Auto-generated commit

CHANGELOG.md

@@ -2912,6 +2912,10 @@ A total of 17 people contributed to this release. Thank you to the following con
 
 <details>
 
+-   [`4a70790`](https://github.com/stdlib-js/stdlib/commit/4a707903dfef7c2b56216000165706497d19a251) - **style:** add missing spaces _(by Philipp Burckhardt)_
+-   [`2965f8f`](https://github.com/stdlib-js/stdlib/commit/2965f8f4d1469ed76c7d9c150b39a87b62d73fbd) - **chore:** minor clean-up _(by Philipp Burckhardt)_
+-   [`ed5c4cc`](https://github.com/stdlib-js/stdlib/commit/ed5c4cccc06ad98f4de90310bf24a5a373761b43) - **chore:** minor clean-up _(by Philipp Burckhardt)_
+-   [`90e96d0`](https://github.com/stdlib-js/stdlib/commit/90e96d01b7a32ec3b71caf3e5f57528338199a8f) - **chore:** minor clean-up _(by Philipp Burckhardt)_
 -   [`04fda1b`](https://github.com/stdlib-js/stdlib/commit/04fda1b6f333b6ac26d2f4b65d903d25701e6877) - **refactor:** update `stats/base/dnanrange` native addon from C++ to C [(#4163)](https://github.com/stdlib-js/stdlib/pull/4163) _(by Vivek maurya)_
 -   [`70dde47`](https://github.com/stdlib-js/stdlib/commit/70dde4759fdc94408dc4ee058cd83e6edf6ebda7) - **refactor:** update `stats/base/dnanmeanwd` native addon from C++ to C  [(#4157)](https://github.com/stdlib-js/stdlib/pull/4157) _(by Neeraj Pathak)_
 -   [`b7867cb`](https://github.com/stdlib-js/stdlib/commit/b7867cbb3a4fc453e19203794402c36f19b264fd) - **chore:** minor clean-up _(by Philipp Burckhardt)_

base/dists/bernoulli/mode/manifest.json

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

base/dists/cosine/kurtosis/test/test.js

@@ -91,7 +91,7 @@ tape( 'the function returns the excess kurtosis of a raised cosine distribution'
 	s = data.s;
 	for ( i = 0; i < mu.length; i++ ) {
 		y = kurtosis( mu[i], s[i] );
-		if ( expected[i] !== null) {
+		if ( expected[i] !== null ) {
 			if ( y === expected[i] ) {
 				t.equal( y, expected[i], 'mu:'+mu[i]+', s: '+s[i]+', y: '+y+', expected: '+expected[i] );
 			} else {

base/dists/cosine/logpdf/test/test.logpdf.js

@@ -141,7 +141,7 @@ tape( 'the function evaluates the logpdf for `x` given positive `mu`', function
 	s = positiveMean.s;
 	for ( i = 0; i < x.length; i++ ) {
 		y = logpdf( x[i], mu[i], s[i] );
-		if ( expected[i] !== null) {
+		if ( expected[i] !== null ) {
 			if ( y === expected[i] ) {
 				t.equal( y, expected[i], 'x: '+x[i]+', mu:'+mu[i]+', s: '+s[i]+', y: '+y+', expected: '+expected[i] );
 			} else {

base/dists/cosine/mean/test/test.js

@@ -91,7 +91,7 @@ tape( 'the function returns the expected value of a raised cosine distribution',
 	s = data.s;
 	for ( i = 0; i < mu.length; i++ ) {
 		y = mean( mu[i], s[i] );
-		if ( expected[i] !== null) {
+		if ( expected[i] !== null ) {
 			if ( y === expected[i] ) {
 				t.equal( y, expected[i], 'mu:'+mu[i]+', s: '+s[i]+', y: '+y+', expected: '+expected[i] );
 			} else {

base/dists/cosine/median/test/test.js

@@ -91,7 +91,7 @@ tape( 'the function returns the median of a raised cosine distribution', functio
 	s = data.s;
 	for ( i = 0; i < mu.length; i++ ) {
 		y = median( mu[i], s[i] );
-		if ( expected[i] !== null) {
+		if ( expected[i] !== null ) {
 			if ( y === expected[i] ) {
 				t.equal( y, expected[i], 'mu:'+mu[i]+', s: '+s[i]+', y: '+y+', expected: '+expected[i] );
 			} else {

