feat: add stats/incr/nanewmean

lib/node_modules/@stdlib/stats/incr/nanewmean/README.md

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+<!--
+
+@license Apache-2.0
+
+Copyright (c) 2025 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.
+
+-->
+
+# incrnanewmean
+
+> Compute an [exponentially weighted mean][moving-average] incrementally, ignoring `NaN` values.
+
+<section class="intro">
+
+An [exponentially weighted mean][moving-average] can be defined recursively as
+
+<!-- <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."> -->
+
+```math
+\mu_t = \begin{cases} x_0 & \textrm{if}\ t = 0 \\ \alpha x_t + (1-\alpha) \mu_{t-1} & \textrm{if}\ t > 0 \end{cases}
+```
+
+<!-- <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">
+    <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.">
+    <br>
+</div> -->
+
+<!-- </equation> -->
+
+</section>
+
+<!-- /.intro -->
+
+<section class="usage">
+
+## Usage
+
+```javascript
+var incrnanewmean = require( '@stdlib/stats/incr/nanewmean' );
+```
+
+#### incrnanewmean( alpha )
+
+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.
+
+```javascript
+var accumulator = incrnanewmean( 0.5 );
+```
+
+#### accumulator( \[x] )
+
+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.
+
+```javascript
+var accumulator = incrnanewmean( 0.5 );
+
+var v = accumulator();
+// returns null
+
+v = accumulator( 2.0 );
+// returns 2.0
+
+v = accumulator( 1.0 );
+// returns 1.5
+
+v = accumulator( NaN );
+// returns 1.5
+
+v = accumulator( 3.0 );
+// returns 2.25
+
+v = accumulator();
+// returns 2.25
+```
+
+</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 randu = require( '@stdlib/random/base/randu' );
+var incrnanewmean = require( '@stdlib/stats/incr/nanewmean' );
+
+var accumulator;
+var v;
+var i;
+
+// Initialize an accumulator:
+accumulator = incrnanewmean( 0.5 );
+
+// For each simulated datum, update the exponentially weighted mean...
+for ( i = 0; i < 100; i++ ) {
+    if ( randu() < 0.2 ) {
+        v = NaN;
+    } else {
+        v = randu() * 100.0;
+    }
+    accumulator( v );
+}
+console.log( accumulator() );
+```
+
+</section>
+
+<!-- /.examples -->
+
+<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->
+
+<section class="related">
+
+* * *
+
+## See Also
+
+-   <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>
+-   <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>
+-   <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>
+-   <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>
+
+</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">
+
+[moving-average]: https://en.wikipedia.org/wiki/Moving_average
+
+<!-- <related-links> -->
+
+[@stdlib/stats/incr/ewvariance]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/incr/ewvariance
+
+[@stdlib/stats/incr/mean]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/incr/mean
+
+[@stdlib/stats/incr/mmean]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/incr/mmean
+
+[@stdlib/stats/incr/wmean]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/incr/wmean
+
+<!-- </related-links> -->
+
+</section>
+
+<!-- /.links -->

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

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+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 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.
+*/
+
+'use strict';
+
+// MODULES //
+
+var bench = require( '@stdlib/bench' );
+var randu = require( '@stdlib/random/base/randu' );
+var pkg = require( './../package.json' ).name;
+var incrnanewmean = require( './../lib' );
+
+
+// MAIN //
+
+bench( pkg, function benchmark( b ) {
+	var f;
+	var i;
+	b.tic();
+	for ( i = 0; i < b.iterations; i++ ) {
+		f = incrnanewmean( 0.5 );
+		if ( typeof f !== 'function' ) {
+			b.fail( 'should return a function' );
+		}
+	}
+	b.toc();
+	if ( typeof f !== 'function' ) {
+		b.fail( 'should return a function' );
+	}
+	b.pass( 'benchmark finished' );
+	b.end();
+});
+
+bench( pkg+'::accumulator', function benchmark( b ) {
+	var acc;
+	var v;
+	var i;
+
+	acc = incrnanewmean( 0.5 );
+
+	b.tic();
+	for ( i = 0; i < b.iterations; i++ ) {
+		v = acc( randu() );
+		if ( v !== v ) {
+			b.fail( 'should not return NaN' );
+		}
+	}
+	b.toc();
+	if ( v !== v ) {
+		b.fail( 'should not return NaN' );
+	}
+	b.pass( 'benchmark finished' );
+	b.end();
+});

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

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