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feat: add stats/incr/nanewmean
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‎lib/node_modules/@stdlib/stats/incr/nanewmean/README.md‎

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<!--
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@license Apache-2.0
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Copyright (c) 2025 The Stdlib Authors.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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-->
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# incrnanewmean
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> Compute an [exponentially weighted mean][moving-average] incrementally, ignoring `NaN` values.
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<section class="intro">
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An [exponentially weighted mean][moving-average] can be defined recursively as
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<!-- <equation class="equation" label="eq:exponentially_weighted_mean" align="center" raw="\mu_t = \begin{cases} x_0 & \textrm{if}\ t = 0 \\ \alpha x_t + (1-\alpha) \mu_{t-1} & \textrm{if}\ t > 0 \end{cases}" alt="Recursive definition for computing an exponentially weighted mean."> -->
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```math
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\mu_t = \begin{cases} x_0 & \textrm{if}\ t = 0 \\ \alpha x_t + (1-\alpha) \mu_{t-1} & \textrm{if}\ t > 0 \end{cases}
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```
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<!-- <div class="equation" align="center" data-raw-text="\mu_t = \begin{cases} x_0 &amp; \textrm{if}\ t = 0 \\ \alpha x_t + (1-\alpha) \mu_{t-1} &amp; \textrm{if}\ t &gt; 0 \end{cases}" data-equation="eq:exponentially_weighted_mean">
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<img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@1445ad5c454bc3c1a86bde2be87d6cec87781174/lib/node_modules/@stdlib/stats/incr/ewmean/docs/img/equation_exponentially_weighted_mean.svg" alt="Recursive definition for computing an exponentially weighted mean.">
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<br>
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</div> -->
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<!-- </equation> -->
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</section>
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<!-- /.intro -->
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<section class="usage">
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## Usage
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```javascript
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var incrnanewmean = require( '@stdlib/stats/incr/nanewmean' );
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```
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#### incrnanewmean( alpha )
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Returns an accumulator `function` which incrementally computes an [exponentially weighted mean][moving-average], where `alpha` is a smoothing factor between `0` and `1`, ignoring `NaN` values.
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```javascript
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var accumulator = incrnanewmean( 0.5 );
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```
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#### accumulator( \[x] )
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If provided an input value `x`, the accumulator function returns an updated mean. If not provided an input value `x`, the accumulator function returns the current mean.
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```javascript
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var accumulator = incrnanewmean( 0.5 );
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var v = accumulator();
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// returns null
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v = accumulator( 2.0 );
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// returns 2.0
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v = accumulator( 1.0 );
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// returns 1.5
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v = accumulator( NaN );
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// returns 1.5
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v = accumulator( 3.0 );
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// returns 2.25
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v = accumulator();
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// returns 2.25
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```
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</section>
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<!-- /.usage -->
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<section class="notes">
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## Notes
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- 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.
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</section>
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<!-- /.notes -->
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<section class="examples">
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## Examples
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<!-- eslint no-undef: "error" -->
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```javascript
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var randu = require( '@stdlib/random/base/randu' );
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var incrnanewmean = require( '@stdlib/stats/incr/nanewmean' );
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var accumulator;
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var v;
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var i;
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// Initialize an accumulator:
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accumulator = incrnanewmean( 0.5 );
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// For each simulated datum, update the exponentially weighted mean...
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for ( i = 0; i < 100; i++ ) {
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if ( randu() < 0.2 ) {
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v = NaN;
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} else {
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v = randu() * 100.0;
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}
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accumulator( v );
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}
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console.log( accumulator() );
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```
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</section>
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<!-- /.examples -->
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<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->
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<section class="related">
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* * *
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## See Also
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- <span class="package-name">[`@stdlib/stats/incr/ewvariance`][@stdlib/stats/incr/ewvariance]</span><span class="delimiter">: </span><span class="description">compute an exponentially weighted variance incrementally.</span>
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- <span class="package-name">[`@stdlib/stats/incr/mean`][@stdlib/stats/incr/mean]</span><span class="delimiter">: </span><span class="description">compute an arithmetic mean incrementally.</span>
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- <span class="package-name">[`@stdlib/stats/incr/mmean`][@stdlib/stats/incr/mmean]</span><span class="delimiter">: </span><span class="description">compute a moving arithmetic mean incrementally.</span>
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- <span class="package-name">[`@stdlib/stats/incr/wmean`][@stdlib/stats/incr/wmean]</span><span class="delimiter">: </span><span class="description">compute a weighted arithmetic mean incrementally.</span>
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</section>
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<!-- /.related -->
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<!-- Section for all links. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
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<section class="links">
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[moving-average]: https://en.wikipedia.org/wiki/Moving_average
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<!-- <related-links> -->
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[@stdlib/stats/incr/ewvariance]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/incr/ewvariance
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[@stdlib/stats/incr/mean]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/incr/mean
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[@stdlib/stats/incr/mmean]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/incr/mmean
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[@stdlib/stats/incr/wmean]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/incr/wmean
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<!-- </related-links> -->
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</section>
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<!-- /.links -->

‎lib/node_modules/@stdlib/stats/incr/nanewmean/benchmark/benchmark.js‎

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/**
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* @license Apache-2.0
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*
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* Copyright (c) 2025 The Stdlib Authors.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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'use strict';
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// MODULES //
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var bench = require( '@stdlib/bench' );
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var randu = require( '@stdlib/random/base/randu' );
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var pkg = require( './../package.json' ).name;
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var incrnanewmean = require( './../lib' );
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// MAIN //
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bench( pkg, function benchmark( b ) {
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var f;
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var i;
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b.tic();
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for ( i = 0; i < b.iterations; i++ ) {
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f = incrnanewmean( 0.5 );
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if ( typeof f !== 'function' ) {
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b.fail( 'should return a function' );
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}
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}
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b.toc();
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if ( typeof f !== 'function' ) {
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b.fail( 'should return a function' );
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}
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b.pass( 'benchmark finished' );
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b.end();
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});
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bench( pkg+'::accumulator', function benchmark( b ) {
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var acc;
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var v;
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var i;
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acc = incrnanewmean( 0.5 );
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b.tic();
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for ( i = 0; i < b.iterations; i++ ) {
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v = acc( randu() );
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if ( v !== v ) {
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b.fail( 'should not return NaN' );
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}
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}
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b.toc();
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if ( v !== v ) {
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b.fail( 'should not return NaN' );
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}
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b.pass( 'benchmark finished' );
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b.end();
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});

‎lib/node_modules/@stdlib/stats/incr/nanewmean/docs/img/equation_exponentially_weighted_mean.svg‎

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‎lib/node_modules/@stdlib/stats/incr/nanewmean/docs/repl.txt‎

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{{alias}}( α )
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Returns an accumulator function which incrementally computes an
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exponentially weighted mean, where α is a smoothing factor between 0 and 1, ignoring
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`NaN` values.
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If provided a value, the accumulator function returns an updated mean. If
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not provided a value, the accumulator function returns the current mean.
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Parameters
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----------
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α: number
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Smoothing factor (value between 0 and 1).
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Returns
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-------
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acc: Function
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Accumulator function.
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Examples
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--------
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> var accumulator = {{alias}}( 0.5 );
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> var v = accumulator()
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null
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> v = accumulator( 2.0 )
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2.0
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> v = accumulator( NaN )
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2.0
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> v = accumulator( -5.0 )
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-1.5
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> v = accumulator()
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-1.5
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See Also
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--------
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