feat: add C implementation for stasts/base/dists/erlang/mgf

lib/node_modules/@stdlib/namespace/lib/namespace/base/strided/d.js

@@ -130,7 +130,7 @@
 });
 
 ns.push({
 	'alias': 'base.strided.dasum',
 	'path': '@stdlib/blas/base/dasum',
 	'value': require( '@stdlib/blas/base/dasum' ),
 	'type': 'Function',
@@ -144,7 +144,7 @@
 });
 
 ns.push({
 	'alias': 'base.strided.dasumpw',
 	'path': '@stdlib/blas/ext/base/dasumpw',
 	'value': require( '@stdlib/blas/ext/base/dasumpw' ),
 	'type': 'Function',
@@ -172,7 +172,7 @@
 });
 
 ns.push({
 	'alias': 'base.strided.dcbrt',
 	'path': '@stdlib/math/strided/special/dcbrt',
 	'value': require( '@stdlib/math/strided/special/dcbrt' ),
 	'type': 'Function',
@@ -184,7 +184,7 @@
 });
 
 ns.push({
 	'alias': 'base.strided.dceil',
 	'path': '@stdlib/math/strided/special/dceil',
 	'value': require( '@stdlib/math/strided/special/dceil' ),
 	'type': 'Function',
@@ -243,7 +243,7 @@
 });
 
 ns.push({
 	'alias': 'base.strided.dcumin',
 	'path': '@stdlib/stats/base/dcumin',
 	'value': require( '@stdlib/stats/base/dcumin' ),
 	'type': 'Function',
@@ -257,7 +257,7 @@
 });
 
 ns.push({
 	'alias': 'base.strided.dcuminabs',
 	'path': '@stdlib/stats/strided/dcuminabs',
 	'value': require( '@stdlib/stats/strided/dcuminabs' ),
 	'type': 'Function',
@@ -271,7 +271,7 @@
 });
 
 ns.push({
 	'alias': 'base.strided.dcusum',
 	'path': '@stdlib/blas/ext/base/dcusum',
 	'value': require( '@stdlib/blas/ext/base/dcusum' ),
 	'type': 'Function',
@@ -284,7 +284,7 @@
 });
 
 ns.push({
 	'alias': 'base.strided.dcusumkbn',
 	'path': '@stdlib/blas/ext/base/dcusumkbn',
 	'value': require( '@stdlib/blas/ext/base/dcusumkbn' ),
 	'type': 'Function',
@@ -297,7 +297,7 @@
 });
 
 ns.push({
 	'alias': 'base.strided.dcusumkbn2',
 	'path': '@stdlib/blas/ext/base/dcusumkbn2',
 	'value': require( '@stdlib/blas/ext/base/dcusumkbn2' ),
 	'type': 'Function',
@@ -310,7 +310,7 @@
 });
 
 ns.push({
 	'alias': 'base.strided.dcusumors',
 	'path': '@stdlib/blas/ext/base/dcusumors',
 	'value': require( '@stdlib/blas/ext/base/dcusumors' ),
 	'type': 'Function',
@@ -2168,7 +2168,7 @@
 		'@stdlib/stats/strided/dsvariance',
 		'@stdlib/stats/base/variancepn',
 		'@stdlib/stats/base/sdsvariance',
-		'@stdlib/stats/base/svariancepn'
+		'@stdlib/stats/strided/svariancepn'
 	]
 });
 
@@ -2280,7 +2280,7 @@
 		'@stdlib/stats/strided/dnanvariancepn',
 		'@stdlib/stats/strided/dstdevpn',
 		'@stdlib/stats/strided/dvariance',
-		'@stdlib/stats/base/svariancepn',
+		'@stdlib/stats/strided/svariancepn',
 		'@stdlib/stats/base/variancepn'
 	]
 });
@@ -2294,7 +2294,7 @@
 		'@stdlib/stats/strided/dnanvariancetk',
 		'@stdlib/stats/strided/dstdevtk',
 		'@stdlib/stats/strided/dvariance',
-		'@stdlib/stats/base/svariancetk',
+		'@stdlib/stats/strided/svariancetk',
 		'@stdlib/stats/base/variancetk'
 	]
 });
@@ -2322,7 +2322,7 @@
 		'@stdlib/stats/strided/dnanvarianceyc',
 		'@stdlib/stats/strided/dstdevyc',
 		'@stdlib/stats/strided/dvariance',
-		'@stdlib/stats/base/svarianceyc',
+		'@stdlib/stats/strided/svarianceyc',
 		'@stdlib/stats/base/varianceyc'
 	]
 });

lib/node_modules/@stdlib/namespace/lib/namespace/base/strided/s.js

@@ -1406,7 +1406,7 @@ ns.push({
 		'@stdlib/stats/base/nanvariancepn',
 		'@stdlib/stats/base/snanstdevpn',
 		'@stdlib/stats/base/snanvariance',
-		'@stdlib/stats/base/svariancepn'
+		'@stdlib/stats/strided/svariancepn'
 	]
 });
 
