feat: add implementation for stats/base/dists/halfnormal/logpdf

lib/node_modules/@stdlib/stats/base/dists/halfnormal/logpdf/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.
+
+-->
+
+# Logarithm of Probability Density Function
+
+> [Half-Normal][half-normal-distribution] distribution logarithm of [probability density function (PDF)][pdf].
+
+<section class="intro">
+
+The [probability density function][pdf] (PDF) for a [half-normal][half-normal-distribution] random variable is
+
+<!-- <equation class="equation" label="eq:halfnormal_pdf" align="center" raw="f(x;\sigma) = \frac{\sqrt{2}}{\sigma\sqrt{\pi}} e^{-\frac{x^2}{2\sigma^2}}" alt="Probability density function (PDF) for a half-normal distribution."> -->
+
+```math
+f(x;\sigma) = \frac{\sqrt{2}}{\sigma\sqrt{\pi}} e^{-\frac{x^2}{2\sigma^2}}
+```
+
+<!-- <div class="equation" align="center" data-raw-text="f(x;\sigma) = \frac{\sqrt{2}}{\sigma\sqrt{\pi}} e^{-\frac{x^2}{2\sigma^2}}" data-equation="eq:halfnormal_pdf">
+    <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@main/lib/node_modules/@stdlib/stats/base/dists/halfnormal/logpdf/docs/img/equation_halfnormal_pdf.svg" alt="Probability density function (PDF) for a half-normal distribution.">
+    <br>
+</div> -->
+
+<!-- </equation> -->
+
+for `x >= 0`, where `sigma > 0` is the scale parameter. For `x < 0`, the PDF is `0`.
+
+</section>
+
+<!-- /.intro -->
+
+<section class="usage">
+
+## Usage
+
+```javascript
+var logpdf = require( '@stdlib/stats/base/dists/halfnormal/logpdf' );
+```
+
+#### logpdf( x, sigma )
+
+Evaluates the logarithm of the [probability density function][pdf] (PDF) for a [half-normal][half-normal-distribution] distribution with parameter `sigma` (scale parameter).
+
+```javascript
+var y = logpdf( 0.8, 1.0 );
+// returns ~-0.546
+
+y = logpdf( 0.5, 1.0 );
+// returns ~-0.351
+
+y = logpdf( 1.2, 2.0 );
+// returns ~-1.099
+```
+
+If `x < 0`, the function returns `-Infinity`.
+
+```javascript
+var y = logpdf( -0.2, 1.0 );
+// returns -Infinity
+```
+
+If provided `NaN` as any argument, the function returns `NaN`.
+
+```javascript
+var y = logpdf( NaN, 1.0 );
+// returns NaN
+
+y = logpdf( 0.0, NaN );
+// returns NaN
+```
+
+If provided `sigma <= 0`, the function returns `NaN`.
+
+```javascript
+var y = logpdf( 2.0, -1.0 );
+// returns NaN
+
+y = logpdf( 2.0, 0.0 );
+// returns NaN
+```
+
+#### logpdf.factory( sigma )
+
+Returns a `function` for evaluating the logarithm of the [PDF][pdf] for a [half-normal][half-normal-distribution] distribution with parameter `sigma` (scale parameter).
+
+```javascript
+var mylogpdf = logpdf.factory( 1.0 );
+
+var y = mylogpdf( 0.8 );
+// returns ~-0.546
+
+y = mylogpdf( 1.2 );
+// returns ~-0.946
+```
+
+</section>
+
+<!-- /.usage -->
+
+<section class="notes">
+
+## Notes
+
+-   In virtually all cases, using the `logpdf` or `logcdf` functions is preferable to manually computing the logarithm of the `pdf` or `cdf`, respectively, since the latter is prone to overflow and underflow.
