chore: minor clean-up

---
type: pre_commit_static_analysis_report
description: Results of running static analysis checks when committing changes.
report:
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    status: passed
  - task: lint_editorconfig
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  - task: lint_markdown
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  - task: lint_package_json
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  - task: lint_javascript_src
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---

Author: Planeshifter

lib/node_modules/@stdlib/array/base/unary5d-by/docs/types/test.ts

@@ -139,4 +139,4 @@ function identity( value: number ): number {
 	unary5dBy( [ x, y ], [ 1, 1, 1, 2, 2 ] ); // $ExpectError
 	unary5dBy( [ x, y ], [ 1, 1, 1, 2, 2 ], identity ); // $ExpectError
 	unary5dBy( [ x, y ], [ 1, 1, 1, 2, 2 ], identity, identity, {}, {} ); // $ExpectError
-}
+}

lib/node_modules/@stdlib/math/base/special/acotd/test/fixtures/julia/REQUIRE

@@ -1,2 +1,2 @@
-julia 1.5
-JSON 0.21
+julia 1.5
+JSON 0.21

lib/node_modules/@stdlib/stats/base/dists/bernoulli/mode/manifest.json

@@ -73,4 +73,4 @@
       ]
     }
   ]
-}
+}

lib/node_modules/@stdlib/stats/base/dists/exponential/mode/test/test.native.js

@@ -24,9 +24,7 @@ var resolve = require( 'path' ).resolve;
 var tape = require( 'tape' );
 var tryRequire = require( '@stdlib/utils/try-require' );
 var isnan = require( '@stdlib/math/base/assert/is-nan' );
-var abs = require( '@stdlib/math/base/special/abs' );
 var NINF = require( '@stdlib/constants/float64/ninf' );
-var EPS = require( '@stdlib/constants/float64/eps' );
 
 
 // VARIABLES //
@@ -71,22 +69,14 @@ tape( 'if provided a rate parameter `lambda` that is not a nonnegative number, t
 tape( 'the function returns the mode of an exponential distribution', opts, function test( t ) {
 	var expected;
 	var lambda;
-	var delta;
-	var tol;
 	var i;
 	var y;
 
 	expected = data.expected;
 	lambda = data.lambda;
 	for ( i = 0; i < expected.length; i++ ) {
 		y = mode( lambda[i] );
-		if ( y === expected[ i ] ) {
-			t.equal( y, expected[i], 'lambda: '+lambda[i]+', y: '+y+', expected: '+expected[i] );
-		} else {
-			delta = abs( y - expected[ i ] );
-			tol = 2.0 * EPS * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
-		}
+		t.equal( y, expected[i], 'lambda: '+lambda[i]+', y: '+y+', expected: '+expected[i] );
 	}
 	t.end();
 });

lib/node_modules/@stdlib/stats/base/dists/f/entropy/README.md

@@ -182,7 +182,7 @@ double out = stdlib_base_dists_f_entropy( 3.0, 7.0 );
 The function accepts the following arguments:
 
 -   **d1**: `[in] double` numerator degrees of freedom.
--   **d2**: `[in] double` maximum support.
+-   **d2**: `[in] double` denominator degrees of freedom.
 
 ```c
 double stdlib_base_dists_f_entropy( const double d1, const double d2 );

lib/node_modules/@stdlib/stats/base/dists/gumbel/skewness/test/test.js

@@ -72,15 +72,15 @@ tape( 'if provided a nonpositive `beta`, the function returns `NaN`', function t
 });
 
 tape( 'the function returns `~1.14` as the skewness of a Gumbel distribution ', function test( t ) {
-	var a;
-	var b;
+	var beta;
+	var mu;
 	var i;
 	var v;
 
 	for ( i = 0; i < 10; i++ ) {
-		a = ( randu()*10.0 );
-		b = ( randu()*10.0 ) + a;
-		v = skewness( a, b );
+		mu = ( randu()*10.0 );
+		beta = ( randu()*10.0 ) + 1.0;
+		v = skewness( mu, beta );
 		t.equal( v, 1.1395470994046488, 'returns ~1.14' );
 	}
 	t.end();

lib/node_modules/@stdlib/stats/base/dists/t/logpdf/README.md

@@ -168,9 +168,9 @@ for ( i = 0; i < 10; i++ ) {
 #include "stdlib/stats/base/dists/t/logpdf.h"
 ```
 
