About stdlib...
We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.
The library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.
When you use stdlib, you can be absolutely certain that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code out there.
To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!
Erlang
Erlang distribution constructor.
Installation
npm install @stdlib/stats-base-dists-erlang-ctor
Usage
var Erlang = require( '@stdlib/stats-base-dists-erlang-ctor' );
Erlang( [k, lambda] )
Returns an Erlang distribution object.
var erlang = new Erlang();
var mode = erlang.mode;
// returns 0.0
By default, k = 1.0
and lambda = 1.0
. To create a distribution having a different k
(shape parameter) and lambda
(rate parameter), provide the corresponding arguments.
var erlang = new Erlang( 2, 4.0 );
var mode = erlang.mode;
// returns 0.25
erlang
An Erlang distribution object has the following properties and methods...
Writable Properties
erlang.k
Shape parameter of the distribution. k
must be a positive integer.
var erlang = new Erlang();
var k = erlang.k;
// returns 1.0
erlang.k = 3.0;
k = erlang.k;
// returns 3.0
erlang.lambda
Rate parameter of the distribution. lambda
must be a positive number.
var erlang = new Erlang( 2, 4.0 );
var lambda = erlang.lambda;
// returns 4.0
erlang.lambda = 3.0;
lambda = erlang.lambda;
// returns 3.0
Computed Properties
Erlang.prototype.entropy
Returns the differential entropy.
var erlang = new Erlang( 4, 12.0 );
var entropy = erlang.entropy;
// returns ~-0.462
Erlang.prototype.kurtosis
Returns the excess kurtosis.
var erlang = new Erlang( 4, 12.0 );
var kurtosis = erlang.kurtosis;
// returns 1.5
Erlang.prototype.mean
Returns the expected value.
var erlang = new Erlang( 4, 12.0 );
var mu = erlang.mean;
// returns ~0.333
Erlang.prototype.mode
Returns the mode.
var erlang = new Erlang( 4, 12.0 );
var mode = erlang.mode;
// returns 0.25
Erlang.prototype.skewness
Returns the skewness.
var erlang = new Erlang( 4, 12.0 );
var skewness = erlang.skewness;
// returns 1.0
Erlang.prototype.stdev
Returns the standard deviation.
var erlang = new Erlang( 4, 12.0 );
var s = erlang.stdev;
// returns ~0.167
Erlang.prototype.variance
Returns the variance.
var erlang = new Erlang( 4, 12.0 );
var s2 = erlang.variance;
// returns ~0.028
Methods
Erlang.prototype.cdf( x )
Evaluates the cumulative distribution function (CDF).
var erlang = new Erlang( 2, 4.0 );
var y = erlang.cdf( 0.5 );
// returns ~0.594
Erlang.prototype.logpdf( x )
Evaluates the natural logarithm of the probability density function (PDF).
var erlang = new Erlang( 2, 4.0 );
var y = erlang.logpdf( 0.8 );
// returns ~-0.65
Erlang.prototype.mgf( t )
Evaluates the moment-generating function (MGF).
var erlang = new Erlang( 2, 4.0 );
var y = erlang.mgf( 0.5 );
// returns ~1.306
Erlang.prototype.pdf( x )
Evaluates the probability density function (PDF).
var erlang = new Erlang( 2, 4.0 );
var y = erlang.pdf( 0.8 );
// returns ~0.522
Erlang.prototype.quantile( p )
Evaluates the quantile function at probability p
.
var erlang = new Erlang( 2, 4.0 );
var y = erlang.quantile( 0.5 );
// returns ~0.42
y = erlang.quantile( 1.9 );
// returns NaN
Examples
var Erlang = require( '@stdlib/stats-base-dists-erlang-ctor' );
var erlang = new Erlang( 2, 4.0 );
var mu = erlang.mean;
// returns 0.5
var mode = erlang.mode;
// returns 0.25
var s2 = erlang.variance;
// returns 0.125
var y = erlang.cdf( 0.8 );
// returns ~0.829
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
License
See LICENSE.
Copyright
Copyright © 2016-2024. The Stdlib Authors.