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## essy-distribution

1.1.22 • Public • Published

# Javascript Distributions and Sampling

## Installation

``````npm install essy-distribution
``````

## Description

Defines multiple distributions with methods for random sampling and calculating distribution properties. Sampling functions are largely ported from CERN's cern.jet.random Java package. See the source code for details.

This package was created during the development of Essy Tree to support Monte Carlo simulations.

## Basic Usage (node.js)

``````var dists  = require('essy-distribution');

var normal = new dists.Normal(0, 1);
var mean   = normal.mean();     // 0
var sample = normal.sample();   // eg, 0.2314311234
``````

## Basic Usage (ES2015)

``````import { Normal } from 'essy-distribution';

var normal = new Normal(0, 1);
var mean   = normal.mean();     // 0
var sample = normal.sample();   // eg, 0.2314311234
``````

Or you can load the entire package:

``````import * as dists from 'essy-distribution';

var normal = new dists.Normal(0, 1);
var mean   = normal.mean();     // 0
var sample = normal.sample();   // eg, 0.2314311234
``````

Each distribution defines the following methods:

#### cdf(x {Number})

Cumulative distribution function.

#### mean()

Returns distribution mean.

#### median()

Returns distribution median.

#### pdf(x {Number})

Probability density function.

#### sample([n {Number}] [,generator {Object}])

Samples the distribution. If no arguments are provided or `n = 1` a single sampled value is returned. If `n` is greater than 1, an array of `n` sampled values is returned.

The method accepts an optional `generator` object that defines a method `random()`. If no generator is provided a mersenne-twister is used.

#### variance()

Returns variance.

## Distributions

#### Beta(alpha, beta)

See documentation.

#### Binomial(samples, probability)

See documentation.

See article.

See article.

#### Custom(values)

A custom distribution. The `values` argument should be an array of numbers.

#### Erlang(shape, rate)

See documentation.

#### Exponential(lambda)

See documentation.

See article.

#### Gamma(shape, scale)

See documentation.

See article.

#### Laplace(location, scale)

See documentation.

See article.

#### Logarithmic(probability)

See documentation.

#### Logistic(mean, scale)

See documentation.

#### LogLogistic(scale, shape)

See documentation.

#### LogNormal(mean, se)

See documentation.

#### Normal(mean, se)

See documentation.

See article.

#### Poisson(lambda)

See documentation.

#### Rayleigh(scale)

See documentation.

See article.

#### Triangular(min, mode, max)

See documentation.

#### Uniform(min, max)

See documentation.

#### Weibull(shape, scale)

See documentation.

## Keywords

### Install

`npm i essy-distribution`

31

1.1.22

MIT

### Repository

github.com/essy2017/essy-distribution