diff --git a/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/README.md b/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/README.md
new file mode 100644
index 000000000000..957ad380fb88
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/README.md
@@ -0,0 +1,175 @@
+
+
+# incrnanmpcorrdist
+
+> Compute a moving [sample Pearson product-moment correlation distance][pearson-correlation] incrementally, ignoring `NaN` values.
+
+
+
+The [sample Pearson product-moment correlation distance][pearson-correlation] is defined as
+
+
+
+```math
+d_{x,y} = 1 - r_{x,y} = 1 - \frac{\mathop{\mathrm{cov_n(x,y)}}}{\sigma_x \sigma_y}
+```
+
+
+
+
+
+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]`.
+
+
+
+
+
+
+
+## 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
+```
+
+
+
+
+
+
+
+## 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.
+
+
+
+
+
+
+
+## Examples
+
+
+
+```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 ) );
+}
+```
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+[pearson-correlation]: https://en.wikipedia.org/wiki/Pearson_correlation_coefficient
+
+
+
+
diff --git a/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/benchmark/benchmark.js b/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/benchmark/benchmark.js
new file mode 100644
index 000000000000..a21dc347570a
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/benchmark/benchmark.js
@@ -0,0 +1,91 @@
+/**
+* @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();
+});
diff --git a/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/docs/img/equation_pearson_distance.svg b/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/docs/img/equation_pearson_distance.svg
new file mode 100644
index 000000000000..6df633f17b81
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/docs/img/equation_pearson_distance.svg
@@ -0,0 +1,70 @@
+
\ No newline at end of file
diff --git a/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/docs/repl.txt b/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/docs/repl.txt
new file mode 100644
index 000000000000..e9488748c4ce
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/docs/repl.txt
@@ -0,0 +1,64 @@
+
+{{alias}}( W[, mx, my] )
+ Returns an accumulator function which incrementally computes a moving
+ sample Pearson product-moment correlation distance, ignoring
+ `NaN` values.
+
+ The correlation distance is defined as one minus the Pearson product-moment
+ correlation coefficient and, thus, resides on the interval [0,2].
+
+ However, due to limitations inherent in representing numeric values using
+ floating-point format (i.e., the inability to represent numeric values with
+ infinite precision), the correlation distance between perfectly correlated
+ random variables may *not* be `0` or `2`. In fact, the correlation distance
+ is *not* guaranteed to be strictly on the interval [0,2]. Any computed
+ distance should, however, be within floating-point roundoff error.
+
+ The `W` parameter defines the number of values over which to compute the
+ moving sample correlation distance.
+
+ If provided values, the accumulator function returns an updated moving
+ sample correlation distance. If not provided values, the accumulator
+ function returns the current moving sample correlation distance.
+
+ 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.
+
+ Parameters
+ ----------
+ W: integer
+ Window size.
+
+ mx: number (optional)
+ Known mean.
+
+ my: number (optional)
+ Known mean.
+
+ Returns
+ -------
+ acc: Function
+ Accumulator function.
+
+ Examples
+ --------
+ > var accumulator = {{alias}}( 3 );
+ > var d = accumulator()
+ null
+ > d = accumulator( 2.0, 1.0 )
+ 1.0
+ > d = accumulator( NaN, 3.0 )
+ 1.0
+ > d = accumulator( 4.0, NaN )
+ 1.0
+ > d = accumulator( NaN, NaN )
+ 1.0
+ > d = accumulator( 5.0, 2.0 )
+ 0.0
+ > d = accumulator()
+ 0.0
+
+ See Also
+ --------
+
diff --git a/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/docs/types/index.d.ts b/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/docs/types/index.d.ts
new file mode 100644
index 000000000000..d33bf2c245be
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/docs/types/index.d.ts
@@ -0,0 +1,97 @@
+/*
+* @license Apache-2.0
+*
+* Copyright (c) 2019 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.
+*/
+
+// TypeScript Version: 4.1
+
+///
+
+/**
+* With ignoring `NaN` values, if provided input values, the accumulator function returns an updated moving sample correlation distance ignoring . If not provided input values, the accumulator function returns the current moving sample correlation distance.
+*
+* ## Notes
+*
+* - The correlation distance is defined as one minus the Pearson product-moment correlation coefficient and, thus, resides on the interval [0,2].
+* - If provided `NaN` or a value which, when used in computations, results in `NaN`, the accumulated value is `NaN` for all future invocations.
+*
+* @param x - value
+* @param y - value
+* @returns updated moving sample correlation distance
+*/
+type accumulator = ( x?: number, y?: number ) => number | null;
+
+/**
+* Returns an accumulator function which incrementally computes a moving sample Pearson product-moment correlation distance ignoring ignoring `NaN` values.
+*
+* ## Notes
+*
+* - The `W` parameter defines the number of values over which to compute the moving sample correlation distance.
