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feat(stats): add nanpcorrdist
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‎lib/node_modules/@stdlib/stats/incr/nanpcorrdist/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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# incrnanpcorrdist
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> Compute a [sample Pearson product-moment correlation distance][pearson-correlation] incrementally, ignoring `NaN` value.
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<section class="intro">
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The [sample Pearson product-moment correlation distance][pearson-correlation] is defined as
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<!-- <equation class="equation" label="eq:pearson_distance" align="center" raw="d_{x,y} = 1 - r_{x,y} = 1 - \frac{\operatorname{cov_n(x,y)}}{\sigma_x \sigma_y}" alt="Equation for the Pearson product-moment correlation distance."> -->
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```math
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d_{x,y} = 1 - r_{x,y} = 1 - \frac{\mathop{\mathrm{cov_n(x,y)}}}{\sigma_x \sigma_y}
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```
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<!-- <div class="equation" align="center" data-raw-text="d_{x,y} = 1 - r_{x,y} = 1 - \frac{\operatorname{cov_n(x,y)}}{\sigma_x \sigma_y}" data-equation="eq:pearson_distance">
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<img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@7e0a95722efd9c771b129597380c63dc6715508b/lib/node_modules/@stdlib/stats/incr/pcorrdist/docs/img/equation_pearson_distance.svg" alt="Equation for the Pearson product-moment correlation distance.">
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<br>
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</div> -->
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<!-- </equation> -->
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where `r` is the [sample Pearson product-moment correlation coefficient][pearson-correlation], `cov(x,y)` is the sample covariance, and `σ` corresponds to the sample standard deviation. As `r` resides on the interval `[-1,1]`, `d` resides on the interval `[0,2]`.
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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 incrnanpcorrdist = require( '@stdlib/stats/incr/nanpcorrdist' );
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```
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#### incrnanpcorrdist( \[mx, my] )
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Returns an accumulator `function` which incrementally computes a [sample Pearson product-moment correlation distance][pearson-correlation], ignoring `NaN` value.
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```javascript
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var accumulator = incrnanpcorrdist();
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```
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If the means are already known, provide `mx` and `my` arguments.
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```javascript
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var accumulator = incrnanpcorrdist( 3.0, -5.5 );
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```
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#### accumulator( \[x, y] )
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If provided input value `x` and `y`, the accumulator function returns an updated [sample correlation coefficient][pearson-correlation]. If not provided input values `x` and `y`, the accumulator function returns the current [sample correlation coefficient][pearson-correlation].
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```javascript
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var accumulator = incrnanpcorrdist();
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var d = accumulator( 2.0, 1.0 );
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// returns 1.0
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d = accumulator( NaN, 1.0 );
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// returns 1.0
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d = accumulator( 1.0, -5.0 );
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// returns 0.0
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d = accumulator( 1.0, NaN );
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// returns 0.0
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d = accumulator( 3.0, 3.14 );
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// returns ~0.035
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d = accumulator();
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// returns ~0.035
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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 provided `NaN` or a value which, when used in computations, results in `NaN`, the accumulator function skips updating its state and continues to return the most recent valid accumulated value. 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 incrnanpcorrdist = require( '@stdlib/stats/incr/nanpcorrdist' );
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var accumulator;
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var x;
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var y;
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var i;
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// Initialize an accumulator:
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accumulator = incrnanpcorrdist();
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// For each simulated datum, update the sample correlation distance...
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for ( i = 0; i < 100; i++ ) {
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if ( randu() < 0.2 ) {
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x = NaN;
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} else {
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x = randu() * 100.0;
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}
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if ( randu() < 0.2 ) {
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y = NaN;
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} else {
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y = randu() * 100.0;
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}
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accumulator( x, y );
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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/covariance`][@stdlib/stats/incr/covariance]</span><span class="delimiter">: </span><span class="description">compute an unbiased sample covariance incrementally.</span>
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- <span class="package-name">[`@stdlib/stats/incr/pcorr`][@stdlib/stats/incr/pcorr]</span><span class="delimiter">: </span><span class="description">compute a sample Pearson product-moment correlation coefficient.</span>
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- <span class="package-name">[`@stdlib/stats/incr/summary`][@stdlib/stats/incr/summary]</span><span class="delimiter">: </span><span class="description">compute a statistical summary 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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[pearson-correlation]: https://en.wikipedia.org/wiki/Pearson_correlation_coefficient
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<!-- <related-links> -->
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[@stdlib/stats/incr/covariance]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/incr/covariance
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[@stdlib/stats/incr/pcorr]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/incr/pcorr
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[@stdlib/stats/incr/summary]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/incr/summary
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<!-- </related-links> -->
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</section>
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<!-- /.links -->

‎lib/node_modules/@stdlib/stats/incr/nanpcorrdist/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) 2018 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 incrnanpcorrdist = 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 = incrnanpcorrdist();
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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 = incrnanpcorrdist();
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b.tic();
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for ( i = 0; i < b.iterations; i++ ) {
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v = acc( randu(), 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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});
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bench( pkg+'::accumulator,known_means', 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 = incrnanpcorrdist( 3.0, -2.0 );
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b.tic();
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for ( i = 0; i < b.iterations; i++ ) {
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v = acc( randu(), 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/nanpcorrdist/docs/img/equation_pearson_distance.svg‎

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

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{{alias}}( [mx, my] )
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Returns an accumulator function which incrementally computes a sample
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Pearson product-moment correlation distance, ignoring `NaN` value.
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The correlation distance is defined as one minus the Pearson product-moment
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correlation coefficient and, thus, resides on the interval [0,2].
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If provided values, the accumulator function returns an updated sample
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correlation distance. If not provided values, the accumulator function
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returns the current sample correlation distance.
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If provided `NaN` or a value which, when used in computations, results in
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`NaN`, the accumulated value is `NaN` for all future invocations.
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Parameters
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----------
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mx: number (optional)
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Known mean.
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my: number (optional)
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Known mean.
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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}}();
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> var d = accumulator()
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null
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> d = accumulator( 2.0, 1.0 )
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~1.0
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> d = accumulator( NaN, 1.0 )
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~1.0
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> d = accumulator( -5.0, 3.14 )
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~2.0
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> d = accumulator( -5.0, NaN )
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~2.0
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> d = accumulator()
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~2.0
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See Also
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--------
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