Merge branch 'develop' of https://github.com/stdlib-js/stdlib into develop

Author: kgryte

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

@@ -80,14 +80,7 @@ tape( 'the function returns the skewness of a bradford distribution', function t
 			delta = abs( y - expected[ i ] );
 
 			/*
-			* NOTE: the tolerance is set high in this case due to the numerically challenging nature of the Bradford distribution skewness formula, which involves:
-			*
-			* 1. Complex expressions with nested logarithmic terms ln(1+c)
-			* 2. Square roots in both numerator and denominator
-			* 3. Products and differences of terms involving c and ln(1+c) that can be sensitive to floating-point precision
-			* 4. The SQRT2 factor amplifying any accumulated numerical errors
-			*
-			* Out of 1000 test cases, only two require tolerance higher than 500*EPS (specifically c=0.4 needs ~20000*EPS).
+			* TODO: the significant divergence from SciPy appears to stem from the computation of the natural log. We should follow up to ensure that our ln implementation is sufficiently accurate.
 			*/
 			tol = 20000.0 * EPS * abs( expected[ i ] );
 			t.ok( delta <= tol, 'within tolerance. x: '+x[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );

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

@@ -89,14 +89,7 @@ tape( 'the function returns the skewness of a Bradford distribution', opts, func
 			delta = abs( y - expected[ i ] );
 
 			/*
-			* NOTE: the tolerance is set high in this case due to the numerically challenging nature of the Bradford distribution skewness formula, which involves:
-			*
-			* 1. Complex expressions with nested logarithmic terms ln(1+c)
-			* 2. Square roots in both numerator and denominator
-			* 3. Products and differences of terms involving c and ln(1+c) that can be sensitive to floating-point precision
-			* 4. The SQRT2 factor amplifying any accumulated numerical errors
-			*
-			* Out of 1000 test cases, only two require tolerance higher than 500*EPS (specifically c=0.4 needs ~20000*EPS).
+			* TODO: the significant divergence from SciPy appears to stem from the computation of the natural log. We should follow up to ensure that our ln implementation is sufficiently accurate.
 			*/
 			tol = 20000.0 * EPS * abs( expected[ i ] );
 			t.ok( delta <= tol, 'within tolerance. c: '+c[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );

lib/node_modules/@stdlib/stats/base/dists/pareto-type1/quantile/README.md

@@ -55,7 +55,7 @@ var quantile = require( '@stdlib/stats/base/dists/pareto-type1/quantile' );
 
 #### quantile( p, alpha, beta )
 
-Evaluates the [quantile function][quantile-function] for a [Pareto (Type I)][pareto-distribution] distribution with parameters `alpha` (shape parameter) and `beta` ( scale parameter).
+Evaluates the [quantile function][quantile-function] for a [Pareto (Type I)][pareto-distribution] distribution with parameters `alpha` (shape parameter) and `beta` (scale parameter).
 
 ```javascript
 var y = quantile( 0.8, 2.0, 1.0 );
@@ -113,7 +113,7 @@ y = quantile( 0.4, 1.0, 0.0 );
 
 #### quantile.factory( alpha, beta )
 
-Returns a function for evaluating the [quantile function][quantile-function] of a [Pareto (Type I)][pareto-distribution] distribution with parameters `alpha` (shape parameter) and `beta` ( scale parameter).
+Returns a function for evaluating the [quantile function][quantile-function] of a [Pareto (Type I)][pareto-distribution] distribution with parameters `alpha` (shape parameter) and `beta` (scale parameter).
 
 ```javascript
 var myquantile = quantile.factory( 2.5, 0.5 );
@@ -157,6 +157,104 @@ for ( i = 0; i < 10; i++ ) {
 
