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@stdlib/stats

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Standard library statistical functions.

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<!-- @license Apache-2.0 Copyright (c) 2018 The Stdlib Authors. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # Gamma > Gamma distribution. <section class="usage"> ## Usage ```javascript var gamma = require( '@stdlib/stats/base/dists/gamma' ); ``` #### gamma Gamma distribution. ```javascript var dist = gamma; // returns {...} ``` The namespace contains the following distribution functions: <!-- <toc pattern="*+(cdf|pdf|mgf|quantile)*"> --> <div class="namespace-toc"> - <span class="signature">[`cdf( x, alpha, beta )`][@stdlib/stats/base/dists/gamma/cdf]</span><span class="delimiter">: </span><span class="description">gamma distribution cumulative distribution function.</span> - <span class="signature">[`logcdf( x, alpha, beta )`][@stdlib/stats/base/dists/gamma/logcdf]</span><span class="delimiter">: </span><span class="description">gamma distribution logarithm of cumulative distribution function (CDF).</span> - <span class="signature">[`logpdf( x, alpha, beta )`][@stdlib/stats/base/dists/gamma/logpdf]</span><span class="delimiter">: </span><span class="description">gamma distribution logarithm of probability density function (PDF).</span> - <span class="signature">[`mgf( t, alpha, beta )`][@stdlib/stats/base/dists/gamma/mgf]</span><span class="delimiter">: </span><span class="description">gamma distribution moment-generating function (MGF).</span> - <span class="signature">[`pdf( x, alpha, beta )`][@stdlib/stats/base/dists/gamma/pdf]</span><span class="delimiter">: </span><span class="description">gamma distribution probability density function (PDF).</span> - <span class="signature">[`quantile( p, alpha, beta )`][@stdlib/stats/base/dists/gamma/quantile]</span><span class="delimiter">: </span><span class="description">gamma distribution quantile function.</span> </div> <!-- </toc> --> The namespace contains the following functions for calculating distribution properties: <!-- <toc pattern="*+(entropy|kurtosis|mean|median|mode|skewness|stdev|variance)*"> --> <div class="namespace-toc"> - <span class="signature">[`entropy( alpha, beta )`][@stdlib/stats/base/dists/gamma/entropy]</span><span class="delimiter">: </span><span class="description">gamma distribution differential entropy.</span> - <span class="signature">[`kurtosis( alpha, beta )`][@stdlib/stats/base/dists/gamma/kurtosis]</span><span class="delimiter">: </span><span class="description">gamma distribution excess kurtosis.</span> - <span class="signature">[`mean( alpha, beta )`][@stdlib/stats/base/dists/gamma/mean]</span><span class="delimiter">: </span><span class="description">gamma distribution expected value.</span> - <span class="signature">[`mode( alpha, beta )`][@stdlib/stats/base/dists/gamma/mode]</span><span class="delimiter">: </span><span class="description">gamma distribution mode.</span> - <span class="signature">[`skewness( alpha, beta )`][@stdlib/stats/base/dists/gamma/skewness]</span><span class="delimiter">: </span><span class="description">gamma distribution skewness.</span> - <span class="signature">[`stdev( alpha, beta )`][@stdlib/stats/base/dists/gamma/stdev]</span><span class="delimiter">: </span><span class="description">gamma distribution standard deviation.</span> - <span class="signature">[`variance( alpha, beta )`][@stdlib/stats/base/dists/gamma/variance]</span><span class="delimiter">: </span><span class="description">gamma distribution variance.</span> </div> <!-- </toc> --> The namespace contains a constructor function for creating a [gamma][gamma-distribution] distribution object. <!-- <toc pattern="*ctor*"> --> <div class="namespace-toc"> - <span class="signature">[`Gamma( [alpha, beta] )`][@stdlib/stats/base/dists/gamma/ctor]</span><span class="delimiter">: </span><span class="description">gamma distribution constructor.</span> </div> <!-- </toc> --> ```javascript var Gamma = require( '@stdlib/stats/base/dists/gamma' ).Gamma; var dist = new Gamma( 2.0, 4.0 ); var y = dist.cdf( 0.5 ); // returns ~0.594 ``` </section> <!-- /.usage --> <section class="examples"> ## Examples <!-- TODO: better examples --> <!