-#### stdlib_base_dists_t_logpdf( x, a, b )
+#### stdlib_base_dists_t_logpdf( x, v )
 
-Evaluates the natural logarithm of the [PDF][pdf] for a [Student's t][t-distribution] distribution with degree of freedom `v`.
+Evaluates the natural logarithm of the [probability density function][pdf] (PDF) for a [Student's t][t-distribution] distribution with degrees of freedom `v`.
 
 ```c
 double out = stdlib_base_dists_t_logpdf( 0.5, 1.0 );

lib/node_modules/@stdlib/stats/base/dists/t/logpdf/benchmark/c/benchmark.c

@@ -16,12 +16,12 @@
 * limitations under the License.
 */
 
-#include <sys/time.h>
 #include "stdlib/stats/base/dists/t/logpdf.h"
 #include <math.h>
 #include <stdio.h>
 #include <stdlib.h>
 #include <time.h>
+#include <sys/time.h>
 
 #define NAME "t-logpdf"
 #define ITERATIONS 1000000

lib/node_modules/@stdlib/stats/base/dists/t/logpdf/src/main.c

@@ -16,12 +16,12 @@
 * limitations under the License.
 */
 
+#include "stdlib/stats/base/dists/t/logpdf.h"
 #include "stdlib/math/base/assert/is_nan.h"
 #include "stdlib/math/base/special/betaln.h"
 #include "stdlib/math/base/special/ln.h"
 #include "stdlib/math/base/special/pow.h"
 #include "stdlib/math/base/special/sqrt.h"
-#include "stdlib/stats/base/dists/t/logpdf.h"
 
 /**
 * Evaluates the log probability density function (logPDF) for a Student's t distribution with degrees of freedom `v` at a value `x`.

lib/node_modules/@stdlib/stats/base/dists/t/logpdf/test/test.native.js

@@ -25,6 +25,8 @@ var tape = require( 'tape' );
 var tryRequire = require( '@stdlib/utils/try-require' );
 var isnan = require( '@stdlib/math/base/assert/is-nan' );
 var abs = require( '@stdlib/math/base/special/abs' );
+var PINF = require( '@stdlib/constants/float64/pinf' );
+var NINF = require( '@stdlib/constants/float64/ninf' );
 var EPS = require( '@stdlib/constants/float64/eps' );
 
 
@@ -60,19 +62,55 @@ tape( 'if provided `NaN` for any parameter, the function returns `NaN`', opts, f
 	t.end();
 });
 
-tape( 'if provided `v <= 0`, the function returns `NaN`', opts, function test( t ) {
+tape( 'if provided `Infinity` for `x` and a finite `v`, the function returns `-Infinity`', opts, function test( t ) {
+	var y = logpdf( PINF, 1.0 );
+	t.equal( y, NINF, 'returns -Infinity' );
+	t.end();
+});
+
+tape( 'if provided `-Infinity` for `x` and a finite `v`, the function returns `-Infinity`', opts, function test( t ) {
+	var y = logpdf( NINF, 1.0 );
+	t.equal( y, NINF, 'returns -Infinity' );
+	t.end();
+});
+
+tape( 'if provided `Infinity` for `v`, the function returns `NaN`', opts, function test( t ) {
 	var y;
 
+	y = logpdf( 0.0, PINF );
+	t.equal( isnan( y ), true, 'returns NaN' );
+
+	y = logpdf( NaN, PINF );
+	t.equal( isnan( y ), true, 'returns NaN' );
+
+	y = logpdf( PINF, PINF );
+	t.equal( isnan( y ), true, 'returns NaN' );
+
+	y = logpdf( NINF, PINF );
+	t.equal( isnan( y ), true, 'returns NaN' );
+
+	t.end();
+});
+
