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159 changes: 159 additions & 0 deletions lib/node_modules/@stdlib/stats/incr/nanmvariance/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.

-->

# incrnanmvariance

> Compute a moving [unbiased sample variance][sample-variance] incrementally, ignoring `NaN` values.

<section class="intro">

For a window of size `W`, 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="./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 incrnanmvariance = require( '@stdlib/stats/incr/nanmvariance' );
```

#### incrnanmvariance( window\[, mean] )

Returns an accumulator function which incrementally computes a moving [unbiased sample variance][sample-variance], ignoring `NaN` values. The `window` parameter defines the number of values over which to compute the moving [unbiased sample variance][sample-variance].

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

If the mean is already known, provide a `mean` argument.

```javascript
var accumulator = incrnanmvariance( 3, 5.0 );
```

#### accumulator( \[x] )

If provided an input value `x`, the accumulator function returns an updated [unbiased sample variance][sample-variance]. If not provided an input value `x`, the accumulator function returns the current [unbiased sample variance][sample-variance]. If provided `NaN`, the accumulator function ignores the value and returns the current [unbiased sample variance][sample-variance] without updating the window.


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

var s2 = accumulator();
// returns null

// Fill the window with non-NaN values...
s2 = accumulator( 2.0 ); // [2.0]
// returns 0.0

// NaN ignored...
s2 = accumulator( NaN );
// returns 0.0

s2 = accumulator( -5.0 ); // [2.0, -5.0]
// returns 24.5

s2 = accumulator( 3.0 ); // [2.0, -5.0, 3.0]
// returns 19.0

// NaN ignored...
s2 = accumulator( NaN );
// returns 19.0

s2 = accumulator();
// returns 19.0
```

</section>

<!-- /.usage -->

<section class="notes">

## Notes

- `NaN` values are ignored. If all values in the window are `NaN`, the accumulator returns the last valid variance or `null` if no valid values have been provided.
- Input values are **not** type checked beyond `NaN` detection. If other non-numeric inputs are possible, you are advised to type check and handle accordingly **before** passing the value to the accumulator function.
- As `W` values are needed to fill the window buffer, the first `W-1` returned values are calculated from smaller sample sizes. Until the window is full, each returned value is calculated from all provided non-`NaN` values.

</section>

<!-- /.notes -->

<section class="examples">

## Examples

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

```javascript
var uniform = require( '@stdlib/random/base/uniform' );
var bernoulli = require( '@stdlib/random/base/bernoulli' );
var incrnanmvariance = require( '@stdlib/stats/incr/nanmvariance' );

// Initialize an accumulator:
var accumulator = incrnanmvariance( 5 );

// For each simulated datum, update the moving variance...
var i;
for ( i = 0; i < 100; i++ ) {
accumulator( ( bernoulli( 0.8 ) < 1 ) ? NaN : uniform( -50.0, 50.0 ) );
}
console.log( accumulator() );
```

</section>

<!-- /.examples -->

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

<section class="related">

</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">

[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 format = require( '@stdlib/string/format' );
var pkg = require( './../package.json' ).name;
var incrnanmvariance = require( './../lib' );


// MAIN //

bench( pkg, function benchmark( b ) {
var f;
var i;
b.tic();
for ( i = 0; i < b.iterations; i++ ) {
f = incrnanmvariance( (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( format( '%s::accumulator', pkg ), function benchmark( b ) {
var acc;
var v;
var i;

acc = incrnanmvariance( 5 );

b.tic();
for ( i = 0; i < b.iterations; i++ ) {
v = acc( (i%5) ? i : NaN );
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();
});

bench( format( '%s::accumulator,known_mean', pkg ), function benchmark( b ) {
var acc;
var v;
var i;

acc = incrnanmvariance( 5, 3.0 );

b.tic();
for ( i = 0; i < b.iterations; i++ ) {
v = acc( (i%5) ? i : NaN );
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();
});
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50 changes: 50 additions & 0 deletions lib/node_modules/@stdlib/stats/incr/nanmvariance/docs/repl.txt
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{{alias}}( W[, mean] )
Returns an accumulator function which incrementally computes a moving
unbiased sample variance, ignoring `NaN` values.

The `W` parameter defines the number of values over which to compute the
moving unbiased sample variance.

If provided a value, the accumulator function returns an updated moving
unbiased sample variance. If not provided a value, the accumulator
function returns the current moving unbiased sample variance.

As `W` values are needed to fill the window buffer, the first `W-1` returned
values are calculated from smaller sample sizes. Until the window is full,
each returned value is calculated from all provided values.

NaN values are ignored during computation. If provided NaN, the
accumulator returns the current moving unbiased sample variance.

Parameters
----------
W: integer
Window size.

mean: number (optional)
Known mean.
Returns
-------
acc: Function
Accumulator function.

Examples
--------
> var accumulator = {{alias}}( 3 );
> var s2 = accumulator()
null
> s2 = accumulator( 2.0 )
0.0
> s2 = accumulator( NaN )
0.0
> s2 = accumulator( -5.0 )
24.5
> s2 = accumulator( 3.0 )
19.0
> s2 = accumulator()
19.0

See Also
--------

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