+* - 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.
+*
+* @param W - window size
+* @param meanx - mean value
+* @param meany - mean value
+* @throws first argument must be a positive integer
+* @returns accumulator function
+*
+* @example
+* var accumulator = incrnanmpcorrdist( 3, -2.0, 10.0 );
+*/
+declare function incrnanmpcorrdist( W: number, meanx: number, meany: number ): accumulator;
+
+/**
+* Returns an accumulator function which incrementally computes a moving sample Pearson product-moment correlation distance ignoring `NaN` values.
+*
+* ## Notes
+*
+* - The `W` parameter defines the number of values over which to compute the moving sample correlation distance.
+* - 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.
+*
+* @param W - window size
+* @throws first argument must be a positive integer
+* @returns accumulator function
+*
+* @example
+* var accumulator = incrnanmpcorrdist( 3 );
+*
+* var d = accumulator();
+* // returns null
+*
+* d = accumulator( 2.0, 1.0 );
+* // returns 1.0
+*
+* d = accumulator( NaN, 3.0 );
+* // returns 1.0
+*
+* d = accumulator( 4.0, NaN );
+* // returns 1.0
+*
+* d = accumulator( NaN, NaN );
+* // returns 1.0
+*
+* d = accumulator( 5.0, 2.0 );
+* // returns 0.0
+*
+* d = accumulator();
+* // returns 0.0
+*/
+declare function incrnanmpcorrdist( W: number ): accumulator;
+
+
+// EXPORTS //
+
+export = incrnanmpcorrdist;
diff --git a/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/docs/types/test.ts b/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/docs/types/test.ts
new file mode 100644
index 000000000000..2f45627eae36
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/docs/types/test.ts
@@ -0,0 +1,123 @@
+/*
+* @license Apache-2.0
+*
+* Copyright (c) 2019 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.
+*/
+
+import incrnanmpcorrdist = require( './index' );
+
+
+// TESTS //
+
+// The function returns an accumulator function...
+{
+ incrnanmpcorrdist( 3 ); // $ExpectType accumulator
+ incrnanmpcorrdist( 3, 2.5, 4.5 ); // $ExpectType accumulator
+}
+
+// The compiler throws an error if the function is provided non-numeric arguments...
+{
+ incrnanmpcorrdist( 2, '5' ); // $ExpectError
+ incrnanmpcorrdist( 2, true ); // $ExpectError
+ incrnanmpcorrdist( 2, false ); // $ExpectError
+ incrnanmpcorrdist( 2, null ); // $ExpectError
+ incrnanmpcorrdist( 2, undefined ); // $ExpectError
+ incrnanmpcorrdist( 2, [] ); // $ExpectError
+ incrnanmpcorrdist( 2, {} ); // $ExpectError
+ incrnanmpcorrdist( 2, ( x: number ): number => x ); // $ExpectError
+
+ incrnanmpcorrdist( '5', 4 ); // $ExpectError
+ incrnanmpcorrdist( true, 4 ); // $ExpectError
+ incrnanmpcorrdist( false, 4 ); // $ExpectError
+ incrnanmpcorrdist( null, 4 ); // $ExpectError
+ incrnanmpcorrdist( undefined, 4 ); // $ExpectError
+ incrnanmpcorrdist( [], 4 ); // $ExpectError
+ incrnanmpcorrdist( {}, 4 ); // $ExpectError
+ incrnanmpcorrdist( ( x: number ): number => x, 4 ); // $ExpectError
+
+ incrnanmpcorrdist( '5', 2.5, 3.5 ); // $ExpectError
+ incrnanmpcorrdist( true, 2.5, 3.5 ); // $ExpectError
+ incrnanmpcorrdist( false, 2.5, 3.5 ); // $ExpectError
+ incrnanmpcorrdist( null, 2.5, 3.5 ); // $ExpectError
+ incrnanmpcorrdist( undefined, 2.5, 3.5 ); // $ExpectError
+ incrnanmpcorrdist( [], 2.5, 3.5 ); // $ExpectError
+ incrnanmpcorrdist( {}, 2.5, 3.5 ); // $ExpectError
+ incrnanmpcorrdist( ( x: number ): number => x, 2.5, 3.5 ); // $ExpectError
+}
+
+// The compiler throws an error if the function is provided an invalid number of arguments...
+{
+ incrnanmpcorrdist(); // $ExpectError
+ incrnanmpcorrdist( 2, 2 ); // $ExpectError
+ incrnanmpcorrdist( 2, 2, 3, 4 ); // $ExpectError
+}
+
+// The function returns an accumulator function which returns an accumulated result...
+{
+ const acc = incrnanmpcorrdist( 3 );
+
+ acc(); // $ExpectType number | null
+ acc( 3.14, 2.0 ); // $ExpectType number | null
+}
+
+// The function returns an accumulator function which returns an accumulated result (known means)...
