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# @stdlib/stats-base-dists-hypergeometric-pmf

Hypergeometric distribution probability mass function (PMF).

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# Probability Mass Function   Hypergeometric distribution probability mass function (PMF).

Imagine a scenario with a population of size N, of which a subpopulation of size K can be considered successes. We draw n observations from the total population. Defining the random variable X as the number of successes in the n draws, X is said to follow a hypergeometric distribution. The probability mass function (PMF) for a hypergeometric random variable is given by ## Installation

npm install @stdlib/stats-base-dists-hypergeometric-pmf 

## Usage

var pmf = require( '@stdlib/stats-base-dists-hypergeometric-pmf' ); 

#### pmf( x, N, K, n )

Evaluates the probability mass function (PMF) for a hypergeometric distribution with parameters N (population size), K (subpopulation size), and n (number of draws).

var y = pmf( 1.0, 8, 4, 2 ); // returns ~0.571 y = pmf( 2.0, 8, 4, 2 ); // returns ~0.214 y = pmf( 0.0, 8, 4, 2 ); // returns ~0.214 y = pmf( 1.5, 8, 4, 2 ); // returns 0.0 

If provided NaN as any argument, the function returns NaN.

var y = pmf( NaN, 10, 5, 2 ); // returns NaN y = pmf( 0.0, NaN, 5, 2 ); // returns NaN y = pmf( 0.0, 10, NaN, 2 ); // returns NaN y = pmf( 0.0, 10, 5, NaN ); // returns NaN 

If provided a population size N, subpopulation size K or draws n which is not a nonnegative integer, the function returns NaN.

var y = pmf( 2.0, 10.5, 5, 2 ); // returns NaN y = pmf( 2.0, 10, 1.5, 2 ); // returns NaN y = pmf( 2.0, 10, 5, -2.0 ); // returns NaN 

If the number of draws n exceeds population size N, the function returns NaN.

var y = pmf( 2.0, 10, 5, 12 ); // returns NaN y = pmf( 2.0, 8, 3, 9 ); // returns NaN 

#### pmf.factory( N, K, n )

Returns a function for evaluating the probability mass function (PMF) of a hypergeometric distribution with parameters N (population size), K (subpopulation size), and n (number of draws).

var mypmf = pmf.factory( 30, 20, 5 ); var y = mypmf( 4.0 ); // returns ~0.34 y = mypmf( 1.0 ); // returns ~0.029 

## Examples

var randu = require( '@stdlib/random-base-randu' ); var round = require( '@stdlib/math-base-special-round' ); var pmf = require( '@stdlib/stats-base-dists-hypergeometric-pmf' ); var i; var N; var K; var n; var x; var y; for ( i = 0; i < 10; i++ ) { x = round( randu() * 5.0 ); N = round( randu() * 20.0 ); K = round( randu() * N ); n = round( randu() * N ); y = pmf( x, N, K, n ); console.log( 'x: %d, N: %d, K: %d, n: %d, P(X=x;N,K,n): %d', x, N, K, n, y.toFixed( 4 ) ); } 

## 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 ## What is @stdlib&#x2F;stats-base-dists-hypergeometric-pmf?

Hypergeometric distribution probability mass function (PMF).

## Is @stdlib&#x2F;stats-base-dists-hypergeometric-pmf popular?

The npm package @stdlib&#x2F;stats-base-dists-hypergeometric-pmf receives a total of 153 weekly downloads. As such, @stdlib&#x2F;stats-base-dists-hypergeometric-pmf popularity was classified as not popular.

## Is @stdlib&#x2F;stats-base-dists-hypergeometric-pmf well maintained?

We found that @stdlib&#x2F;stats-base-dists-hypergeometric-pmf demonstrated a healthy version release cadence and project activity. It has 4 open source maintainers collaborating on the project.

Last updated on 16 Feb 2022 Product