distributions-normal v2.0.2

Normal

Normal distribution.

Installation

$ npm install distributions-normal

For use in the browser, use browserify.

Usage

To use the module,

var createDist = require( 'distributions-normal' );

To create a normal distribution,

var normal = createDist();

The distribution is configurable and has the following methods...

normal.support()

Returns the distribution support, which is the set of all real values.

var support = normal.support();
// returns [-inf, inf]

normal.mean( value )

This method is a setter/getter. If no value is provided, returns the distribution mean. To set the distribution mean,

normal.mean( 100 );

The default distribution mean is 0.

normal.variance( value )

This method is a setter/getter. If no value is provided, returns the distribution variance. To set the distribution variance,

normal.variance( 25 );

The default distribution variance is 1.

normal.median()

Returns the distribution median, which is equal to the distribution mean.

var median = normal.median();
// equals normal.mean()

normal.mode()

Returns the distribution mode, which is equal to the distribution mean.

var mode = normal.mode();
// equals normal.mean()

normal.skewness()

Returns the distribution skewness, which is equal to 0.

var skewness = normal.skewness();
// returns 0

normal.ekurtosis()

Returns the distribution excess kurtosis, which is equal to 0.

var excess = normal.ekurtosis();
// returns 0

normal.information()

Returns the Fisher information.

var info = normal.information();
// returns [...]

normal.entropy()

Returns the distribution's differential entropy.

var entropy = normal.entropy();
// approx 1.42 for mu=0, variance=1

normal.pdf( arr )

If a vector is not provided, returns the probability density function (PDF). If a vector is provided, evaluates the PDF for each vector element.

var data = [ -1, -0.5, 0, 0.5, 1 ];

var pdf = normal.pdf( data );
// returns [...]

normal.cdf( arr )

If a vector is not provided, returns the cumulative density function (CDF). If a vector is provided, evaluates the CDF for each vector element.

var data = [ -1, -0.5, 0, 0.5, 1 ];

var cdf = normal.cdf( data );
// returns [...]

normal.inv( arr )

If a cumulative probability vector is not provided, returns the inverse cumulative distribution function (aka the quantile function). If a cumulative probability vector is provided, evaluates the quantile function for each vector element.

var probs = [ 0.025, 0.5, 0.975 ];

var quantiles = normal.inv( probs );
// returns [...]

Note: all vector values must exist on the interval [0, 1].

Examples

var createDist = require( 'distributions-normal' ),
	median = require( 'compute-median' ),
	mean = require( 'compute-mean' );

// Define the distribution parameters...
var mu = 100,
	s2 = 25,
	xLow = 0,
	xHigh = 200;

// Create a vector...
var vec = new Array( 1000 ),
	len = vec.length,
	inc;

inc = ( xHigh - xLow ) / len;

for ( var i = 0; i < len; i++ ) {
	vec[ i ] = inc*i + xLow;
}

// Create a normal distribution and configure...
var normal = createDist()
	.mean( mu )
	.variance( s2 );

// Evaluate the probability density function over the vector...
var pdf = normal.pdf( vec );

// Find the max...
var max = pdf[ 0 ],
	idx = 0;
for ( var j = 1; j < pdf.length; j++ ) {
	if ( pdf[ j ] > max ) {
		max = pdf[ j ];
		idx = j;
	}
}
console.log( 'Max: ' + vec[ idx ] );

// Calculate the median...
console.log( 'Median: ' + median( vec ) );

// Calculate the mean...
console.log( 'Mean: ' + mean( vec ) );

// Evaluate the quantile function for canonical cumulative probability values...
var quantiles = normal.inv( [ 0.025, 0.5, 0.975 ] );

console.log( quantiles );

To run the example code from the top-level application directory,

$ node ./examples/index.js

Tests

Unit

Unit tests use the Mocha test framework with Chai assertions. To run the tests, execute the following command in the top-level application directory:

$ make test

All new feature development should have corresponding unit tests to validate correct functionality.

Test Coverage

This repository uses Istanbul as its code coverage tool. To generate a test coverage report, execute the following command in the top-level application directory:

$ make test-cov

Istanbul creates a ./reports/coverage directory. To access an HTML version of the report,

$ open reports/coverage/lcov-report/index.html

License

MIT license.

Copyright

Copyright © 2014. Athan Reines.