@stdlib/stats-base-dists-lognormal NPM

Lognormal

Lognormal distribution.

Installation

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

Usage

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

lognormal

Lognormal distribution.

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

The namespace contains the following distribution functions:

cdf( x, mu, sigma ): lognormal distribution cumulative distribution function.
logcdf( x, mu, sigma ): evaluate the natural logarithm of the cumulative distribution function (CDF) for a lognormal distribution.
logpdf( x, mu, sigma ): evaluate the natural logarithm of the probability density function (PDF) for a lognormal distribution.
pdf( x, mu, sigma ): lognormal distribution probability density function (PDF).
quantile( p, mu, sigma ): lognormal distribution quantile function.

The namespace contains the following functions for calculating distribution properties:

entropy( mu, sigma ): lognormal distribution differential entropy.
kurtosis( mu, sigma ): lognormal distribution excess kurtosis.
mean( mu, sigma ): lognormal distribution expected value.
median( mu, sigma ): lognormal distribution median.
mode( mu, sigma ): lognormal distribution mode.
skewness( mu, sigma ): lognormal distribution skewness.
stdev( mu, sigma ): lognormal distribution standard deviation.
variance( mu, sigma ): lognormal distribution variance.

The namespace contains a constructor function for creating a lognormal distribution object.

LogNormal( [mu, sigma] ): lognormal distribution constructor.

var LogNormal = require( '@stdlib/stats-base-dists-lognormal' ).LogNormal;

var dist = new LogNormal( 2.0, 4.0 );

var y = dist.cdf( 0.5 );
// returns ~0.25

Examples

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

console.log( objectKeys( lognormal ) );

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.