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9281e0c
feat: add incrnanmmeanvar (moving mean/variance with NaN skipping)
anoofmhd Nov 11, 2025
80cbbdb
chore: update copyright years
stdlib-bot Nov 11, 2025
f7c0075
fix: correct import variable name in example and also added trailing …
anoofmhd Nov 11, 2025
4ae8a4b
fix:lint_changed_files / Lint Changed Files (pull_request) and also f…
anoofmhd Dec 2, 2025
f39415c
fix: run_affected_examples / Run changed examples (pull_request) and …
anoofmhd Dec 3, 2025
2c7c6d6
Tests updated with more meaningful in terms of actual computation.
anoofmhd Dec 13, 2025
0348aaf
Cleared linting issues
anoofmhd Dec 15, 2025
4fdb603
fixed run_affected_examples / Run changed examples (pull_request) and…
anoofmhd Dec 16, 2025
9bb404d
Fixed : lint_changed_files / Lint Changed Files (pull_request)
anoofmhd Dec 30, 2025
2e32f59
Removed the unwanted related notations in README
anoofmhd Dec 30, 2025
a93936b
Removed the unwanted image file
anoofmhd Dec 31, 2025
c7a945a
Update lib/node_modules/@stdlib/stats/incr/nanmmeanvar/lib/main.js
anoofmhd Jan 15, 2026
afea9ee
Merge branch 'develop' of https://github.com/anoofmhd/stdlib into nan…
anoofmhd Mar 10, 2026
1c50600
Merge branch 'nanmmeanvar' of https://github.com/anoofmhd/stdlib into…
anoofmhd Mar 10, 2026
d7777cc
feat: add incremental NaN-aware moving mean and variance module.
anoofmhd Mar 10, 2026
04898b4
docs: add REPL documentation for `nanmmeanvar`
anoofmhd Mar 10, 2026
40821d3
feat: add `incrnanmmeanvar` for incrementally computing moving mean a…
anoofmhd Mar 10, 2026
32b6320
feat: add incremental moving mean and variance accumulator that skips…
anoofmhd Mar 24, 2026
f9d80ce
feat: add `incrnanmmeanvar` module for incrementally computing a movi…
anoofmhd Mar 24, 2026
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223 changes: 223 additions & 0 deletions lib/node_modules/@stdlib/stats/incr/nanmmeanvar/README.md
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<!--

@license Apache-2.0

Copyright (c) 2026 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.

-->

# incrnanmmeanvar

> Compute a moving [arithmetic mean][arithmetic-mean] and [unbiased sample variance][sample-variance] incrementally, **skipping `NaN` values**.

<section class="intro">

For a window of size `W`, the [arithmetic mean][arithmetic-mean] is defined as

<!-- <equation class="equation" label="eq:arithmetic_mean" align="center" raw="\bar{x} = \frac{1}{W} \sum_{i=0}^{W-1} x_i" alt="Equation for the arithmetic mean."> -->

```math
\bar{x} = \frac{1}{W} \sum_{i=0}^{W-1} x_i
```

<!-- <div class="equation" align="center" data-raw-text="\bar{x} = \frac{1}{W} \sum_{i=0}^{W-1} x_i" data-equation="eq:arithmetic_mean">
<img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@c8c3c87eeab590bfdff924ec0fb269fb33a7de2b/lib/node_modules/@stdlib/stats/incr/mmeanvar/docs/img/equation_arithmetic_mean.svg" alt="Equation for the arithmetic mean.">
<br>
</div> -->

<!-- </equation> -->

and the [unbiased sample variance][sample-variance] is defined as

<!-- <equation class="equation" label="eq:unbiased_sample_variance" align="center" raw="s^2 = \frac{1}{W-1} \sum_{i=0}^{W-1} ( x_i - \bar{x} )^2" alt="Equation for the unbiased sample variance."> -->

```math
s^2 = \frac{1}{W-1} \sum_{i=0}^{W-1} ( x_i - \bar{x} )^2
```

<!-- <div class="equation" align="center" data-raw-text="s^2 = \frac{1}{W-1} \sum_{i=0}^{W-1} ( x_i - \bar{x} )^2" data-equation="eq:unbiased_sample_variance">
<img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@563a8587d936008c82db675be84f8ce1474fee27/lib/node_modules/@stdlib/stats/incr/mmeanvar/docs/img/equation_unbiased_sample_variance.svg" alt="Equation for the unbiased sample variance.">
<br>
</div> -->

<!-- </equation> -->

</section>

<!-- /.intro -->

<section class="usage">

## Usage

```javascript
var incrnanmmeanvar = require( '@stdlib/stats/incr/nanmmeanvar' );
```

#### incrnanmmeanvar( \[out,] window )

Returns an accumulator `function` which incrementally computes a moving [arithmetic mean][arithmetic-mean] and [unbiased sample variance][sample-variance] **while skipping `NaN` values**. The `window` parameter defines the maximum number of most recent **non-NaN** values used to compute the moving statistics.

```javascript
var accumulator = incrnanmmeanvar( 3 );
```