@@ -1420,7 +1420,7 @@ ns.push({
 		'@stdlib/stats/base/nanvariancetk',
 		'@stdlib/stats/base/snanstdevtk',
 		'@stdlib/stats/base/snanvariance',
-		'@stdlib/stats/base/svariancetk'
+		'@stdlib/stats/strided/svariancetk'
 	]
 });
 
@@ -1448,7 +1448,7 @@ ns.push({
 		'@stdlib/stats/base/nanvarianceyc',
 		'@stdlib/stats/base/snanstdevyc',
 		'@stdlib/stats/base/snanvariance',
-		'@stdlib/stats/base/svarianceyc'
+		'@stdlib/stats/strided/svarianceyc'
 	]
 });
 
@@ -1652,7 +1652,7 @@ ns.push({
 		'@stdlib/stats/base/sstdev',
 		'@stdlib/stats/base/sstdevmpn',
 		'@stdlib/stats/base/stdevpn',
-		'@stdlib/stats/base/svariancepn'
+		'@stdlib/stats/strided/svariancepn'
 	]
 });
 
@@ -1666,7 +1666,7 @@ ns.push({
 		'@stdlib/stats/base/snanstdevtk',
 		'@stdlib/stats/base/sstdev',
 		'@stdlib/stats/base/stdevtk',
-		'@stdlib/stats/base/svariancetk'
+		'@stdlib/stats/strided/svariancetk'
 	]
 });
 
@@ -1694,7 +1694,7 @@ ns.push({
 		'@stdlib/stats/base/snanstdevyc',
 		'@stdlib/stats/base/sstdev',
 		'@stdlib/stats/base/stdevyc',
-		'@stdlib/stats/base/svarianceyc'
+		'@stdlib/stats/strided/svarianceyc'
 	]
 });
 
@@ -1920,8 +1920,8 @@ ns.push({
 
 ns.push({
 	'alias': 'base.strided.svariancepn',
-	'path': '@stdlib/stats/base/svariancepn',
-	'value': require( '@stdlib/stats/base/svariancepn' ),
+	'path': '@stdlib/stats/strided/svariancepn',
+	'value': require( '@stdlib/stats/strided/svariancepn' ),
 	'type': 'Function',
 	'related': [
 		'@stdlib/stats/strided/dvariancepn',
@@ -1935,8 +1935,8 @@ ns.push({
 
 ns.push({
 	'alias': 'base.strided.svariancetk',
-	'path': '@stdlib/stats/base/svariancetk',
-	'value': require( '@stdlib/stats/base/svariancetk' ),
+	'path': '@stdlib/stats/strided/svariancetk',
+	'value': require( '@stdlib/stats/strided/svariancetk' ),
 	'type': 'Function',
 	'related': [
 		'@stdlib/stats/strided/dvariancetk',
@@ -1963,8 +1963,8 @@ ns.push({
 
 ns.push({
 	'alias': 'base.strided.svarianceyc',
-	'path': '@stdlib/stats/base/svarianceyc',
-	'value': require( '@stdlib/stats/base/svarianceyc' ),
+	'path': '@stdlib/stats/strided/svarianceyc',
+	'value': require( '@stdlib/stats/strided/svarianceyc' ),
 	'type': 'Function',
 	'related': [
 		'@stdlib/stats/strided/dvarianceyc',

