feat: add stats/base/ndarray/dztest2

lib/node_modules/@stdlib/stats/base/ndarray/dztest2/README.md

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+<!--
+
+@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.
+
+-->
+
+# dztest2
+
+> Compute a two-sample Z-test for two one-dimensional double-precision floating-point ndarrays.
+
+<section class="intro">
+
+A Z-test commonly refers to a two-sample location test which compares the means of two independent sets of measurements `X` and `Y` when the population standard deviations are known. A Z-test supports testing three different null hypotheses `H0`:
+
+-   `H0: μX - μY ≥ Δ` versus the alternative hypothesis `H1: μX - μY < Δ`.
+-   `H0: μX - μY ≤ Δ` versus the alternative hypothesis `H1: μX - μY > Δ`.
+-   `H0: μX - μY = Δ` versus the alternative hypothesis `H1: μX - μY ≠ Δ`.
+
+Here, `μX` and `μY` are the true population means of samples `X` and `Y`, respectively, and `Δ` is the hypothesized difference in means (typically `0` by default).
+
+</section>
+
+<!-- /.intro -->
+
+<section class="usage">
+
+## Usage
+
+```javascript
+var dztest2 = require( '@stdlib/stats/base/ndarray/dztest2' );
+```
+
+#### dztest2( arrays )
+
+Computes a two-sample Z-test for two one-dimensional double-precision floating-point ndarrays.
+
+```javascript
+var Float64Results = require( '@stdlib/stats/base/ztest/two-sample/results/float64' );
+var resolveEnum = require( '@stdlib/stats/base/ztest/alternative-resolve-enum' );
+var structFactory = require( '@stdlib/array/struct-factory' );
+var Float64Array = require( '@stdlib/array/float64' );
+var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
+var ndarray = require( '@stdlib/ndarray/ctor' );
+
+var opts = {
+    'dtype': 'float64'
+};
+
+var xbuf = new Float64Array( [ 4.0, 4.0, 6.0, 6.0, 5.0 ] );
+var x = new ndarray( opts.dtype, xbuf, [ 5 ], [ 1 ], 0, 'row-major' );
+
+var ybuf = new Float64Array( [ 3.0, 3.0, 5.0, 7.0, 7.0 ] );
+var y = new ndarray( opts.dtype, ybuf, [ 5 ], [ 1 ], 0, 'row-major' );
+
+var alt = scalar2ndarray( resolveEnum( 'two-sided' ), {
+    'dtype': 'int8'
+});
+var alpha = scalar2ndarray( 0.05, opts );
+var diff = scalar2ndarray( 0.0, opts );
+var sigmax = scalar2ndarray( 1.0, opts );
+var sigmay = scalar2ndarray( 2.0, opts );
+
+var ResultsArray = structFactory( Float64Results );
+var out = new ndarray( Float64Results, new ResultsArray( 1 ), [], [ 0 ], 0, 'row-major' );
+
+var v = dztest2( [ x, y, out, alt, alpha, diff, sigmax, sigmay ] );
+
+var bool = ( v === out );
+// returns true
+```
+
+The function has the following parameters:
+
+-   **arrays**: array-like object containing the following ndarrays in order:
+
+    1.  first one-dimensional input ndarray.
+    2.  second one-dimensional input ndarray.
+    3.  a zero-dimensional output ndarray containing a [results object][@stdlib/stats/base/ztest/two-sample/results/float64].
+    4.  a zero-dimensional ndarray specifying the alternative hypothesis.
+    5.  a zero-dimensional ndarray specifying the significance level.
+    6.  a zero-dimensional ndarray specifying the difference in means under the null hypothesis.
+    7.  a zero-dimensional ndarray specifying the known standard deviation of the first one-dimensional input ndarray.
+    8.  a zero-dimensional ndarray specifying the known standard deviation of the second one-dimensional input ndarray.
+
+</section>
+
+<!-- /.usage -->
+
+<section class="notes">
+
+## Notes
+
+-   As a general rule of thumb, a Z-test is most reliable for sample sizes greater than `50`. For smaller sample sizes or when the standard deviation is unknown, prefer a t-test.
+
+</section>
+
+<!-- /.notes -->
+
+<section class="examples">
+
+## Examples
+
+<!-- eslint no-undef: "error" -->
+
+```javascript
+var Float64Results = require( '@stdlib/stats/base/ztest/two-sample/results/float64' );
+var resolveEnum = require( '@stdlib/stats/base/ztest/alternative-resolve-enum' );
+var structFactory = require( '@stdlib/array/struct-factory' );
+var normal = require( '@stdlib/random/array/normal' );
+var ndarray = require( '@stdlib/ndarray/ctor' );
+var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
+var ndarray2array = require( '@stdlib/ndarray/to-array' );
+var dztest2 = require( '@stdlib/stats/base/ndarray/dztest2' );
+
+var opts = {
+    'dtype': 'float64'
+};
+
+// Create one-dimensional ndarrays containing pseudorandom numbers drawn from a normal distribution:
+var xbuf = normal( 100, 0.0, 1.0, opts );
+var x = new ndarray( opts.dtype, xbuf, [ xbuf.length ], [ 1 ], 0, 'row-major' );
+console.log( ndarray2array( x ) );
+
+var ybuf = normal( 100, 0.0, 1.0, opts );
+var y = new ndarray( opts.dtype, ybuf, [ ybuf.length ], [ 1 ], 0, 'row-major' );
+console.log( ndarray2array( y ) );
+
+// Specify the alternative hypothesis:
+var alt = scalar2ndarray( resolveEnum( 'two-sided' ), {
+    'dtype': 'int8'
+});
+
+// Specify the significance level:
+var alpha = scalar2ndarray( 0.05, opts );
+
+// Specify the difference in means under the null hypothesis:
+var diff = scalar2ndarray( 0.0, opts );
+
+// Specify the known standard deviations:
+var sigmax = scalar2ndarray( 1.0, opts );
+var sigmay = scalar2ndarray( 1.0, opts );
+
+// Create a zero-dimensional results ndarray:
+var ResultsArray = structFactory( Float64Results );
+var out = new ndarray( Float64Results, new ResultsArray( 1 ), [], [ 0 ], 0, 'row-major' );
+
+// Perform a Z-test:
+var v = dztest2( [ x, y, out, alt, alpha, diff, sigmax, sigmay ] );
+console.log( v.get().toString() );
+```
+
+</section>
+
+<!-- /.examples -->
+
+<!-- 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">
+
+[@stdlib/stats/base/ztest/two-sample/results/float64]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ztest/two-sample/results/float64
+
+</section>
+
+<!-- /.links -->

