diff --git a/lib/node_modules/@stdlib/stats/mskmax/README.md b/lib/node_modules/@stdlib/stats/mskmax/README.md
new file mode 100644
index 000000000000..bb4b8dfc148c
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/mskmax/README.md
@@ -0,0 +1,295 @@
+
+
+# mskmax
+
+> Compute the maximum value along one or more [ndarray][@stdlib/ndarray/ctor] dimensions according to a mask.
+
+
+
+## Usage
+
+```javascript
+var mskmax = require( '@stdlib/stats/mskmax' );
+```
+
+#### mskmax( x, mask\[, options] )
+
+Compute the maximum value along one or more [ndarray][@stdlib/ndarray/ctor] dimensions according to a mask.
+
+```javascript
+var Uint8Array = require( '@stdlib/array/uint8' );
+var array = require( '@stdlib/ndarray/array' );
+
+var x = array( [ -1.0, 2.0, -3.0 ] );
+var mask = array( new Uint8Array( [ 0, 0, 1 ] ) );
+
+var y = mskmax( x, mask );
+// returns
+
+var v = y.get();
+// returns 2.0
+```
+
+The function has the following parameters:
+
+- **x**: input [ndarray][@stdlib/ndarray/ctor]. Must have a real-valued or "generic" [data type][@stdlib/ndarray/dtypes].
+- **mask**: mask [ndarray][@stdlib/ndarray/ctor]. Must be [broadcast-compatible][@stdlib/ndarray/base/broadcast-shapes] with `x`. If a mask element is `0`, the corresponding element in `x` is considered valid. If a mask element is non-zero, the corresponding element in `x` is ignored.
+- **options**: function options (_optional_).
+
+The function accepts the following options:
+
+- **dims**: list of dimensions over which to perform a reduction. If not provided, the function performs a reduction over all elements in a provided input [ndarray][@stdlib/ndarray/ctor].
+- **dtype**: output ndarray [data type][@stdlib/ndarray/dtypes]. Must be a real-valued or "generic" [data type][@stdlib/ndarray/dtypes].
+- **keepdims**: boolean indicating whether the reduced dimensions should be included in the returned [ndarray][@stdlib/ndarray/ctor] as singleton dimensions. Default: `false`.
+
+By default, the function performs a reduction over all elements in a provided input [ndarray][@stdlib/ndarray/ctor]. To perform a reduction over specific dimensions, provide a `dims` option.
+
+```javascript
+var Uint8Array = require( '@stdlib/array/uint8' );
+var mskmax = require( '@stdlib/stats/mskmax' );
+var ndarray2array = require( '@stdlib/ndarray/to-array' );
+var array = require( '@stdlib/ndarray/array' );
+
+var x = array( [ -1.0, 2.0, -3.0, 4.0 ], {
+ 'shape': [ 2, 2 ],
+ 'order': 'row-major'
+});
+var mask = array( new Uint8Array( [ 0, 0, 1, 0 ] ), {
+ 'shape': [ 2, 2 ],
+ 'order': 'row-major'
+});
+var v = ndarray2array( x );
+// returns [ [ -1.0, 2.0 ], [ -3.0, 4.0 ] ]
+
+var y = mskmax( x, mask, {
+ 'dims': [ 0 ]
+});
+// returns
+
+v = ndarray2array( y );
+// returns [ -1.0, 4.0 ]
+
+y = mskmax( x, mask, {
+ 'dims': [ 1 ]
+});
+// returns
+
+v = ndarray2array( y );
+// returns [ 2.0, 4.0 ]
+
+y = mskmax( x, mask, {
+ 'dims': [ 0, 1 ]
+});
+// returns
+
+v = y.get();
+// returns 4.0
+```
+
+By default, the function excludes reduced dimensions from the output [ndarray][@stdlib/ndarray/ctor]. To include the reduced dimensions as singleton dimensions, set the `keepdims` option to `true`.
+
+```javascript
+var Uint8Array = require( '@stdlib/array/uint8' );
+var mskmax = require( '@stdlib/stats/mskmax' );
+var ndarray2array = require( '@stdlib/ndarray/to-array' );
+var array = require( '@stdlib/ndarray/array' );
+
+var x = array( [ -1.0, 2.0, -3.0, 4.0 ], {
+ 'shape': [ 2, 2 ],
+ 'order': 'row-major'
+});
+var mask = array( new Uint8Array( [ 0, 0, 1, 0 ] ), {
+ 'shape': [ 2, 2 ],
+ 'order': 'row-major'
+});
+
+var v = ndarray2array( x );
+// returns [ [ -1.0, 2.0 ], [ -3.0, 4.0 ] ]
+
+var y = mskmax( x, mask, {
+ 'dims': [ 0 ],
+ 'keepdims': true
+});
+// returns
+
+v = ndarray2array( y );
+// returns [ [ -1.0, 4.0 ] ]
+
+y = mskmax( x, mask, {
+ 'dims': [ 1 ],
+ 'keepdims': true
+});
+// returns
+
+v = ndarray2array( y );
+// returns [ [ 2.0 ], [ 4.0 ] ]
+
+y = mskmax( x, mask, {
+ 'dims': [ 0, 1 ],
+ 'keepdims': true
+});
+// returns
+
+v = ndarray2array( y );
+// returns [ [ 4.0 ] ]
+```
+
+By default, the function returns an [ndarray][@stdlib/ndarray/ctor] having a [data type][@stdlib/ndarray/dtypes] determined by the function's output data type [policy][@stdlib/ndarray/output-dtype-policies]. To override the default behavior, set the `dtype` option.
+
+```javascript
+var Uint8Array = require( '@stdlib/array/uint8' );
+var mskmax = require( '@stdlib/stats/mskmax' );
+var getDType = require( '@stdlib/ndarray/dtype' );
+var array = require( '@stdlib/ndarray/array' );
+
+var x = array( [ -1.0, 2.0, -3.0 ], {
+ 'dtype': 'generic'
+});
+var mask = array( new Uint8Array( [ 0, 0, 1 ] ) );
+
+var y = mskmax( x, mask, {
+ 'dtype': 'float64'
+});
+// returns
+
+var dt = String( getDType( y ) );
+// returns 'float64'
+```
+
+#### mskmax.assign( x, mask, out\[, options] )
+
+Computes the maximum value of [ndarray][@stdlib/ndarray/ctor] along one or more dimensions and assigns results to a provided output [ndarray][@stdlib/ndarray/ctor] according to mask.
+
+```javascript
+var Uint8Array = require( '@stdlib/array/uint8' );
+var mskmax = require( '@stdlib/stats/mskmax' );
+var array = require( '@stdlib/ndarray/array' );
+var zeros = require( '@stdlib/ndarray/zeros' );
+
+var x = array( [ -1.0, 2.0, -3.0 ] );
+var mask = array( new Uint8Array( [ 0, 0, 1 ] ) );
+var y = zeros( [] );
+
+var out = mskmax.assign( x, mask, y );
+// returns
+
+var v = out.get();
+// returns 2.0
+
+var bool = ( out === y );
+// returns true
+```
+
+The method has the following parameters:
+
+- **x**: input [ndarray][@stdlib/ndarray/ctor]. Must have a real-valued or generic [data type][@stdlib/ndarray/dtypes].
+- **mask**: mask [ndarray][@stdlib/ndarray/ctor]. Must be [broadcast-compatible][@stdlib/ndarray/base/broadcast-shapes] with `x`. If a mask element is `0`, the corresponding element in `x` is considered valid. If a mask element is non-zero, the corresponding element in `x` is ignored.
+- **out**: output [ndarray][@stdlib/ndarray/ctor].
+- **options**: function options (_optional_).
+
+The method accepts the following options:
+
+- **dims**: list of dimensions over which to perform a reduction. If not provided, the function performs a reduction over all elements in a provided input [ndarray][@stdlib/ndarray/ctor].
+
+
+
+
+
+
+
+## Notes
+
+- Setting the `keepdims` option to `true` can be useful when wanting to ensure that the output [ndarray][@stdlib/ndarray/ctor] is [broadcast-compatible][@stdlib/ndarray/base/broadcast-shapes] with ndarrays having the same shape as the input [ndarray][@stdlib/ndarray/ctor].
+- The output data type [policy][@stdlib/ndarray/output-dtype-policies] only applies to the main function and specifies that, by default, the function must return an [ndarray][@stdlib/ndarray/ctor] having the same [data type][@stdlib/ndarray/dtypes] as the input [ndarray][@stdlib/ndarray/ctor]. For the `assign` method, the output [ndarray][@stdlib/ndarray/ctor] is allowed to have any supported output [data type][@stdlib/ndarray/dtypes].
+
+
+
+
+
+
+
+## Examples
+
+
+
+```javascript
+var discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
+var getDType = require( '@stdlib/ndarray/dtype' );
+var ndarray2array = require( '@stdlib/ndarray/to-array' );
+var ndarray = require( '@stdlib/ndarray/ctor' );
+var mskmax = require( '@stdlib/stats/mskmax' );
+
+// Generate an array of random numbers:
+var xbuf = discreteUniform( 25, 0, 20, {
+ 'dtype': 'generic'
+});
+
+// Generate a mask array:
+var mbuf = discreteUniform( 25, 0, 1, {
+ 'dtype': 'uint8'
+});
+
+// Wrap in ndarrays:
+var x = new ndarray( 'generic', xbuf, [ 5, 5 ], [ 5, 1 ], 0, 'row-major' );
+var mask = new ndarray( 'uint8', mbuf, [ 5, 5 ], [ 5, 1 ], 0, 'row-major' );
+console.log( ndarray2array( x ) );
+console.log( ndarray2array( mask ) );
+
+// Perform a reduction:
+var y = mskmax( x, mask, {
+ 'dims': [ 0 ]
+});
+
+// Resolve the output array data type:
+var dt = getDType( y );
+console.log( dt );
+
+// Print the results:
+console.log( ndarray2array( y ) );
+```
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+[@stdlib/ndarray/ctor]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/ndarray/ctor
+
+[@stdlib/ndarray/dtypes]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/ndarray/dtypes
+
+[@stdlib/ndarray/output-dtype-policies]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/ndarray/output-dtype-policies
+
+[@stdlib/ndarray/base/broadcast-shapes]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/ndarray/base/broadcast-shapes
+
+
+
+
diff --git a/lib/node_modules/@stdlib/stats/mskmax/benchmark/benchmark.assign.js b/lib/node_modules/@stdlib/stats/mskmax/benchmark/benchmark.assign.js
new file mode 100644
index 000000000000..674f055d30d6
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/mskmax/benchmark/benchmark.assign.js
@@ -0,0 +1,121 @@
+/**
+* @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 isnan = require( '@stdlib/math/base/assert/is-nan' );
+var pow = require( '@stdlib/math/base/special/pow' );
+var uniform = require( '@stdlib/random/array/uniform' );
+var zeros = require( '@stdlib/array/zeros' );
+var ndarray = require( '@stdlib/ndarray/base/ctor' );
+var pkg = require( './../package.json' ).name;
+var mskmax = 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 mask;
+ var mbuf;
+ var out;
+ var x;
+ var i;
+
+ x = uniform( len, -50.0, 50.0, options );
+ x = new ndarray( options.dtype, x, [ len ], [ 1 ], 0, 'row-major' );
+
+ // Create a mask (mask out every 5th element):
+ mbuf = [];
+ for ( i = 0; i < len; i++ ) {
+ mbuf.push( ( i % 5 === 0 ) ? 1 : 0 );
+ }
+ mask = new ndarray( 'uint8', mbuf, [ len ], [ 1 ], 0, 'row-major' );
+
+ out = new ndarray( options.dtype, zeros( 1, options.dtype ), [], [ 0 ], 0, 'row-major' );
+
+ return benchmark;
+
+ /**
+ * Benchmark function.