+tape( 'if provided a nonpositive `v`, the function always returns `NaN`', opts, function test( t ) {
+	var y;
+
+	y = logpdf( 2.0, 0.0 );
+	t.equal( isnan( y ), true, 'returns NaN' );
+
+	y = logpdf( 2.0, -1.0 );
+	t.equal( isnan( y ), true, 'returns NaN' );
+
 	y = logpdf( 0.0, -1.0 );
 	t.equal( isnan( y ), true, 'returns NaN' );
 
-	y = logpdf( 0.0, 0.0 );
+	y = logpdf( 2.0, NINF );
 	t.equal( isnan( y ), true, 'returns NaN' );
 
 	t.end();
 });
 
-tape( 'the function evaluates the logpdf for `x` given a large range of `v` and `x`', opts, function test( t ) {
+tape( 'the function evaluates the logpdf for `x` given parameter `v` (when `x` and `v` are small)', opts, function test( t ) {
 	var expected;
 	var delta;
 	var tol;
@@ -81,26 +119,23 @@ tape( 'the function evaluates the logpdf for `x` given a large range of `v` and
 	var y;
 	var i;
 
-	expected = largeLarge.expected;
-	x = largeLarge.x;
-	v = largeLarge.v;
+	expected = smallSmall.expected;
+	x = smallSmall.x;
+	v = smallSmall.v;
 	for ( i = 0; i < x.length; i++ ) {
-		y = logpdf( x[ i ], v[ i ] );
-		if ( y === expected[ i ] ) {
-			t.equal( y, expected[ i ], 'x: ' + x[ i ] + ', v: ' + v[ i ] + ', y: ' + y + ', expected: ' + expected[ i ] );
+		y = logpdf( x[i], v[i] );
+		if ( y === expected[i] ) {
+			t.equal( y, expected[i], 'x: '+x[i]+'. v: '+v[i]+', y: '+y+', expected: '+expected[i] );
 		} else {
 			delta = abs( y - expected[ i ] );
-			tol = 2.0 * EPS * abs( expected[ i ] );
-			if ( tol < 1e-14 ) {
-				tol = 1e-14; // Set a minimum tolerance
-			}
-			t.ok( delta <= tol, 'within tolerance. x: ' + x[ i ] + '. v: ' + v[ i ] + '. y: ' + y + '. E: ' + expected[ i ] + '. Δ: ' + delta + '. tol: ' + tol + '.' );
+			tol = 10.0 * EPS * abs( expected[ i ] );
+			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. v: '+v[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
 });
 
-tape( 'the function evaluates the logpdf for `x` given a large range of `v` and small range of `x`', opts, function test( t ) {
+tape( 'the function evaluates the logpdf for `x` given parameter `v` (when `x` is large and `v` small)', opts, function test( t ) {
 	var expected;
 	var delta;
 	var tol;
@@ -113,22 +148,19 @@ tape( 'the function evaluates the logpdf for `x` given a large range of `v` and
 	x = largeSmall.x;
 	v = largeSmall.v;
 	for ( i = 0; i < x.length; i++ ) {
-		y = logpdf( x[ i ], v[ i ] );
-		if ( y === expected[ i ] ) {
-			t.equal( y, expected[ i ], 'x: ' + x[ i ] + ', v: ' + v[ i ] + ', y: ' + y + ', expected: ' + expected[ i ] );
+		y = logpdf( x[i], v[i] );
+		if ( y === expected[i] ) {
+			t.equal( y, expected[i], 'x: '+x[i]+'. v: '+v[i]+', y: '+y+', expected: '+expected[i] );
 		} else {
 			delta = abs( y - expected[ i ] );
-			tol = 2.0 * EPS * abs( expected[ i ] );
-			if ( tol < 1e-14 ) {
-				tol = 1e-14; // Set a minimum tolerance
-			}
-			t.ok( delta <= tol, 'within tolerance. x: ' + x[ i ] + '. v: ' + v[ i ] + '. y: ' + y + '. E: ' + expected[ i ] + '. Δ: ' + delta + '. tol: ' + tol + '.' );