lib/node_modules/@stdlib/stats/base/ndarray/dztest2/benchmark/benchmark.js

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+/**
+* @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 bench = require( '@stdlib/bench' );
+var normal = require( '@stdlib/random/array/normal' );
+var isnan = require( '@stdlib/math/base/assert/is-nan' );
+var pow = require( '@stdlib/math/base/special/pow' );
+var ndarray = require( '@stdlib/ndarray/base/ctor' );
+var scalar2ndarray = require( '@stdlib/ndarray/base/from-scalar' );
+var Float64Results = require( '@stdlib/stats/base/ztest/two-sample/results/float64' );
+var resolveEnum = require( '@stdlib/stats/base/ztest/alternative-resolve-enum' );
+var structFactory = require( '@stdlib/array/struct-factory' );
+var pkg = require( './../package.json' ).name;
+var dztest2 = require( './../lib' );
+
+
+// VARIABLES //
+
+var options = {
+	'dtype': 'float64'
+};
+var ResultsArray = structFactory( Float64Results );
+
+
+// FUNCTIONS //
+
+/**
+* Creates a benchmark function.
+*
+* @private
+* @param {PositiveInteger} len - array length
+* @returns {Function} benchmark function
+*/
+function createBenchmark( len ) {
+	var sigmax;
+	var sigmay;
+	var alpha;
+	var diff;
+	var xbuf;
+	var ybuf;
+	var obuf;
+	var out;
+	var alt;
+	var x;
+	var y;
+
+	xbuf = normal( len, 0.0, 1.0, options );
+	x = new ndarray( options.dtype, xbuf, [ len ], [ 1 ], 0, 'row-major' );
+
+	ybuf = normal( len, 0.0, 1.0, options );
+	y = new ndarray( options.dtype, ybuf, [ len ], [ 1 ], 0, 'row-major' );
+
+	obuf = new ResultsArray( 1 );
+	out = new ndarray( Float64Results, obuf, [], [ 0 ], 0, 'row-major' );
+
+	alt = scalar2ndarray( resolveEnum( 'two-sided' ), 'int8', 'row-major' );
+	alpha = scalar2ndarray( 0.05, options.dtype, 'row-major' );
+	diff = scalar2ndarray( 0.0, options.dtype, 'row-major' );
+	sigmax = scalar2ndarray( 1.0, options.dtype, 'row-major' );
+	sigmay = scalar2ndarray( 1.0, options.dtype, 'row-major' );
+
+	return benchmark;
+
+	function benchmark( b ) {
+		var v;
+		var i;
+
+		b.tic();
+		for ( i = 0; i < b.iterations; i++ ) {
+			v = dztest2( [ x, y, out, alt, alpha, diff, sigmax, sigmay ] );
+			if ( typeof v !== 'object' ) {
+				b.fail( 'should return an ndarray' );
+			}
+		}
+		b.toc();
+		if ( isnan( v.get().statistic ) || isnan( 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( pkg+':len='+len, f );
+	}
+}
+
+main();