+ *
+ * @private
+ * @param {Benchmark} b - benchmark instance
+ */
+ function benchmark( b ) {
+ var o;
+ var i;
+
+ b.tic();
+ for ( i = 0; i < b.iterations; i++ ) {
+ o = mskmax.assign( x, mask, out );
+ if ( typeof o !== 'object' ) {
+ b.fail( 'should return an ndarray' );
+ }
+ }
+ b.toc();
+ if ( isnan( o.get() ) ) {
+ 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+':assign:dtype='+options.dtype+',len='+len, f );
+ }
+}
+
+main();
diff --git a/lib/node_modules/@stdlib/stats/mskmax/benchmark/benchmark.js b/lib/node_modules/@stdlib/stats/mskmax/benchmark/benchmark.js
new file mode 100644
index 000000000000..7b99e9b1c45e
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/mskmax/benchmark/benchmark.js
@@ -0,0 +1,117 @@
+/**
+* @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 isnan = require( '@stdlib/math/base/assert/is-nan' );
+var pow = require( '@stdlib/math/base/special/pow' );
+var uniform = require( '@stdlib/random/array/uniform' );
+var ndarray = require( '@stdlib/ndarray/base/ctor' );
+var pkg = require( './../package.json' ).name;
+var mskmax = 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 mask;
+ var mbuf;
+ var x;
+ var i;
+
+ x = uniform( len, -50.0, 50.0, options );
+ x = new ndarray( options.dtype, x, [ len ], [ 1 ], 0, 'row-major' );
+
+ // Create a mask (mask out every 5th element):
+ mbuf = [];
+ for ( i = 0; i < len; i++ ) {
+ mbuf.push( ( i % 5 === 0 ) ? 1 : 0 );
+ }
+ mask = new ndarray( 'uint8', mbuf, [ len ], [ 1 ], 0, 'row-major' );
+
+ return benchmark;
+
+ /**
+ * Benchmark function.
+ *
+ * @private
+ * @param {Benchmark} b - benchmark instance
+ */
+ function benchmark( b ) {
+ var o;
+ var i;
+
+ b.tic();
+ for ( i = 0; i < b.iterations; i++ ) {
+ o = mskmax( x, mask );
+ if ( typeof o !== 'object' ) {
+ b.fail( 'should return an ndarray' );
+ }
+ }
+ b.toc();
+ if ( isnan( o.get() ) ) {
+ 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+':dtype='+options.dtype+',len='+len, f );
+ }
+}
+
+main();
diff --git a/lib/node_modules/@stdlib/stats/mskmax/docs/repl.txt b/lib/node_modules/@stdlib/stats/mskmax/docs/repl.txt
new file mode 100644
index 000000000000..00db8fb13905
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/mskmax/docs/repl.txt
@@ -0,0 +1,90 @@
+
+{{alias}}( x, mask[, options] )
+ Computes the maximum value along one or more ndarray dimensions
+ according to a mask.
+
+ If a `mask` array element is `0`, the corresponding element in `x` is
+ considered valid and included in computation.
+
+ If a `mask` array element is `1`, the corresponding element in `x` is
+ considered invalid/missing and excluded from computation.
+
+ Parameters
+ ----------
+ x: ndarray
+ Input array. Must have a real-valued or "generic" data type.
+
+ mask: ndarray
+ Mask array. Must have an integer data type and the same shape as `x`.
+
+ options: Object (optional)
+ Function options.
+
+ options.dtype: string (optional)
+ Output array data type. Must be a real-valued or "generic" data type.
+
+ options.dims: Array (optional)
+ List of dimensions over which to perform a reduction. If not provided,
+ the function performs a reduction over all elements in a provided input
+ ndarray.
+
+ options.keepdims: boolean (optional)
+ Boolean indicating whether the reduced dimensions should be included in
+ the returned ndarray as singleton dimensions. Default: false.
+
+ Returns
+ -------
+ y: ndarray
+ Output array.
+
+ Examples
+ --------
+ > var x = {{alias:@stdlib/ndarray/array}}( [ -1.0, 2.0, -3.0, 4.0 ] );
+ > var mask = {{alias:@stdlib/ndarray/array}}( new Uint8Array( [ 0, 0, 1, 0 ] ) );
+ > var y = {{alias}}( x, mask );
+ > var v = y.get()
+ 4.0
+
+
+{{alias}}.assign( x, mask, out[, options] )
+ Computes the maximum value along one or more ndarray dimensions and assigns
+ results to a provided output ndarray according to a mask.
+
+ Parameters
+ ----------
+ x: ndarray
+ Input array. Must have a real-valued or generic data type.
+
+ mask: ndarray
+ Mask array. Must have an integer data type and the same shape as `x`.
+
+ out: ndarray
+ Output array.
+
+ options: Object (optional)
+ Function options.
+
+ options.dims: Array (optional)
+ List of dimensions over which to perform a reduction. If not provided,
+ the function performs a reduction over all elements in a provided input
+ ndarray.
+
+ Returns
+ -------
+ out: ndarray
+ Output array.
+
+ Examples
+ --------
+ > var x = {{alias:@stdlib/ndarray/array}}( [ -1.0, 2.0, -3.0, 4.0 ] );
+ > var mask = {{alias:@stdlib/ndarray/array}}( new Uint8Array( [ 0, 0, 1, 0 ] ) );
+ > var out = {{alias:@stdlib/ndarray/zeros}}( [] );
+ > var y = {{alias}}.assign( x, mask, out )
+
+ > var bool = ( out === y )
+ true
+ > var v = out.get()
+ 4.0
+
+ See Also
+ --------
diff --git a/lib/node_modules/@stdlib/stats/mskmax/docs/types/index.d.ts b/lib/node_modules/@stdlib/stats/mskmax/docs/types/index.d.ts
new file mode 100644
index 000000000000..4373d4390377
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/mskmax/docs/types/index.d.ts
@@ -0,0 +1,158 @@
+/*
+* @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.
+*/
+
+// TypeScript Version: 4.1
+
+///
+
+import { ArrayLike } from '@stdlib/types/array';
+import { RealAndGenericDataType as DataType, typedndarray } from '@stdlib/types/ndarray';
+
+/**
+* Input array.
+*/
+type InputArray = typedndarray;
+
+/**
+* Output array.
+*/
+type OutputArray = typedndarray;
+
+/**
+* Interface defining "base" options.
+*/
+interface BaseOptions {
+ /**
+ * List of dimensions over which to perform a reduction.
+ */
+ dims?: ArrayLike;
+}
+
+/**
+* Interface defining options.
+*/
+interface Options extends BaseOptions {
+ /**
+ * Output array data type.
+ */
+ dtype?: DataType;
+
+ /**
+ * Boolean indicating whether the reduced dimensions should be included in the returned array as singleton dimensions. Default: `false`.
+ */
+ keepdims?: boolean;
+}
+
+/**
+* Interface for performing a reduction on an ndarray.
+*/
+interface Unary {
+ /**
+ * Computes the maximum value along one or more ndarray dimensions according to a mask.
+ *
+ * @param x - input ndarray
+ * @param mask - mask ndarray
+ * @param options - function options
+ * @returns output ndarray
+ *
+ * @example
+ * var array = require( '@stdlib/ndarray/array' );
+ *
+ * var x = array( [ -1.0, 2.0, -3.0, 4.0 ] );
+ * var mask = array( new Uint8Array( [ 0, 0, 1, 0 ] ) );
+ *
+ * var y = mskmax( x, mask );
+ * // returns
+ *
+ * var v = y.get();
+ * // returns 4.0
+ */
+ ( x: InputArray, mask: InputArray, options?: Options ): OutputArray; // NOTE: we lose type specificity here, but retaining specificity would likely be difficult and/or tedious to completely enumerate, as the output ndarray data type is dependent on how `x` interacts with output data type policy and whether that policy has been overridden by `options.dtype`.
+
+ /**
+ * Computes the maximum value along one or more ndarray dimensions and assigns results to a provided output ndarray according to a mask.
+ *
+ * @param x - input ndarray
+ * @param mask - mask ndarray
+ * @param out - output ndarray
+ * @param options - function options
+ * @returns output ndarray
+ *
+ * @example
+ * var array = require( '@stdlib/ndarray/array' );
+ * var zeros = require( '@stdlib/ndarray/zeros' );
+ *
+ * var x = array( [ -1.0, 2.0, -3.0, 4.0 ] );
+ * var mask = array( new Uint8Array( [ 0, 0, 1, 0 ] ) );
+ * var y = zeros( [] );
+ *
+ * var out = mskmax.assign( x, mask, y );
+ * // returns
+ *
+ * var v = out.get();
+ * // returns 4.0
+ *
+ * var bool = ( out === y );
+ * // returns true
+ */
+ assign = OutputArray>( x: InputArray, mask: InputArray, out: U, options?: BaseOptions ): U;
+}
+
+/**
+* Computes the maximum value along one or more ndarray dimensions according to a mask.
+*
+* @param x - input ndarray
+* @param mask - mask ndarray
+* @param options - function options
+* @returns output ndarray
+*
+* @example
+* var array = require( '@stdlib/ndarray/array' );
+*
+* var x = array( [ -1.0, 2.0, -3.0, 4.0 ] )
+* var mask = array( new Uint8Array( [ 0, 0, 1, 0 ] ) );
+*
+* var y = mskmax( x, mask );
+* // returns
+*
+* var v = y.get();
+* // returns 4.0
+*
+* @example
+* var array = require( '@stdlib/ndarray/array' );
+* var zeros = require( '@stdlib/ndarray/zeros' );
+*
+* var x = array( [ -1.0, 2.0, -3.0, 4.0 ] )
+* var mask = array( new Uint8Array( [ 0, 0, 1, 0 ] ) );
+* var y = zeros( [] );
+*
+* var out = mskmax.assign( x, mask, y );
+* // returns
+*
+* var v = out.get();
+* // returns 4.0
+*
+* var bool = ( out === y );
+* // returns true
+*/
+declare const mskmax: Unary;
+
+
+// EXPORTS //
+
+export = mskmax;
diff --git a/lib/node_modules/@stdlib/stats/mskmax/docs/types/test.ts b/lib/node_modules/@stdlib/stats/mskmax/docs/types/test.ts
new file mode 100644
index 000000000000..24a831499f9c
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/mskmax/docs/types/test.ts
@@ -0,0 +1,225 @@
+/*
+* @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.
+*/
+
+/* eslint-disable space-in-parens */
+
+///
+
+import zeros = require( '@stdlib/ndarray/zeros' );
+import mskmax = require( './index' );
+
+
+// TESTS //
+
+// The function returns an ndarray...
+{
+ const x = zeros( [ 2, 2 ], {
+ 'dtype': 'float64'
+ });
+
+ mskmax( x ); // $ExpectType OutputArray
+ mskmax( x, {} ); // $ExpectType OutputArray
+}
+
+// The compiler throws an error if the function is provided a first argument which is not an ndarray...
+{
+ mskmax( '5' ); // $ExpectError
+ mskmax( 5 ); // $ExpectError
+ mskmax( true ); // $ExpectError
+ mskmax( false ); // $ExpectError
+ mskmax( null ); // $ExpectError
+ mskmax( void 0 ); // $ExpectError
+ mskmax( {} ); // $ExpectError
+ mskmax( ( x: number ): number => x ); // $ExpectError
+
+ mskmax( '5', {} ); // $ExpectError
+ mskmax( 5, {} ); // $ExpectError
+ mskmax( true, {} ); // $ExpectError
+ mskmax( false, {} ); // $ExpectError
+ mskmax( null, {} ); // $ExpectError
+ mskmax( void 0, {} ); // $ExpectError
+ mskmax( {}, {} ); // $ExpectError
+ mskmax( ( x: number ): number => x, {} ); // $ExpectError
+}
+
+// The compiler throws an error if the function is provided a second argument which is not an object...