+{
+ const acc = incrnanmpcorrdist( 3, 2, -3 );
+
+ acc(); // $ExpectType number | null
+ acc( 3.14, 2.0 ); // $ExpectType number | null
+}
+
+// The compiler throws an error if the returned accumulator function is provided invalid arguments...
+{
+ const acc = incrnanmpcorrdist( 3 );
+
+ acc( '5', 1.0 ); // $ExpectError
+ acc( true, 1.0 ); // $ExpectError
+ acc( false, 1.0 ); // $ExpectError
+ acc( null, 1.0 ); // $ExpectError
+ acc( [], 1.0 ); // $ExpectError
+ acc( {}, 1.0 ); // $ExpectError
+ acc( ( x: number ): number => x, 1.0 ); // $ExpectError
+
+ acc( 3.14, '5' ); // $ExpectError
+ acc( 3.14, true ); // $ExpectError
+ acc( 3.14, false ); // $ExpectError
+ acc( 3.14, null ); // $ExpectError
+ acc( 3.14, [] ); // $ExpectError
+ acc( 3.14, {} ); // $ExpectError
+ acc( 3.14, ( x: number ): number => x ); // $ExpectError
+}
+
+// The compiler throws an error if the returned accumulator function is provided invalid arguments (known means)...
+{
+ const acc = incrnanmpcorrdist( 3, 2, -3 );
+
+ acc( '5', 1.0 ); // $ExpectError
+ acc( true, 1.0 ); // $ExpectError
+ acc( false, 1.0 ); // $ExpectError
+ acc( null, 1.0 ); // $ExpectError
+ acc( [], 1.0 ); // $ExpectError
+ acc( {}, 1.0 ); // $ExpectError
+ acc( ( x: number ): number => x, 1.0 ); // $ExpectError
+
+ acc( 3.14, '5' ); // $ExpectError
+ acc( 3.14, true ); // $ExpectError
+ acc( 3.14, false ); // $ExpectError
+ acc( 3.14, null ); // $ExpectError
+ acc( 3.14, [] ); // $ExpectError
+ acc( 3.14, {} ); // $ExpectError
+ acc( 3.14, ( x: number ): number => x ); // $ExpectError
+}
diff --git a/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/examples/index.js b/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/examples/index.js
new file mode 100644
index 000000000000..1e837fc367df
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/examples/index.js
@@ -0,0 +1,50 @@
+/**
+* @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';
+
+var randu = require( '@stdlib/random/base/randu' );
+var incrnanmpcorrdist = require( './../lib' );
+
+var accumulator;
+var d;
+var x;
+var y;
+var i;
+
+// Initialize an accumulator:
+accumulator = incrnanmpcorrdist( 5 );
+
+// For each simulated datum, update the moving sample correlation distance...
+console.log( '\nx\ty\tCorrelation Distance\n' );
+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 ) );
+}
diff --git a/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/lib/index.js b/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/lib/index.js
new file mode 100644
index 000000000000..85da6ba3b046
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/lib/index.js
@@ -0,0 +1,60 @@
+/**
+* @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';
+
+/**
+* Compute a moving sample Pearson product-moment correlation distance incrementally, ignoring `NaN` values.
+*
+* @module @stdlib/stats/incr/nanmpcorrdist
+*
+* @example
+* var incrnanmpcorrdist = require( '@stdlib/stats/incr/nanmpcorrdist' );
+*
+* var accumulator = incrnanmpcorrdist( 3 );
+*
+* var d = accumulator();
+* // returns null
+*
+* d = accumulator( 2.0, 1.0 );
+* // returns 1.0
+*
+* d = accumulator( NaN, 3.0 );
+* // returns 1.0
+*
+* d = accumulator( 4.0, NaN );
+* // returns 1.0
+*
+* d = accumulator( NaN, NaN );
+* // returns 1.0
+*
+* d = accumulator( 5.0, 2.0 );
+* // returns 0.0
+*
+* d = accumulator();
+* // returns 0.0
+*/
+
+// MODULES //
+
+var main = require( './main.js' );
+
+
+// EXPORTS //
+
+module.exports = main;
diff --git a/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/lib/main.js b/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/lib/main.js
new file mode 100644
index 000000000000..b6b15612aa90
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/lib/main.js
@@ -0,0 +1,99 @@
+/**
+* @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 isnan = require( '@stdlib/math/base/assert/is-nan' );
+var incrmpcorrdist = require( '@stdlib/stats/incr/mpcorrdist' );
+
+
+// MAIN //
+
+/**
+* Returns an accumulator function which incrementally computes a moving sample Pearson product-moment correlation distance, ignoring `NaN` values.
+*
+* @param {PositiveInteger} W - Sliding window size for the moving calculation. Must be a positive integer.
