https://github.com/stdlib-js/stats-base-dists-hypergeometric-mean
Hypergeometric distribution expected value.
https://github.com/stdlib-js/stats-base-dists-hypergeometric-mean
discrete dist distribution hypergeometric javascript node node-js nodejs parameter statistics stats stdlib univariate
Last synced: 7 months ago
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Hypergeometric distribution expected value.
- Host: GitHub
- URL: https://github.com/stdlib-js/stats-base-dists-hypergeometric-mean
- Owner: stdlib-js
- License: apache-2.0
- Created: 2021-06-14T16:29:24.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2024-12-30T03:40:53.000Z (over 1 year ago)
- Last Synced: 2024-12-30T04:26:46.421Z (over 1 year ago)
- Topics: discrete, dist, distribution, hypergeometric, javascript, node, node-js, nodejs, parameter, statistics, stats, stdlib, univariate
- Language: JavaScript
- Homepage: https://github.com/stdlib-js/stdlib
- Size: 377 KB
- Stars: 1
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
- Security: SECURITY.md
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README
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# Mean
[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url]
> [Hypergeometric][hypergeometric-distribution] distribution [expected value][expected-value].
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][hypergeometric-distribution]. The [mean][expected-value] for a [hypergeometric][hypergeometric-distribution] random variable is
```math
\mathbb{E} \left[ X \right] = n{K \over N}
```
## Installation
```bash
npm install @stdlib/stats-base-dists-hypergeometric-mean
```
Alternatively,
- To load the package in a website via a `script` tag without installation and bundlers, use the [ES Module][es-module] available on the [`esm`][esm-url] branch (see [README][esm-readme]).
- If you are using Deno, visit the [`deno`][deno-url] branch (see [README][deno-readme] for usage intructions).
- For use in Observable, or in browser/node environments, use the [Universal Module Definition (UMD)][umd] build available on the [`umd`][umd-url] branch (see [README][umd-readme]).
The [branches.md][branches-url] file summarizes the available branches and displays a diagram illustrating their relationships.
To view installation and usage instructions specific to each branch build, be sure to explicitly navigate to the respective README files on each branch, as linked to above.
## Usage
```javascript
var mean = require( '@stdlib/stats-base-dists-hypergeometric-mean' );
```
#### mean( N, K, n )
Returns the [expected value][expected-value] of a [hypergeometric][hypergeometric-distribution] distribution with parameters `N` (population size), `K` (subpopulation size), and `n` (number of draws).
```javascript
var v = mean( 16, 11, 4 );
// returns 2.75
v = mean( 2, 1, 1 );
// returns 0.5
```
If provided `NaN` as any argument, the function returns `NaN`.
```javascript
var v = mean( NaN, 10, 4 );
// returns NaN
v = mean( 20, NaN, 4 );
// returns NaN
v = mean( 20, 10, 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`.
```javascript
var v = mean( 10.5, 5, 2 );
// returns NaN
v = mean( 10, 1.5, 2 );
// returns NaN
v = mean( 10, 5, -2.0 );
// returns NaN
```
If the number of draws `n` or the subpopulation size `K` exceed population size `N`, the function returns `NaN`.
```javascript
var v = mean( 10, 5, 12 );
// returns NaN
v = mean( 10, 12, 5 );
// returns NaN
```
## Examples
```javascript
var randu = require( '@stdlib/random-base-randu' );
var round = require( '@stdlib/math-base-special-round' );
var mean = require( '@stdlib/stats-base-dists-hypergeometric-mean' );
var v;
var i;
var N;
var K;
var n;
for ( i = 0; i < 10; i++ ) {
N = round( randu() * 20 );
K = round( randu() * N );
n = round( randu() * K );
v = mean( N, K, n );
console.log( 'N: %d, K: %d, n: %d, E(X;N,K,n): %d', N, K, n, v.toFixed( 4 ) );
}
```
* * *
## C APIs
### Usage
```c
#include "stdlib/stats/base/dists/hypergeometric/mean.h"
```
#### stdlib_base_dists_hypergeometric_mean( N, K, n )
Returns the expected value of a hypergeometric distribution.
```c
double out = stdlib_base_dists_hypergeometric_mean( 16, 11, 4 );
// returns 2.75
```
The function accepts the following arguments:
- **N**: `[in] int32_t` population size.
- **K**: `[in] int32_t` subpopulation size.
- **n**: `[in] int32_t` number of draws.
```c
double stdlib_base_dists_hypergeometric_mean( const int32_t N, const int32_t K, const int32_t n );
```
### Examples
```c
#include "stdlib/stats/base/dists/hypergeometric/mean.h"
#include
#include
#include
static int32_t random_int( const int32_t min, const int32_t max ) {
int32_t v = rand() % ( max - min + 1 );
return min + v;
}
int main( void ) {
int32_t N;
int32_t K;
int32_t n;
double y;
int i;
for ( i = 0; i < 10; i++ ) {
N = random_int( 1, 20 );
K = random_int( 0, N );
n = random_int( 0, K );
y = stdlib_base_dists_hypergeometric_mean( N, K, n );
printf( "N: %d, K: %d, n: %d, E(X;N,K,n): %.4f\n", N, K, n, y );
}
}
```
* * *
## Notice
This package is part of [stdlib][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][stdlib], see the main project [repository][stdlib].
#### Community
[![Chat][chat-image]][chat-url]
---
## License
See [LICENSE][stdlib-license].
## Copyright
Copyright © 2016-2025. The Stdlib [Authors][stdlib-authors].
[npm-image]: http://img.shields.io/npm/v/@stdlib/stats-base-dists-hypergeometric-mean.svg
[npm-url]: https://npmjs.org/package/@stdlib/stats-base-dists-hypergeometric-mean
[test-image]: https://github.com/stdlib-js/stats-base-dists-hypergeometric-mean/actions/workflows/test.yml/badge.svg?branch=main
[test-url]: https://github.com/stdlib-js/stats-base-dists-hypergeometric-mean/actions/workflows/test.yml?query=branch:main
[coverage-image]: https://img.shields.io/codecov/c/github/stdlib-js/stats-base-dists-hypergeometric-mean/main.svg
[coverage-url]: https://codecov.io/github/stdlib-js/stats-base-dists-hypergeometric-mean?branch=main
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[chat-url]: https://app.gitter.im/#/room/#stdlib-js_stdlib:gitter.im
[stdlib]: https://github.com/stdlib-js/stdlib
[stdlib-authors]: https://github.com/stdlib-js/stdlib/graphs/contributors
[umd]: https://github.com/umdjs/umd
[es-module]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules
[deno-url]: https://github.com/stdlib-js/stats-base-dists-hypergeometric-mean/tree/deno
[deno-readme]: https://github.com/stdlib-js/stats-base-dists-hypergeometric-mean/blob/deno/README.md
[umd-url]: https://github.com/stdlib-js/stats-base-dists-hypergeometric-mean/tree/umd
[umd-readme]: https://github.com/stdlib-js/stats-base-dists-hypergeometric-mean/blob/umd/README.md
[esm-url]: https://github.com/stdlib-js/stats-base-dists-hypergeometric-mean/tree/esm
[esm-readme]: https://github.com/stdlib-js/stats-base-dists-hypergeometric-mean/blob/esm/README.md
[branches-url]: https://github.com/stdlib-js/stats-base-dists-hypergeometric-mean/blob/main/branches.md
[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/stats-base-dists-hypergeometric-mean/main/LICENSE
[hypergeometric-distribution]: https://en.wikipedia.org/wiki/Hypergeometric_distribution
[expected-value]: https://en.wikipedia.org/wiki/Expected_value