+{
+ const x = zeros( [ 2, 2 ], {
+ 'dtype': 'float64'
+ });
+
+ mskmax( x, '5' ); // $ExpectError
+ mskmax( x, true ); // $ExpectError
+ mskmax( x, false ); // $ExpectError
+ mskmax( x, null ); // $ExpectError
+ mskmax( x, [] ); // $ExpectError
+ mskmax( x, ( x: number ): number => x ); // $ExpectError
+}
+
+// The compiler throws an error if the function is provided an invalid `dtype` option...
+{
+ const x = zeros( [ 2, 2 ], {
+ 'dtype': 'float64'
+ });
+
+ mskmax( x, { 'dtype': '5' } ); // $ExpectError
+ mskmax( x, { 'dtype': 5 } ); // $ExpectError
+ mskmax( x, { 'dtype': true } ); // $ExpectError
+ mskmax( x, { 'dtype': false } ); // $ExpectError
+ mskmax( x, { 'dtype': null } ); // $ExpectError
+ mskmax( x, { 'dtype': [] } ); // $ExpectError
+ mskmax( x, { 'dtype': {} } ); // $ExpectError
+ mskmax( x, { 'dtype': ( x: number ): number => x } ); // $ExpectError
+}
+
+// The compiler throws an error if the function is provided an invalid `keepdims` option...
+{
+ const x = zeros( [ 2, 2 ], {
+ 'dtype': 'float64'
+ });
+
+ mskmax( x, { 'keepdims': '5' } ); // $ExpectError
+ mskmax( x, { 'keepdims': 5 } ); // $ExpectError
+ mskmax( x, { 'keepdims': null } ); // $ExpectError
+ mskmax( x, { 'keepdims': [] } ); // $ExpectError
+ mskmax( x, { 'keepdims': {} } ); // $ExpectError
+ mskmax( x, { 'keepdims': ( x: number ): number => x } ); // $ExpectError
+}
+
+// The compiler throws an error if the function is provided an invalid `dims` option...
+{
+ const x = zeros( [ 2, 2 ], {
+ 'dtype': 'float64'
+ });
+
+ mskmax( x, { 'dims': '5' } ); // $ExpectError
+ mskmax( x, { 'dims': 5 } ); // $ExpectError
+ mskmax( x, { 'dims': true } ); // $ExpectError
+ mskmax( x, { 'dims': false } ); // $ExpectError
+ mskmax( x, { 'dims': null } ); // $ExpectError
+ mskmax( x, { 'dims': {} } ); // $ExpectError
+ mskmax( x, { 'dims': ( x: number ): number => x } ); // $ExpectError
+}
+
+// The compiler throws an error if the function is provided an unsupported number of arguments...
+{
+ const x = zeros( [ 2, 2 ], {
+ 'dtype': 'float64'
+ });
+
+ mskmax(); // $ExpectError
+ mskmax( x, {}, {} ); // $ExpectError
+}
+
+// Attached to the function is an `assign` method which returns an ndarray...
+{
+ const x = zeros( [ 2, 2 ], {
+ 'dtype': 'float64'
+ });
+
+ mskmax.assign( x, x ); // $ExpectType float64ndarray
+ mskmax.assign( x, x, {} ); // $ExpectType float64ndarray
+}
+
+// The compiler throws an error if the `assign` method is provided a first argument which is not an ndarray...
+{
+ const x = zeros( [ 2, 2 ], {
+ 'dtype': 'float64'
+ });
+
+ mskmax.assign( '5', x ); // $ExpectError
+ mskmax.assign( 5, x ); // $ExpectError
+ mskmax.assign( true, x ); // $ExpectError
+ mskmax.assign( false, x ); // $ExpectError
+ mskmax.assign( null, x ); // $ExpectError
+ mskmax.assign( void 0, x ); // $ExpectError
+ mskmax.assign( {}, x ); // $ExpectError
+ mskmax.assign( ( x: number ): number => x, x ); // $ExpectError
+
+ mskmax.assign( '5', x, {} ); // $ExpectError
+ mskmax.assign( 5, x, {} ); // $ExpectError
+ mskmax.assign( true, x, {} ); // $ExpectError
+ mskmax.assign( false, x, {} ); // $ExpectError
+ mskmax.assign( null, x, {} ); // $ExpectError
+ mskmax.assign( void 0, x, {} ); // $ExpectError
+ mskmax.assign( {}, x, {} ); // $ExpectError
+ mskmax.assign( ( x: number ): number => x, x, {} ); // $ExpectError
+}
+
+// The compiler throws an error if the `assign` method is provided a second argument which is not an ndarray...
+{
+ const x = zeros( [ 2, 2 ], {
+ 'dtype': 'float64'
+ });
+
+ mskmax.assign( x, '5' ); // $ExpectError
+ mskmax.assign( x, 5 ); // $ExpectError
+ mskmax.assign( x, true ); // $ExpectError
+ mskmax.assign( x, false ); // $ExpectError
+ mskmax.assign( x, null ); // $ExpectError
+ mskmax.assign( x, void 0 ); // $ExpectError
+ mskmax.assign( x, ( x: number ): number => x ); // $ExpectError
+
+ mskmax.assign( x, '5', {} ); // $ExpectError
+ mskmax.assign( x, 5, {} ); // $ExpectError
+ mskmax.assign( x, true, {} ); // $ExpectError
+ mskmax.assign( x, false, {} ); // $ExpectError
+ mskmax.assign( x, null, {} ); // $ExpectError
+ mskmax.assign( x, void 0, {} ); // $ExpectError
+ mskmax.assign( x, ( x: number ): number => x, {} ); // $ExpectError
+}
+
+// The compiler throws an error if the `assign` method is provided a third argument which is not an object...
+{
+ const x = zeros( [ 2, 2 ], {
+ 'dtype': 'float64'
+ });
+
+ mskmax.assign( x, x, '5' ); // $ExpectError
+ mskmax.assign( x, x, true ); // $ExpectError
+ mskmax.assign( x, x, false ); // $ExpectError
+ mskmax.assign( x, x, null ); // $ExpectError
+ mskmax.assign( x, x, [] ); // $ExpectError
+ mskmax.assign( x, x, ( x: number ): number => x ); // $ExpectError
+}
+
+// The compiler throws an error if the `assign` method is provided an invalid `dims` option...
+{
+ const x = zeros( [ 2, 2 ], {
+ 'dtype': 'float64'
+ });
+
+ mskmax.assign( x, x, { 'dims': '5' } ); // $ExpectError
+ mskmax.assign( x, x, { 'dims': 5 } ); // $ExpectError
+ mskmax.assign( x, x, { 'dims': true } ); // $ExpectError
+ mskmax.assign( x, x, { 'dims': false } ); // $ExpectError
+ mskmax.assign( x, x, { 'dims': null } ); // $ExpectError
+ mskmax.assign( x, x, { 'dims': {} } ); // $ExpectError
+ mskmax.assign( x, x, { 'dims': ( x: number ): number => x } ); // $ExpectError
+}
+
+// The compiler throws an error if the `assign` method is provided an unsupported number of arguments...
+{
+ const x = zeros( [ 2, 2 ], {
+ 'dtype': 'float64'
+ });
+
+ mskmax.assign(); // $ExpectError
+ mskmax.assign( x ); // $ExpectError
+ mskmax.assign( x, x, {}, {} ); // $ExpectError
+}
diff --git a/lib/node_modules/@stdlib/stats/mskmax/examples/index.js b/lib/node_modules/@stdlib/stats/mskmax/examples/index.js
new file mode 100644
index 000000000000..d459cb992d70
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/mskmax/examples/index.js
@@ -0,0 +1,55 @@
+/**
+* @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';
+
+var discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
+var getDType = require( '@stdlib/ndarray/dtype' );
+var ndarray2array = require( '@stdlib/ndarray/to-array' );
+var ndarray = require( '@stdlib/ndarray/ctor' );
+var mskmax = require( './../lib' );
+
+// Generate an array of random numbers:
+var xbuf = discreteUniform( 25, 0, 20, {
+ 'dtype': 'generic'
+});
+
+// Wrap in an ndarray:
+var x = new ndarray( 'generic', xbuf, [ 5, 5 ], [ 5, 1 ], 0, 'row-major' );
+console.log( ndarray2array( x ) );
+
+// Generate a mask (mask out random elements):
+var mbuf = discreteUniform( 25, 0, 1, {
+ 'dtype': 'uint8'
+});
+
+// Wrap mask in an ndarray:
+var mask = new ndarray( 'uint8', mbuf, [ 5, 5 ], [ 5, 1 ], 0, 'row-major' );
+console.log( ndarray2array( mask ) );
+
+// Perform a reduction:
+var y = mskmax( x, mask, {
+ 'dims': [ 0 ]
+});
+
+// Resolve the output array data type:
+var dt = getDType( y );
+console.log( dt );
+
+// Print the results:
+console.log( ndarray2array( y ) );
diff --git a/lib/node_modules/@stdlib/stats/mskmax/lib/assign.js b/lib/node_modules/@stdlib/stats/mskmax/lib/assign.js
new file mode 100644
index 000000000000..c03a2dbf6f30
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/mskmax/lib/assign.js
@@ -0,0 +1,111 @@
+/**
+* @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 isNdarrayLike = require( '@stdlib/assert/is-ndarray-like' );
+var format = require( '@stdlib/string/format' );
+var maybeBroadcastArrays = require( '@stdlib/ndarray/base/maybe-broadcast-arrays' );
+var base = require( './base.js' );
+
+
+// MAIN //
+
+/**
+* Computes the maximum value along one or more ndarray dimensions and assigns the results to a provided output ndarray according to a mask.
