npm package: @stdlib/stats-base-dists-kumaraswamy-logcdf
Description: Natural logarithm of the cumulative distribution function (CDF)for a Kumaraswamy's double bounded distribution.
Author: The Stdlib Authors
Latest version: 0.2.2

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# Logarithm of Cumulative Distribution Function

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> Evaluate the natural logarithm of the [cumulative distribution function][cdf] for a [Kumaraswamy's double bounded][kumaraswamy-distribution] distribution.

<section class="intro">

The [cumulative distribution function][cdf] for a [Kumaraswamy's double bounded][kumaraswamy-distribution] random variable is

<!-- <equation class="equation" label="eq:kumaraswamy_cdf" align="center" raw="F(x;a,b) = 1-(1-x^{a})^{b}" alt="Cumulative distribution function for a Kumaraswamy's double bounded distribution."> -->

<div class="equation" align="center" data-raw-text="F(x;a,b) = 1-(1-x^{a})^{b}" data-equation="eq:kumaraswamy_cdf">
    <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@51534079fef45e990850102147e8945fb023d1d0/lib/node_modules/@stdlib/stats/base/dists/kumaraswamy/logcdf/docs/img/equation_kumaraswamy_cdf.svg" alt="Cumulative distribution function for a Kumaraswamy's double bounded distribution.">
    <br>
</div>

<!-- </equation> -->

where `a > 0` is the first shape parameter and `b > 0` is the second shape parameter.

</section>

<!-- /.intro -->

<section class="installation">

## Installation

```bash
npm install @stdlib/stats-base-dists-kumaraswamy-logcdf
```

</section>

<section class="usage">

## Usage

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

#### logcdf( x, a, b )

Evaluates the natural logarithm of the [cumulative distribution function][cdf] (CDF) for a [Kumaraswamy's double bounded][kumaraswamy-distribution] distribution with parameters `a` (first shape parameter) and `b` (second shape parameter).

```javascript
var y = logcdf( 0.5, 1.0, 1.0 );
// returns ~-0.693

y = logcdf( 0.5, 2.0, 4.0 );
// returns ~-0.38

y = logcdf( 0.2, 2.0, 2.0 );
// returns ~-2.546

y = logcdf( 0.8, 4.0, 4.0 );
// returns ~-0.13

y = logcdf( -0.5, 4.0, 2.0 );
// returns -Infinity

y = logcdf( -Infinity, 4.0, 2.0 );
// returns -Infinity

y = logcdf( 1.5, 4.0, 2.0 );
// returns 0.0

y = logcdf( +Infinity, 4.0, 2.0 );
// returns 0.0
```

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

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

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

y = logcdf( 0.0, 1.0, NaN );
// returns NaN
```

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

```javascript
var y = logcdf( 2.0, -1.0, 0.5 );
// returns NaN

y = logcdf( 2.0, 0.0, 0.5 );
// returns NaN
```

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

```javascript
var y = logcdf( 2.0, 0.5, -1.0 );
// returns NaN

y = logcdf( 2.0, 0.5, 0.0 );
// returns NaN
```

#### logcdf.factory( a, b )

Returns a function for evaluating the natural logarithm of the [cumulative distribution function][cdf] for a [Kumaraswamy's double bounded][kumaraswamy-distribution] distribution with parameters `a` (first shape parameter) and `b` (second shape parameter).

```javascript
var mylogcdf = logcdf.factory( 0.5, 0.5 );

var y = mylogcdf( 0.8 );
// returns ~-0.393

y = mylogcdf( 0.3 );
// returns ~-1.116
```

</section>

<!-- /.usage -->

<section class="notes">

## Notes

-   In virtually all cases, using the `logpdf` or `logcdf` functions is preferable to manually computing the logarithm of the `pdf` or `cdf`, respectively, since the latter is prone to overflow and underflow.

</section>

<!-- /.notes -->

<section class="examples">

## Examples

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

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

var opts = {
    'dtype': 'float64'
};
var x = uniform( 10, 0.0, 1.0, opts );
var a = uniform( 10, EPS, 5.0, opts );
var b = uniform( 10, EPS, 5.0, opts );

logEachMap( 'x: %0.4f, a: %0.4f, b: %0.4f, ln(F(x;a,b)): %0.4f', x, a, b, logcdf );
```

</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/kumaraswamy/logcdf.h"
```

#### stdlib_base_dists_kumaraswamy_logcdf( x, a, b )

Evaluates the natural logarithm of the cumulative distribution function (CDF) for a Kumaraswamy's double bounded distribution.

```c
double out = stdlib_base_dists_kumaraswamy_logcdf( 0.5, 1.0, 1.0 );
// returns ~-0.693
```

The function accepts the following arguments:

-   **x**: `[in] double` input value.
-   **a**: `[in] double` first shape parameter.
-   **b**: `[in] double` second shape parameter.

```c
double stdlib_base_dists_kumaraswamy_logcdf( const double x, const double a, const double b );
```

</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/kumaraswamy/logcdf.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 x;
    double a;
    double b;
    double y;
    int i;
    for ( i = 0; i < 25; i++ ) {
        x = random_uniform( 0, 1.0 );
        a = random_uniform( 0, 5.0 );
        b = random_uniform( 0, 5.0 );
        y = stdlib_base_dists_kumaraswamy_logcdf( x, a, b );
        printf( "x: %lf, a: %lf, b: %lf, ln(F(x;a,b)): %lf\n", x, a, b, y );
    }
}
```

</section>

<!-- /.examples -->

</section>

<!-- /.c -->

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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].

#### Community

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---

## License

See [LICENSE][stdlib-license].


## Copyright

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

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[kumaraswamy-distribution]: https://en.wikipedia.org/wiki/Kumaraswamy_distribution

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

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