@stdlib/stats-base-dists-kumaraswamy-ctor

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Kumaraswamy

Kumaraswamy's double bounded distribution constructor.

Installation

npm install @stdlib/stats-base-dists-kumaraswamy-ctor

Usage

var Kumaraswamy = require( '@stdlib/stats-base-dists-kumaraswamy-ctor' );

Kumaraswamy( [a, b] )

Returns a Kumaraswamy's double bounded distribution object.

var kumaraswamy = new Kumaraswamy();

var mu = kumaraswamy.mean;
// returns 0.5

By default, a = 1.0 and b = 1.0. To create a distribution having a different a (first shape parameter) and b (second shape parameter), provide the corresponding arguments.

var kumaraswamy = new Kumaraswamy( 2.0, 4.0 );

var mu = kumaraswamy.mean;
// returns ~0.406

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kumaraswamy

A Kumaraswamy's double bounded distribution object has the following properties and methods...

Writable Properties

kumaraswamy.a

First shape parameter of the distribution. a must be a positive number.

var kumaraswamy = new Kumaraswamy();

var a = kumaraswamy.a;
// returns 1.0

kumaraswamy.a = 3.0;

a = kumaraswamy.a;
// returns 3.0

kumaraswamy.b

Second shape parameter of the distribution. b must be a positive number.

var kumaraswamy = new Kumaraswamy( 2.0, 4.0 );

var b = kumaraswamy.b;
// returns 4.0

kumaraswamy.b = 3.0;

b = kumaraswamy.b;
// returns 3.0

---

Computed Properties

Kumaraswamy.prototype.kurtosis

Returns the excess kurtosis.

var kumaraswamy = new Kumaraswamy( 4.0, 12.0 );

var kurtosis = kumaraswamy.kurtosis;
// returns ~2.704

Kumaraswamy.prototype.mean

Returns the expected value.

var kumaraswamy = new Kumaraswamy( 4.0, 12.0 );

var mu = kumaraswamy.mean;
// returns ~0.481

Kumaraswamy.prototype.mode

Returns the mode.

var kumaraswamy = new Kumaraswamy( 4.0, 12.0 );

var mode = kumaraswamy.mode;
// returns ~0.503

Kumaraswamy.prototype.skewness

Returns the skewness.

var kumaraswamy = new Kumaraswamy( 4.0, 12.0 );

var skewness = kumaraswamy.skewness;
// returns ~-0.201

Kumaraswamy.prototype.stdev

Returns the standard deviation.

var kumaraswamy = new Kumaraswamy( 4.0, 12.0 );

var s = kumaraswamy.stdev;
// returns ~0.13

Kumaraswamy.prototype.variance

Returns the variance.

var kumaraswamy = new Kumaraswamy( 4.0, 12.0 );

var s2 = kumaraswamy.variance;
// returns ~0.017

---

Methods

Kumaraswamy.prototype.cdf( x )

Evaluates the cumulative distribution function (CDF).

var kumaraswamy = new Kumaraswamy( 2.0, 4.0 );

var y = kumaraswamy.cdf( 0.5 );
// returns ~0.684

Kumaraswamy.prototype.logcdf( x )

Evaluates the natural logarithm of the cumulative distribution function (CDF).

var kumaraswamy = new Kumaraswamy( 2.0, 4.0 );

var y = kumaraswamy.logcdf( 0.5 );
// returns ~-0.38

Kumaraswamy.prototype.logpdf( x )

Evaluates the natural logarithm of the probability density function (PDF).

var kumaraswamy = new Kumaraswamy( 2.0, 4.0 );

var y = kumaraswamy.logpdf( 0.8 );
// returns ~-1.209

Kumaraswamy.prototype.pdf( x )

Evaluates the probability density function (PDF).

var kumaraswamy = new Kumaraswamy( 2.0, 4.0 );

var y = kumaraswamy.pdf( 0.8 );
// returns ~0.299

Kumaraswamy.prototype.quantile( p )

Evaluates the quantile function at probability p.

var kumaraswamy = new Kumaraswamy( 2.0, 4.0 );

var y = kumaraswamy.quantile( 0.5 );
// returns ~0.399

y = kumaraswamy.quantile( 1.9 );
// returns NaN

---

Examples

var Kumaraswamy = require( '@stdlib/stats-base-dists-kumaraswamy-ctor' );

var kumaraswamy = new Kumaraswamy( 2.0, 4.0 );

var mu = kumaraswamy.mean;
// returns ~0.406

var mode = kumaraswamy.mode;
// returns ~0.378

var s2 = kumaraswamy.variance;
// returns ~0.035

var y = kumaraswamy.cdf( 0.8 );
// returns ~0.983

---

Notice

This package is part of 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, see the main project repository.

Community

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License

See LICENSE.

Copyright

Copyright © 2016-2024. The Stdlib Authors.