feat: add new package to the stats/incr/* namespace: @stdlib/stats/incr/nanmpcorrdist

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

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+<!--
+
+@license Apache-2.0
+
+Copyright (c) 2018 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.
+
+-->
+
+# incrnanmpcorrdist
+
+> Compute a moving [sample Pearson product-moment correlation distance][pearson-correlation] incrementally, ignoring `NaN` values.
+
+<section class="intro">
+
+The [sample Pearson product-moment correlation distance][pearson-correlation] is defined as
+
+<!-- <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."> -->
+
+```math
+d_{x,y} = 1 - r_{x,y} = 1 - \frac{\mathop{\mathrm{cov_n(x,y)}}}{\sigma_x \sigma_y}
+```
+
+<!-- <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">
+    <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@49d8cabda84033d55d7b8069f19ee3dd8b8d1496/lib/node_modules/@stdlib/stats/incr/mpcorrdist/docs/img/equation_pearson_distance.svg" alt="Equation for the Pearson product-moment correlation distance.">
+    <br>
+</div> -->
+
+<!-- </equation> -->
+
+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]`.
+
+</section>
+
+<!-- /.intro -->
+
+<section class="usage">
+
+## Usage
+
+```javascript
+var incrnanmpcorrdist = require( '@stdlib/stats/incr/nanmpcorrdist' );
+```
+
+#### incrnanmpcorrdist( window\[, mx, my] )
+
+Returns an accumulator `function` which incrementally computes a moving [sample Pearson product-moment correlation distance][pearson-correlation] while ignoring `NaN` values. The `window` parameter defines the number of values over which to compute the moving [sample Pearson product-moment correlation distance][pearson-correlation].
+
+```javascript
+var accumulator = incrnanmpcorrdist( 3 );
+```
+
+If means are already known, provide `mx` and `my` arguments.
+
+```javascript
+var accumulator = incrnanmpcorrdist( 3, 5.0, -3.14 );
+```
+
+#### accumulator( \[x, y] )
+
+If provided input values `x` and `y`, the accumulator function returns an updated [sample Pearson product-moment correlation distance][pearson-correlation]. If not provided input values `x` and `y`, the accumulator function returns the current [sample Pearson product-moment correlation distance][pearson-correlation].
+
+```javascript
+var accumulator = incrnanmpcorrdist( 3 );
+
+var r = accumulator();
+// returns null
+
+// Fill the window...
+r = accumulator( 2.0, 1.0 ); // [(2.0, 1.0)]
+// returns 1.0
+
+r = accumulator( NaN, 3.0 ); // [(2.0, 1.0), (NaN, 3.0)]
+// returns 1.0
+
+r = accumulator( 4.0, NaN ); // [(2.0, 1.0), (NaN, 3.0), (4.0, NaN)]
+// returns 1.0
+
+// Window begins sliding...
+r = accumulator( NaN, NaN ); // [(NaN, 3.0), (4.0, NaN), (NaN, NaN)]
+// returns 1.0
+
+r = accumulator( 5.0, 2.0 ); // [(4.0, NaN), (NaN, NaN), 5.0, 2.0]
+// returns 0.0
+
+r = accumulator();
+// returns 0.0
+```
+
+</section>
+
+<!-- /.usage -->
+
+<section class="notes">
+
+## Notes
+
+-   Input values are **not** type checked. If provided `NaN` or a value which, when used in computations, results in `NaN`, it will be ignored, and the accumulated value is the last one. If non-numeric inputs are possible, you are advised to type check and handle accordingly **before** passing the value to the accumulator function.
+-   As `W` (x,y) pairs 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.
+-   Due to limitations inherent in representing numeric values using floating-point format (i.e., the inability to represent numeric values with infinite precision), the [sample correlation distance][pearson-correlation] between perfectly correlated random variables may **not** be `0` or `2`. In fact, the [sample correlation distance][pearson-correlation] is **not** guaranteed to be strictly on the interval `[0,2]`. Any computed distance should, however, be within floating-point roundoff error.
+
+</section>
+
+<!-- /.notes -->
+
+<section class="examples">
+
+## Examples
+
+<!-- eslint no-undef: "error" -->
+
+```javascript
+var randu = require( '@stdlib/random/base/randu' );
+var incrnanmpcorrdist = require( '@stdlib/stats/incr/nanmpcorrdist' );
+
+var accumulator;
+var x;
+var y;
+var i;
+
+// Initialize an accumulator:
+accumulator = incrnanmpcorrdist( 5 );
+var d;
+
+// For each simulated datum, update the moving sample correlation distance...
+for ( i = 0; i < 100; i++ ) {
+    if ( randu() < 0.2 ) {
+        x = NaN;
+    }
+    else {
+        x = randu() * 100.0;
+    }
+    if ( randu() < 0.2 ) {
+        y = NaN;
+    }
+    else {
+        y = randu() * 100.0;
+    }
+    d = accumulator( x, y );
+    console.log( '%d\t%d\t%d', x.toFixed( 4 ), y.toFixed( 4 ), ( d === null ) ? NaN : d.toFixed( 4 ) );
+}
+```
+
+</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">
+
+[pearson-correlation]: https://en.wikipedia.org/wiki/Pearson_correlation_coefficient
+
+</section>
+
+<!-- /.links -->

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

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+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2018 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 incrnanmpcorrdist = require( './../lib' );
+
+
+// MAIN //
+
+bench( pkg, function benchmark( b ) {
+	var f;
+	var i;
+	b.tic();
+	for ( i = 0; i < b.iterations; i++ ) {
+		f = incrnanmpcorrdist( ( 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 = incrnanmpcorrdist( 5 );
+
+	b.tic();
+	for ( i = 0; i < b.iterations; i++ ) {
+		v = acc( randu(), 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();
+});
+
+bench( pkg+'::accumulator,known_means', function benchmark( b ) {
+	var acc;
+	var v;
+	var i;
+
+	acc = incrnanmpcorrdist( 5, 3.0, -1.0 );
+
+	b.tic();
+	for ( i = 0; i < b.iterations; i++ ) {
+		v = acc( randu(), 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();
+});