+*
+* @param {ndarray} x - input ndarray
+* @param {ndarray} mask - mask ndarray
+* @param {ndarray} out - output ndarray
+* @param {Options} [options] - function options
+* @param {IntegerArray} [options.dims] - list of dimensions over which to perform a reduction
+* @param {boolean} [options.keepdims=false] - boolean indicating whether the reduced dimensions should be included in the returned ndarray as singleton dimensions
+* @throws {TypeError} first argument must be an ndarray-like object
+* @throws {TypeError} second argument must be an ndarray-like object
+* @throws {TypeError} third argument must be an ndarray-like object
+* @throws {TypeError} options argument must be an object
+* @throws {RangeError} dimension indices must not exceed input ndarray bounds
+* @throws {RangeError} number of dimension indices must not exceed the number of input ndarray dimensions
+* @throws {Error} must provide valid options
+* @returns {ndarray} output ndarray
+*
+* @example
+* var Float64Array = require( '@stdlib/array/float64' );
+* var Uint8Array = require( '@stdlib/array/uint8' );
+* var ndarray = require( '@stdlib/ndarray/ctor' );
+*
+* // Create a data buffer:
+* var xbuf = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
+*
+* // Create a mask buffer:
+* var mbuf = new Uint8Array( [ 0, 0, 0, 1, 0, 1 ] );
+*
+* // Create an output buffer:
+* var obuf = new Float64Array( [ 0.0 ] );
+*
+* // Define the shape of the input array:
+* var sh = [ 3, 2 ];
+*
+* // Define the array strides:
+* var sx = [ 2, 1 ];
+* var sm = [ 2, 1 ];
+*
+* // Define the index offset:
+* var ox = 0;
+* var om = 0;
+*
+* // Create the input ndarray:
+* var x = new ndarray( 'float64', xbuf, sh, sx, ox, 'row-major' );
+*
+* // Create the mask ndarray:
+* var mask = new ndarray( 'uint8', mbuf, sh, sm, om, 'row-major' ); // cspell:disable-line
+*
+* // Create the output ndarray:
+* var out = new ndarray( 'float64', obuf, [], [ 0 ], 0, 'row-major' );
+*
+* // Perform reduction:
+* var res = assign( x, mask, out );
+* // returns
+*
+* var v = res.get();
+* // returns 5.0
+*/
+function assign( x, mask, out ) {
+ var arrs;
+ if ( !isNdarrayLike( x ) ) {
+ throw new TypeError( format( 'invalid argument. First argument must be an ndarray-like object. Value: `%s`.', x ) );
+ }
+ if ( !isNdarrayLike( mask ) ) {
+ throw new TypeError( format( 'invalid argument. Second argument must be an ndarray-like object. Value: `%s`.', mask ) );
+ }
+ if ( !isNdarrayLike( out ) ) {
+ throw new TypeError( format( 'invalid argument. Third argument must be an ndarray-like object. Value: `%s`.', out ) );
+ }
+ arrs = maybeBroadcastArrays( [ x, mask ] );
+ if ( arguments.length > 3 ) {
+ return base.assign( arrs[ 0 ], arrs[ 1 ], out, arguments[ 3 ] );
+ }
+ return base.assign( arrs[ 0 ], arrs[ 1 ], out );
+}
+
+
+// EXPORTS //
+
+module.exports = assign;
diff --git a/lib/node_modules/@stdlib/stats/mskmax/lib/base.js b/lib/node_modules/@stdlib/stats/mskmax/lib/base.js
new file mode 100644
index 000000000000..ae0879551978
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/mskmax/lib/base.js
@@ -0,0 +1,80 @@
+/**
+* @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 dtypes = require( '@stdlib/ndarray/dtypes' );
+var gmskmax = require( '@stdlib/stats/base/ndarray/mskmax' );
+var dmskmax = require( '@stdlib/stats/base/ndarray/dmskmax' );
+var smskmax = require( '@stdlib/stats/base/ndarray/smskmax' );
+var factory = require( '@stdlib/ndarray/base/binary-reduce-strided1d-dispatch-factory' );
+
+
+// VARIABLES //
+
+var idtypes = dtypes( 'real_and_generic' );
+var mdtypes = dtypes( 'mask_index_and_generic' );
+var odtypes = dtypes( 'real_and_generic' );
+var policies = {
+ 'output': 'promoted',
+ 'casting': 'none'
+};
+var table = {
+ 'types': [
+ 'float64',
+ 'uint8', // dmskmax: x, mask
+ 'float32',
+ 'uint8' // smskmax: x, mask
+ ],
+ 'fcns': [
+ dmskmax,
+ smskmax
+ ],
+ 'default': gmskmax
+};
+
+
+// MAIN //
+
+/**
+* Base implementation for computing the maximum value along one or more ndarray dimensions according to a mask.
+*
+* @name mskmax
+* @type {Function}
+* @param {ndarray} x - input ndarray
+* @param {ndarray} mask - mask ndarray
+* @param {Options} [options] - function options
+* @param {IntegerArray} [options.dims] - list of dimensions over which to perform a reduction
+* @param {boolean} [options.keepdims=false] - boolean indicating whether the reduced dimensions should be included in the returned ndarray as singleton dimensions
+* @param {string} [options.dtype] - output ndarray data type
+* @throws {TypeError} first argument must be an ndarray-like object
+* @throws {TypeError} second argument must be an ndarray-like object
+* @throws {TypeError} options argument must be an object
+* @throws {RangeError} dimension indices must not exceed input ndarray bounds
+* @throws {RangeError} number of dimension indices must not exceed the number of input ndarray dimensions
+* @throws {Error} must provide valid options
+* @returns {ndarray} output ndarray
+*/
+var base = factory( table, [ idtypes, mdtypes ], odtypes, policies );
+
+
+// EXPORTS //
+
+module.exports = base;
diff --git a/lib/node_modules/@stdlib/stats/mskmax/lib/index.js b/lib/node_modules/@stdlib/stats/mskmax/lib/index.js
new file mode 100644
index 000000000000..36900adf392e
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/mskmax/lib/index.js
@@ -0,0 +1,75 @@
+/**
+* @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';
+
+/**
+* Compute the maximum value along one or more ndarray dimensions according to a mask.
+*
+* @module @stdlib/stats/mskmax
+*
+* @example
+* var Float64Array = require( '@stdlib/array/float64' );
+* var Uint8Array = require( '@stdlib/array/uint8' );
+* var ndarray = require( '@stdlib/ndarray/ctor' );
+* var mskmax = require( '@stdlib/stats/mskmax' );
+*
+* // Create a data buffer:
+* var xbuf = new Float64Array( [ 0.0, 2.0, 3.0, 0.0, 0.0, 6.0, 7.0, 0.0, 0.0, 10.0, 11.0, 0.0 ] );
+*
+* // Create a mask buffer:
+* var mbuf = new Uint8Array( [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0 ] ); // eslint-disable-line id-length
+*
+* // Define the shape of the input array:
+* var sh = [ 3, 1, 2 ];
+*
+* // Define the array strides:
+* var sx = [ 4, 4, 1 ];
+*
+* // Define the index offset:
+* var ox = 1;
+*
+* // Create an input ndarray:
+* var x = new ndarray( 'float64', xbuf, sh, sx, ox, 'row-major' );
+*
+* // Create a mask ndarray:
+* var mask = new ndarray( 'uint8', mbuf, sh, sx, ox, 'row-major' ); // cspell:disable-line
+*
+* // Perform reduction:
+* var out = mskmax( x, mask );
+* // returns
+*
+* var v = out.get();
+* // returns 10.0
+*/
+
+// MODULES //
+
+var setReadOnly = require( '@stdlib/utils/define-nonenumerable-read-only-property' );
+var main = require( './main.js' );
+var assign = require( './assign.js' );
+
+
+// MAIN //
+
+setReadOnly( main, 'assign', assign );
+
+
+// EXPORTS //
+
+module.exports = main;
diff --git a/lib/node_modules/@stdlib/stats/mskmax/lib/main.js b/lib/node_modules/@stdlib/stats/mskmax/lib/main.js
new file mode 100644
index 000000000000..fcd026b029f8
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/mskmax/lib/main.js
@@ -0,0 +1,101 @@
+/**
+* @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 isNdarrayLike = require( '@stdlib/assert/is-ndarray-like' );
+var format = require( '@stdlib/string/format' );
+var maybeBroadcastArrays = require( '@stdlib/ndarray/base/maybe-broadcast-arrays' );
+var base = require( './base.js' );
+
+
+// MAIN //
+
+/**
+* Computes the maximum value along one or more ndarray dimensions according to a mask.
+*
+* @param {ndarray} x - input ndarray
+* @param {ndarray} mask - mask ndarray
+* @param {Options} [options] - function options
+* @param {IntegerArray} [options.dims] - list of dimensions over which to perform a reduction
+* @param {boolean} [options.keepdims=false] - boolean indicating whether the reduced dimensions should be included in the returned ndarray as singleton dimensions
+* @param {string} [options.dtype] - output ndarray data type
+* @throws {TypeError} first argument must be an ndarray-like object
+* @throws {TypeError} second argument must be an ndarray-like object
+* @throws {TypeError} options argument must be an object
+* @throws {RangeError} dimension indices must not exceed input ndarray bounds
+* @throws {RangeError} number of dimension indices must not exceed the number of input ndarray dimensions
+* @throws {Error} must provide valid options
+* @returns {ndarray} output ndarray
+*
+* @example
+* var Float64Array = require( '@stdlib/array/float64' );
+* var Uint8Array = require( '@stdlib/array/uint8' );
+* var ndarray = require( '@stdlib/ndarray/ctor' );
+*
+* // Create a data buffer:
+* var xbuf = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
+*
+* // Create a mask buffer:
+* var mbuf = new Uint8Array( [ 0, 0, 0, 1, 0, 1 ] );
+*
+* // Define the shape of the input array:
+* var sh = [ 3, 2 ];
+*
+* // Define the array strides:
+* var sx = [ 2, 1 ];
+* var sm = [ 2, 1 ];
+*
+* // Define the index offset:
+* var ox = 0;
+* var om = 0;
+*
+* // Create the input ndarray:
+* var x = new ndarray( 'float64', xbuf, sh, sx, ox, 'row-major' );
+*
+* // Create the mask ndarray:
+* var mask = new ndarray( 'uint8', mbuf, sh, sm, om, 'row-major' ); // cspell:disable-line
+*
+* // Perform reduction:
+* var out = mskmax( x, mask );
+* // returns
+*
+* var v = out.get();
+* // returns 5.0
+*/
+function mskmax( x, mask ) {
+ var arrs;
+ if ( !isNdarrayLike( x ) ) {
+ throw new TypeError( format( 'invalid argument. First argument must be an ndarray-like object. Value: `%s`.', x ) );
+ }
+ if ( !isNdarrayLike( mask ) ) {
+ throw new TypeError( format( 'invalid argument. Second argument must be an ndarray-like object. Value: `%s`.', mask ) );
+ }
+ arrs = maybeBroadcastArrays( [ x, mask ] );
+ if ( arguments.length > 2 ) {
+ return base( arrs[ 0 ], arrs[ 1 ], arguments[ 2 ] );
+ }
+ return base( arrs[ 0 ], arrs[ 1 ] );
+}
+
+
+// EXPORTS //
+
+module.exports = mskmax;
diff --git a/lib/node_modules/@stdlib/stats/mskmax/package.json b/lib/node_modules/@stdlib/stats/mskmax/package.json
new file mode 100644
index 000000000000..a1890c8ee07a
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/mskmax/package.json
@@ -0,0 +1,67 @@
+{
+ "name": "@stdlib/stats/mskmax",
+ "version": "0.0.0",
