feat: add blas/ext/base/ndarray/dvander

lib/node_modules/@stdlib/blas/ext/base/ndarray/dvander/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.
+
+-->
+
+# dvander
+
+> Generate a double-precision floating-point Vandermonde matrix.
+
+<section class="intro">
+
+</section>
+
+<!-- /.intro -->
+
+<section class="usage">
+
+## Usage
+
+```javascript
+var dvander = require( '@stdlib/blas/ext/base/ndarray/dvander' );
+```
+
+#### dvander( arrays )
+
+Generates a double-precision floating-point Vandermonde matrix.
+
+```javascript
+var Float64Vector = require( '@stdlib/ndarray/vector/float64' );
+var zeros = require( '@stdlib/ndarray/zeros' );
+var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
+
+var x = new Float64Vector( [ 1.0, 2.0, 3.0 ] );
+var out = zeros( [ 3, 3 ], {
+    'dtype': 'float64'
+});
+
+var mode = scalar2ndarray( 1, {
+    'dtype': 'float64'
+});
+
+var v = dvander( [ x, out, mode ] );
+// returns <ndarray>[ [ 1.0, 1.0, 1.0 ], [ 1.0, 2.0, 4.0 ], [ 1.0, 3.0, 9.0 ] ]
+
+var bool = ( v === out );
+// returns true
+```
+
+The function has the following parameters:
+
+-   **arrays**: array-like object containing the following ndarrays:
+
+    -   a one-dimensional input ndarray.
+    -   a two-dimensional output ndarray.
+    -   a zero-dimensional ndarray specifying the mode.
+
+Let the output ndarray have shape `[M, N]`. When the mode is positive, the matrix is generated such that
+
+```text
+[
+    1   x_0^1   x_0^2   ...   x_0^(N-1)
+    1   x_1^1   x_1^2   ...   x_1^(N-1)
+    ...
+]
+```
+
+with increasing powers along the rows.
+
+When the mode is negative, the matrix is generated such that
+
+```text
+[
+    x_0^(N-1)   ...   x_0^2   x_0^1   1
+    x_1^(N-1)   ...   x_1^2   x_1^1   1
+    ...
+]
+```
+
+with decreasing powers along the rows.
+
+</section>
+
+<!-- /.usage -->
+
+<section class="notes">
+
+## Notes
+
+-   Let the output ndarray have shape `[M, N]`. If `M <= 0` or `N <= 0`, the function returns the output ndarray unchanged.
+
+</section>
+
+<!-- /.notes -->
+
+<section class="examples">
+
+## Examples
+
+<!-- eslint no-undef: "error" -->
+
+```javascript
+var discreteUniform = require( '@stdlib/random/discrete-uniform' );
+var zeros = require( '@stdlib/ndarray/zeros' );
+var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
+var ndarray2array = require( '@stdlib/ndarray/to-array' );
+var dvander = require( '@stdlib/blas/ext/base/ndarray/dvander' );
+
+var M = 3;
+var N = 4;
+
+var opts = {
+    'dtype': 'float64'
+};
+
+var x = discreteUniform( [ M ], 0, 10, opts );
+console.log( ndarray2array( x ) );
+
+var out = zeros( [ M, N ], opts );
+
+var mode = scalar2ndarray( -1, {
+    'dtype': 'float64'
+});
+
+var v = dvander( [ x, out, mode ] );
+console.log( ndarray2array( v ) );
+```
+
+</section>
+
+<!-- /.examples -->
+
+<section class="references">
+
+</section>
+
+<!-- /.references -->
+
+<!-- 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">
+
+<!-- <related-links> -->
+
+<!-- </related-links> -->
+
+</section>
+
+<!-- /.links -->

lib/node_modules/@stdlib/blas/ext/base/ndarray/dvander/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 uniform = require( '@stdlib/random/uniform' );
+var zeros = require( '@stdlib/ndarray/zeros' );
+var isnan = require( '@stdlib/math/base/assert/is-nan' );
+var pow = require( '@stdlib/math/base/special/pow' );
+var format = require( '@stdlib/string/format' );
+var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
+var pkg = require( './../package.json' ).name;
+var dvander = 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 mode;
+	var out;
+	var x;
+
+	x = uniform( [ len ], -10.0, 10.0, options );
+	out = zeros( [ len, len ], options );
+
+	mode = scalar2ndarray( 1, {
+		'dtype': 'float64'
+	});
+
+	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 = dvander( [ x, out, mode ] );
+			if ( typeof v !== 'object' ) {
+				b.fail( 'should return an ndarray' );
+			}
+		}
+		b.toc();
+		if ( isnan( v.get( i%len, i%len ) ) ) {
+			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 = 3; // 10^max
+
+	for ( i = min; i <= max; i++ ) {
+		len = pow( 10, i );
+		f = createBenchmark( len );
+		bench( format( '%s:len=%d', pkg, len ), f );
+	}
+}
+
+main();

lib/node_modules/@stdlib/blas/ext/base/ndarray/dvander/docs/repl.txt

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+
+{{alias}}( arrays )
+    Generates a double-precision floating-point Vandermonde matrix.
+
+    Let the output ndarray have shape `[M, N]`. When the mode is positive,
+    the matrix is generated such that
+
+        [
+            1   x_0^1   x_0^2   ...   x_0^(N-1)
+            1   x_1^1   x_1^2   ...   x_1^(N-1)
+            ...
+        ]
+
+    with increasing powers along the rows.
+
+    When the mode is negative, the matrix is generated such that
+
+        [
+            x_0^(N-1)   ...   x_0^2   x_0^1   1
+            x_1^(N-1)   ...   x_1^2   x_1^1   1
+            ...
+        ]
+
+    with decreasing powers along the rows.
+
+    If `M <= 0` or `N <= 0`, the function returns the output ndarray
+    unchanged.
+
+    Parameters
+    ----------
+    arrays: ArrayLikeObject<ndarray>
+        Array-like object containing the following ndarrays:
+
+        - a one-dimensional input ndarray.
+        - a two-dimensional output ndarray.
+        - a zero-dimensional ndarray specifying the mode.
+
+    Returns
+    -------
+    out: ndarray
+        Output ndarray.
+
+    Examples
+    --------
+    > var x = new {{alias:@stdlib/ndarray/vector/float64}}( [ 1.0, 2.0, 3.0 ] );
+    > var out = {{alias:@stdlib/ndarray/zeros}}( [ 3, 3 ], { 'dtype': 'float64' } );
+    > var mode = {{alias:@stdlib/ndarray/from-scalar}}( 1, { 'dtype': 'float64' } );
+    > {{alias}}( [ x, out, mode ] );
+    > out
+    <ndarray>[ [ 1.0, 1.0, 1.0 ], [ 1.0, 2.0, 4.0 ], [ 1.0, 3.0, 9.0 ] ]
+
+    See Also
+    --------
+