npm package: @stdlib/stats-base-dists-betaprime-skewness
Description: Beta prime distribution skewness.
Author: The Stdlib Authors
Latest version: 0.3.1

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# Skewness

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

> [Beta prime][betaprime-distribution] distribution [skewness][skewness].

<!-- Section to include introductory text. Make sure to keep an empty line after the intro `section` element and another before the `/section` close. -->

<section class="intro">

The [skewness][skewness] for a [beta prime][betaprime-distribution] random variable with first shape parameter `α` and second shape parameter `β` is

<!-- <equation class="equation" label="eq:betaprime_skewness" align="center" raw="\operatorname{skew}\left( X \right) = \frac{2(2\alpha + \beta - 1)}{\beta - 3}{\sqrt{\frac{\beta - 2}{\alpha (\alpha + \beta - 1)}}}" alt="Skewness for a beta prime distribution."> -->

<div class="equation" align="center" data-raw-text="\operatorname{skew}\left( X \right) = \frac{2(2\alpha + \beta - 1)}{\beta - 3}{\sqrt{\frac{\beta - 2}{\alpha (\alpha + \beta - 1)}}}" data-equation="eq:betaprime_skewness">
    <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@51534079fef45e990850102147e8945fb023d1d0/lib/node_modules/@stdlib/stats/base/dists/betaprime/skewness/docs/img/equation_betaprime_skewness.svg" alt="Skewness for a beta prime distribution.">
    <br>
</div>

<!-- </equation> -->

when `α > 0` and `β > 3`. Otherwise, the skewness is not defined.

</section>

<!-- /.intro -->

<!-- Package usage documentation. -->

<section class="installation">

## Installation

```bash
npm install @stdlib/stats-base-dists-betaprime-skewness
```

</section>

<section class="usage">

## Usage

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

#### skewness( alpha, beta )

Returns the [skewness][skewness] of a [beta prime][betaprime-distribution] distribution with parameters `alpha` (first shape parameter) and `beta` (second shape parameter).

```javascript
var v = skewness( 2.0, 4.0 );
// returns ~6.261

v = skewness( 4.0, 12.0 );
// returns ~1.724

v = skewness( 8.0, 4.0 );
// returns ~5.729
```

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

```javascript
var v = skewness( NaN, 4.0 );
// returns NaN

v = skewness( 2.0, NaN );
// returns NaN
```

If provided `alpha <= 0`, the function returns `NaN`.

```javascript
var v = skewness( 0.0, 4.0 );
// returns NaN

v = skewness( -1.0, 4.0 );
// returns NaN
```

If provided `beta <= 3`, the function returns `NaN`.

```javascript
var v = skewness( 1.0, 2.5 );
// returns NaN

v = skewness( 1.0, 0.0 );
// returns NaN

v = skewness( 1.0, -1.0 );
// returns NaN
```

</section>

<!-- /.usage -->

<!-- Package usage notes. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->

<section class="notes">

</section>

<!-- /.notes -->

<!-- Package usage examples. -->

<section class="examples">

## Examples

<!-- eslint no-undef: "error" -->

```javascript
var uniform = require( '@stdlib/random-array-uniform' );
var logEachMap = require( '@stdlib/console-log-each-map' );
var EPS = require( '@stdlib/constants-float64-eps' );
var skewness = require( '@stdlib/stats-base-dists-betaprime-skewness' );

var opts = {
    'dtype': 'float64'
};
var alpha = uniform( 10, EPS, 10.0, opts );
var beta = uniform( 10, 3.0 + EPS, 10.0, opts );

logEachMap( 'α: %0.4f, β: %0.4f, skew(X;α,β): %0.4f', alpha, beta, skewness );
```

</section>

<!-- /.examples -->

<!-- C interface documentation. -->

* * *

<section class="c">

## C APIs

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<section class="intro">

</section>

<!-- /.intro -->

<!-- C usage documentation. -->

<section class="usage">

### Usage

```c
#include "stdlib/stats/base/dists/betaprime/skewness.h"
```

#### stdlib_base_dists_betaprime_skewness( alpha, beta )

Returns the skewness of a beta prime distribution.

```c
double out = stdlib_base_dists_betaprime_skewness( 2.0, 4.0 );
// returns ~6.261
```

The function accepts the following arguments:

-   **alpha**: `[in] double` first shape parameter.
-   **beta**: `[in] double` second shape parameter.

```c
double stdlib_base_dists_betaprime_skewness( const double alpha, const double beta );
```

</section>

<!-- /.usage -->

<!-- C API usage notes. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->

<section class="notes">

</section>

<!-- /.notes -->

<!-- C API usage examples. -->

<section class="examples">

### Examples

```c
#include "stdlib/stats/base/dists/betaprime/skewness.h"
#include <stdlib.h>
#include <stdio.h>

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 alpha;
    double beta;
    double y;
    int i;

    for ( i = 0; i < 25; i++ ) {
        alpha = random_uniform( 0.0, 20.0 );
        beta = random_uniform( 3.0, 23.0 );
        y = stdlib_base_dists_betaprime_skewness( alpha, beta );
        printf( "α: %lf, β: %lf, skew(X;α,β): %lf\n", alpha, beta, y );
    }
}
```

</section>

<!-- /.examples -->

</section>

<!-- /.c -->

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<section class="references">

</section>

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<section class="related">

</section>

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<section class="main-repo" >

* * *

## 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].

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---

## License

See [LICENSE][stdlib-license].


## Copyright

Copyright &copy; 2016-2026. The Stdlib [Authors][stdlib-authors].

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[betaprime-distribution]: https://en.wikipedia.org/wiki/Beta_prime_distribution

[skewness]: https://en.wikipedia.org/wiki/Skewness

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