feat: add C implementation for stats/base/ndarray/sztest

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

@@ -152,6 +152,229 @@ console.log( v.get().toString() );
 
 <!-- /.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/ndarray/sztest.h"
+```
+
+#### stdlib_stats_sztest( arrays )
+
+Computes a one-sample Z-test for a one-dimensional single-precision floating-point ndarray.
+
+```c
+#include "stdlib/stats/base/ztest/one-sample/results/float32.h"
+#include "stdlib/stats/base/ztest/alternatives.h"
+#include "stdlib/ndarray/ctor.h"
+#include "stdlib/ndarray/dtypes.h"
+#include "stdlib/ndarray/index_modes.h"
+#include "stdlib/ndarray/orders.h"
+#include "stdlib/ndarray/base/bytes_per_element.h"
+#include <stdint.h>
+
+// Create a one-dimensional input ndarray:
+const float xbuf[] = { 1.0f, 3.0f, 4.0f, 2.0f };
+int64_t xshape[] = { 4 };
+int64_t xstrides[] = { STDLIB_NDARRAY_FLOAT32_BYTES_PER_ELEMENT };
+int8_t submodes[] = { STDLIB_NDARRAY_INDEX_ERROR };
+struct ndarray *x = stdlib_ndarray_allocate( STDLIB_NDARRAY_FLOAT32, (uint8_t *)xbuf, 1, xshape, xstrides, 0, STDLIB_NDARRAY_ROW_MAJOR, STDLIB_NDARRAY_INDEX_ERROR, 1, submodes );
+
+// Create a zero-dimensional output ndarray to hold the results (its buffer is written directly as a `stdlib_stats_ztest_one_sample_float32_results` struct):
+uint8_t ybuf[ sizeof( struct stdlib_stats_ztest_one_sample_float32_results ) ];
+int64_t *zshape = NULL;
+int64_t zstrides[] = { 0 };
+struct ndarray *y = stdlib_ndarray_allocate( STDLIB_NDARRAY_UINT8, ybuf, 0, zshape, zstrides, 0, STDLIB_NDARRAY_ROW_MAJOR, STDLIB_NDARRAY_INDEX_ERROR, 0, submodes );
+
+// Create a zero-dimensional ndarray specifying the alternative hypothesis:
+const int8_t altbuf[] = { STDLIB_STATS_ZTEST_TWO_SIDED };
+struct ndarray *alt = stdlib_ndarray_allocate( STDLIB_NDARRAY_INT8, (uint8_t *)altbuf, 0, zshape, zstrides, 0, STDLIB_NDARRAY_ROW_MAJOR, STDLIB_NDARRAY_INDEX_ERROR, 0, submodes );
+
+// Create a zero-dimensional ndarray specifying the significance level:
+const float alphabuf[] = { 0.05f };
+struct ndarray *alpha = stdlib_ndarray_allocate( STDLIB_NDARRAY_FLOAT32, (uint8_t *)alphabuf, 0, zshape, zstrides, 0, STDLIB_NDARRAY_ROW_MAJOR, STDLIB_NDARRAY_INDEX_ERROR, 0, submodes );
+
+// Create a zero-dimensional ndarray specifying the mean under the null hypothesis:
+const float mubuf[] = { 0.0f };
+struct ndarray *mu = stdlib_ndarray_allocate( STDLIB_NDARRAY_FLOAT32, (uint8_t *)mubuf, 0, zshape, zstrides, 0, STDLIB_NDARRAY_ROW_MAJOR, STDLIB_NDARRAY_INDEX_ERROR, 0, submodes );
+
+// Create a zero-dimensional ndarray specifying the known standard deviation:
+const float sigmabuf[] = { 1.0f };
+struct ndarray *sigma = stdlib_ndarray_allocate( STDLIB_NDARRAY_FLOAT32, (uint8_t *)sigmabuf, 0, zshape, zstrides, 0, STDLIB_NDARRAY_ROW_MAJOR, STDLIB_NDARRAY_INDEX_ERROR, 0, submodes );
+
+// Perform a Z-test:
+const struct ndarray *arrays[] = { x, y, alt, alpha, mu, sigma };
+stdlib_stats_sztest( arrays );
+
+// Interpret the output buffer as a results struct:
+struct stdlib_stats_ztest_one_sample_float32_results *results = (struct stdlib_stats_ztest_one_sample_float32_results *)ybuf;
+// results->rejected, results->statistic, results->pValue, etc.
