feat: add stats/base/ndarray/dmeanstdev

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

@@ -0,0 +1,171 @@
+<!--
+
+@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.
+
+-->
+
+# dmeanstdev
+
+> Compute the [arithmetic mean][arithmetic-mean] and [standard deviation][standard-deviation] of a one-dimensional double-precision floating-point ndarray.
+
+<section class="intro">
+
+The [arithmetic mean][arithmetic-mean] and [standard deviation][standard-deviation] are defined as
+
+<!-- <equation class="equation" label="eq:arithmetic_mean" align="center" raw="\mu = \frac{1}{n} \sum_{i=0}^{n-1} x_i" alt="Equation for the arithmetic mean."> -->
+
+```math
+\mu = \frac{1}{n} \sum_{i=0}^{n-1} x_i
+```
+
+<!-- <div class="equation" align="center" data-raw-text="\mu = \frac{1}{n} \sum_{i=0}^{n-1} x_i" data-equation="eq:arithmetic_mean">
+    <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@42d8f64d805113ab899c79c7c39d6c6bac7fe25c/lib/node_modules/@stdlib/stats/base/ndarray/mean/docs/img/equation_arithmetic_mean.svg" alt="Equation for the arithmetic mean.">
+    <br>
+</div> -->
+
+<!-- </equation> -->
+
+and
+
+<!-- <equation class="equation" label="eq:standard_deviation" align="center" raw="\sigma = \sqrt{\frac{1}{n-1} \sum_{i=0}^{n-1} (x_i - \mu)^2}" alt="Equation for the standard deviation."> -->
+
+```math
+\sigma = \sqrt{\frac{1}{n-1} \sum_{i=0}^{n-1} (x_i - \mu)^2}
+```
+
+<!-- <div class="equation" align="center" data-raw-text="\sigma = \sqrt{\frac{1}{n-1} \sum_{i=0}^{n-1} (x_i - \mu)^2}" data-equation="eq:standard_deviation">
+    <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@42d8f64d805113ab899c79c7c39d6c6bac7fe25c/lib/node_modules/@stdlib/stats/base/ndarray/dmeanstdev/docs/img/equation_corrected_sample_standard_deviation.svg" alt="Equation for the standard deviation.">
+    <br>
+</div> -->
+
+<!-- </equation> -->
+
+where the use of the term `n-1` is commonly referred to as Bessel's correction.
+
+</section>
+
+<!-- /.intro -->
+
+<section class="usage">
+
+## Usage
+
+```javascript
+var dmeanstdev = require( '@stdlib/stats/base/ndarray/dmeanstdev' );
+```
+
+#### dmeanstdev( arrays )
+
+Computes the [arithmetic mean][arithmetic-mean] and [standard deviation][standard-deviation] of a one-dimensional double-precision floating-point ndarray.
+
+```javascript
+var Float64Array = require( '@stdlib/array/float64' );
+var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
+var ndarray = require( '@stdlib/ndarray/base/ctor' );
+
+var opts = {
+    'dtype': 'float64'
+};
+
+var xbuf = new Float64Array( [ 1.0, 3.0, 4.0, 2.0 ] );
+var x = new ndarray( opts.dtype, xbuf, [ 4 ], [ 1 ], 0, 'row-major' );
+var out = new ndarray( opts.dtype, new Float64Array( 2 ), [ 2 ], [ 1 ], 0, 'row-major' );
+
+var correction = scalar2ndarray( 1.0, opts );
+
+var v = dmeanstdev( [ x, out, correction ] );
+// returns <ndarray>
+```
+
+The function has the following parameters:
+
+-   **arrays**: array-like object containing the following ndarrays in order:
+
+    1.  a one-dimensional input ndarray.
+    2.  a one-dimensional output ndarray (of length 2) to store the [mean][arithmetic-mean] and [standard deviation][standard-deviation].
+    3.  a zero-dimensional ndarray specifying the degrees of freedom adjustment.
+
+The output ndarray contains two elements: the [arithmetic mean][arithmetic-mean] at index 0 and the [standard deviation][standard-deviation] at index 1. The [standard deviation][standard-deviation] is computed using the provided degrees of freedom adjustment. Setting the correction parameter to `1` corresponds to Bessel's correction (i.e., the corrected sample standard deviation). Setting it to `0` computes the population standard deviation.
+
+</section>
+
+<!-- /.usage -->
+
+<section class="notes">
+
+## Notes
+
+-   If provided an empty one-dimensional ndarray, the computed [arithmetic mean][arithmetic-mean] and [standard deviation][standard-deviation] are equal to `NaN`.
+
+</section>
+
+<!-- /.notes -->
+
+<section class="examples">
+
+## Examples
+
+<!-- eslint no-undef: "error" -->
+
+```javascript
+var discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
+var Float64Array = require( '@stdlib/array/float64' );
+var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
+var ndarray = require( '@stdlib/ndarray/base/ctor' );
+var ndarray2array = require( '@stdlib/ndarray/to-array' );
+var dmeanstdev = require( '@stdlib/stats/base/ndarray/dmeanstdev' );
+
+var opts = {
+    'dtype': 'float64'
+};
+
+var xbuf = discreteUniform( 10, -50, 50, {
+    'dtype': 'float64'
+});
+var x = new ndarray( opts.dtype, xbuf, [ xbuf.length ], [ 1 ], 0, 'row-major' );
+var out = new ndarray( opts.dtype, new Float64Array( 2 ), [ 2 ], [ 1 ], 0, 'row-major' );
+var correction = scalar2ndarray( 1.0, opts );
+
+console.log( ndarray2array( x ) );
+
+var v = dmeanstdev( [ x, out, correction ] );
+console.log( v );
+```
+
+</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">
+
+[arithmetic-mean]: https://en.wikipedia.org/wiki/Arithmetic_mean
+
+[standard-deviation]: https://en.wikipedia.org/wiki/Standard_deviation
+
+</section>
+
+<!-- /.links -->