+* @param {number} [x] - Initial mean value of the first dataset. Defaults to `NaN`.
+* @param {number} [y] - Initial mean value of the second dataset. Defaults to `NaN`.
+* @returns {Function} Accumulator function which computes the Pearson correlation distance.
+*
+* @example
+* var accumulator = incrnanmpcorrdist( 3 );
+*
+* var d = accumulator();
+* // returns null
+*
+* d = accumulator( 2.0, 1.0 );
+* // returns 1.0
+*
+* d = accumulator( NaN, 3.0 );
+* // returns 1.0
+*
+* d = accumulator( 4.0, NaN );
+* // returns 1.0
+*
+* d = accumulator( NaN, NaN );
+* // returns 1.0
+*
+* d = accumulator( 5.0, 2.0 );
+* // returns 0.0
+*
+* d = accumulator();
+* // returns 0.0
+*
+* @example
+* var accumulator = incrnanmpcorrdist( 3, -2.0, 10.0 );
+*/
+function incrnanmpcorrdist( W, x, y ) {
+ var d;
+ if ( arguments.length > 1 ) {
+ if ( isnan( x ) || isnan( y ) ) {
+ d = incrmpcorrdist( W );
+ }
+ else {
+ d = incrmpcorrdist( W, x, y );
+ }
+ }
+ else {
+ d = incrmpcorrdist( W );
+ }
+ return accumulator;
+
+ /**
+ * If provided a value, the accumulator function returns an updated moving sample Pearson product-moment correlation distance. If not provided a value, the accumulator function returns the current moving sample Pearson product-moment correlation distance.
+ *
+ * @private
+ * @param {number} [x] - new value
+ * @param {number} [y] - new value
+ * @returns {(number|null)} moving sample Pearson product-moment correlation distance or null
+ */
+ function accumulator( x, y ) {
+ if ( arguments.length === 0 || isnan( x ) || isnan( y ) ) {
+ return d();
+ }
+
+ return d( x, y );
+ }
+}
+
+
+// EXPORTS //
+
+module.exports = incrnanmpcorrdist;
diff --git a/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/package.json b/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/package.json
new file mode 100644
index 000000000000..cc4d4bc12d76
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/package.json
@@ -0,0 +1,64 @@
+{
+ "name": "@stdlib/stats/incr/nanmpcorrdist",
+ "version": "0.0.0",
+ "description": "Compute a moving sample Pearson product-moment correlation distance incrementally while ignoring `NaN` values.",
+ "license": "Apache-2.0",
+ "author": {
+ "name": "The Stdlib Authors",
+ "url": "https://github.com/stdlib-js/stdlib/graphs/contributors"
+ },
+ "contributors": [
+ {
+ "name": "The Stdlib Authors",
+ "url": "https://github.com/stdlib-js/stdlib/graphs/contributors"
+ }
+ ],
+ "main": "./lib",
+ "directories": {
+ "benchmark": "./benchmark",
+ "doc": "./docs",
+ "example": "./examples",
+ "lib": "./lib",
+ "test": "./test"
+ },
+ "types": "./docs/types",
+ "scripts": {},
+ "homepage": "https://github.com/stdlib-js/stdlib",
+ "repository": {
+ "type": "git",
+ "url": "git://github.com/stdlib-js/stdlib.git"
+ },
+ "bugs": {
+ "url": "https://github.com/stdlib-js/stdlib/issues"
+ },
+ "dependencies": {},
+ "devDependencies": {},
+ "engines": {
+ "node": ">=0.10.0",
+ "npm": ">2.7.0"
+ },
+ "os": [
+ "aix",
+ "darwin",
+ "freebsd",
+ "linux",
+ "macos",
+ "openbsd",
+ "sunos",
+ "win32",
+ "windows"
+ ],
+ "keywords": [
+ "stdlib",
+ "stdmath",
+ "statistics",
+ "stats",
+ "mathematics",
+ "math",
+ "summation",
+ "sum",
+ "total",
+ "incremental",
+ "accumulator"
+ ]
+ }
diff --git a/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/test/test.js b/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/test/test.js
new file mode 100644
index 000000000000..488f0cf0df9b
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/incr/nanmpcorrdist/test/test.js
@@ -0,0 +1,671 @@
+/**
+* @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 tape = require( 'tape' );
+var randu = require( '@stdlib/random/base/randu' );
+var abs = require( '@stdlib/math/base/special/abs' );
+var pow = require( '@stdlib/math/base/special/pow' );
+var sqrt = require( '@stdlib/math/base/special/sqrt' );
+var isnan = require( '@stdlib/math/base/assert/is-nan' );
+var EPS = require( '@stdlib/constants/float64/eps' );
+var min = require( '@stdlib/math/base/special/min' );
+var incrnanmpcorrdist = require( './../lib' );
+
+
+// FUNCTIONS //
+
+/**
+* Computes sample means using Welford's algorithm.