lib/node_modules/@stdlib/stats/base/README.md

@@ -239,10 +239,10 @@ The namespace contains the following statistical functions:
 -   <span class="signature">[`stdevyc( N, correction, x, stride )`][@stdlib/stats/base/stdevyc]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a strided array using a one-pass algorithm proposed by Youngs and Cramer.</span>
 -   <span class="signature">[`svariance( N, correction, x, stride )`][@stdlib/stats/base/svariance]</span><span class="delimiter">: </span><span class="description">calculate the variance of a single-precision floating-point strided array.</span>
 -   <span class="signature">[`svariancech( N, correction, x, strideX )`][@stdlib/stats/strided/svariancech]</span><span class="delimiter">: </span><span class="description">calculate the variance of a single-precision floating-point strided array using a one-pass trial mean algorithm.</span>
--   <span class="signature">[`svariancepn( N, correction, x, strideX )`][@stdlib/stats/base/svariancepn]</span><span class="delimiter">: </span><span class="description">calculate the variance of a single-precision floating-point strided array using a two-pass algorithm.</span>
--   <span class="signature">[`svariancetk( N, correction, x, strideX )`][@stdlib/stats/base/svariancetk]</span><span class="delimiter">: </span><span class="description">calculate the variance of a single-precision floating-point strided array using a one-pass textbook algorithm.</span>
+-   <span class="signature">[`svariancepn( N, correction, x, strideX )`][@stdlib/stats/strided/svariancepn]</span><span class="delimiter">: </span><span class="description">calculate the variance of a single-precision floating-point strided array using a two-pass algorithm.</span>
+-   <span class="signature">[`svariancetk( N, correction, x, strideX )`][@stdlib/stats/strided/svariancetk]</span><span class="delimiter">: </span><span class="description">calculate the variance of a single-precision floating-point strided array using a one-pass textbook algorithm.</span>
 -   <span class="signature">[`svariancewd( N, correction, x, stride )`][@stdlib/stats/base/svariancewd]</span><span class="delimiter">: </span><span class="description">calculate the variance of a single-precision floating-point strided array using Welford's algorithm.</span>
--   <span class="signature">[`svarianceyc( N, correction, x, strideX )`][@stdlib/stats/base/svarianceyc]</span><span class="delimiter">: </span><span class="description">calculate the variance of a single-precision floating-point strided array using a one-pass algorithm proposed by Youngs and Cramer.</span>
+-   <span class="signature">[`svarianceyc( N, correction, x, strideX )`][@stdlib/stats/strided/svarianceyc]</span><span class="delimiter">: </span><span class="description">calculate the variance of a single-precision floating-point strided array using a one-pass algorithm proposed by Youngs and Cramer.</span>
 -   <span class="signature">[`variance( N, correction, x, stride )`][@stdlib/stats/base/variance]</span><span class="delimiter">: </span><span class="description">calculate the variance of a strided array.</span>
 -   <span class="signature">[`variancech( N, correction, x, stride )`][@stdlib/stats/base/variancech]</span><span class="delimiter">: </span><span class="description">calculate the variance of a strided array using a one-pass trial mean algorithm.</span>
 -   <span class="signature">[`variancepn( N, correction, x, stride )`][@stdlib/stats/base/variancepn]</span><span class="delimiter">: </span><span class="description">calculate the variance of a strided array using a two-pass algorithm.</span>
@@ -663,13 +663,13 @@ console.log( objectKeys( ns ) );
 
 [@stdlib/stats/strided/svariancech]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/svariancech
 
-[@stdlib/stats/base/svariancepn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/svariancepn
+[@stdlib/stats/strided/svariancepn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/svariancepn
 
-[@stdlib/stats/base/svariancetk]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/svariancetk
+[@stdlib/stats/strided/svariancetk]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/svariancetk
 
 [@stdlib/stats/base/svariancewd]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/svariancewd
 
-[@stdlib/stats/base/svarianceyc]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/svarianceyc
+[@stdlib/stats/strided/svarianceyc]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/svarianceyc
 
 [@stdlib/stats/base/variance]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/variance
 