+ "description": "Compute the maximum value along one or more ndarray dimensions according to a mask.",
+ "license": "Apache-2.0",
+ "author": {
+ "name": "The Stdlib Authors",
+ "url": "https://github.com/stdlib-js/stdlib/graphs/contributors"
+ },
+ "contributors": [
+ {
+ "name": "The Stdlib Authors",
+ "url": "https://github.com/stdlib-js/stdlib/graphs/contributors"
+ }
+ ],
+ "main": "./lib",
+ "directories": {
+ "benchmark": "./benchmark",
+ "doc": "./docs",
+ "example": "./examples",
+ "lib": "./lib",
+ "test": "./test"
+ },
+ "types": "./docs/types",
+ "scripts": {},
+ "homepage": "https://github.com/stdlib-js/stdlib",
+ "repository": {
+ "type": "git",
+ "url": "git://github.com/stdlib-js/stdlib.git"
+ },
+ "bugs": {
+ "url": "https://github.com/stdlib-js/stdlib/issues"
+ },
+ "dependencies": {},
+ "devDependencies": {},
+ "engines": {
+ "node": ">=0.10.0",
+ "npm": ">2.7.0"
+ },
+ "os": [
+ "aix",
+ "darwin",
+ "freebsd",
+ "linux",
+ "macos",
+ "openbsd",
+ "sunos",
+ "win32",
+ "windows"
+ ],
+ "keywords": [
+ "stdlib",
+ "stdmath",
+ "statistics",
+ "stats",
+ "mathematics",
+ "math",
+ "maximum",
+ "max",
+ "range",
+ "extremes",
+ "domain",
+ "extent",
+ "ndarray"
+ ],
+ "__stdlib__": {}
+}
diff --git a/lib/node_modules/@stdlib/stats/mskmax/test/test.assign.js b/lib/node_modules/@stdlib/stats/mskmax/test/test.assign.js
new file mode 100644
index 000000000000..56042b75ce73
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/mskmax/test/test.assign.js
@@ -0,0 +1,819 @@
+/**
+* @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 tape = require( 'tape' );
+var ndarray = require( '@stdlib/ndarray/ctor' );
+var zeros = require( '@stdlib/ndarray/zeros' );
+var empty = require( '@stdlib/ndarray/empty' );
+var emptyLike = require( '@stdlib/ndarray/empty-like' );
+var ndarray2array = require( '@stdlib/ndarray/to-array' );
+var mskmax = require( './../lib' ).assign;
+
+
+// TESTS //
+
+tape( 'main export is a function', function test( t ) {
+ t.ok( true, __filename );
+ t.strictEqual( typeof mskmax, 'function', 'main export is a function' );
+ t.end();
+});
+
+tape( 'the function throws an error if provided a first argument which is not an ndarray-like object', function test( t ) {
+ var values;
+ var mask;
+ var out;
+ var i;
+
+ mask = zeros( [ 2, 2 ], {
+ 'dtype': 'uint8'
+ });
+ out = zeros( [ 2, 2 ], {
+ 'dtype': 'generic'
+ });
+
+ values = [
+ '5',
+ 5,
+ NaN,
+ true,
+ false,
+ null,
+ void 0,
+ [],
+ {},
+ function noop() {}
+ ];
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ mskmax( value, mask, out );
+ };
+ }
+});
+
+tape( 'the function throws an error if provided a first argument which is not an ndarray-like object (options)', function test( t ) {
+ var values;
+ var mask;
+ var out;
+ var i;
+
+ mask = zeros( [ 2, 2 ], {
+ 'dtype': 'uint8'
+ });
+ out = zeros( [ 2, 2 ], {
+ 'dtype': 'generic'
+ });
+
+ values = [
+ '5',
+ 5,
+ NaN,
+ true,
+ false,
+ null,
+ void 0,
+ [],
+ {},
+ function noop() {}
+ ];
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ mskmax( value, mask, out, {} );
+ };
+ }
+});
+
+tape( 'the function throws an error if provided a first argument which is not an ndarray-like object having a supported data type', function test( t ) {
+ var values;
+ var mask;
+ var out;
+ var i;
+
+ mask = zeros( [ 2, 2 ], {
+ 'dtype': 'uint8'
+ });
+ out = zeros( [ 2, 2 ], {
+ 'dtype': 'generic'
+ });
+
+ values = [
+ empty( [ 2, 2 ], {
+ 'dtype': 'bool'
+ })
+ ];
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ mskmax( value, mask, out );
+ };
+ }
+});
+
+tape( 'the function throws an error if provided a first argument which is not an ndarray-like object having a supported data type (options)', function test( t ) {
+ var values;
+ var mask;
+ var out;
+ var i;
+
+ mask = zeros( [ 2, 2 ], {
+ 'dtype': 'uint8'
+ });
+ out = zeros( [ 2, 2 ], {
+ 'dtype': 'generic'
+ });
+
+ values = [
+ empty( [ 2, 2 ], {
+ 'dtype': 'bool'
+ })
+ ];
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ mskmax( value, mask, out, {} );
+ };
+ }
+});
+
+tape( 'the function throws an error if provided a second argument which is not an ndarray-like object', function test( t ) {
+ var values;
+ var mask;
+ var x;
+ var i;
+
+ x = zeros( [ 2, 2 ], {
+ 'dtype': 'generic'
+ });
+ mask = zeros( [ 2, 2 ], {
+ 'dtype': 'uint8'
+ });
+
+ values = [
+ '5',
+ 5,
+ NaN,
+ true,
+ false,
+ null,
+ void 0,
+ [],
+ {},
+ function noop() {}
+ ];
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ mskmax( x, mask, value );
+ };
+ }
+});
+
+tape( 'the function throws an error if provided a second argument which is not an ndarray-like object (options)', function test( t ) {
+ var values;
+ var mask;
+ var x;
+ var i;
+
+ x = zeros( [ 2, 2 ], {
+ 'dtype': 'generic'
+ });
+ mask = zeros( [ 2, 2 ], {
+ 'dtype': 'uint8'
+ });
+
+ values = [
+ '5',
+ 5,
+ NaN,
+ true,
+ false,
+ null,
+ void 0,
+ [],
+ {},
+ function noop() {}
+ ];
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ mskmax( x, mask, value, {} );
+ };
+ }
+});
+
+tape( 'the function throws an error if provided a third argument which is not an object', function test( t ) {
+ var values;
+ var mask;
+ var out;
+ var x;
+ var i;
+
+ x = zeros( [ 2, 2 ], {
+ 'dtype': 'generic'
+ });
+ mask = zeros( [ 2, 2 ], {
+ 'dtype': 'uint8'
+ });
+ out = zeros( [], {
+ 'dtype': 'generic'
+ });
+
+ values = [
+ '5',
+ 5,
+ NaN,
+ true,
+ false,
+ null,
+ void 0,
+ [],
+ function noop() {}
+ ];
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ mskmax( x, mask, out, value );
+ };
+ }
+});
+
+tape( 'the function throws an error if provided a `dims` option which is not an array-like object of integers', function test( t ) {
+ var values;
+ var mask;
+ var out;
+ var x;
+ var i;
+
+ x = zeros( [ 2, 2 ], {
+ 'dtype': 'generic'
+ });
+ mask = zeros( [ 2, 2 ], {
+ 'dtype': 'uint8'
+ });
+ out = zeros( [], {
+ 'dtype': 'generic'
+ });
+
+ values = [
+ '5',
+ 5,
+ NaN,
+ true,
+ false,
+ null,
+ void 0,
+ [ 'a' ],
+ {},
+ function noop() {}
+ ];
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ mskmax( x, mask, out, {
+ 'dims': value
+ });
+ };
+ }
+});
+
+tape( 'the function throws an error if provided a `dims` option which contains out-of-bounds indices', function test( t ) {
+ var values;
+ var mask;
+ var out;
+ var x;
+ var i;
+
+ x = zeros( [ 2, 2 ], {
+ 'dtype': 'generic'
+ });
+ mask = zeros( [ 2, 2 ], {
+ 'dtype': 'uint8'
+ });
+ out = zeros( [ 2 ], {
+ 'dtype': 'generic'
+ });
+
+ values = [
+ [ -10 ],
+ [ 20 ]
+ ];
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), RangeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ mskmax( x, mask, out, {
+ 'dims': value
+ });
+ };
+ }
+});
+
+tape( 'the function throws an error if provided a `dims` option which contains too many indices', function test( t ) {
+ var values;
+ var mask;
+ var out;
+ var x;
+ var i;
+
+ x = zeros( [ 2, 2 ], {
+ 'dtype': 'generic'
+ });
+ mask = zeros( [ 2, 2 ], {
+ 'dtype': 'uint8'
+ });
+ out = zeros( [], {
+ 'dtype': 'generic'
+ });
+
+ values = [
+ [ 0, 1, 2 ],
+ [ 0, 1, 2, 3 ]
+ ];
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), RangeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ mskmax( x, mask, out, {
+ 'dims': value
+ });
+ };
+ }
+});
+
+tape( 'the function throws an error if provided a `dims` option which contains duplicate indices', function test( t ) {
+ var values;
+ var mask;
+ var out;
+ var x;
+ var i;
+
+ x = zeros( [ 2, 2 ], {
+ 'dtype': 'generic'
+ });
+ mask = zeros( [ 2, 2 ], {
+ 'dtype': 'uint8'
+ });
+ out = zeros( [], {
+ 'dtype': 'generic'
+ });
+
+ values = [
+ [ 0, 0 ],
+ [ 1, 1 ],
+ [ 0, 1, 0 ],
+ [ 1, 0, 1 ]
+ ];
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), Error, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ mskmax( x, mask, out, {
+ 'dims': value
+ });
+ };
+ }
+});
+
+tape( 'the function throws an error if provided an output array which has an invalid shape (default)', function test( t ) {
+ var values;
+ var mask;
+ var x;
+ var i;
+
+ x = zeros( [ 2, 2 ], {
+ 'dtype': 'generic'
+ });
+ mask = zeros( [ 2, 2 ], {
+ 'dtype': 'uint8'
+ });
+
+ values = [
+ [ 2, 2 ],
+ [ 2 ],
+ [ 4, 4 ],
+ [ 4 ],
+ [ 1 ],
+ [ 1, 1 ]
+ ];
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), Error, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ var out = zeros( value, {
+ 'dtype': 'generic'
+ });
+ mskmax( x, mask, out );
+ };
+ }
+});
+
+tape( 'the function throws an error if provided an output array which has an invalid shape (all dimensions)', function test( t ) {
+ var values;
+ var mask;
+ var x;
+ var i;
+
+ x = zeros( [ 2, 2 ], {
+ 'dtype': 'generic'
+ });
+ mask = zeros( [ 2, 2 ], {
+ 'dtype': 'uint8'
+ });
+
+ values = [
+ [ 2, 2 ],
+ [ 2 ],
+ [ 4, 4 ],
+ [ 4 ],
+ [ 1 ],
+ [ 1, 1 ]
+ ];
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), Error, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ var out = zeros( value, {
+ 'dtype': 'generic'
+ });
+ mskmax( x, mask, out, {
+ 'dims': [ 0, 1 ]
+ });
+ };
+ }
+});
+
+tape( 'the function throws an error if provided an output array which has an invalid shape (some dimensions)', function test( t ) {
+ var values;
+ var mask;
+ var x;
+ var i;
+
+ x = zeros( [ 2, 2 ], {
+ 'dtype': 'generic'
+ });
+ mask = zeros( [ 2, 2 ], {
+ 'dtype': 'uint8'
+ });
+
+ values = [
+ [],
+ [ 4, 4 ],
+ [ 4 ],
+ [ 1 ],
+ [ 1, 1 ]
+ ];
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), Error, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ var out = zeros( value, {
+ 'dtype': 'generic'
+ });
+ mskmax( x, mask, out, {
+ 'dims': [ 0 ]
+ });
+ };
+ }
+});
+
+tape( 'the function performs a reduction on an ndarray (default, row-major)', function test( t ) {
+ var expected;
+ var actual;
+ var mbuf;
+ var mask;
+ var xbuf;
+ var out;
+ var x;
+
+ xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
+ x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 2, 1 ], 0, 'row-major' );
+
+ mbuf = [ 0, 0, 0, 1 ];
+ mask = new ndarray( 'uint8', mbuf, [ 2, 2 ], [ 2, 1 ], 0, 'row-major' );
+
+ out = emptyLike( x, {
+ 'shape': []
+ });
+
+ actual = mskmax( x, mask, out );
+ expected = 2.0;
+
+ t.strictEqual( actual, out, 'returns expected value' );
+ t.strictEqual( actual.get(), expected, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function performs a reduction on an ndarray (default, column-major)', function test( t ) {
+ var expected;