+*
+* @private
+* @param {Array} out - output array
+* @param {ArrayArray} arr - input array
+* @returns {Array} output array
+*/
+function mean( out, arr ) {
+ var delta;
+ var mx;
+ var my;
+ var N;
+ var i;
+
+ mx = 0.0;
+ my = 0.0;
+
+ N = 0;
+ for ( i = 0; i < arr.length; i++ ) {
+ N += 1;
+ delta = arr[ i ][ 0 ] - mx;
+ mx += delta / N;
+ delta = arr[ i ][ 1 ] - my;
+ my += delta / N;
+ }
+ out[ 0 ] = mx;
+ out[ 1 ] = my;
+ return out;
+}
+
+/**
+* Computes standard deviations.
+*
+* @private
+* @param {Array} out - output array
+* @param {ArrayArray} arr - input array
+* @param {number} mx - `x` mean
+* @param {number} my - `y` mean
+* @param {boolean} bool - boolean indicating whether to compute a biased standard deviation
+* @returns {Array} output array
+*/
+function stdev( out, arr, mx, my, bool ) {
+ var delta;
+ var M2x;
+ var M2y;
+ var N;
+ var i;
+
+ M2x = 0.0;
+ M2y = 0.0;
+
+ N = 0;
+ for ( i = 0; i < arr.length; i++ ) {
+ N += 1;
+ delta = arr[ i ][ 0 ] - mx;
+ M2x += delta * delta;
+ delta = arr[ i ][ 1 ] - my;
+ M2y += delta * delta;
+ }
+ if ( bool ) {
+ out[ 0 ] = sqrt( M2x / N );
+ out[ 1 ] = sqrt( M2y / N );
+ return out;
+ }
+ if ( N < 2 ) {
+ out[ 0 ] = 0.0;
+ out[ 1 ] = 0.0;
+ return out;
+ }
+ out[ 0 ] = sqrt( M2x / ( N - 1 ) );
+ out[ 1 ] = sqrt( M2y / ( N - 1 ) );
+ return out;
+}
+
+/**
+* Computes the covariance using textbook formula.
+*
+* @private
+* @param {ArrayArray} arr - input array
+* @param {number} mx - `x` mean
+* @param {number} my - `y` mean
+* @param {boolean} bool - boolean indicating whether to compute the population covariance
+* @returns {number} covariance
+*/
+function covariance( arr, mx, my, bool ) {
+ var N;
+ var C;
+ var i;
+
+ N = arr.length;
+ C = 0.0;
+ for ( i = 0; i < N; i++ ) {
+ C += ( arr[ i ][ 0 ] - mx ) * ( arr[ i ][ 1 ] - my );
+ }
+ if ( bool ) {
+ return C / N;
+ }
+ if ( N === 1 ) {
+ return 0.0;
+ }
+ return C / ( N - 1 );
+}
+
+/**
+* Computes the sample Pearson product-moment correlation distance using textbook formula.
+*
+* @private
+* @param {ArrayArray} arr - input array
+* @param {number} mx - `x` mean
+* @param {number} my - `y` mean
+* @param {boolean} bool - boolean indicating whether to compute the population correlation distance
+* @returns {number} correlation distance
+*/
+function pcorrdist( arr, mx, my, bool ) {
+ var cov;
+ var sd;
+ var d;
+ if ( bool === false && arr.length < 2 ) {
+ return 1.0;
+ }
+ sd = stdev( [ 0.0, 0.0 ], arr, mx, my, bool );
+ cov = covariance( arr, mx, my, bool );
+ d = 1.0 - ( cov / ( sd[ 0 ] * sd[ 1 ] ) );
+ if ( d < 0.0 ) {
+ return 0.0;
+ }
+ if ( d > 2.0 ) {
+ return 2.0;
+ }
+ return d;
+}
+
+/**
+* Generates a set of sample datasets.
+*
+* @private
+* @param {PositiveInteger} N - number of datasets
+* @param {PositiveInteger} M - dataset length
+* @param {PositiveInteger} [seed] - PRNG seed
+* @returns {ArrayArray} sample datasets
+*/
+function nandatasets( N, M, seed ) {
+ var data;
+ var rand;
+ var tmp;
+ var i;
+ var j;
+
+ rand = randu.factory({
+ 'seed': seed || ( randu() * pow( 2.0, 31 ) )|0
+ });
+
+ // Generate datasets consisting of (x,y) pairs of varying value ranges, where some of the pairs may contain `(NaN, y)`, `(x, NaN)`, or `(NaN, NaN)`...