lib/node_modules/@stdlib/stats/base/dists/erlang/mgf/README.md

@@ -163,6 +163,104 @@ 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/erlang/mgf.h"
+```
+
+#### stdlib_base_dists_erlang_mgf( t, k, lambda )
+
+Evaluates the [moment-generating function][mgf] (mgf) for an [Erlang][erlang-distribution] distribution with parameters `k` (shape parameter) and `lambda` (rate parameter).
+
+```c
+double y = stdlib_base_dists_erlang_mgf( 0.3, 1, 1.0 );
+// returns ~1.429
+```
+
+The function accepts the following arguments:
+
+-   **t**: `[in] double` input value.
+-   **k**: `[in] int32_t` shape parameter.
+-   **lambda**: `[in] double` rate parameter.
+
+```c
+double stdlib_base_dists_erlang_mgf( const double t, const int32_t k, const double lambda );
+```
+
+</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/erlang/mgf.h"
+#include "stdlib/math/base/special/round.h"
+#include <stdint.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 lambda;
+    int32_t k;
+    double t;
+    double y;
+    int i;
+
+    for ( i = 0; i < 10; i++ ) {
+        k = stdlib_base_round( random_uniform( 0.0, 10.0 ) );
+        lambda = random_uniform( 0.0, 10.0 ) ;
+        t = random_uniform( 0.0, lambda );
+        y = stdlib_base_dists_erlang_mgf( t, k, lambda );
+        printf( "t: %lf, k: %d, λ: %lf, M_X(t;k,λ): %lf\n", t, k, lambda, y );
+    }
+}
+```
+
+</section>
+
+<!-- /.examples -->
+
+</section>
+
+<!-- /.c -->
+
 <!-- Section to include cited references. If references are included, add a horizontal rule *before* the section. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
 
 <section class="references">

lib/node_modules/@stdlib/stats/base/dists/erlang/mgf/benchmark/benchmark.js

@@ -21,9 +21,10 @@
 // MODULES //
 
 var bench = require( '@stdlib/bench' );
-var Float64Array = require( '@stdlib/array/float64' );
-var uniform = require( '@stdlib/random/base/uniform' );
 var discreteUniform = require( '@stdlib/random/base/discrete-uniform' );
+var uniform = require( '@stdlib/random/base/uniform' );
+var Float64Array = require( '@stdlib/array/float64' );
+var Int32Array = require( '@stdlib/array/int32' );
 var isnan = require( '@stdlib/math/base/assert/is-nan' );
 var EPS = require( '@stdlib/constants/float64/eps' );
 var pkg = require( './../package.json' ).name;
@@ -41,11 +42,11 @@ bench( pkg, function benchmark( b ) {
 	var i;
 
 	len = 100;
-	k = new Float64Array( len );
+	k = new Int32Array( len );
 	lambda = new Float64Array( len );
 	t = new Float64Array( len );
 	for ( i = 0; i < len; i++ ) {
-		k[ i ] = discreteUniform( 0, 100 );
+		k[ i ] = discreteUniform( 1.0, 100.0 );
 		lambda[ i ] = uniform( EPS, 20.0 );
 		t[ i ] = uniform( 0.0, lambda[ i ] );
 	}
@@ -77,6 +78,7 @@ bench( pkg+':factory', function benchmark( b ) {
 	k = 2;
 	lambda = 1.5;
 	mymgf = mgf.factory( k, lambda );
+
 	len = 100;
 	t = new Float64Array( len );
 	for ( i = 0; i < len; i++ ) {

lib/node_modules/@stdlib/stats/base/dists/erlang/mgf/benchmark/benchmark.native.js

@@ -0,0 +1,76 @@
+/**
+* @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 discreteUniform = require( '@stdlib/random/base/discrete-uniform' );
+var uniform = require( '@stdlib/random/base/uniform' );
+var Float64Array = require( '@stdlib/array/float64' );
+var Int32Array = require( '@stdlib/array/int32' );
+var isnan = require( '@stdlib/math/base/assert/is-nan' );
+var tryRequire = require( '@stdlib/utils/try-require' );
+var EPS = require( '@stdlib/constants/float64/eps' );
+var pkg = require( './../package.json' ).name;
+
+
+// VARIABLES //
+
+var mgf = tryRequire( resolve( __dirname, './../lib/native.js' ) );
+var opts = {
+	'skip': ( mgf instanceof Error )
+};
+
+
+// MAIN //
+
+bench( pkg+'::native', opts, function benchmark( b ) {
+	var lambda;
+	var len;
+	var k;
+	var t;
+	var y;
+	var i;
+
+	len = 100;
+	k = new Int32Array( len );
+	lambda = new Float64Array( len );
+	t = new Float64Array( len );
+	for ( i = 0; i < len; i++ ) {
+		k[ i ] = discreteUniform( 1.0, 100.0 );
+		lambda[ i ] = uniform( EPS, 20.0 );
+		t[ i ] = uniform( 0.0, lambda[ i ] );
+	}
+
+	b.tic();
+	for ( i = 0; i < b.iterations; i++ ) {
+		y = mgf( t[ i % len ], k[ i % len ], lambda[ 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();
+});