+
+</section>
+
+<!-- /.notes -->
+
+<section class="examples">
+
+## Examples
+
+<!-- eslint no-undef: "error" -->
+
+```javascript
+var randu = require( '@stdlib/random/base/randu' );
+var logpdf = require( '@stdlib/stats/base/dists/halfnormal/logpdf' );
+var i;
+var x;
+var y;
+var sigma;
+
+for ( i = 0; i < 25; i++ ) {
+    x = randu() * 3.0;
+    sigma = randu() * 3.0;
+    y = logpdf( x, sigma );
+    console.log( 'x: %d, σ: %d, ln(f(x;σ)): %d', x.toFixed( 4 ), sigma.toFixed( 4 ), y.toFixed( 4 ) );
+}
+```
+
+</section>
+
+<!-- /.examples -->
+
+<!-- C usage documentation. -->
+
+<section class="usage">
+
+### Usage
+
+```c
+#include "stdlib/stats/base/dists/halfnormal/logpdf.h"
+```
+
+#### stdlib_base_dists_halfnormal_logpdf( x, sigma )
+
+Evaluates the logarithm of the [probability density function][pdf] (PDF) for a [Half-Normal][half-normal-distribution] distribution with parameter `sigma` (scale parameter).
+
+```c
+double out = stdlib_base_dists_halfnormal_logpdf( 0.8, 1.0 );
+// returns ~-0.546
+```
+
+The function accepts the following arguments:
+
+-   **x**: `[in] double` input value.
+-   **sigma**: `[in] double` scale parameter.
+
+```c
+double stdlib_base_dists_halfnormal_logpdf( const double x, const double sigma );
+```
+
+</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/halfnormal/logpdf.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 sigma;
+    double x;
+    double y;
+    int i;
+
+    for ( i = 0; i < 25; i++ ) {
+        x = random_uniform( 0.0, 10.0 );
+        sigma = random_uniform( 0.1, 10.0 ); // sigma must be > 0
+        y = stdlib_base_dists_halfnormal_logpdf( x, sigma );
+        printf( "x: %lf, σ: %lf, ln(f(x;σ)): %lf\n", x, sigma, y );
+    }
+}
+```
+
+</section>
+
+<!-- /.examples -->
+
+<!-- /.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">
+
+[half-normal-distribution]: https://en.wikipedia.org/wiki/Half-normal_distribution
+
+[pdf]: https://en.wikipedia.org/wiki/Probability_density_function
+
+</section>
+
+<!-- /.links -->

lib/node_modules/@stdlib/stats/base/dists/halfnormal/logpdf/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 Float64Array = require( '@stdlib/array/float64' );
+var uniform = require( '@stdlib/random/base/uniform' );
+var isnan = require( '@stdlib/math/base/assert/is-nan' );
+var EPS = require( '@stdlib/constants/float64/eps' );
+var pkg = require( './../package.json' ).name;
+var logpdf = require( './../lib' );
+
+
+// MAIN //
+
+bench( pkg, function benchmark( b ) {
+	var sigma;
+	var len;
+	var x;
+	var y;
+	var i;
+
+	len = 100;
+	x = new Float64Array( len );
+	sigma = new Float64Array( len );
+	for ( i = 0; i < len; i++ ) {
+		x[ i ] = uniform( 0.0, 10.0 );
+		sigma[ i ] = uniform( EPS, 10.0 );
+	}
+
+	b.tic();
+	for ( i = 0; i < b.iterations; i++ ) {
+		y = logpdf( x[ i % len ], sigma[ 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();
+});
+
+bench( pkg+':factory', function benchmark( b ) {
+	var mylogpdf;
+	var sigma;
+	var len;
+	var x;
+	var y;
+	var i;
+
+	sigma = 4.0;
+	mylogpdf = logpdf.factory( sigma );
+	len = 100;
+	x = new Float64Array( len );
+	for ( i = 0; i < len; i++ ) {
+		x[ i ] = uniform( 0.0, 20.0 );
+	}
+
+	b.tic();
+	for ( i = 0; i < b.iterations; i++ ) {
+		y = mylogpdf( x[ 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();
+});