feat: add stats/base/dists/log-logistic/pdf

lib/node_modules/@stdlib/stats/base/dists/log-logistic/pdf/README.md

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+<!--
+
+@license Apache-2.0
+
+Copyright (c) 2026 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.
+
+-->
+
+# Probability Density Function
+
+> [Log-logistic][log-logistic-distribution] distribution [probability density function (PDF)][pdf].
+
+<section class="intro">
+
+The [probability density function][pdf] (PDF) for a [log-logistic][log-logistic-distribution] random variable is
+
+<!-- <equation class="equation" label="eq:log-logistic_pdf" align="center" raw="f(x;\alpha,\beta) = \frac{\dfrac{\beta}{\alpha}\left(\dfrac{x}{\alpha}\right)^{\beta-1}}{\left(1+\left(\dfrac{x}{\alpha}\right)^{\beta}\right)^{2}}" alt="Probability density function (PDF) for a log-logistic distribution."> -->
+
+```math
+f(x;\alpha,\beta) = \frac{\dfrac{\beta}{\alpha}\left(\dfrac{x}{\alpha}\right)^{\beta-1}}{\left(1+\left(\dfrac{x}{\alpha}\right)^{\beta}\right)^{2}}
+```
+
+<!-- <div class="equation" align="center" data-raw-text="f(x;\alpha,\beta) = \frac{\dfrac{\beta}{\alpha}\left(\dfrac{x}{\alpha}\right)^{\beta-1}}{\left(1+\left(\dfrac{x}{\alpha}\right)^{\beta}\right)^{2}}" data-equation="eq:log_logistic_pdf">
+    <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@.../lib/node_modules/@stdlib/stats/base/dists/log-logistic/pdf/docs/img/equation_log_logistic_pdf.svg" alt="Probability density function (PDF) for a log-logistic distribution.">
+    <br>
+</div> -->
+
+<!-- </equation> -->
+
+where `alpha` is the scale parameter and `beta` is the shape parameter.
+
+</section>
+
+<!-- /.intro -->
+
+<section class="usage">
+
+## Usage
+
+```javascript
+var pdf = require( '@stdlib/stats/base/dists/log-logistic/pdf' );
+```
+
+#### pdf( x, alpha, beta )
+
+Evaluates the [probability density function][pdf] (PDF) for a [log-logistic][log-logistic-distribution] distribution with parameters `alpha` (scale parameter) and `beta` (shape parameter).
+
+```javascript
+var y = pdf( 2.0, 1.0, 1.0 );
+// returns ~0.111
+
+y = pdf( 4.0, 2.0, 3.0 );
+// returns ~0.074
+```
+
+If provided `NaN` as any argument, the function returns `NaN`.
+
+```javascript
+var y = pdf( NaN, 1.0, 1.0 );
+// returns NaN
+
+y = pdf( 1.0, NaN, 1.0 );
+// returns NaN
+
+y = pdf( 1.0, 1.0, NaN );
+// returns NaN
+```
+
+If provided `alpha <= 0`, the function returns `NaN`.
+
+```javascript
+var y = pdf( 2.0, -1.0, 1.0 );
+// returns NaN
+```
+
+If provided `beta <= 0`, the function returns `NaN`.
+
+```javascript
+var y = pdf( 2.0, 1.0, -1.0 );
+// returns NaN
+```
+
+#### pdf.factory( alpha, beta )
+
+Returns a function for evaluating the [probability density function][pdf] (PDF) of a [log-logistic][log-logistic-distribution] distribution with parameters `alpha` (scale parameter) and `beta` (shape parameter).
+
+```javascript
+var mypdf = pdf.factory( 1.0, 1.0 );
+
+var y = mypdf( 2.0 );
+// returns ~0.111
+
+y = mypdf( -1.0 );
+// returns 0.0
+```
+
+</section>
+
+<!-- /.usage -->
+
+<section class="examples">
+
+## Examples
+
+<!-- eslint no-undef: "error" -->
+
+```javascript
+var uniform = require( '@stdlib/random/array/uniform' );
+var logEachMap = require( '@stdlib/console/log-each-map' );
+var pdf = require( '@stdlib/stats/base/dists/log-logistic/pdf' );
+
+var opts = {
+    'dtype': 'float64'
+};
+var x = uniform( 10, 0.1, 10.0, opts );
+var alpha = uniform( 10, 0.1, 10.0, opts );
+var beta = uniform( 10, 0.1, 10.0, opts );
+
+logEachMap( 'x: %0.4f, alpha: %0.4f, beta: %0.4f, f(x;alpha,beta): %0.4f', x, alpha, beta, pdf );
+```
+
+</section>
+
+<!-- /.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/log-logistic/pdf.h"
+```
+
+#### stdlib_base_dists_log_logistic_pdf( x, alpha, beta )
+
+Evaluates the [probability density function][pdf] (PDF) for a [log-logistic][log-logistic-distribution] distribution with parameters `alpha` (scale parameter) and `beta` (shape parameter).
+
+```c
+double out = stdlib_base_dists_log_logistic_pdf( 2.0, 1.0, 1.0 );
+// returns ~0.111
+```
+
+The function accepts the following arguments:
+
+-   **x**: `[in] double` input parameter.
+-   **alpha**: `[in] double` scale parameter.
+-   **beta**: `[in] double` shape parameter.
+
+```c
+double stdlib_base_dists_log_logistic_pdf( const double x, 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/log-logistic/pdf.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 x;
+    double y;
+    int i;
+
+    for ( i = 0; i < 25; i++ ) {
+        x = random_uniform( 0.1, 10.0 );
+        alpha = random_uniform( 0.1, 10.0 );
+        beta = random_uniform( 0.1, 10.0 );
+        y = stdlib_base_dists_log_logistic_pdf( x, alpha, beta );
+        printf( "x: %lf, alpha: %lf, beta: %lf, f(x;alpha,beta): %lf\n", x, 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">
+
+</section>
+
+<!-- /.related -->
+
+<!-- Section for all links. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
+
+<section class="links">
+
+[log-logistic-distribution]: https://en.wikipedia.org/wiki/Log-logistic_distribution
+
+[pdf]: https://en.wikipedia.org/wiki/Probability_density_function
+
+</section>
+
+<!-- /.links -->