By default, the returned accumulator `function` returns the accumulated values as a two-element `array`. To avoid unnecessary memory allocation, the function supports providing an output (destination) object. Unlike `incrmmeanvar`, this accumulator ignores (skips) any `NaN` values and does not allow them to propagate into the moving mean and variance.

```javascript
var Float64Array = require( '@stdlib/array/float64' );

var accumulator = incrnanmmeanvar( new Float64Array( 2 ), 3 );
```

#### accumulator( \[x] )

If provided an input value `x`, the accumulator function updates and returns the current moving arithmetic mean and unbiased sample variance. If `x` is `NaN`, the value is ignored (i.e., it does not affect the window). If not provided an input value `x`, the accumulator function returns the current accumulated values without updating.

```javascript
var incrnanmmeanvar = require( '@stdlib/stats/incr/nanmmeanvar' );

var accumulator = incrnanmmeanvar( 3 );
var out = accumulator();
// returns null

out = accumulator( 2.0 );
// returns [ 2, 0 ]

out = accumulator( NaN );
// returns [ 2, 0 ]

out = accumulator( 1.0 );
// returns [ 1.5, 0.5 ]

out = accumulator( 3.0 );
// returns [ 2, 1 ]

out = accumulator( -7.0 );
// returns [ -1, 28 ]

out = accumulator( NaN );
// returns [ -1, 28 ]

out = accumulator( -5.0 );
// returns [ -3, 28 ]

out = accumulator();
// returns [ -3, 28 ]
```

</section>

<!-- /.usage -->

<section class="notes">

## Notes

- Input values are **not** type checked. If provided `NaN`, the value is ignored and does not affect the moving window or the accumulated statistics. If non-numeric inputs are possible, you are advised to type check and handle them **before** passing values to the accumulator function.
- As `W` valid (non-`NaN`) values are needed to fill the window buffer, the first `W-1` returned values are calculated from smaller sample sizes. Until the window has received `W` valid values, each returned value is calculated from all valid inputs seen so far.

</section>

<!-- /.notes -->

<section class="examples">

## Examples

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

```javascript
var randu = require( '@stdlib/random/base/randu' );
var Float64Array = require( '@stdlib/array/float64' );
var ArrayBuffer = require( '@stdlib/array/buffer' );
var incrnanmmeanvar = require( '@stdlib/stats/incr/nanmmeanvar' );

var offset;
var acc;
var buf;
var out;
var mv;
var N;
var v;
var i;
var j;

// Define the number of accumulators:
N = 5;

// Create an array buffer for storing accumulator output:
buf = new ArrayBuffer( N*2*8 );

// Initialize accumulators:
acc = [];
for ( i = 0; i < N; i++ ) {
// Compute the byte offset for the i­th accumulator:
offset = i * 2 * 8;

// Create a typed array view over the correct section of the buffer:
out = new Float64Array( buf, offset, 2 );

// Create a moving mean/variance accumulator with window W = 5:
acc.push( incrnanmmeanvar( out, 5 ) );
}

// Simulate streaming data updates:
for ( i = 0; i < 100; i++ ) {
for ( j = 0; j < N; j++ ) {
// Generate random values, but occasionally insert NaN.
// Any NaNs are ignored by the accumulator.
v = ( randu() > 0.1 ) ? randu() * 100 : NaN;

// Update accumulator j:
acc[ j ]( v );
}
}

// Display final moving means and variances:
console.log( 'Mean\tVariance' );
for ( i = 0; i < N; i++ ) {
mv = acc[ i ](); // Get the current result
console.log( mv[ 0 ].toFixed( 3 ) + '\t' + mv[ 1 ].toFixed( 3 ) );
}
```

</section>

<!-- /.examples -->

<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->

<section class="related">

* * *

## See Also

</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">

[arithmetic-mean]: https://en.wikipedia.org/wiki/Arithmetic_mean

[sample-variance]: https://en.wikipedia.org/wiki/Variance

</section>

<!-- /.links -->
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/**
* @license Apache-2.0
*
* Copyright (c) 2026 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 incrnanmmeanvar = require( './../lib' );


// MAIN //

bench(pkg, function benchmark(b) {
var f;
var i;
b.tic();
for (i = 0; i < b.iterations; i++) {
f = incrnanmmeanvar((i % 5) + 1);
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 = incrnanmmeanvar(5);

b.tic();
for (i = 0; i < b.iterations; i++) {
v = acc(randu());
if (v.length !== 2) {
b.fail('should contain two elements');
}
}
b.toc();
if (v[0] !== v[0] || v[1] !== v[1]) {
b.fail('should not return NaN');
}
b.pass('benchmark finished');
b.end();
});
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