( '@stdlib/random-array-uniform' ); var logEachMap = require( '@stdlib/console-log-each-map' ); var pdf = require( '@stdlib/stats-base-dists-weibull-pdf' );

var opts = { 'dtype': 'float64' }; var lambda = uniform( 10, 0.0, 10.0, opts ); var k = uniform( 10, 0.0, 10.0, opts ); var x = uniform( 10, 0.0, 10.0, opts );

logEachMap( 'x: %0.4f, k: %0.4f, λ: %0.4f, f(x;k,λ): %0.4f', x, k, lambda, pdf ); `

*

`C APIs`

`$3`

`c #include "stdlib/stats/base/dists/weibull/pdf.h" `

#### stdlib_base_dists_weibull_pdf( x, k, lambda )

Evaluates the [probability density function][pdf] (PDF) for a [Weibull][weibull-distribution] distribution with [shape parameter][shape] k and [scale parameter][scale] lambda.

`c double out = stdlib_base_dists_weibull_pdf( 2.0, 1.0, 1.0 ); // returns ~0.037 `

The function accepts the following arguments:

- x: [in] double input value. - k: [in] double shape parameter. - lambda: [in] double scale parameter.

`c double stdlib_base_dists_weibull_pdf( const double x, const double k, const double lambda ); `

`$3`

`c #include "stdlib/stats/base/dists/weibull/pdf.h" #include #include

static double random_uniform( const double min, const double max ) { double v = (double)rand() / ( (double)RAND_MAX + 1.0 ); return min + ( v*(max-min) ); }

int main( void ) { double lambda; double x; double k; double y; int i;

for ( i = 0; i < 25; i++ ) { x = random_uniform( 0.0, 10.0 ); lambda = random_uniform( 0.0, 10.0 ); k = random_uniform( 0.0, 10.0 ); y = stdlib_base_dists_weibull_pdf( x, k, lambda ); printf( "x: %lf, k: %lf, λ: %lf, f(x;k,λ): %lf\n", x, k, lambda, y ); } } `

*

`Notice`

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/stats-base-dists-weibull-pdf.svg [npm-url]: https://npmjs.org/package/@stdlib/stats-base-dists-weibull-pdf

[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

[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/stats-base-dists-weibull-pdf/main/LICENSE

[pdf]: https://en.wikipedia.org/wiki/Probability_density_function

[weibull-distribution]: https://en.wikipedia.org/wiki/Weibull_distribution

[shape]: https://en.wikipedia.org/wiki/Shape_parameter

[scale]: https://en.wikipedia.org/wiki/Scale_parameter