+ var actual;
+ var mbuf;
+ var mask;
+ var xbuf;
+ var out;
+ var x;
+
+ xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
+ x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 1, 2 ], 0, 'column-major' );
+
+ mbuf = [ 0, 0, 0, 1 ];
+ mask = new ndarray( 'uint8', mbuf, [ 2, 2 ], [ 1, 2 ], 0, 'column-major' );
+
+ out = emptyLike( x, {
+ 'shape': []
+ });
+
+ actual = mskmax( x, mask, out );
+ expected = 2.0;
+
+ t.strictEqual( actual, out, 'returns expected value' );
+ t.strictEqual( actual.get(), expected, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function performs a reduction on an ndarray (all dimensions, row-major)', function test( t ) {
+ var expected;
+ var actual;
+ var mbuf;
+ var mask;
+ var xbuf;
+ var out;
+ var x;
+
+ xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
+ x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 2, 1 ], 0, 'row-major' );
+
+ mbuf = [ 0, 0, 0, 1 ];
+ mask = new ndarray( 'uint8', mbuf, [ 2, 2 ], [ 2, 1 ], 0, 'row-major' );
+
+ out = emptyLike( x, {
+ 'shape': []
+ });
+
+ actual = mskmax( x, mask, out, {
+ 'dims': [ 0, 1 ]
+ });
+ expected = 2.0;
+
+ t.strictEqual( actual, out, 'returns expected value' );
+ t.strictEqual( actual.get(), expected, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function performs a reduction on an ndarray (all dimensions, column-major)', function test( t ) {
+ var expected;
+ var actual;
+ var mbuf;
+ var mask;
+ var xbuf;
+ var out;
+ var x;
+
+ xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
+ x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 1, 2 ], 0, 'column-major' );
+
+ mbuf = [ 0, 0, 0, 1 ];
+ mask = new ndarray( 'uint8', mbuf, [ 2, 2 ], [ 1, 2 ], 0, 'column-major' );
+
+ out = emptyLike( x, {
+ 'shape': []
+ });
+
+ actual = mskmax( x, mask, out, {
+ 'dims': [ 0, 1 ]
+ });
+ expected = 2.0;
+
+ t.strictEqual( actual, out, 'returns expected value' );
+ t.strictEqual( actual.get(), expected, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function performs a reduction on an ndarray (no dimensions, row-major)', function test( t ) {
+ var actual;
+ var mbuf;
+ var mask;
+ var xbuf;
+ var out;
+ var arr;
+ var x;
+
+ xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
+ x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 2, 1 ], 0, 'row-major' );
+
+ mbuf = [ 0, 0, 0, 1 ];
+ mask = new ndarray( 'uint8', mbuf, [ 2, 2 ], [ 2, 1 ], 0, 'row-major' );
+
+ out = emptyLike( x, {
+ 'shape': [ 2, 2 ]
+ });
+
+ actual = mskmax( x, mask, out, {
+ 'dims': []
+ });
+
+ t.strictEqual( actual, out, 'returns expected value' );
+ arr = ndarray2array( actual );
+ t.strictEqual( arr[0][0], -1.0, 'returns expected value' );
+ t.strictEqual( arr[0][1], 2.0, 'returns expected value' );
+ t.strictEqual( arr[1][0], -3.0, 'returns expected value' );
+ t.ok( arr[1][1] !== arr[1][1], 'returns NaN for masked value' );
+
+ t.end();
+});
+
+tape( 'the function performs a reduction on an ndarray (no dimensions, column-major)', function test( t ) {
+ var actual;
+ var mbuf;
+ var mask;
+ var xbuf;
+ var out;
+ var arr;
+ var x;
+
+ xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
+ x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 1, 2 ], 0, 'column-major' );
+
+ mbuf = [ 0, 0, 0, 1 ];
+ mask = new ndarray( 'uint8', mbuf, [ 2, 2 ], [ 1, 2 ], 0, 'column-major' );
+
+ out = emptyLike( x, {
+ 'shape': [ 2, 2 ]
+ });
+
+ actual = mskmax( x, mask, out, {
+ 'dims': []
+ });
+
+ t.strictEqual( actual, out, 'returns expected value' );
+ arr = ndarray2array( actual );
+ t.strictEqual( arr[0][0], -1.0, 'returns expected value' );
+ t.strictEqual( arr[0][1], -3.0, 'returns expected value' );
+ t.strictEqual( arr[1][0], 2.0, 'returns expected value' );
+ t.ok( arr[1][1] !== arr[1][1], 'returns NaN for masked value' );
+
+ t.end();
+});
+
+tape( 'the function supports specifying reduction dimensions (row-major)', function test( t ) {
+ var expected;
+ var actual;
+ var mbuf;
+ var mask;
+ var xbuf;
+ var out;
+ var x;
+
+ xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
+ x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 2, 1 ], 0, 'row-major' );
+
+ mbuf = [ 0, 0, 0, 1 ];
+ mask = new ndarray( 'uint8', mbuf, [ 2, 2 ], [ 2, 1 ], 0, 'row-major' );
+
+ out = emptyLike( x, {
+ 'shape': [ 2 ]
+ });
+
+ actual = mskmax( x, mask, out, {
+ 'dims': [ 0 ]
+ });
+ expected = [ -1.0, 2.0 ];
+
+ t.strictEqual( actual, out, 'returns expected value' );
+ t.deepEqual( ndarray2array( actual ), expected, 'returns expected value' );
+
+ xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
+ x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 2, 1 ], 0, 'row-major' );
+
+ out = emptyLike( x, {
+ 'shape': [ 2 ]
+ });
+
+ actual = mskmax( x, mask, out, {
+ 'dims': [ 1 ]
+ });
+ expected = [ 2.0, -3.0 ];
+
+ t.strictEqual( actual, out, 'returns expected value' );
+ t.deepEqual( ndarray2array( actual ), expected, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function supports specifying reduction dimensions (column-major)', function test( t ) {
+ var expected;
+ var actual;
+ var mbuf;
+ var mask;
+ var xbuf;
+ var out;
+ var x;
+
+ xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
+ x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 1, 2 ], 0, 'column-major' );
+
+ mbuf = [ 0, 0, 0, 1 ];
+ mask = new ndarray( 'uint8', mbuf, [ 2, 2 ], [ 1, 2 ], 0, 'column-major' );
+
+ out = emptyLike( x, {
+ 'shape': [ 2 ]
+ });
+
+ actual = mskmax( x, mask, out, {
+ 'dims': [ 0 ]
+ });
+ expected = [ 2.0, -3.0 ];
+
+ t.strictEqual( actual, out, 'returns expected value' );
+ t.deepEqual( ndarray2array( actual ), expected, 'returns expected value' );
+
+ xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
+ x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 1, 2 ], 0, 'column-major' );
+
+ out = emptyLike( x, {
+ 'shape': [ 2 ]
+ });
+
+ actual = mskmax( x, mask, out, {
+ 'dims': [ 1 ]
+ });
+ expected = [ -1.0, 2.0 ];
+
+ t.strictEqual( actual, out, 'returns expected value' );
+ t.deepEqual( ndarray2array( actual ), expected, 'returns expected value' );
+
+ t.end();
+});
diff --git a/lib/node_modules/@stdlib/stats/mskmax/test/test.js b/lib/node_modules/@stdlib/stats/mskmax/test/test.js
new file mode 100644
index 000000000000..e53eacc98f42
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/mskmax/test/test.js
@@ -0,0 +1,39 @@
+/**
+* @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 tape = require( 'tape' );
+var isMethod = require( '@stdlib/assert/is-method' );
+var mskmax = require( './../lib' );
+
+
+// TESTS //
+
+tape( 'main export is a function', function test( t ) {
+ t.ok( true, __filename );
+ t.strictEqual( typeof mskmax, 'function', 'main export is a function' );
+ t.end();
+});
+
+tape( 'attached to the main export is an `assign` method', function test( t ) {
+ t.strictEqual( isMethod( mskmax, 'assign' ), true, 'returns expected value' );
+ t.end();
+});
diff --git a/lib/node_modules/@stdlib/stats/mskmax/test/test.main.js b/lib/node_modules/@stdlib/stats/mskmax/test/test.main.js
new file mode 100644
index 000000000000..5256254d6be4
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/mskmax/test/test.main.js
@@ -0,0 +1,873 @@
+/**
+* @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 tape = require( 'tape' );
+var isndarrayLike = require( '@stdlib/assert/is-ndarray-like' );
+var ndarray = require( '@stdlib/ndarray/ctor' );
+var zeros = require( '@stdlib/ndarray/zeros' );
+var empty = require( '@stdlib/ndarray/empty' );
+var ndarray2array = require( '@stdlib/ndarray/to-array' );
+var getDType = require( '@stdlib/ndarray/dtype' );
+var getShape = require( '@stdlib/ndarray/shape' );
+var getOrder = require( '@stdlib/ndarray/order' );
+var mskmax = require( './../lib' );
+
+
+// TESTS //
+
+tape( 'main export is a function', function test( t ) {
+ t.ok( true, __filename );
+ t.strictEqual( typeof mskmax, 'function', 'main export is a function' );
+ t.end();
+});
+
+tape( 'the function throws an error if provided a first argument which is not an ndarray-like object', function test( t ) {
+ var values;
+ var i;
+
+ values = [
+ '5',
+ 5,
+ NaN,
+ true,
+ false,
+ null,
+ void 0,
+ [],
+ {},
+ function noop() {}
+ ];
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ mskmax( value );
+ };
+ }
+});
+
+tape( 'the function throws an error if provided a first argument which is not an ndarray-like object (options)', function test( t ) {
+ var values;
+ var i;
+
+ values = [
+ '5',
+ 5,
+ NaN,
+ true,
+ false,
+ null,
+ void 0,
+ [],
+ {},
+ function noop() {}
+ ];
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ mskmax( value, {} );
+ };
+ }
+});
+
+tape( 'the function throws an error if provided a first argument which is not an ndarray-like object having a supported data type', function test( t ) {
+ var values;
+ var i;
+
+ values = [
+ empty( [ 2, 2 ], {
+ 'dtype': 'bool'
+ })
+ ];
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ mskmax( value );
+ };
+ }
+});
+
+tape( 'the function throws an error if provided a first argument which is not an ndarray-like object having a supported data type (options)', function test( t ) {
+ var values;
+ var i;
+
+ values = [
+ empty( [ 2, 2 ], {
+ 'dtype': 'bool'
+ })
+ ];
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ mskmax( value, {} );
+ };
+ }
+});
+
+tape( 'the function throws an error if provided a second argument which is not an object', function test( t ) {
+ var values;
+ var x;
+ var i;
+
+ x = zeros( [ 2, 2 ], {
+ 'dtype': 'generic'
+ });
+
+ values = [
+ '5',
+ 5,
+ NaN,
+ true,
+ false,
+ null,
+ void 0,
+ [],
+ function noop() {}
+ ];
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ mskmax( x, value );
+ };
+ }
+});
+
+tape( 'the function throws an error if provided a `dtype` option which is not a supported data type', function test( t ) {
+ var values;
+ var x;
+ var i;
+
+ x = zeros( [ 2, 2 ], {
+ 'dtype': 'generic'
+ });
+
+ values = [
+ 'foo',
+ 'bar',
+ 'beep',
+ 'boop'
+ ];
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ mskmax( x, {
+ 'dtype': value
+ });
+ };
+ }
+});
+
+tape( 'the function throws an error if provided a `keepdims` option which is not a boolean', function test( t ) {
+ var values;
+ var x;
+ var i;
+
+ x = zeros( [ 2, 2 ], {
+ 'dtype': 'generic'
+ });
+
+ values = [
+ '5',
+ 5,
+ NaN,
+ null,
+ void 0,
+ [],
+ {},
+ function noop() {}
