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

---
type: pre_commit_static_analysis_report
description: Results of running static analysis checks when committing changes.
report:
  - task: lint_filenames
    status: passed
  - task: lint_editorconfig
    status: passed
  - task: lint_markdown
    status: na
  - task: lint_package_json
    status: na
  - task: lint_repl_help
    status: na
  - task: lint_javascript_src
    status: na
  - task: lint_javascript_cli
    status: na
  - task: lint_javascript_examples
    status: na
  - task: lint_javascript_tests
    status: passed
  - task: lint_javascript_benchmarks
    status: na
  - task: lint_python
    status: na
  - task: lint_r
    status: na
  - task: lint_c_src
    status: na
  - task: lint_c_examples
    status: na
  - task: lint_c_benchmarks
    status: na
  - task: lint_c_tests_fixtures
    status: na
  - task: lint_shell
    status: na
  - task: lint_typescript_declarations
    status: na
  - task: lint_typescript_tests
    status: na
  - task: lint_license_headers
    status: passed
---

Author: MohannedAhmed67

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

@@ -54,9 +54,9 @@ function mean( out, arr ) {
 	N = 0;
 	for ( i = 0; i < arr.length; i++ ) {
 		N += 1;
-		delta = arr[i][0] - mx;
+		delta = arr[ i ][ 0 ] - mx;
 		mx += delta / N;
-		delta = arr[i][1] - my;
+		delta = arr[ i ][ 1 ] - my;
 		my += delta / N;
 	}
 	out[ 0 ] = mx;
@@ -88,9 +88,9 @@ function stdev( out, arr, mx, my, bool ) {
 	N = 0;
 	for ( i = 0; i < arr.length; i++ ) {
 		N += 1;
-		delta = arr[i][0] - mx;
+		delta = arr[ i ][ 0 ] - mx;
 		M2x += delta * delta;
-		delta = arr[i][1] - my;
+		delta = arr[ i ][ 1 ] - my;
 		M2y += delta * delta;
 	}
 	if ( bool ) {
@@ -103,8 +103,8 @@ function stdev( out, arr, mx, my, bool ) {
 		out[ 1 ] = 0.0;
 		return out;
 	}
-	out[ 0 ] = sqrt( M2x / (N-1) );
-	out[ 1 ] = sqrt( M2y / (N-1) );
+	out[ 0 ] = sqrt( M2x / ( N - 1 ) );
+	out[ 1 ] = sqrt( M2y / ( N - 1 ) );
 	return out;
 }
 
@@ -126,15 +126,15 @@ function covariance( arr, mx, my, bool ) {
 	N = arr.length;
 	C = 0.0;
 	for ( i = 0; i < N; i++ ) {
-		C += ( arr[i][0]-mx ) * ( arr[i][1]-my );
+		C += ( arr[ i ][ 0 ] - mx ) * ( arr[ i ][ 1 ] - my );
 	}
 	if ( bool ) {
 		return C / N;
 	}
 	if ( N === 1 ) {
 		return 0.0;
 	}
-	return C / (N-1);
+	return C / ( N - 1 );
 }
 
 /**
@@ -156,7 +156,7 @@ function pcorrdist( arr, mx, my, bool ) {
 	}
 	sd = stdev( [ 0.0, 0.0 ], arr, mx, my, bool );
 	cov = covariance( arr, mx, my, bool );
-	d = 1.0 - ( cov / ( sd[0]*sd[1] ) );
+	d = 1.0 - ( cov / ( sd[ 0 ] * sd[ 1 ] ) );
 	if ( d < 0.0 ) {
 		return 0.0;
 	}
@@ -183,7 +183,7 @@ function nandatasets( N, M, seed ) {
 	var j;
 
 	rand = randu.factory({
-		'seed': seed || ( randu()*pow( 2.0, 31 ) )|0
+		'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)`...
@@ -229,7 +229,7 @@ tape( 'the function throws an error if not provided a positive integer for the w
 	];
 
 	for ( i = 0; i < values.length; i++ ) {
-		t.throws( badValue( values[i] ), TypeError, 'throws an error when provided '+values[i] );
+		t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided '+values[ i ] );
 	}
 	t.end();
 