lib/node_modules/@stdlib/stats/base/ndarray/dztest2/docs/repl.txt

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+
+{{alias}}( arrays )
+    Computes a two-sample Z-test for two one-dimensional double-precision
+    floating-point ndarrays.
+
+    Parameters
+    ----------
+    arrays: ArrayLikeObject<ndarray>
+        Array-like object containing the following ndarrays in order:
+
+        - first one-dimensional input ndarray.
+        - second one-dimensional input ndarray.
+        - a zero-dimensional output ndarray containing a results object.
+        - a zero-dimensional ndarray specifying the alternative hypothesis.
+        - a zero-dimensional ndarray specifying the significance level.
+        - a zero-dimensional ndarray specifying the difference in means under
+        the null hypothesis.
+        - a zero-dimensional ndarray specifying the known standard deviation of
+        the first one-dimensional input ndarray.
+        - a zero-dimensional ndarray specifying the known standard deviation of
+        the second one-dimensional input ndarray.
+
+    Returns
+    -------
+    out: ndarray
+        Output ndarray.
+
+    Examples
+    --------
+    // Create input ndarrays:
+    > var xbuf = new {{alias:@stdlib/array/float64}}( [ 4.0, 4.0, 6.0, 6.0, 5.0 ] );
+    > var ybuf = new {{alias:@stdlib/array/float64}}( [ 3.0, 3.0, 5.0, 7.0, 7.0 ] );
+    > var dt = 'float64';
+    > var sh = [ xbuf.length ];
+    > var st = [ 1 ];
+    > var oo = 0;
+    > var ord = 'row-major';
+    > var x = new {{alias:@stdlib/ndarray/ctor}}( dt, xbuf, sh, st, oo, ord );
+    > var y = new {{alias:@stdlib/ndarray/ctor}}( dt, ybuf, sh, st, oo, ord );
+
+    // Create the output ndarray:
+    > var S = {{alias:@stdlib/stats/base/ztest/two-sample/results/float64}};
+    > var Results = {{alias:@stdlib/array/struct-factory}}( S );
+    > var obuf = new Results( 1 );
+    > var out = new {{alias:@stdlib/ndarray/ctor}}( S, obuf, [], [ 0 ], 0, ord );
+
+    // Specify the alternative hypothesis:
+    > var alt = {{alias:@stdlib/ndarray/from-scalar}}( 'two-sided' );
+
+    // Specify the significance level:
+    > var opts = { 'dtype': dt };
+    > var alpha = {{alias:@stdlib/ndarray/from-scalar}}( 0.05, opts );
+
+    // Specify the difference in means under the null hypothesis:
+    > var diff = {{alias:@stdlib/ndarray/from-scalar}}( 0.0, opts );
+
+    // Specify the known standard deviations:
+    > var sigmax = {{alias:@stdlib/ndarray/from-scalar}}( 1.0, opts );
+    > var sigmay = {{alias:@stdlib/ndarray/from-scalar}}( 2.0, opts );
+
+    // Perform a Z-test:
+    > {{alias}}( [ x, y, out, alt, alpha, diff, sigmax, sigmay ] );
+
+    // Print the results:
+    > out.get().toString()
+
+    See Also
+    --------
+