+ ];
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ mskmax( x, {
+ 'keepdims': value
+ });
+ };
+ }
+});
+
+tape( 'the function throws an error if provided a `dims` option which is not an array-like object of integers', function test( t ) {
+ var values;
+ var x;
+ var i;
+
+ x = zeros( [ 2, 2 ], {
+ 'dtype': 'generic'
+ });
+
+ values = [
+ '5',
+ 5,
+ NaN,
+ true,
+ false,
+ null,
+ void 0,
+ [ 'a' ],
+ {},
+ function noop() {}
+ ];
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ mskmax( x, {
+ 'dims': value
+ });
+ };
+ }
+});
+
+tape( 'the function throws an error if provided a `dims` option which contains out-of-bounds indices', function test( t ) {
+ var values;
+ var mask;
+ var x;
+ var i;
+
+ x = zeros( [ 2, 2 ], {
+ 'dtype': 'generic'
+ });
+
+ mask = zeros( [ 2, 2 ], {
+ 'dtype': 'uint8'
+ });
+
+ values = [
+ [ -10 ],
+ [ 0, 20 ],
+ [ 20 ]
+ ];
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), RangeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ mskmax( x, mask, {
+ 'dims': value
+ });
+ };
+ }
+});
+
+tape( 'the function throws an error if provided a `dims` option which contains too many indices', function test( t ) {
+ var values;
+ var mask;
+ var x;
+ var i;
+
+ x = zeros( [ 2, 2 ], {
+ 'dtype': 'generic'
+ });
+
+ mask = zeros( [ 2, 2 ], {
+ 'dtype': 'uint8'
+ });
+
+ values = [
+ [ 0, 1, 2 ],
+ [ 0, 1, 2, 3 ]
+ ];
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), RangeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ mskmax( x, mask, {
+ 'dims': value
+ });
+ };
+ }
+});
+
+tape( 'the function throws an error if provided a `dims` option which contains duplicate indices', function test( t ) {
+ var values;
+ var x;
+ var i;
+
+ x = zeros( [ 2, 2 ], {
+ 'dtype': 'generic'
+ });
+
+ values = [
+ [ 0, 0 ],
+ [ 1, 1 ],
+ [ 0, 1, 0 ],
+ [ 1, 0, 1 ]
+ ];
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), Error, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ mskmax( x, {
+ 'dims': value
+ });
+ };
+ }
+});
+
+tape( 'the function performs a reduction on an ndarray (default, row-major)', function test( t ) {
+ var expected;
+ var actual;
+ var mbuf;
+ var mask;
+ var xbuf;
+ var x;
+
+ xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
+ x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 2, 1 ], 0, 'row-major' );
+
+ mbuf = [ 0, 0, 0, 1 ];
+ mask = new ndarray( 'uint8', mbuf, [ 2, 2 ], [ 2, 1 ], 0, 'row-major' );
+
+ actual = mskmax( x, mask );
+ expected = 2.0;
+
+ t.strictEqual( isndarrayLike( actual ), true, 'returns expected value' );
+ t.strictEqual( String( getDType( actual ) ), 'generic', 'returns expected value' );
+ t.deepEqual( getShape( actual ), [], 'returns expected value' );
+ t.strictEqual( getOrder( actual ), getOrder( x ), 'returns expected value' );
+ t.strictEqual( actual.get(), expected, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function performs a reduction on an ndarray (default, column-major)', function test( t ) {
+ var expected;
+ var actual;
+ var mbuf;
+ var mask;
+ var xbuf;
+ var x;
+
+ xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
+ x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 1, 2 ], 0, 'column-major' );
+
+ mbuf = [ 0, 0, 0, 1 ];
+ mask = new ndarray( 'uint8', mbuf, [ 2, 2 ], [ 1, 2 ], 0, 'column-major' );
+
+ actual = mskmax( x, mask );
+ expected = 2.0;
+
+ t.strictEqual( isndarrayLike( actual ), true, 'returns expected value' );
+ t.strictEqual( String( getDType( actual ) ), 'generic', 'returns expected value' );
+ t.deepEqual( getShape( actual ), [], 'returns expected value' );
+ t.strictEqual( getOrder( actual ), getOrder( x ), 'returns expected value' );
+ t.strictEqual( actual.get(), expected, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function performs a reduction on an ndarray (all dimensions, row-major)', function test( t ) {
+ var expected;
+ var actual;
+ var mbuf;
+ var mask;
+ var xbuf;
+ var x;
+
+ xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
+ x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 2, 1 ], 0, 'row-major' );
+
+ mbuf = [ 0, 0, 0, 1 ];
+ mask = new ndarray( 'uint8', mbuf, [ 2, 2 ], [ 2, 1 ], 0, 'row-major' );
+
+ actual = mskmax( x, mask, {
+ 'dims': [ 0, 1 ]
+ });
+ expected = 2.0;
+
+ t.strictEqual( isndarrayLike( actual ), true, 'returns expected value' );
+ t.strictEqual( String( getDType( actual ) ), 'generic', 'returns expected value' );
+ t.deepEqual( getShape( actual ), [], 'returns expected value' );
+ t.strictEqual( getOrder( actual ), getOrder( x ), 'returns expected value' );
+ t.strictEqual( actual.get(), expected, 'returns expected value' );
+
+ xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
+ x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 2, 1 ], 0, 'row-major' );
+
+ mbuf = [ 0, 0, 0, 1 ];
+ mask = new ndarray( 'uint8', mbuf, [ 2, 2 ], [ 2, 1 ], 0, 'row-major' );
+
+ actual = mskmax( x, mask, {
+ 'dims': [ 0, 1 ],
+ 'keepdims': false
+ });
+ expected = 2.0;
+
+ t.strictEqual( isndarrayLike( actual ), true, 'returns expected value' );
+ t.strictEqual( String( getDType( actual ) ), 'generic', 'returns expected value' );
+ t.deepEqual( getShape( actual ), [], 'returns expected value' );
+ t.strictEqual( getOrder( actual ), getOrder( x ), 'returns expected value' );
+ t.strictEqual( actual.get(), expected, 'returns expected value' );
+
+ xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
+ x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 2, 1 ], 0, 'row-major' );
+
+ mbuf = [ 0, 0, 0, 1 ];
+ mask = new ndarray( 'uint8', mbuf, [ 2, 2 ], [ 2, 1 ], 0, 'row-major' );
+
+ actual = mskmax( x, mask, {
+ 'dims': [ 0, 1 ],
+ 'keepdims': true
+ });
+ expected = [ [ 2.0 ] ];
+
+ t.strictEqual( isndarrayLike( actual ), true, 'returns expected value' );
+ t.strictEqual( String( getDType( actual ) ), 'generic', 'returns expected value' );
+ t.deepEqual( getShape( actual ), [ 1, 1 ], 'returns expected value' );
+ t.strictEqual( getOrder( actual ), getOrder( x ), 'returns expected value' );
+ t.deepEqual( ndarray2array( actual ), expected, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function performs a reduction on an ndarray (all dimensions, column-major)', function test( t ) {
+ var expected;
+ var actual;
+ var mbuf;
+ var mask;
+ var xbuf;
+ var x;
+
+ xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
+ x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 1, 2 ], 0, 'column-major' );
+
+ mbuf = [ 0, 0, 0, 1 ];
+ mask = new ndarray( 'uint8', mbuf, [ 2, 2 ], [ 1, 2 ], 0, 'column-major' );
+
+ actual = mskmax( x, mask, {
+ 'dims': [ 0, 1 ]
+ });
+ expected = 2.0;
+
+ t.strictEqual( isndarrayLike( actual ), true, 'returns expected value' );
+ t.strictEqual( String( getDType( actual ) ), 'generic', 'returns expected value' );
+ t.deepEqual( getShape( actual ), [], 'returns expected value' );
+ t.strictEqual( getOrder( actual ), getOrder( x ), 'returns expected value' );
+ t.strictEqual( actual.get(), expected, 'returns expected value' );
+
+ xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
+ x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 1, 2 ], 0, 'column-major' );
+
+ mbuf = [ 0, 0, 0, 1 ];
+ mask = new ndarray( 'uint8', mbuf, [ 2, 2 ], [ 1, 2 ], 0, 'column-major' );
+
+ actual = mskmax( x, mask, {
+ 'dims': [ 0, 1 ],
+ 'keepdims': false
+ });
+ expected = 2.0;
+
+ t.strictEqual( isndarrayLike( actual ), true, 'returns expected value' );
+ t.strictEqual( String( getDType( actual ) ), 'generic', 'returns expected value' );
+ t.deepEqual( getShape( actual ), [], 'returns expected value' );
+ t.strictEqual( getOrder( actual ), getOrder( x ), 'returns expected value' );
+ t.strictEqual( actual.get(), expected, 'returns expected value' );
+
+ xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
+ x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 1, 2 ], 0, 'column-major' );
+
+ mbuf = [ 0, 0, 0, 1 ];
+ mask = new ndarray( 'uint8', mbuf, [ 2, 2 ], [ 1, 2 ], 0, 'column-major' );
+
+ actual = mskmax( x, mask, {
+ 'dims': [ 0, 1 ],
+ 'keepdims': true
+ });
+ expected = [ [ 2.0 ] ];
+
+ t.strictEqual( isndarrayLike( actual ), true, 'returns expected value' );
+ t.strictEqual( String( getDType( actual ) ), 'generic', 'returns expected value' );
+ t.deepEqual( getShape( actual ), [ 1, 1 ], 'returns expected value' );
+ t.strictEqual( getOrder( actual ), getOrder( x ), 'returns expected value' );
+ t.deepEqual( ndarray2array( actual ), expected, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function performs a reduction on an ndarray (no dimensions, row-major)', function test( t ) {
+ var actual;
+ var xbuf;
+ var mbuf;
+ var mask;
+ var arr;
+ var x;
+
+ xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
+ x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 2, 1 ], 0, 'row-major' );
+
+ mbuf = [ 0, 0, 0, 1 ];
+ mask = new ndarray( 'uint8', mbuf, [ 2, 2 ], [ 2, 1 ], 0, 'row-major' );
+
+ actual = mskmax( x, mask, {
+ 'dims': [],
+ 'keepdims': false
+ });
+
+ t.strictEqual( isndarrayLike( actual ), true, 'returns expected value' );
+ t.strictEqual( String( getDType( actual ) ), 'generic', 'returns expected value' );
+ t.deepEqual( getShape( actual ), [ 2, 2 ], 'returns expected value' );
+ t.strictEqual( getOrder( actual ), getOrder( x ), 'returns expected value' );
+ arr = ndarray2array( actual );
+ t.strictEqual( arr[0][0], -1.0, 'returns expected value' );
+ t.strictEqual( arr[0][1], 2.0, 'returns expected value' );
+ t.strictEqual( arr[1][0], -3.0, 'returns expected value' );
+ t.ok( arr[1][1] !== arr[1][1], 'returns NaN for masked value' );
+
+ xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
+ x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 2, 1 ], 0, 'row-major' );
+
+ mbuf = [ 0, 0, 0, 1 ];
+ mask = new ndarray( 'uint8', mbuf, [ 2, 2 ], [ 2, 1 ], 0, 'row-major' );
+
+ actual = mskmax( x, mask, {
+ 'dims': [],
+ 'keepdims': true
+ });
+
+ t.strictEqual( isndarrayLike( actual ), true, 'returns expected value' );
+ t.strictEqual( String( getDType( actual ) ), 'generic', 'returns expected value' );
+ t.deepEqual( getShape( actual ), [ 2, 2 ], 'returns expected value' );
+ t.strictEqual( getOrder( actual ), getOrder( x ), 'returns expected value' );
+ arr = ndarray2array( actual );
+ t.strictEqual( arr[0][0], -1.0, 'returns expected value' );
+ t.strictEqual( arr[0][1], 2.0, 'returns expected value' );