+ data = [];
+ for ( i = 0; i < N; i++ ) {
+ tmp = [];
+ for ( j = 0; j < M; j++ ) {
+ tmp.push([
+ ( rand() < 0.2 ) ? NaN : rand() * pow( 10.0, i ),
+ ( rand() < 0.2 ) ? NaN : rand() * pow( 10.0, i )
+ ]);
+ }
+ data.push( tmp );
+ }
+ return data;
+}
+
+
+// TESTS //
+
+tape( 'main export is a function', function test( t ) {
+ t.ok( true, __filename );
+ t.strictEqual( typeof incrnanmpcorrdist, 'function', 'main export is a function' );
+ t.end();
+});
+
+tape( 'the function throws an error if not provided a positive integer for the window size', function test( t ) {
+ var values;
+ var i;
+
+ values = [
+ '5',
+ -5.0,
+ 0.0,
+ 3.14,
+ true,
+ null,
+ void 0,
+ NaN,
+ [],
+ {},
+ function noop() {}
+ ];
+
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided '+values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ incrnanmpcorrdist( value );
+ };
+ }
+});
+
+tape( 'the function throws an error if not provided a positive integer for the window size (known means)', function test( t ) {
+ var values;
+ var i;
+
+ values = [
+ '5',
+ -5.0,
+ 0.0,
+ 3.14,
+ true,
+ null,
+ void 0,
+ NaN,
+ [],
+ {},
+ function noop() {}
+ ];
+
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided '+values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ incrnanmpcorrdist( value, 3.0, 3.14 );
+ };
+ }
+});
+
+tape( 'the function throws an error if not provided a number as the mean value', function test( t ) {
+ var values;
+ var i;
+
+ values = [
+ '5',
+ true,
+ false,
+ null,
+ void 0,
+ [],
+ {},
+ function noop() {}
+ ];
+
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided '+values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ incrnanmpcorrdist( 3, value, 3.14 );
+ };
+ }
+});
+
+tape( 'the function throws an error if not provided a number as the mean value', function test( t ) {
+ var values;
+ var i;
+
+ values = [
+ '5',
+ true,
+ false,
+ null,
+ void 0,
+ [],
+ {},
+ function noop() {}
+ ];
+
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided '+values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ incrnanmpcorrdist( 3, 3.14, value );
+ };
+ }
+});
+
+tape('the function properly ignores NaN values in x and y inputs', function test( t ) {
+ var result;
+ var acc;
+
+ acc = incrnanmpcorrdist( 3, 3.14, 0.0 );
+ result = acc();
+
+ t.ok(!isnan( result ), 'result should not be NaN');
+ t.end();
+});
+
+tape( 'the function returns an accumulator function', function test( t ) {
+ t.equal( typeof incrnanmpcorrdist( 3 ), 'function', 'returns a function' );
+ t.end();
+});
+
+tape( 'the function returns an accumulator function (known means)', function test( t ) {
+ t.equal( typeof incrnanmpcorrdist( 3, 3.0, 3.14 ), 'function', 'returns a function' );
+ t.end();
+});
+
+tape( 'the accumulator function computes a moving sample Pearson product-moment correlation distance incrementally', function test( t ) {
+ var countNonNanPairs;
+ var expected;
+ var actual;
+ var delta;
+ var means;
+ var data;
+ var acc;
+ var arr;
+ var tol;
+ var d;
+ var N;
+ var M;
+ var W;
+ var i;
+ var j;
+ var k;
+
+ N = 10;
+ M = 100;
+ data = nandatasets( N, M, randu.seed );
+
+ t.pass( 'seed: ' + randu.seed );
+
+ // Define the window size:
+ W = 10;
+
+ // For each dataset, compute the actual and expected correlation distances...
+ for ( i = 0; i < N; i++ ) {
+ d = data[ i ];
+
+ acc = incrnanmpcorrdist( W );
+ countNonNanPairs = 0;
+ for ( j = 0; j < M; j++ ) {
+ actual = acc( d[ j ][ 0 ], d[ j ][ 1 ] );
+ countNonNanPairs += ( !isnan( d[ j ][ 0 ] ) && !isnan( d[ j ][ 1 ] ) ) ? 1 : 0;
+
+ arr = [];
+ k = j;
+ while ( arr.length < min( countNonNanPairs, W ) && k >= 0 ) {
+ if ( !isnan( d[ k ][ 0 ] ) && !isnan( d[ k ][ 1 ] ) ) {
+ arr.push( d[ k ] );
+ }
+ k -= 1;
+ }
+
+ if ( arr.length > 0 ) {
+ means = mean( [ 0.0, 0.0 ], arr );
+ } else {
+ means = null;
+ }
+
+ if ( arr.length > 0 ) {
+ expected = pcorrdist( arr, means[ 0 ], means[ 1 ], false );
+ } else {
+ expected = null;
+ }
+
+ if ( actual === expected ) {
+ t.equal( actual, expected, 'returns expected value. dataset: ' + i + '. window: ' + j + '.' );
+ } else {
+ if ( actual < 0.0 ) {
+ actual = 0.0; // NOTE: this addresses occasional negative values due to accumulated floating-point error. Based on observation, typically `|actual| ≅ |expected|`, but `actual < 0` and `expected > 0`, suggesting that a sign got "flipped" along the way due to, e.g., operations which theoretically should compute to the same value, but do not due to floating-point error.