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

@@ -0,0 +1,107 @@
+/**
+* @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 uniform = require( '@stdlib/random/array/uniform' );
+var isnan = require( '@stdlib/math/base/assert/is-nan' );
+var pow = require( '@stdlib/math/base/special/pow' );
+var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
+var ndarray = require( '@stdlib/ndarray/base/ctor' );
+var Float64Array = require( '@stdlib/array/float64' );
+var pkg = require( './../package.json' ).name;
+var dmeanstdev = require( './../lib' );
+
+
+// VARIABLES //
+
+var options = {
+	'dtype': 'float64'
+};
+
+
+// FUNCTIONS //
+
+/**
+* Creates a benchmark function.
+*
+* @private
+* @param {PositiveInteger} len - array length
+* @returns {Function} benchmark function
+*/
+function createBenchmark( len ) {
+	var correction;
+	var xbuf;
+	var out;
+	var x;
+
+	xbuf = uniform( len, -10.0, 10.0, options );
+	x = new ndarray( options.dtype, xbuf, [ len ], [ 1 ], 0, 'row-major' );
+	out = new ndarray( 'float64', new Float64Array( 2 ), [ 2 ], [ 1 ], 0, 'row-major' );
+	correction = scalar2ndarray( 1.0, options );
+
+	return benchmark;
+
+	function benchmark( b ) {
+		var v;
+		var i;
+
+		b.tic();
+		for ( i = 0; i < b.iterations; i++ ) {
+			v = dmeanstdev( [ x, out, correction ] );
+			if ( isnan( v[ 0 ] ) ) {
+				b.fail( 'should not return NaN' );
+			}
+		}
+		b.toc();
+		if ( isnan( v[ 0 ] ) ) {
+			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/dmeanstdev/docs/repl.txt

@@ -0,0 +1,43 @@
+
+{{alias}}( arrays )
+    Computes the mean and standard deviation of a one-dimensional double-
+    precision floating-point ndarray.
+
+    If provided an empty ndarray, the function returns `NaN` values.
+
+    Parameters
+    ----------
+    arrays: ArrayLikeObject<ndarray>
+        Array-like object containing the following ndarrays in order:
+
+        - a one-dimensional input ndarray.
+        - a one-dimensional output ndarray (of length 2) to store the mean
+        and standard deviation.
+        - a zero-dimensional ndarray specifying the degrees of freedom
+        adjustment.
+
+    Returns
+    -------
+    out: ndarray
+        An ndarray containing the mean and standard deviation.
+
+    Examples
+    --------
+    > var xbuf = new {{alias:@stdlib/array/float64}}( [ 2.0, 1.0, 2.0, -2.0 ] );
+    > var dt = 'float64';
+    > var sh = [ xbuf.length ];
+    > var sx = [ 1 ];
+    > var ox = 0;
+    > var ord = 'row-major';
+    > var x = new {{alias:@stdlib/ndarray/ctor}}( dt, xbuf, sh, sx, ox, ord );
+    > var o = new {{alias:@stdlib/array/float64}}( 2 );
+    > var out = new {{alias:@stdlib/ndarray/ctor}}( dt, o, [ 2 ], [ 1 ], ox, ord );
+    > var opts = { 'dtype': dt };
+    > var correction = {{alias:@stdlib/ndarray/from-scalar}}( 1.0, opts );
+    > {{alias}}( [ x, out, correction ] );
+    > {{alias:@stdlib/ndarray/to-array}}( out )
+    [ ~0.75, ~1.8930 ]
+
+    See Also
+    --------
+