+			tol = 10.0 * EPS * abs( expected[ i ] );
+			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. v: '+v[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
 });
 
-tape( 'the function evaluates the logpdf for `x` given a small range of `v` and large range of `x`', opts, function test( t ) {
+tape( 'the function evaluates the logpdf for `x` given parameter `v` (when `x` is small and `v` large)', opts, function test( t ) {
 	var expected;
 	var delta;
 	var tol;
@@ -141,22 +173,19 @@ tape( 'the function evaluates the logpdf for `x` given a small range of `v` and
 	x = smallLarge.x;
 	v = smallLarge.v;
 	for ( i = 0; i < x.length; i++ ) {
-		y = logpdf( x[ i ], v[ i ] );
-		if ( y === expected[ i ] ) {
-			t.equal( y, expected[ i ], 'x: ' + x[ i ] + ', v: ' + v[ i ] + ', y: ' + y + ', expected: ' + expected[ i ] );
+		y = logpdf( x[i], v[i] );
+		if ( y === expected[i] ) {
+			t.equal( y, expected[i], 'x: '+x[i]+'. v: '+v[i]+', y: '+y+', expected: '+expected[i] );
 		} else {
 			delta = abs( y - expected[ i ] );
-			tol = 2.0 * EPS * abs( expected[ i ] );
-			if ( tol < 1e-14 ) {
-				tol = 1e-14; // Set a minimum tolerance
-			}
-			t.ok( delta <= tol, 'within tolerance. x: ' + x[ i ] + '. v: ' + v[ i ] + '. y: ' + y + '. E: ' + expected[ i ] + '. Δ: ' + delta + '. tol: ' + tol + '.' );
+			tol = 50.0 * EPS * abs( expected[ i ] );
+			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. v: '+v[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
 });
 
-tape( 'the function evaluates the logpdf for `x` given a small range of `v` and `x`', opts, function test( t ) {
+tape( 'the function evaluates the logpdf for `x` given parameter `v` (when `x` and `v` are large)', opts, function test( t ) {
 	var expected;
 	var delta;
 	var tol;
@@ -165,20 +194,17 @@ tape( 'the function evaluates the logpdf for `x` given a small range of `v` and
 	var y;
 	var i;
 
-	expected = smallSmall.expected;
-	x = smallSmall.x;
-	v = smallSmall.v;
+	expected = largeLarge.expected;
+	x = largeLarge.x;
+	v = largeLarge.v;
 	for ( i = 0; i < x.length; i++ ) {
-		y = logpdf( x[ i ], v[ i ] );
-		if ( y === expected[ i ] ) {
-			t.equal( y, expected[ i ], 'x: ' + x[ i ] + ', v: ' + v[ i ] + ', y: ' + y + ', expected: ' + expected[ i ] );
+		y = logpdf( x[i], v[i] );
+		if ( y === expected[i] ) {
+			t.equal( y, expected[i], 'x: '+x[i]+'. v: '+v[i]+', y: '+y+', expected: '+expected[i] );
 		} else {
 			delta = abs( y - expected[ i ] );
-			tol = 2.0 * EPS * abs( expected[ i ] );
-			if ( tol < 1e-14 ) {
-				tol = 1e-14; // Set a minimum tolerance
-			}
-			t.ok( delta <= tol, 'within tolerance. x: ' + x[ i ] + '. v: ' + v[ i ] + '. y: ' + y + '. E: ' + expected[ i ] + '. Δ: ' + delta + '. tol: ' + tol + '.' );
+			tol = 40.0 * EPS * abs( expected[ i ] );
+			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. v: '+v[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();