@@ -259,7 +259,7 @@ tape( 'the function throws an error if not provided a positive integer for the w
 	];
 
 	for ( i = 0; i < values.length; i++ ) {
-		t.throws( badValue( values[i] ), TypeError, 'throws an error when provided '+values[i] );
+		t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided '+values[ i ] );
 	}
 	t.end();
 
@@ -286,7 +286,7 @@ tape( 'the function throws an error if not provided a number as the mean value',
 	];
 
 	for ( i = 0; i < values.length; i++ ) {
-		t.throws( badValue( values[i] ), TypeError, 'throws an error when provided '+values[i] );
+		t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided '+values[ i ] );
 	}
 	t.end();
 
@@ -313,7 +313,7 @@ tape( 'the function throws an error if not provided a number as the mean value',
 	];
 
 	for ( i = 0; i < values.length; i++ ) {
-		t.throws( badValue( values[i] ), TypeError, 'throws an error when provided '+values[i] );
+		t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided '+values[ i ] );
 	}
 	t.end();
 
@@ -324,14 +324,14 @@ tape( 'the function throws an error if not provided a number as the mean value',
 	}
 });
 
-tape('the function properly ignores NaN values in x and y inputs', function test(t) {
+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);
+	acc = incrnanmpcorrdist( 3, 3.14, 0.0 );
 	result = acc();
 
-	t.ok(!isnan(result), 'result should not be NaN');
+	t.ok(!isnan( result ), 'result should not be NaN');
 	t.end();
 });
 
@@ -367,7 +367,7 @@ tape( 'the accumulator function computes a moving sample Pearson product-moment
 	M = 100;
 	data = nandatasets( N, M, randu.seed );
 
-	t.pass( 'seed: '+randu.seed );
+	t.pass( 'seed: ' + randu.seed );
 
 	// Define the window size:
 	W = 10;
@@ -379,14 +379,14 @@ tape( 'the accumulator function computes a moving sample Pearson product-moment
 		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;
+			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]);
+			while ( arr.length < min( countNonNanPairs, W ) && k >= 0 ) {
+				if ( !isnan( d[ k ][ 0 ] ) && !isnan( d[ k ][ 1 ] ) ) {
+					arr.push( d[ k ] );
 				}
 				k -= 1;
 			}
@@ -404,7 +404,7 @@ tape( 'the accumulator function computes a moving sample Pearson product-moment
 			}
 
 			if ( actual === expected ) {
-				t.equal( actual, expected, 'returns expected value. dataset: '+i+'. window: '+j+'.' );
+				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.
@@ -415,7 +415,7 @@ tape( 'the accumulator function computes a moving sample Pearson product-moment
 				} 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.equal( delta <= tol, true, 'dataset: ' + i + '. window: ' + j + '. expected: ' + expected + '. actual: ' + actual + '. tol: ' + tol + '. delta: ' + delta + '.' );
 			}
 		}
 	}
@@ -445,14 +445,14 @@ tape( 'the accumulator function computes a moving sample Pearson product-moment
 	M = 100;
 	data = nandatasets( N, M, randu.seed );
 
-	t.pass( 'seed: '+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]);
+	function isNotNaN( pair ) {
+		return !isnan( pair[ 0 ] ) && !isnan( pair[ 1 ] );
 	}
 	for ( i = 0; i < N; i++ ) {
 		d = data[ i ];
@@ -465,14 +465,14 @@ tape( 'the accumulator function computes a moving sample Pearson product-moment
 		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;
+			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]);
+			while ( arr.length < min( countNonNanPairs, W ) && k >= 0 ) {
+				if ( !isnan( d[ k ][ 0 ] ) && !isnan( d[ k ][ 1 ] ) ) {
+					arr.push( d[ k ] );
 				}
 				k -= 1;
 			}
@@ -484,11 +484,11 @@ tape( 'the accumulator function computes a moving sample Pearson product-moment
 			}
 