+ }
+ delta = abs( actual - expected );
+ if ( expected === 0.0 || actual === 0.0 ) {
+ tol = 10.0 * EPS;
+ } else {
+ tol = 1.0e6 * EPS * abs( expected );
+ }
+ t.equal( delta <= tol, true, 'dataset: ' + i + '. window: ' + j + '. expected: ' + expected + '. actual: ' + actual + '. tol: ' + tol + '. delta: ' + delta + '.' );
+ }
+ }
+ }
+ t.end();
+});
+
+tape( 'the accumulator function computes a moving sample Pearson product-moment correlation distance incrementally (known means)', function test( t ) {
+ var countNonNanPairs;
+ var filteredArr;
+ var expected;
+ var actual;
+ var means;
+ var delta;
+ var data;
+ var acc;
+ var arr;
+ var tol;
+ var d;
+ var N;
+ var M;
+ var W;
+ var i;
+ var j;
+ var k;
+
+ N = 10;
+ M = 100;
+ data = nandatasets( N, M, randu.seed );
+
+ t.pass( 'seed: ' + randu.seed );
+
+ // Define the window size:
+ W = 10;
+
+ // For each dataset, compute the actual and expected correlation distances...
+ function isNotNaN( pair ) {
+ return !isnan( pair[ 0 ] ) && !isnan( pair[ 1 ] );
+ }
+ for ( i = 0; i < N; i++ ) {
+ d = data[ i ];
+ filteredArr = d.filter( isNotNaN );
+ if ( filteredArr.length > 0 ) {
+ means = mean( [ 0.0, 0.0 ], filteredArr );
+ } else {
+ means = null;
+ }
+ acc = incrnanmpcorrdist( W, means[ 0 ], means[ 1 ] );
+ countNonNanPairs = 0;
+ for ( j = 0; j < M; j++ ) {
+ actual = acc( d[ j ][ 0 ], d[ j ][ 1 ] );
+ countNonNanPairs += ( !isnan( d[ j ][ 0 ] ) && !isnan( d[ j ][ 1 ] ) ) ? 1 : 0;
+
+ arr = [];
+ k = j;
+ while ( arr.length < min( countNonNanPairs, W ) && k >= 0 ) {
+ if ( !isnan( d[ k ][ 0 ] ) && !isnan( d[ k ][ 1 ] ) ) {
+ arr.push( d[ k ] );
+ }
+ k -= 1;
+ }
+
+ if ( means !== null && arr.length > 0 ) {
+ expected = pcorrdist( arr, means[ 0 ], means[ 1 ], true );
+ } else {
+ expected = null;
+ }
+
+ if ( actual === expected ) {
+ t.equal( actual, expected, 'returns expected value. dataset: ' + i + '. window: ' + j + '.' );
+ } else {
+ delta = abs( actual - expected );
+ tol = 1.0e6 * EPS * abs( expected );
+ t.equal( delta <= tol, true, 'dataset: ' + i + '. window: ' + j + '. expected: ' + expected + '. actual: ' + actual + '. tol: ' + tol + '. delta: ' + delta + '.' );
+ }
+ }
+ }
+ t.end();
+});
+
+tape( 'if not provided an input value, the accumulator function returns the current sample correlation distance (unknown means)', function test( t ) {
+ var expected;
+ var actual;
+ var means;
+ var delta;
+ var data;
+ var tol;
+ var acc;
+ var W;
+ var N;
+ var d;
+ var i;
+
+ data = [
+ [ 2.0, 1.0 ],
+ [ -5.0, NaN ],
+ [ 3.0, -1.0 ],
+ [ NaN, NaN ]
+ ];
+ N = data.length;
+
+ // Window size:
+ W = 4;
+
+ acc = incrnanmpcorrdist( W );
+ for ( i = 0; i < N; i++ ) {
+ acc( data[ i ][ 0 ], data[ i ][ 1 ] );
+ actual = acc();
+ if ( i < W - 1 ) {
+ d = data.slice( 0, i + 1 );
+ } else {
+ d = data.slice( i - W + 1, i + 1 );
+ }
+ d = d.filter( function isNotNaN( pair ) {
+ return !isnan( pair[ 0 ] ) && !isnan( pair[ 1 ] );
+ });
+
+ if ( d.length > 0 ) {
+ means = mean( [ 0.0, 0.0 ], d );
+ } else {
+ means = null;
+ }
+
+ if ( means !== null ) {
+ expected = pcorrdist( d, means[ 0 ], means[ 1 ], false );
+ }
+
+ if ( actual === expected ) {
+ t.equal( actual, expected, 'returns expected value. window: ' + i + '.' );
+ } else {
+ delta = abs( actual - expected );
+ tol = 1.0 * EPS * abs( expected );