lib/node_modules/@stdlib/stats/base/dists/log-logistic/pdf/benchmark/benchmark.js

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+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2026 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 uniform = require( '@stdlib/random/array/uniform' );
+var isnan = require( '@stdlib/math/base/assert/is-nan' );
+var EPS = require( '@stdlib/constants/float64/eps' );
+var format = require( '@stdlib/string/format' );
+var pkg = require( './../package.json' ).name;
+var pdf = require( './../lib' );
+
+
+// MAIN //
+
+bench( pkg, function benchmark( b ) {
+	var alpha;
+	var beta;
+	var opts;
+	var x;
+	var y;
+	var i;
+
+	opts = {
+		'dtype': 'float64'
+	};
+	x = uniform( 100, EPS, 20.0, opts );
+	alpha = uniform( 100, EPS, 5.0, opts );
+	beta = uniform( 100, EPS, 5.0, opts );
+
+	b.tic();
+	for ( i = 0; i < b.iterations; i++ ) {
+		y = pdf( x[ i % x.length ], alpha[ i % alpha.length ], beta[ i % beta.length ] );
+		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();
+});
+
+bench( format( '%s::factory', pkg ), function benchmark( b ) {
+	var mypdf;
+	var alpha;
+	var beta;
+	var opts;
+	var x;
+	var y;
+	var i;
+
+	alpha = 2.0;
+	beta = 3.0;
+	mypdf = pdf.factory( alpha, beta );
+
+	opts = {
+		'dtype': 'float64'
+	};
+	x = uniform( 100, EPS, 20.0, opts );
+
+	b.tic();
+	for ( i = 0; i < b.iterations; i++ ) {
+		y = mypdf( x[ i % x.length ] );
+		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();
+});