@stdlib/stats-base-dists-exponential NPM

Exponential

Exponential distribution.

Installation

npm install @stdlib/stats-base-dists-exponential

Usage

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

exponential

Exponential distribution.

var dist = exponential;
// returns {...}

The namespace contains the following distribution functions:

cdf( x, lambda ): exponential distribution cumulative distribution function.
logcdf( x, lambda ): evaluate the natural logarithm of the cumulative distribution function for an exponential distribution.
logpdf( x, lambda ): evaluate the natural logarithm of the probability density function (PDF) for an exponential distribution.
mgf( t, lambda ): exponential distribution moment-generating function (MGF).
pdf( x, lambda ): exponential distribution probability density function (PDF).
quantile( p, lambda ): exponential distribution quantile function.

The namespace contains the following functions for calculating distribution properties:

entropy( lambda ): exponential distribution differential entropy.
kurtosis( lambda ): exponential distribution excess kurtosis.
mean( lambda ): exponential distribution expected value.
median( lambda ): exponential distribution median.
mode( lambda ): exponential distribution mode.
skewness( lambda ): exponential distribution skewness.
stdev( lambda ): exponential distribution standard deviation.
variance( lambda ): exponential distribution variance.

The namespace contains a constructor function for creating an exponential distribution object.

Exponential( [lambda] ): exponential distribution constructor.

var Exponential = require( '@stdlib/stats-base-dists-exponential' ).Exponential;

var dist = new Exponential( 2.0 );

var y = dist.logpdf( 0.8 );
// returns ~-0.907

Examples

var objectKeys = require( '@stdlib/utils-keys' );
var exponential = require( '@stdlib/stats-base-dists-exponential' );

console.log( objectKeys( exponential ) );

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.