npm package: @stdlib/stats-base-dists-chi-cdf
Description: Chi distribution cumulative distribution function (CDF).
Author: The Stdlib Authors
Latest version: 0.3.1

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# Cumulative Distribution Function

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

> [Chi][chi-distribution] distribution [cumulative distribution function][cdf].

<section class="intro">

The [cumulative distribution function][cdf] for a [chi][chi-distribution] random variable is

<!-- <equation class="equation" label="eq:chi_chi_cdf" align="center" raw="F(x;\,k) = P\left(k/2,x^{2}/2\right)" alt="Cumulative distribution function for a chi distribution."> -->

<div class="equation" align="center" data-raw-text="F(x;\,k) = P\left(k/2,x^{2}/2\right)" data-equation="eq:chi_chi_cdf">
    <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@591cf9d5c3a0cd3c1ceec961e5c49d73a68374cb/lib/node_modules/@stdlib/stats/base/dists/chi/cdf/docs/img/equation_chi_chi_cdf.svg" alt="Cumulative distribution function for a chi distribution.">
    <br>
</div>

<!-- </equation> -->

where `k` is the degrees of freedom and `P` is the lower regularized incomplete gamma function.

</section>

<!-- /.intro -->

<section class="installation">

## Installation

```bash
npm install @stdlib/stats-base-dists-chi-cdf
```

</section>

<section class="usage">

## Usage

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

#### cdf( x, k )

Evaluates the [cumulative distribution function][cdf] (CDF) for a [chi][chi-distribution] distribution with degrees of freedom `k`.

```javascript
var y = cdf( 2.0, 1.0 );
// returns ~0.954

y = cdf( 2.0, 3.0 );
// returns ~0.739

y = cdf( 1.0, 0.5 );
// returns ~0.846

y = cdf( -1.0, 2.0 );
// returns 0.0

y = cdf( -Infinity, 4.0 );
// returns 0.0

y = cdf( +Infinity, 4.0 );
// returns 1.0
```

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

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

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

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

```javascript
var y = cdf( 2.0, -2.0 );
// returns NaN
```

If provided `k = 0`, the function evaluates the [CDF][cdf] of a [degenerate distribution][degenerate-distribution] centered at `0`.

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

y = cdf( -2.0, 0.0 );
// returns 0.0

y = cdf( 0.0, 0.0 );
// returns 1.0
```

#### cdf.factory( k )

Returns a function for evaluating the [cumulative distribution function][cdf] for a [chi][chi-distribution] distribution with degrees of freedom `k`.

```javascript
var mycdf = cdf.factory( 3.0 );

var y = mycdf( 6.0 );
// returns ~1.0

y = mycdf( 1.5 );
// returns ~0.478
```

* * *

### C APIs

<!-- 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">

</section>

<!-- /.intro -->

<!-- C usage documentation. -->

<section class="usage">

#### Usage

```c
#include "stdlib/stats/base/dists/chi/cdf.h"
```

#### stdlib_base_dists_chi_cdf( x, k )

Evaluates the cumulative distribution function (CDF) for a chi distribution with degrees of freedom `k`.

```c
double out = stdlib_base_dists_chi_cdf( 2.0, 1.0 );
// returns ~0.954

out = stdlib_base_dists_chi_cdf( 2.0, 3.0 );
// returns ~0.739
```

The function accepts the following arguments:

-   **x**: `[in] double` input value.
-   **k**: `[in] double` degrees of freedom.

```c
double stdlib_base_dists_chi_cdf( const double x, const double k );
```

</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/chi/cdf.h"
#include <stdlib.h>
#include <stdio.h>

static double random_uniform( double a, double b ) {
    double r = ( (double)rand() / ( (double)RAND_MAX + 1.0 ) );
    return a + ( r * ( b - a ) );
}

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

    for ( i = 0; i < 25; i++ ) {
        x = random_uniform( 0.0, 2.0 );
        k = random_uniform( 1.0, 10.0 );
        y = stdlib_base_dists_chi_cdf( x, k );
        printf( "x: %lf, k: %lf, F(x;k): %lf\n", x, k, y );
    }
}
```

</section>

<!-- /.examples -->

</section>

<!-- /.usage -->

<section class="examples">

## Examples

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

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

var opts = {
    'dtype': 'float64'
};
var x = uniform( 20, 0.0, 10.0, opts );
var k = discreteUniform( 20, 0, 10, opts );

logEachMap( 'x: %0.4f, k: %d, F(x;k): %0.4f', x, k, cdf );
```

</section>

<!-- /.examples -->

<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->

<section class="related">

</section>

<!-- /.related -->

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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].

#### Community

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---

## License

See [LICENSE][stdlib-license].


## Copyright

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

</section>

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

[cdf]: https://en.wikipedia.org/wiki/Cumulative_distribution_function

[chi-distribution]: https://en.wikipedia.org/wiki/Chi_distribution

[degenerate-distribution]: https://en.wikipedia.org/wiki/Degenerate_distribution

</section>

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