+
+// Free allocated memory:
+stdlib_ndarray_free( x );
+stdlib_ndarray_free( y );
+stdlib_ndarray_free( alt );
+stdlib_ndarray_free( alpha );
+stdlib_ndarray_free( mu );
+stdlib_ndarray_free( sigma );
+```
+
+The function accepts the following arguments:
+
+-   **arrays**: `[in] struct ndarray**` list containing, in order: a one-dimensional input ndarray; a zero-dimensional output ndarray whose underlying buffer is written with the test results as a `stdlib_stats_ztest_one_sample_float32_results` struct; a zero-dimensional ndarray (`int8`) specifying the alternative hypothesis; a zero-dimensional ndarray specifying the significance level; a zero-dimensional ndarray specifying the mean under the null hypothesis; and a zero-dimensional ndarray specifying the known standard deviation.
+
+```c
+void stdlib_stats_sztest( const struct ndarray *arrays[] );
+```
+
+</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/ndarray/sztest.h"
+#include "stdlib/stats/base/ztest/one-sample/results/float32.h"
+#include "stdlib/stats/base/ztest/alternatives.h"
+#include "stdlib/ndarray/ctor.h"
+#include "stdlib/ndarray/dtypes.h"
+#include "stdlib/ndarray/index_modes.h"
+#include "stdlib/ndarray/orders.h"
+#include "stdlib/ndarray/base/bytes_per_element.h"
+#include <stdint.h>
+#include <stdlib.h>
+#include <stdio.h>
+
+int main( void ) {
+    const enum STDLIB_NDARRAY_INDEX_MODE imode = STDLIB_NDARRAY_INDEX_ERROR;
+    int8_t submodes[] = { STDLIB_NDARRAY_INDEX_ERROR };
+    const enum STDLIB_NDARRAY_ORDER order = STDLIB_NDARRAY_ROW_MAJOR;
+
+    // Create a one-dimensional input ndarray:
+    const float xbuf[] = { 1.0f, 3.0f, 4.0f, 2.0f, 5.0f, 3.0f, 6.0f, 2.0f };
+    int64_t xshape[] = { 8 };
+    int64_t xstrides[] = { STDLIB_NDARRAY_FLOAT32_BYTES_PER_ELEMENT };
+    struct ndarray *x = stdlib_ndarray_allocate( STDLIB_NDARRAY_FLOAT32, (uint8_t *)xbuf, 1, xshape, xstrides, 0, order, imode, 1, submodes );
+    if ( x == NULL ) {
+        fprintf( stderr, "Error allocating memory.\n" );
+        exit( 1 );
+    }
+
+    // Create a zero-dimensional output ndarray to hold the results:
+    uint8_t ybuf[ sizeof( struct stdlib_stats_ztest_one_sample_float32_results ) ];
+    int64_t *yshape = NULL;
+    int64_t ystrides[] = { 0 };
+    struct ndarray *y = stdlib_ndarray_allocate( STDLIB_NDARRAY_UINT8, ybuf, 0, yshape, ystrides, 0, order, imode, 0, submodes );
+    if ( y == NULL ) {
+        fprintf( stderr, "Error allocating memory.\n" );
+        exit( 1 );
+    }
+
+    // Create a zero-dimensional ndarray specifying the alternative hypothesis:
+    const int8_t altbuf[] = { STDLIB_STATS_ZTEST_TWO_SIDED };
+    int64_t *altshape = NULL;
+    int64_t altstrides[] = { 0 };
+    struct ndarray *alt = stdlib_ndarray_allocate( STDLIB_NDARRAY_INT8, (uint8_t *)altbuf, 0, altshape, altstrides, 0, order, imode, 0, submodes );
+    if ( alt == NULL ) {
+        fprintf( stderr, "Error allocating memory.\n" );
+        exit( 1 );
+    }
+
+    // Create a zero-dimensional ndarray specifying the significance level:
+    const float alphabuf[] = { 0.05f };
+    int64_t *alphashape = NULL;
+    int64_t alphastrides[] = { 0 };
+    struct ndarray *alpha = stdlib_ndarray_allocate( STDLIB_NDARRAY_FLOAT32, (uint8_t *)alphabuf, 0, alphashape, alphastrides, 0, order, imode, 0, submodes );
+    if ( alpha == NULL ) {
+        fprintf( stderr, "Error allocating memory.\n" );
+        exit( 1 );
+    }
+
+    // Create a zero-dimensional ndarray specifying the mean under the null hypothesis:
+    const float mubuf[] = { 0.0f };
+    int64_t *mushape = NULL;
+    int64_t mustrides[] = { 0 };
+    struct ndarray *mu = stdlib_ndarray_allocate( STDLIB_NDARRAY_FLOAT32, (uint8_t *)mubuf, 0, mushape, mustrides, 0, order, imode, 0, submodes );
+    if ( mu == NULL ) {
+        fprintf( stderr, "Error allocating memory.\n" );
+        exit( 1 );
+    }
+
+    // Create a zero-dimensional ndarray specifying the known standard deviation:
