npm package: @stdlib/stats-incr-nangmean
Description: Compute a geometric mean incrementally, ignoring NaN values.
Author: The Stdlib Authors
Latest version: 0.1.1

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<details>
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    About stdlib...
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  <p>We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.</p>
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# incrnangmean

[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url] <!-- [![dependencies][dependencies-image]][dependencies-url] -->

> Compute a [geometric mean][geometric-mean] incrementally, ignoring `NaN` values.

<section class="intro">

The [geometric mean][geometric-mean] is defined as the nth root of a product of _n_ numbers.

<!-- <equation class="equation" label="eq:geometric_mean" align="center" raw="\biggl( \prod_{i=0}^{n-1} x_i \biggr)^{\frac{1}{n}} = \sqrt[n]{x_0 x_1 \cdots x_{n-1}}" alt="Equation for the geometric mean."> -->

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</section>

<!-- /.intro -->

<section class="installation">

## Installation

```bash
npm install @stdlib/stats-incr-nangmean
```

</section>

<section class="usage">

## Usage

```javascript
var incrnangmean = require( '@stdlib/stats-incr-nangmean' );
```

#### incrnangmean()

Returns an accumulator `function` which incrementally computes a [geometric mean][geometric-mean], ignoring `NaN` values.

```javascript
var accumulator = incrnangmean();
```

#### accumulator( \[x] )

If provided an input value `x`, the accumulator function returns an updated [geometric mean][geometric-mean]. If not provided an input value `x`, the accumulator function returns the current [geometric mean][geometric-mean].

```javascript
var accumulator = incrnangmean();

var prod = accumulator( 2.0 );
// returns 2.0

prod = accumulator( 1.0 );
// returns ~1.414

prod = accumulator( NaN );
// returns ~1.414

prod = accumulator( 3.0 );
// returns ~1.817

prod = accumulator();
// returns ~1.817
```

</section>

<!-- /.usage -->

<section class="notes">

## Notes

-   Input values are **not** type checked. If non-numeric inputs are possible, you are advised to type check and handle accordingly **before** passing the value to the accumulator function.

</section>

<!-- /.notes -->

<section class="examples">

## Examples

<!-- eslint no-undef: "error" -->

```javascript
var bernoulli = require( '@stdlib/random-base-bernoulli' );
var uniform = require( '@stdlib/random-base-uniform' );
var incrnangmean = require( '@stdlib/stats-incr-nangmean' );

var accumulator;
var v;
var i;

// Initialize an accumulator:
accumulator = incrnangmean();

// For each simulated value, update the geometric mean...
for ( i = 0; i < 100; i++ ) {
    if ( bernoulli( 0.2 ) ) {
        v = NaN;
    } else {
        v = uniform( 0.0, 100.0 );
    }
    accumulator( v );
}
console.log( accumulator() );
```

</section>

<!-- /.examples -->

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<section class="related">

</section>

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<section class="main-repo" >

* * *

## 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 &copy; 2016-2026. The Stdlib [Authors][stdlib-authors].

</section>

<!-- /.stdlib -->

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[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-incr-nangmean/tree/deno
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[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/stats-incr-nangmean/main/LICENSE

[geometric-mean]: https://en.wikipedia.org/wiki/Geometric_mean

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</section>

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