npm package: @stdlib/stats-base-dists-exponential-quantile
Description: Exponential distribution quantile function.
Author: The Stdlib Authors
Latest version: 0.3.1

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# Quantile Function

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

> [Exponential][exponential-distribution] distribution [quantile function][quantile-function].

<section class="intro">

The [quantile function][quantile-function] for an [exponential][exponential-distribution] random variable is

<!-- <equation class="equation" label="eq:exponential_quantile_function" align="center" raw="Q(p;\lambda) = \frac{-\ln(1-p)}{\lambda}" alt="Quantile function for an exponential distribution."> -->

<div class="equation" align="center" data-raw-text="Q(p;\lambda) = \frac{-\ln(1-p)}{\lambda}" data-equation="eq:exponential_quantile_function">
    <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@51534079fef45e990850102147e8945fb023d1d0/lib/node_modules/@stdlib/stats/base/dists/exponential/quantile/docs/img/equation_exponential_quantile_function.svg" alt="Quantile function for an exponential distribution.">
    <br>
</div>

<!-- </equation> -->

for `0 <= p <= 1`, where `λ` is the rate parameter.

</section>

<!-- /.intro -->

<section class="installation">

## Installation

```bash
npm install @stdlib/stats-base-dists-exponential-quantile
```

</section>

<section class="usage">

## Usage

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

#### quantile( p, lambda )

Evaluates the [quantile function][quantile-function] for an [exponential][exponential-distribution] distribution with rate parameter `lambda`.

```javascript
var y = quantile( 0.5, 0.1 );
// returns ~6.931

y = quantile( 0.2, 4.0 );
// returns ~0.056
```

If provided a probability `p` outside the interval `[0,1]`, the function returns `NaN`.

```javascript
var y = quantile( 1.9, 1.0 );
// returns NaN

y = quantile( -0.1, 1.0 );
// returns NaN
```

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

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

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

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

```javascript
var y = quantile( 0.4, -1.0 );
// returns NaN
```

#### quantile.factory( lambda )

Returns a function for evaluating the [quantile function][quantile-function] of an [exponential][exponential-distribution] distribution with rate parameter `lambda`.

```javascript
var myquantile = quantile.factory( 4.0 );

var y = myquantile( 0.2 );
// returns ~0.056

y = myquantile( 0.9 );
// returns ~0.576
```

</section>

<!-- /.usage -->

<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 quantile = require( '@stdlib/stats-base-dists-exponential-quantile' );

var opts = {
    'dtype': 'float64'
};
var p = uniform( 10, 0.0, 1.0, opts );
var lambda = uniform( 10, 0.0, 10.0, opts );

logEachMap( 'p: %0.4f, λ: %0.4f, Q(p;λ): %0.4f', p, lambda, quantile );
```

</section>

<!-- /.examples -->

<!-- C interface documentation. -->

* * *

<section class="c">

## 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/exponential/quantile.h"
```

#### stdlib_base_dists_exponential_quantile( p, lambda )

Evaluates the [quantile function][quantile-function] for an [exponential][exponential-distribution] distribution with rate parameter `lambda`.

```c
double out = stdlib_base_dists_exponential_quantile( 0.8, 1.0 );
// returns ~1.609
```

The function accepts the following arguments:

-   **p**: `[in] double` probability.
-   **lambda**: `[in] double` rate parameter.

```c
double stdlib_base_dists_exponential_quantile( const double p, const double lambda );
```

</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/exponential/quantile.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 lambda;
    double y;
    double p;
    int i;

    for ( i = 0; i < 25; i++ ) {
        p = random_uniform( 0.0, 1.0 );
        lambda = random_uniform( 0.0, 20.0 );
        y = stdlib_base_dists_exponential_quantile( p, lambda );
        printf( "p: %lf, λ: %lf, Q(p;λ): %lf\n", p, lambda, y );
    }
}
```

</section>

<!-- /.examples -->

</section>

<!-- /.c -->

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<section class="references">

</section>

<!-- /.references -->

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<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].

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

[quantile-function]: https://en.wikipedia.org/wiki/Quantile_function

[exponential-distribution]: https://en.wikipedia.org/wiki/Exponential_distribution

</section>

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