base/dists/cosine/mode/test/test.js

@@ -91,7 +91,7 @@ tape( 'the function returns the mode of a raised cosine distribution', function
 	s = data.s;
 	for ( i = 0; i < mu.length; i++ ) {
 		y = mode( mu[i], s[i] );
-		if ( expected[i] !== null) {
+		if ( expected[i] !== null ) {
 			if ( y === expected[i] ) {
 				t.equal( y, expected[i], 'mu:'+mu[i]+', s: '+s[i]+', y: '+y+', expected: '+expected[i] );
 			} else {

base/dists/cosine/quantile/test/test.quantile.js

@@ -120,7 +120,7 @@ tape( 'the function evaluates the quantile function at `p` given positive `mu`',
 	s = positiveMean.s;
 	for ( i = 0; i < p.length; i++ ) {
 		y = quantile( p[i], mu[i], s[i] );
-		if ( expected[i] !== null) {
+		if ( expected[i] !== null ) {
 			if ( y === expected[i] ) {
 				t.equal( y, expected[i], 'p: '+p[i]+', mu:'+mu[i]+', s: '+s[i]+', y: '+y+', expected: '+expected[i] );
 			} else {

base/dists/cosine/skewness/test/test.js

@@ -91,7 +91,7 @@ tape( 'the function returns the skewness of a raised cosine distribution', funct
 	s = data.s;
 	for ( i = 0; i < mu.length; i++ ) {
 		y = skewness( mu[i], s[i] );
-		if ( expected[i] !== null) {
+		if ( expected[i] !== null ) {
 			if ( y === expected[i] ) {
 				t.equal( y, expected[i], 'mu:'+mu[i]+', s: '+s[i]+', y: '+y+', expected: '+expected[i] );
 			} else {

base/dists/cosine/stdev/test/test.js

@@ -91,7 +91,7 @@ tape( 'the function returns the standard deviation of a raised cosine distributi
 	s = data.s;
 	for ( i = 0; i < mu.length; i++ ) {
 		y = stdev( mu[i], s[i] );
-		if ( expected[i] !== null) {
+		if ( expected[i] !== null ) {
 			if ( y === expected[i] ) {
 				t.equal( y, expected[i], 'mu:'+mu[i]+', s: '+s[i]+', y: '+y+', expected: '+expected[i] );
 			} else {

base/dists/cosine/variance/test/test.js

@@ -91,7 +91,7 @@ tape( 'the function returns the variance of a raised cosine distribution', funct
 	s = data.s;
 	for ( i = 0; i < mu.length; i++ ) {
 		y = variance( mu[i], s[i] );
-		if ( expected[i] !== null) {
+		if ( expected[i] !== null ) {
 			if ( y === expected[i] ) {
 				t.equal( y, expected[i], 'mu:'+mu[i]+', s: '+s[i]+', y: '+y+', expected: '+expected[i] );
 			} else {

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();
 });

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 );

base/dists/f/pdf/lib/ibeta_power_terms.js

@@ -226,7 +226,7 @@ function ibetaPowerTerms( a, b, x, y, normalized ) {
 					result *= pow( p1 * b2, b );
 				} else {
 					l2 += l1 + ln( result );
-					if (l2 >= MAX_LN) {
+					if ( l2 >= MAX_LN ) {
 						return NaN;
 					}
 					result = exp( l2 );

base/dists/frechet/entropy/test/test.js

@@ -114,7 +114,7 @@ tape( 'the function returns the differential entropy of a Fréchet distribution'
 	s = data.s;
 	for ( i = 0; i < alpha.length; i++ ) {
 		y = entropy( alpha[i], s[i], 0.0 );
-		if ( expected[i] !== null) {
+		if ( expected[i] !== null ) {
 			if ( y === expected[i] ) {
 				t.equal( y, expected[i], 'alpha:'+alpha[i]+', s: '+s[i]+', m: 0, y: '+y+', expected: '+expected[i] );
 			} else {

