compute-kurtosis

Excess Kurtosis

Computes the sample excess kurtosis of an array of values.

Installation

$ npm install compute-kurtosis

For use in the browser, use browserify.

Usage

To use the module,

var kurtosis = require( 'compute-kurtosis' );

Examples

var data = new Array( 100 );
 
for ( var i = 0; i < data.length; i++ ) {
    data[ i ] = Math.random()*100;
}
 
console.log( kurtosis( data ) );
// A uniform distribution should have an excess kurtosis around -1.2.

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

$ node ./examples/index.js

Notes

The formula for computing the sample excess kurtosis comes from

Jones and Gill (1998). Comparing measures of sample skewness and kurtosis. The Statistician. DOI: 10.1111/1467-9884.00122

The test data comes from Measures of Shape: Skewness and Kurtosis by Stan Brown.

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.

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Copyright

Copyright © 2014. Athan Reines.