-- eslint no-undef: "error" --> ```javascript var gammaRandomFactory = require( '@stdlib/random/base/gamma' ).factory; var filledarrayby = require( '@stdlib/array/filled-by' ); var Float64Array = require( '@stdlib/array/float64' ); var variance = require( '@stdlib/stats/base/variance' ); var linspace = require( '@stdlib/array/base/linspace' ); var mean = require( '@stdlib/stats/base/mean' ); var abs = require( '@stdlib/math/base/special/abs' ); var gamma = require( '@stdlib/stats/base/dists/gamma' ); // Define the shape and scale parameters: var alpha = 3.0; // shape parameter (α) var beta = 2.0; // scale parameter (β) // Generate an array of x values: var x = linspace( 0.0, 20.0, 100 ); // Compute the PDF for each x: var gammaPDF = gamma.pdf.factory( alpha, beta ); var pdf = filledarrayby( x.length, 'float64', gammaPDF ); // Compute the CDF for each x: var gammaCDF = gamma.cdf.factory( alpha, beta ); var cdf = filledarrayby( x.length, 'float64', gammaCDF ); // Output the PDF and CDF values: console.log( 'x values: %s', x ); console.log( 'PDF values: %s', pdf ); console.log( 'CDF values: %s', cdf ); // Compute statistical properties: var theoreticalMean = gamma.mean( alpha, beta ); var theoreticalVariance = gamma.variance( alpha, beta ); var theoreticalSkewness = gamma.skewness( alpha, beta ); var theoreticalKurtosis = gamma.kurtosis( alpha, beta ); console.log( 'Theoretical Mean: %s', theoreticalMean ); console.log( 'Theoretical Variance: %s', theoreticalVariance ); console.log( 'Skewness: %s', theoreticalSkewness ); console.log( 'Kurtosis: %s', theoreticalKurtosis ); // Generate random samples from the gamma distribution: var rgamma = gammaRandomFactory( alpha, beta ); var n = 300; var samples = filledarrayby( n, 'float64', rgamma ); // Compute sample mean and variance: var sampleMean = mean( n, samples, 1 ); var sampleVariance = variance( n, 1, samples, 1 ); console.log( 'Sample Mean: %s', sampleMean ); console.log( 'Sample Variance: %s', sampleVariance ); // Compare sample statistics to theoretical values: console.log( 'Difference in Mean: %s', abs( theoreticalMean - sampleMean ) ); console.log( 'Difference in Variance: %s', abs( theoreticalVariance - sampleVariance ) ); // Demonstrate that the sum of `k` gamma variables is a gamma-distributed sum of `k` gamma(α, β) variables with same β is `gamma(k*α, β)`: var k = 5; var sumSamples = new Float64Array( n ); var sum; var i; var j; for ( i = 0; i < sumSamples.length; i++ ) { sum = 0.0; for ( j = 0; j < k; j++ ) { sum += rgamma(); } sumSamples[ i ] = sum; } // Theoretical parameters for the sum: var sumAlpha = k * alpha; var sumMean = gamma.mean( sumAlpha, beta ); var sumVariance = gamma.variance( sumAlpha, beta ); console.log( 'Sum Theoretical Mean: %s', sumMean ); console.log( 'Sum Theoretical Variance: %s', sumVariance ); // Compute sample mean and variance for the sum: var sumSampleMean = mean( sumSamples.length, sumSamples, 1 ); var sumSampleVariance = variance( sumSamples.length, 1, sumSamples, 1 ); console.log( 'Sum Sample Mean: %s', sumSampleMean ); console.log( 'Sum Sample Variance: %s', sumSampleVariance ); ``` </section> <!-- /.examples --> <!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. --> <section class="related"> </section> <!-- /.related --> <!-- Section for all links. Make sure to keep an empty line after the `section` element and another before the `/section` close. --> <section class="links"> [gamma-distribution]: https://en.wikipedia.org/wiki/Gamma_distribution <!-- <toc-links> --> [@stdlib/stats/base/dists/gamma/ctor]: https://github.com/stdlib-js/stats/tree/main/base/dists/gamma/ctor [@stdlib/stats/base/dists/gamma/entropy]: https://github.com/stdlib-js/stats/tree/main/base/dists/gamma/entropy [@stdlib/stats/base/dists/gamma/kurtosis]: https://github.com/stdlib-js/stats/tree/main/base/dists/gamma/kurtosis [@stdlib/stats/base/dists/gamma/mean]: https://github.com/stdlib-js/stats/tree/main/base/dists/gamma/mean [@stdlib/stats/base/dists/gamma/mode]: https://github.com/stdlib-js/stats/tree/main/base/dists/gamma/mode [@stdlib/stats/base/dists/gamma/skewness]: https://github.com/stdlib-js/stats/tree/main/base/dists/gamma/skewness [@stdlib/stats/base/dists/gamma/stdev]: https://github.com/stdlib-js/stats/tree/main/base/dists/gamma/stdev [@stdlib/stats/base/dists/gamma/variance]: https://github.com/stdlib-js/stats/tree/main/base/dists/gamma/variance [@stdlib/stats/base/dists/gamma/cdf]: https://github.com/stdlib-js/stats/tree/main/base/dists/gamma/cdf [@stdlib/stats/base/dists/gamma/logcdf]: https://github.com/stdlib-js/stats/tree/main/base/dists/gamma/logcdf [@stdlib/stats/base/dists/gamma/logpdf]: https://github.com/stdlib-js/stats/tree/main/base/dists/gamma/logpdf [@stdlib/stats/base/dists/gamma/mgf]: https://github.com/stdlib-js/stats/tree/main/base/dists/gamma/mgf [@stdlib/stats/base/dists/gamma/pdf]: https://github.com/stdlib-js/stats/tree/main/base/dists/gamma/pdf [@stdlib/stats/base/dists/gamma/quantile]: https://github.com/stdlib-js/stats/tree/main/base/dists/gamma/quantile <!-- </toc-links> --> </section> <!-- /.links -->