base/dists/frechet/kurtosis/test/test.js

@@ -114,7 +114,7 @@ tape( 'the function returns the excess kurtosis of a Fréchet distribution', fun
 	s = data.s;
 	for ( i = 0; i < alpha.length; i++ ) {
 		y = kurtosis( alpha[i], s[i], 0.0 );
-		if ( expected[i] !== null) {
+		if ( expected[i] !== null ) {
 			if ( y === expected[i] ) {
 				t.equal( y, expected[i], 'alpha:'+alpha[i]+', s: '+s[i]+', m: 0, y: '+y+', expected: '+expected[i] );
 			} else {

base/dists/frechet/mean/test/test.js

@@ -114,7 +114,7 @@ tape( 'the function returns the expected value of a Fréchet distribution', func
 	s = data.s;
 	for ( i = 0; i < alpha.length; i++ ) {
 		y = mean( alpha[i], s[i], 0.0 );
-		if ( expected[i] !== null) {
+		if ( expected[i] !== null ) {
 			if ( y === expected[i] ) {
 				t.equal( y, expected[i], 'alpha:'+alpha[i]+', s: '+s[i]+', m: 0, y: '+y+', expected: '+expected[i] );
 			} else {

base/dists/frechet/median/test/test.js

@@ -114,7 +114,7 @@ tape( 'the function returns the median of a Fréchet distribution', function tes
 	s = data.s;
 	for ( i = 0; i < alpha.length; i++ ) {
 		y = median( alpha[i], s[i], 0.0 );
-		if ( expected[i] !== null) {
+		if ( expected[i] !== null ) {
 			if ( y === expected[i] ) {
 				t.equal( y, expected[i], 'alpha:'+alpha[i]+', s: '+s[i]+', m: 0, y: '+y+', expected: '+expected[i] );
 			} else {

base/dists/frechet/mode/test/test.js

@@ -114,7 +114,7 @@ tape( 'the function returns the mode of a Fréchet distribution', function test(
 	s = data.s;
 	for ( i = 0; i < alpha.length; i++ ) {
 		y = mode( alpha[i], s[i], 0.0 );
-		if ( expected[i] !== null) {
+		if ( expected[i] !== null ) {
 			if ( y === expected[i] ) {
 				t.equal( y, expected[i], 'alpha:'+alpha[i]+', s: '+s[i]+', m: 0, y: '+y+', expected: '+expected[i] );
 			} else {

base/dists/frechet/skewness/test/test.js

@@ -114,7 +114,7 @@ tape( 'the function returns the skewness of a Fréchet distribution', function t
 	s = data.s;
 	for ( i = 0; i < alpha.length; i++ ) {
 		y = skewness( alpha[i], s[i], 0.0 );
-		if ( expected[i] !== null) {
+		if ( expected[i] !== null ) {
 			if ( y === expected[i] ) {
 				t.equal( y, expected[i], 'alpha:'+alpha[i]+', s: '+s[i]+', m: 0, y: '+y+', expected: '+expected[i] );
 			} else {

base/dists/frechet/stdev/test/test.js

@@ -114,7 +114,7 @@ tape( 'the function returns the standard deviation of a Fréchet distribution',
 	s = data.s;
 	for ( i = 0; i < alpha.length; i++ ) {
 		y = stdev( alpha[i], s[i], 0.0 );
-		if ( expected[i] !== null) {
+		if ( expected[i] !== null ) {
 			if ( y === expected[i] ) {
 				t.equal( y, expected[i], 'alpha:'+alpha[i]+', s: '+s[i]+', m: 0, y: '+y+', expected: '+expected[i] );
 			} else {

base/dists/frechet/variance/test/test.js

@@ -114,7 +114,7 @@ tape( 'the function returns the variance of a Fréchet distribution', function t
 	s = data.s;
 	for ( i = 0; i < alpha.length; i++ ) {
 		y = variance( alpha[i], s[i], 0.0 );
-		if ( expected[i] !== null) {
+		if ( expected[i] !== null ) {
 			if ( y === expected[i] ) {
 				t.equal( y, expected[i], 'alpha:'+alpha[i]+', s: '+s[i]+', m: 0, y: '+y+', expected: '+expected[i] );
 			} else {

