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compute-skewness

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Computes the sample skewness of an array of values.

Package Exports

  • compute-skewness

This package does not declare an exports field, so the exports above have been automatically detected and optimized by JSPM instead. If any package subpath is missing, it is recommended to post an issue to the original package (compute-skewness) to support the "exports" field. If that is not possible, create a JSPM override to customize the exports field for this package.

Readme

Skewness

NPM version Build Status Coverage Status Dependencies

Computes the sample skewness of an array of values.

Installation

$ npm install compute-skewness

For use in the browser, use browserify.

Usage

To use the module,

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

Examples

var data = new Array( 100 );

for ( var i = 0; i < data.length; i++ ) {
    data[ i ] = Math.random();
}

console.log( skewness( data ) );

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

$ node ./examples/index.js

Notes

The formula for computing the sample skewness 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.


Copyright © 2014. Athan Reines.