 <!-- /.examples -->
 
+<!-- C interface documentation. -->
+
+* * *
+
+<section class="c">
+
+## C APIs
+
+<!-- Section to include introductory text. Make sure to keep an empty line after the intro `section` element and another before the `/section` close. -->
+
+<section class="intro">
+
+</section>
+
+<!-- /.intro -->
+
+<!-- C usage documentation. -->
+
+<section class="usage">
+
+### Usage
+
+```c
+#include "stdlib/stats/base/dists/pareto-type1/quantile.h"
+```
+
+#### stdlib_base_dists_pareto_type1_quantile( p, alpha, beta )
+
+Evaluates the [quantile function][quantile-function] for a [Pareto (Type I)][pareto-distribution] distribution with parameters `alpha` (shape parameter) and `beta` (scale parameter).
+
+```c
+double y = stdlib_base_dists_pareto_type1_quantile( 0.8, 2.0, 1.0 );
+// returns ~2.236
+```
+
+The function accepts the following arguments:
+
+-   **p**: `[in] double` input probability.
+-   **alpha**: `[in] double` shape parameter.
+-   **beta**: `[in] double` scale parameter.
+
+```c
+double stdlib_base_dists_pareto_type1_quantile( const double p, const double alpha, const double beta );
+```
+
+</section>
+
+<!-- /.usage -->
+
+<!-- C API usage notes. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
+
+<section class="notes">
+
+</section>
+
+<!-- /.notes -->
+
+<!-- C API usage examples. -->
+
+<section class="examples">
+
+### Examples
+
+```c
+#include "stdlib/stats/base/dists/pareto-type1/quantile.h"
+#include <stdlib.h>
+#include <stdio.h>
+
+static double random_uniform( const double min, const double max ) {
+    double v = (double)rand() / ( (double)RAND_MAX + 1.0 );
+    return min + ( v*(max-min) );
+}
+
+int main( void ) {
+    double alpha;
+    double beta;
+    double p;
+    double y;
+    int i;
+
+    for ( i = 0; i < 25; i++ ) {
+        p = random_uniform( 0.0, 1.0 );
+        alpha = random_uniform( 1.0, 10.0 );
+        beta = random_uniform( 1.0, 10.0 );
+        y = stdlib_base_dists_pareto_type1_quantile( p, alpha, beta );
+        printf( "p: %lf, α: %lf, β: %lf, Q(p;α,β): %lf\n", p, alpha, beta, y );
+    }
+}
+```
+
+</section>
+
+<!-- /.examples -->
+
+</section>
+
+<!-- /.c -->
+
 <!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->
 
 <section class="related">

lib/node_modules/@stdlib/stats/base/dists/pareto-type1/quantile/benchmark/benchmark.js

@@ -21,10 +21,8 @@
 // MODULES //
 
 var bench = require( '@stdlib/bench' );
-var Float64Array = require( '@stdlib/array/float64' );
-var uniform = require( '@stdlib/random/base/uniform' );
+var uniform = require( '@stdlib/random/array/uniform' );
 var isnan = require( '@stdlib/math/base/assert/is-nan' );
-var EPS = require( '@stdlib/constants/float64/eps' );
 var pkg = require( './../package.json' ).name;
 var quantile = require( './../lib' );
 
@@ -40,14 +38,9 @@ bench( pkg, function benchmark( b ) {
 	var i;
 
 	len = 100;
-	alpha = new Float64Array( len );
-	beta = new Float64Array( len );
-	p = new Float64Array( len );
-	for ( i = 0; i < len; i++ ) {
-		p[ i ] = uniform( 0.0, 1.0 );
-		alpha[ i ] = uniform( EPS, 100.0 );
-		beta[ i ] = uniform( EPS, 100.0 );
-	}
+	p = uniform( len, 0.0, 1.0 );
+	alpha = uniform( len, 1.0, 100.0 );
+	beta = uniform( len, 1.0, 100.0 );
 
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {
@@ -74,13 +67,10 @@ bench( pkg+':factory', function benchmark( b ) {
 	var i;
 
 	alpha = 100.56789;
-	len = 100;
 	beta = 55.54321;
 	myquantile = quantile.factory( alpha, beta );
-	p = new Float64Array( len );
-	for ( i = 0; i < len; i++ ) {
-		p[ i ] = uniform( 0.0, 1.0 );
-	}
+	len = 100;
+	p = uniform( len, 0.0, 1.0 );
 
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {

lib/node_modules/@stdlib/stats/base/dists/pareto-type1/quantile/benchmark/benchmark.native.js

@@ -0,0 +1,67 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 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 resolve = require( 'path' ).resolve;
+var bench = require( '@stdlib/bench' );
+var tryRequire = require( '@stdlib/utils/try-require' );
+var uniform = require( '@stdlib/random/array/uniform' );
+var isnan = require( '@stdlib/math/base/assert/is-nan' );
+var pkg = require( './../package.json' ).name;
+
+
+// VARIABLES //
+
+var quantile = tryRequire( resolve( __dirname, './../lib/native.js' ) );
+var opts = {
+	'skip': ( quantile instanceof Error )
+};
+
+
+// MAIN //
+
+bench( pkg+'::native', opts, function benchmark( b ) {
+	var alpha;
+	var beta;
+	var len;
+	var p;
+	var y;
+	var i;
+
+	len = 100;
+	p = uniform( len, 0.0, 1.0 );
+	alpha = uniform( len, 1.0, 10.0 );
+	beta = uniform( len, 1.0, 10.0 );
+
+	b.tic();
+	for ( i = 0; i < b.iterations; i++ ) {
+		y = quantile( p[ i % len ], alpha[ i % len ], beta[ i % len ] );
+		if ( isnan( y ) ) {
+			b.fail( 'should not return NaN' );
+		}
+	}
+	b.toc();
+	if ( isnan( y ) ) {
+		b.fail( 'should not return NaN' );
+	}
+	b.pass( 'benchmark finished' );
+	b.end();
+});