+ t.equal( delta < tol, true, 'window: ' + i + '. expected: ' + expected + '. actual: ' + actual + '. tol: ' + tol + '. delta: ' + delta + '.' );
+ }
+ }
+ t.end();
+});
+
+tape( 'if not provided an input value, the accumulator function returns the current sample correlation distance (known means)', function test( t ) {
+ var filteredArr;
+ var expected;
+ var actual;
+ var means;
+ var delta;
+ var data;
+ var tol;
+ var acc;
+ var W;
+ var N;
+ var d;
+ var i;
+
+ data = [
+ [ 2.0, 1.0 ],
+ [ -5.0, NaN ],
+ [ 3.0, -1.0 ],
+ [ NaN, NaN ]
+ ];
+
+ N = data.length;
+ function isNotNaN( pair ) {
+ return !isnan( pair[ 0 ] ) && !isnan( pair[ 1 ] );
+ }
+ filteredArr = data.filter( isNotNaN );
+ if ( filteredArr.length > 0 ) {
+ means = mean( [ 0.0, 0.0 ], filteredArr );
+ } else {
+ means = null;
+ }
+
+ // Window size:
+ W = 4;
+
+ acc = incrnanmpcorrdist( W, means[ 0 ], means[ 1 ] );
+ for ( i = 0; i < N; i++ ) {
+ acc( data[ i ][ 0 ], data[ i ][ 1 ] );
+ actual = acc();
+ if ( i < W - 1 ) {
+ d = data.slice( 0, i + 1 );
+ } else {
+ d = data.slice( i - W + 1, i + 1 );
+ }
+
+ d = d.filter( function isNotNaN( pair ) {
+ return !isnan( pair[ 0 ] ) && !isnan( pair[ 1 ] );
+ });
+ if ( means === null ) {
+ expected = null;
+ } else {
+ expected = pcorrdist( d, means[ 0 ], means[ 1 ], true );
+ }
+
+ if ( actual === expected ) {
+ t.equal( actual, expected, 'returns expected value. window: ' + i + '.' );
+ } else {
+ delta = abs( actual - expected );
+ tol = 1.0 * EPS * abs( expected );
+ t.equal( delta < tol, true, 'window: ' + i + '. expected: ' + expected + '. actual: ' + actual + '. tol: ' + tol + '. delta: ' + delta + '.' );
+ }
+ }
+ t.end();
+});
+
+tape( 'if data has yet to be provided, the accumulator function returns `null`', function test( t ) {
+ var acc = incrnanmpcorrdist( 3 );
+ t.equal( acc(), null, 'returns null' );
+ t.end();
+});
+
+tape( 'if data has yet to be provided, the accumulator function returns `null` (known means)', function test( t ) {
+ var acc = incrnanmpcorrdist( 3, 3.0, 3.14 );
+ t.equal( acc(), null, 'returns null' );
+ t.end();
+});
+
+tape( 'if only one datum has been provided and the means are unknown, the accumulator function returns `1`', function test( t ) {
+ var acc = incrnanmpcorrdist( 3 );
+ acc( 2.0, 3.14 );
+ t.equal( acc(), 1.0, 'returns 1' );
+ t.end();
+});
+
+tape( 'if only one datum has been provided and the means are known, the accumulator function may not return `1`', function test( t ) {
+ var acc = incrnanmpcorrdist( 3, 30.0, -100.0 );
+ acc( 2.0, 1.0 );
+ t.notEqual( acc(), 1.0, 'does not return 1' );
+ t.end();
+});
+
+tape( 'if the window size is `1` and the means are unknown, the accumulator function always returns `1`', function test( t ) {
+ var acc;
+ var r;
+ var i;
+
+ acc = incrnanmpcorrdist( 1 );
+ for ( i = 0; i < 100; i++ ) {
+ r = acc( randu() * 100.0, randu() * 100.0 );
+ t.equal( r, 1.0, 'returns expected value' );
+ }
+ t.end();
+});
+
+tape( 'if the window size is `1` and the means are known, the accumulator function may not always return `1`', function test( t ) {
+ var acc;
+ var r;
+ var i;
+
+ acc = incrnanmpcorrdist( 1, 500.0, -500.0 ); // means are outside the range of simulated values so the correlation should never be zero
+ for ( i = 0; i < 100; i++ ) {
+ r = acc( randu() * 100.0, randu() * 100.0 );
+ t.notEqual( r, 1.0, 'does not return 1' );
+ }
+ t.end();
+});