+    const float sigmabuf[] = { 1.0f };
+    int64_t *sigmashape = NULL;
+    int64_t sigmastrides[] = { 0 };
+    struct ndarray *sigma = stdlib_ndarray_allocate( STDLIB_NDARRAY_FLOAT32, (uint8_t *)sigmabuf, 0, sigmashape, sigmastrides, 0, order, imode, 0, submodes );
+    if ( sigma == NULL ) {
+        fprintf( stderr, "Error allocating memory.\n" );
+        exit( 1 );
+    }
+
+    // Define a list of ndarrays:
+    const struct ndarray *arrays[] = { x, y, alt, alpha, mu, sigma };
+
+    // Perform a Z-test:
+    stdlib_stats_sztest( arrays );
+
+    // Interpret the output buffer as a results struct:
+    struct stdlib_stats_ztest_one_sample_float32_results *results = (struct stdlib_stats_ztest_one_sample_float32_results *)ybuf;
+
+    // Print the results:
+    printf( "rejected: %s\n", ( results->rejected ) ? "true" : "false" );
+    printf( "statistic: %f\n", results->statistic );
+    printf( "p-value: %f\n", results->pValue );
+    printf( "ci: [%f, %f]\n", results->ci[ 0 ], results->ci[ 1 ] );
+
+    // Free allocated memory:
+    stdlib_ndarray_free( x );
+    stdlib_ndarray_free( y );
+    stdlib_ndarray_free( alt );
+    stdlib_ndarray_free( alpha );
+    stdlib_ndarray_free( mu );
+    stdlib_ndarray_free( sigma );
+}
+```
+
+</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/ndarray/sztest/benchmark/benchmark.native.js

@@ -0,0 +1,129 @@
+/**
+* @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 resolve = require( 'path' ).resolve;
+var bench = require( '@stdlib/bench' );
+var normal = require( '@stdlib/random/normal' );
+var isnanf = require( '@stdlib/math/base/assert/is-nanf' );
+var pow = require( '@stdlib/math/base/special/pow' );
+var ndarray = require( '@stdlib/ndarray/ctor' );
+var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
+var Float32Results = require( '@stdlib/stats/base/ztest/one-sample/results/float32' );
+var resolveEnum = require( '@stdlib/stats/base/ztest/alternative-resolve-enum' );
+var structFactory = require( '@stdlib/array/struct-factory' );
+var format = require( '@stdlib/string/format' );
+var tryRequire = require( '@stdlib/utils/try-require' );
+var pkg = require( './../package.json' ).name;
+
+
+// VARIABLES //
+
+var sztest = tryRequire( resolve( __dirname, './../lib/native.js' ) );
+var opts = {
+	'skip': ( sztest instanceof Error )
+};
+var options = {
+	'dtype': 'float32'
+};
+var ResultsArray = structFactory( Float32Results );
+
+var alt = scalar2ndarray( resolveEnum( 'two-sided' ), {
+	'dtype': 'int8'
+});
+var alpha = scalar2ndarray( 0.05, options );
+var mu = scalar2ndarray( 0.0, options );
+var sigma = scalar2ndarray( 1.0, options );
+
+
+// FUNCTIONS //
+
+/**
+* Creates a benchmark function.
+*
+* @private
+* @param {PositiveInteger} len - array length
+* @returns {Function} benchmark function
+*/
+function createBenchmark( len ) {
+	var obuf;
+	var out;
+	var x;
+
+	x = normal( [ len ], 0.0, 1.0, options );
+
+	obuf = new ResultsArray( 1 );
+	out = new ndarray( Float32Results, obuf, [], [ 0 ], 0, 'row-major' );
+
+	return benchmark;
+
+	/**
+	* Benchmark function.
+	*
+	* @private
+	* @param {Benchmark} b - benchmark instance
+	*/
+	function benchmark( b ) {
+		var v;
+		var i;
+
+		b.tic();
+		for ( i = 0; i < b.iterations; i++ ) {
+			v = sztest( [ x, out, alt, alpha, mu, sigma ] );
+			if ( typeof v !== 'object' ) {
+				b.fail( 'should return an ndarray' );
+			}
+		}
+		b.toc();
+		if ( isnanf( v.get().statistic ) || isnanf( v.get().pValue ) ) {
+			b.fail( 'should not return NaN' );
+		}
+		b.pass( 'benchmark finished' );
+		b.end();
+	}
+}
+
+
+// MAIN //
+
+/**
+* Main execution sequence.
+*
+* @private
+*/
+function main() {
+	var len;
+	var min;
+	var max;
+	var f;
+	var i;
+
+	min = 1; // 10^min
+	max = 6; // 10^max
+
+	for ( i = min; i <= max; i++ ) {
+		len = pow( 10, i );
+		f = createBenchmark( len );
+		bench( format( '%s::native:len=%d', pkg, len ), opts, f );
+	}
+}
+
+main();