 			if ( actual === expected ) {
-				t.equal( actual, expected, 'returns expected value. dataset: '+i+'. window: '+j+'.' );
+				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.equal( delta <= tol, true, 'dataset: ' + i + '. window: ' + j + '. expected: ' + expected + '. actual: ' + actual + '. tol: ' + tol + '. delta: ' + delta + '.' );
 			}
 		}
 	}
@@ -523,13 +523,13 @@ tape( 'if not provided an input value, the accumulator function returns the curr
 	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 );
+		if ( i < W - 1 ) {
+			d = data.slice( 0, i + 1 );
 		} else {
-			d = data.slice( i-W+1, i+1 );
+			d = data.slice( i - W + 1, i + 1 );
 		}
-		d = d.filter( function isNotNaN(pair) {
-			return !isnan(pair[0]) && !isnan(pair[1]);
+		d = d.filter( function isNotNaN( pair ) {
+			return !isnan( pair[ 0 ] ) && !isnan( pair[ 1 ] );
 		});
 
 		if ( d.length > 0 ) {
@@ -543,11 +543,11 @@ tape( 'if not provided an input value, the accumulator function returns the curr
 		}
 
 		if ( actual === expected ) {
-			t.equal( actual, expected, 'returns expected value. window: '+i+'.' );
+			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.equal( delta < tol, true, 'window: ' + i + '. expected: ' + expected + '. actual: ' + actual + '. tol: ' + tol + '. delta: ' + delta + '.' );
 		}
 	}
 	t.end();
@@ -575,8 +575,8 @@ tape( 'if not provided an input value, the accumulator function returns the curr
 	];
 
 	N = data.length;
-	function isNotNaN(pair) {
-		return !isnan(pair[0]) && !isnan(pair[1]);
+	function isNotNaN( pair ) {
+		return !isnan( pair[ 0 ] ) && !isnan( pair[ 1 ] );
 	}
 	filteredArr = data.filter( isNotNaN );
 	if ( filteredArr.length > 0 ) {
@@ -592,14 +592,14 @@ tape( 'if not provided an input value, the accumulator function returns the curr
 	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 );
+		if ( i < W - 1 ) {
+			d = data.slice( 0, i + 1 );
 		} else {
-			d = data.slice( i-W+1, i+1 );
+			d = data.slice( i - W + 1, i + 1 );
 		}
 
-		d = d.filter( function isNotNaN(pair) {
-			return !isnan(pair[0]) && !isnan(pair[1]);
+		d = d.filter( function isNotNaN( pair ) {
+			return !isnan( pair[ 0 ] ) && !isnan( pair[ 1 ] );
 		});
 		if ( means === null ) {
 			expected = null;
@@ -608,11 +608,11 @@ tape( 'if not provided an input value, the accumulator function returns the curr
 		}
 
 		if ( actual === expected ) {
-			t.equal( actual, expected, 'returns expected value. window: '+i+'.' );
+			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.equal( delta < tol, true, 'window: ' + i + '. expected: ' + expected + '. actual: ' + actual + '. tol: ' + tol + '. delta: ' + delta + '.' );
 		}
 	}
 	t.end();
@@ -651,7 +651,7 @@ tape( 'if the window size is `1` and the means are unknown, the accumulator func
 
 	acc = incrnanmpcorrdist( 1 );
 	for ( i = 0; i < 100; i++ ) {
-		r = acc( randu()*100.0, randu()*100.0 );
+		r = acc( randu() * 100.0, randu() * 100.0 );
 		t.equal( r, 1.0, 'returns expected value' );
 	}
 	t.end();
@@ -664,7 +664,7 @@ tape( 'if the window size is `1` and the means are known, the accumulator functi
 
 	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 );
+		r = acc( randu() * 100.0, randu() * 100.0 );
 		t.notEqual( r, 1.0, 'does not return 1' );
 	}
 	t.end();