base/dists/gumbel/entropy/test/test.js

@@ -91,7 +91,7 @@ tape( 'the function returns the differential entropy of a Gumbel distribution',
 	beta = data.beta;
 	for ( i = 0; i < mu.length; i++ ) {
 		y = entropy( mu[i], beta[i] );
-		if ( expected[i] !== null) {
+		if ( expected[i] !== null ) {
 			if ( y === expected[i] ) {
 				t.equal( y, expected[i], 'mu:'+mu[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
 			} else {

base/dists/gumbel/kurtosis/test/test.js

@@ -91,7 +91,7 @@ tape( 'the function returns the excess kurtosis of a Gumbel distribution', funct
 	beta = data.beta;
 	for ( i = 0; i < mu.length; i++ ) {
 		y = kurtosis( mu[i], beta[i] );
-		if ( expected[i] !== null) {
+		if ( expected[i] !== null ) {
 			if ( y === expected[i] ) {
 				t.equal( y, expected[i], 'mu:'+mu[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
 			} else {

base/dists/gumbel/logpdf/test/test.logpdf.js

@@ -112,7 +112,7 @@ tape( 'the function evaluates the logpdf for `x` given positive `mu`', function
 	beta = positiveMean.beta;
 	for ( i = 0; i < x.length; i++ ) {
 		y = logpdf( x[i], mu[i], beta[i] );
-		if ( expected[i] !== null) {
+		if ( expected[i] !== null ) {
 			if ( y === expected[i] ) {
 				t.equal( y, expected[i], 'x: '+x[i]+', mu:'+mu[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
 			} else {

base/dists/gumbel/mean/test/test.js

@@ -91,7 +91,7 @@ tape( 'the function returns the mean of a Gumbel distribution', function test( t
 	beta = data.beta;
 	for ( i = 0; i < mu.length; i++ ) {
 		y = mean( mu[i], beta[i] );
-		if ( expected[i] !== null) {
+		if ( expected[i] !== null ) {
 			if ( y === expected[i] ) {
 				t.equal( y, expected[i], 'mu:'+mu[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
 			} else {

base/dists/gumbel/median/test/test.js

@@ -91,7 +91,7 @@ tape( 'the function returns the median of a Gumbel distribution', function test(
 	beta = data.beta;
 	for ( i = 0; i < mu.length; i++ ) {
 		y = median( mu[i], beta[i] );
-		if ( expected[i] !== null) {
+		if ( expected[i] !== null ) {
 			if ( y === expected[i] ) {
 				t.equal( y, expected[i], 'mu:'+mu[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
 			} else {

base/dists/gumbel/mode/test/test.js

@@ -91,7 +91,7 @@ tape( 'the function returns the mode of a Gumbel distribution', function test( t
 	beta = data.beta;
 	for ( i = 0; i < mu.length; i++ ) {
 		y = mode( mu[i], beta[i] );
-		if ( expected[i] !== null) {
+		if ( expected[i] !== null ) {
 			if ( y === expected[i] ) {
 				t.equal( y, expected[i], 'mu:'+mu[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
 			} else {

base/dists/gumbel/pdf/test/test.pdf.js

@@ -112,7 +112,7 @@ tape( 'the function evaluates the pdf for `x` given positive `mu`', function tes
 	beta = positiveMean.beta;
 	for ( i = 0; i < x.length; i++ ) {
 		y = pdf( x[i], mu[i], beta[i] );
-		if ( expected[i] !== null) {
+		if ( expected[i] !== null ) {
 			if ( y === expected[i] ) {
 				t.equal( y, expected[i], 'x: '+x[i]+', mu:'+mu[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
 			} else {

base/dists/gumbel/quantile/test/test.quantile.js

@@ -108,7 +108,7 @@ tape( 'the function evaluates the quantile function at `p` given positive `mu`',
 	beta = positiveMean.beta;
 	for ( i = 0; i < p.length; i++ ) {
 		y = quantile( p[i], mu[i], beta[i] );
-		if ( expected[i] !== null) {
+		if ( expected[i] !== null ) {
 			if ( y === expected[i] ) {
 				t.equal( y, expected[i], 'p: '+p[i]+', mu:'+mu[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
 			} else {

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();

base/dists/gumbel/stdev/test/test.js

@@ -91,7 +91,7 @@ tape( 'the function returns the standard deviation of a Gumbel distribution', fu
 	beta = data.beta;
 	for ( i = 0; i < mu.length; i++ ) {
 		y = stdev( mu[i], beta[i] );
-		if ( expected[i] !== null) {
+		if ( expected[i] !== null ) {
 			if ( y === expected[i] ) {
 				t.equal( y, expected[i], 'mu:'+mu[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
 			} else {

base/dists/gumbel/variance/test/test.js

@@ -91,7 +91,7 @@ tape( 'the function returns the variance of a Gumbel distribution', function tes
 	beta = data.beta;
 	for ( i = 0; i < mu.length; i++ ) {
 		y = variance( mu[i], beta[i] );
-		if ( expected[i] !== null) {
+		if ( expected[i] !== null ) {
 			if ( y === expected[i] ) {
 				t.equal( y, expected[i], 'mu:'+mu[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
 			} else {

base/dists/gumbel/variance/test/test.native.js

@@ -100,7 +100,7 @@ tape( 'the function returns the variance of a Gumbel distribution', opts, functi
 	beta = data.beta;
 	for ( i = 0; i < mu.length; i++ ) {
 		y = variance( mu[i], beta[i] );
-		if ( expected[i] !== null) {
+		if ( expected[i] !== null ) {
 			if ( y === expected[i] ) {
 				t.equal( y, expected[i], 'mu:'+mu[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
 			} else {

base/dists/kumaraswamy/median/README.md

@@ -171,7 +171,7 @@ for ( i = 0; i < 10; i++ ) {
 
 #### stdlib_base_dists_kumaraswamy_median( a, b )
 
-Evaluates the [median][median] of a [Kumaraswamy][kumaraswamy-distribution] distribution with shape parameters a and b.
+Returns the [median][median] of a [Kumaraswamy's double bounded][kumaraswamy-distribution] distribution with first shape parameter `a` and second shape parameter `b`.
 
 ```c
 double out = stdlib_base_dists_kumaraswamy_median( 2.0, 3.0 );
@@ -180,8 +180,8 @@ double out = stdlib_base_dists_kumaraswamy_median( 2.0, 3.0 );
 
 The function accepts the following arguments:
 
--   **a**: `[in] double` shape parameter.
--   **b**: `[in] double` shape parameter.
+-   **a**: `[in] double` first shape parameter.
+-   **b**: `[in] double` second shape parameter.
 
 ```c
 double stdlib_base_dists_kumaraswamy_median( const double a, const double b );
@@ -207,7 +207,6 @@ double stdlib_base_dists_kumaraswamy_median( const double a, const double b );
 
 ```c
 #include "stdlib/stats/base/dists/kumaraswamy/median.h"
-#include "stdlib/constants/float64/eps.h"
 #include <stdlib.h>
 #include <stdio.h>
 

base/dists/kumaraswamy/median/benchmark/benchmark.native.js

@@ -32,9 +32,9 @@ var pkg = require( './../package.json' ).name;
 
 // VARIABLES //
 
-var pdf = tryRequire( resolve( __dirname, './../lib/native.js' ) );
+var median = tryRequire( resolve( __dirname, './../lib/native.js' ) );
 var opts = {
-	'skip': ( pdf instanceof Error )
+	'skip': ( median instanceof Error )
 };
 
 
@@ -57,7 +57,7 @@ bench( pkg+'::native', opts, function benchmark( b ) {
 
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {
-		y = pdf( alpha[ i % len ], beta[ i % len ] );
+		y = median( alpha[ i % len ], beta[ i % len ] );
 		if ( isnan( y ) ) {
 			b.fail( 'should not return NaN' );
 		}

base/dists/kumaraswamy/median/benchmark/c/Makefile

@@ -143,4 +143,4 @@ run: $(c_targets)
 clean:
 	$(QUIET) -rm -f *.o *.out
 
-.PHONY: clean
+.PHONY: clean