+ t.strictEqual( arr[1][0], -3.0, 'returns expected value' );
+ t.ok( arr[1][1] !== arr[1][1], 'returns NaN for masked value' );
+
+ t.end();
+});
+
+tape( 'the function performs a reduction on an ndarray (no dimensions, column-major)', function test( t ) {
+ var actual;
+ var xbuf;
+ var mbuf;
+ var mask;
+ var arr;
+ var x;
+
+ xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
+ x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 1, 2 ], 0, 'column-major' );
+
+ mbuf = [ 0, 0, 0, 1 ];
+ mask = new ndarray( 'uint8', mbuf, [ 2, 2 ], [ 1, 2 ], 0, 'column-major' );
+
+ actual = mskmax( x, mask, {
+ 'dims': [],
+ 'keepdims': false
+ });
+
+ t.strictEqual( isndarrayLike( actual ), true, 'returns expected value' );
+ t.strictEqual( String( getDType( actual ) ), 'generic', 'returns expected value' );
+ t.deepEqual( getShape( actual ), [ 2, 2 ], 'returns expected value' );
+ t.strictEqual( getOrder( actual ), getOrder( x ), 'returns expected value' );
+ arr = ndarray2array( actual );
+ t.strictEqual( arr[0][0], -1.0, 'returns expected value' );
+ t.strictEqual( arr[0][1], -3.0, 'returns expected value' );
+ t.strictEqual( arr[1][0], 2.0, 'returns expected value' );
+ t.ok( arr[1][1] !== arr[1][1], 'returns NaN for masked value' );
+
+ xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
+ x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 1, 2 ], 0, 'column-major' );
+
+ mbuf = [ 0, 0, 0, 1 ];
+ mask = new ndarray( 'uint8', mbuf, [ 2, 2 ], [ 1, 2 ], 0, 'column-major' );
+
+ actual = mskmax( x, mask, {
+ 'dims': [],
+ 'keepdims': true
+ });
+
+ t.strictEqual( isndarrayLike( actual ), true, 'returns expected value' );
+ t.strictEqual( String( getDType( actual ) ), 'generic', 'returns expected value' );
+ t.deepEqual( getShape( actual ), [ 2, 2 ], 'returns expected value' );
+ t.strictEqual( getOrder( actual ), getOrder( x ), 'returns expected value' );
+ arr = ndarray2array( actual );
+ t.strictEqual( arr[0][0], -1.0, 'returns expected value' );
+ t.strictEqual( arr[0][1], -3.0, 'returns expected value' );
+ t.strictEqual( arr[1][0], 2.0, 'returns expected value' );
+ t.ok( arr[1][1] !== arr[1][1], 'returns NaN for masked value' );
+
+ t.end();
+});
+
+tape( 'the function supports specifying reduction dimensions (row-major)', function test( t ) {
+ var expected;
+ var actual;
+ var xbuf;
+ var mbuf;
+ var mask;
+ var arr;
+ var x;
+
+ xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
+ x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 2, 1 ], 0, 'row-major' );
+
+ mbuf = [ 0, 0, 0, 1 ];
+ mask = new ndarray( 'uint8', mbuf, [ 2, 2 ], [ 2, 1 ], 0, 'row-major' );
+
+ actual = mskmax( x, mask, {
+ 'dims': [],
+ 'keepdims': false
+ });
+
+ t.strictEqual( isndarrayLike( actual ), true, 'returns expected value' );
+ t.strictEqual( String( getDType( actual ) ), 'generic', 'returns expected value' );
+ t.deepEqual( getShape( actual ), [ 2, 2 ], 'returns expected value' );
+ t.strictEqual( getOrder( actual ), getOrder( x ), 'returns expected value' );
+ arr = ndarray2array( actual );
+ t.strictEqual( arr[0][0], -1.0, 'returns expected value' );
+ t.strictEqual( arr[0][1], 2.0, 'returns expected value' );
+ t.strictEqual( arr[1][0], -3.0, 'returns expected value' );
+ t.ok( arr[1][1] !== arr[1][1], 'returns NaN for masked value' );
+
+ xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
+ x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 2, 1 ], 0, 'row-major' );
+
+ mbuf = [ 0, 0, 0, 1 ];
+ mask = new ndarray( 'uint8', mbuf, [ 2, 2 ], [ 2, 1 ], 0, 'row-major' );
+
+ actual = mskmax( x, mask, {
+ 'dims': [ 0 ],
+ 'keepdims': true
+ });
+ expected = [ [ -1.0, 2.0 ] ];
+
+ t.strictEqual( isndarrayLike( actual ), true, 'returns expected value' );
+ t.strictEqual( String( getDType( actual ) ), 'generic', 'returns expected value' );
+ t.deepEqual( getShape( actual ), [ 1, 2 ], 'returns expected value' );
+ t.strictEqual( getOrder( actual ), getOrder( x ), 'returns expected value' );
+ t.deepEqual( ndarray2array( actual ), expected, 'returns expected value' );
+
+ xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
+ x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 2, 1 ], 0, 'row-major' );
+
+ mbuf = [ 0, 0, 0, 1 ];
+ mask = new ndarray( 'uint8', mbuf, [ 2, 2 ], [ 2, 1 ], 0, 'row-major' );
+
+ actual = mskmax( x, mask, {
+ 'dims': [ 1 ],
+ 'keepdims': false
+ });
+ expected = [ 2.0, -3.0 ];
+
+ t.strictEqual( isndarrayLike( actual ), true, 'returns expected value' );
+ t.strictEqual( String( getDType( actual ) ), 'generic', 'returns expected value' );
+ t.deepEqual( getShape( actual ), [ 2 ], 'returns expected value' );
+ t.strictEqual( getOrder( actual ), getOrder( x ), 'returns expected value' );
+ t.deepEqual( ndarray2array( actual ), expected, 'returns expected value' );
+
+ xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
+ x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 2, 1 ], 0, 'row-major' );
+
+ mbuf = [ 0, 0, 0, 1 ];
+ mask = new ndarray( 'uint8', mbuf, [ 2, 2 ], [ 2, 1 ], 0, 'row-major' );
+
+ actual = mskmax( x, mask, {
+ 'dims': [],
+ 'keepdims': true
+ });
+
+ t.strictEqual( isndarrayLike( actual ), true, 'returns expected value' );
+ t.strictEqual( String( getDType( actual ) ), 'generic', 'returns expected value' );
+ t.deepEqual( getShape( actual ), [ 2, 2 ], 'returns expected value' );
+ t.strictEqual( getOrder( actual ), getOrder( x ), 'returns expected value' );
+ arr = ndarray2array( actual );
+ t.strictEqual( arr[0][0], -1.0, 'returns expected value' );
+ t.strictEqual( arr[0][1], 2.0, 'returns expected value' );
+ t.strictEqual( arr[1][0], -3.0, 'returns expected value' );
+ t.ok( arr[1][1] !== arr[1][1], 'returns NaN for masked value' );
+
+ t.end();
+});
+
+tape( 'the function supports specifying reduction dimensions (column-major)', function test( t ) {
+ var expected;
+ var actual;
+ var xbuf;
+ var mbuf;
+ var mask;
+ var arr;
+ var x;
+
+ xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
+ x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 1, 2 ], 0, 'column-major' );
+
+ mbuf = [ 0, 0, 0, 1 ];
+ mask = new ndarray( 'uint8', mbuf, [ 2, 2 ], [ 1, 2 ], 0, 'column-major' );
+
+ actual = mskmax( x, mask, {
+ 'dims': [ 0 ],
+ 'keepdims': false
+ });
+ expected = [ 2.0, -3.0 ];
+
+ t.strictEqual( isndarrayLike( actual ), true, 'returns expected value' );
+ t.strictEqual( String( getDType( actual ) ), 'generic', 'returns expected value' );
+ t.deepEqual( getShape( actual ), [ 2 ], 'returns expected value' );
+ t.strictEqual( getOrder( actual ), getOrder( x ), 'returns expected value' );
+ t.deepEqual( ndarray2array( actual ), expected, 'returns expected value' );
+
+ xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
+ x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 1, 2 ], 0, 'column-major' );
+
+ mbuf = [ 0, 0, 0, 1 ];
+ mask = new ndarray( 'uint8', mbuf, [ 2, 2 ], [ 1, 2 ], 0, 'column-major' );
+
+ actual = mskmax( x, mask, {
+ 'dims': [ 0 ],
+ 'keepdims': true
+ });
+ expected = [ [ 2.0, -3.0 ] ];
+
+ t.strictEqual( isndarrayLike( actual ), true, 'returns expected value' );
+ t.strictEqual( String( getDType( actual ) ), 'generic', 'returns expected value' );
+ t.deepEqual( getShape( actual ), [ 1, 2 ], 'returns expected value' );
+ t.strictEqual( getOrder( actual ), getOrder( x ), 'returns expected value' );
+ t.deepEqual( ndarray2array( actual ), expected, 'returns expected value' );
+
+ xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
+ x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 1, 2 ], 0, 'column-major' );
+
+ mbuf = [ 0, 0, 0, 1 ];
+ mask = new ndarray( 'uint8', mbuf, [ 2, 2 ], [ 1, 2 ], 0, 'column-major' );
+
+ actual = mskmax( x, mask, {
+ 'dims': [],
+ 'keepdims': false
+ });
+
+ t.strictEqual( isndarrayLike( actual ), true, 'returns expected value' );
+ t.strictEqual( String( getDType( actual ) ), 'generic', 'returns expected value' );
+ t.deepEqual( getShape( actual ), [ 2, 2 ], 'returns expected value' );
+ t.strictEqual( getOrder( actual ), getOrder( x ), 'returns expected value' );
+ arr = ndarray2array( actual );
+ t.strictEqual( arr[0][0], -1.0, 'returns expected value' );
+ t.strictEqual( arr[0][1], -3.0, 'returns expected value' );
+ t.strictEqual( arr[1][0], 2.0, 'returns expected value' );
+ t.ok( arr[1][1] !== arr[1][1], 'returns NaN for masked value' );
+
+ xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
+ x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 1, 2 ], 0, 'column-major' );
+
+ mbuf = [ 0, 0, 0, 1 ];
+ mask = new ndarray( 'uint8', mbuf, [ 2, 2 ], [ 1, 2 ], 0, 'column-major' );
+
+ actual = mskmax( x, mask, {
+ 'dims': [],
+ 'keepdims': true
+ });
+
+ t.strictEqual( isndarrayLike( actual ), true, 'returns expected value' );
+ t.strictEqual( String( getDType( actual ) ), 'generic', 'returns expected value' );
+ t.deepEqual( getShape( actual ), [ 2, 2 ], 'returns expected value' );
+ t.strictEqual( getOrder( actual ), getOrder( x ), 'returns expected value' );
+ arr = ndarray2array( actual );
+ t.strictEqual( arr[0][0], -1.0, 'returns expected value' );
+ t.strictEqual( arr[0][1], -3.0, 'returns expected value' );
+ t.strictEqual( arr[1][0], 2.0, 'returns expected value' );
+ t.ok( arr[1][1] !== arr[1][1], 'returns NaN for masked value' );
+
+ t.end();
+});
+
+tape( 'the function supports specifying the output array data type', function test( t ) {
+ var expected;
+ var actual;
+ var mbuf;
+ var mask;
+ var xbuf;
+ var x;
+
+ xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
+ x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 2, 1 ], 0, 'row-major' );
+
+ mbuf = [ 0, 0, 0, 1 ];
+ mask = new ndarray( 'uint8', mbuf, [ 2, 2 ], [ 2, 1 ], 0, 'row-major' );
+
+ actual = mskmax( x, mask, {
+ 'dtype': 'float64'
+ });
+ expected = 2.0;
+
+ t.strictEqual( isndarrayLike( actual ), true, 'returns expected value' );
+ t.strictEqual( String( getDType( actual ) ), 'float64', 'returns expected value' );
+ t.deepEqual( getShape( actual ), [], 'returns expected value' );
+ t.strictEqual( getOrder( actual ), getOrder( x ), 'returns expected value' );
+ t.strictEqual( actual.get(), expected, 'returns expected value' );
+
+ xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
+ x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 1, 2 ], 0, 'column-major' );
+
+ mbuf = [ 0, 0, 0, 1 ];
+ mask = new ndarray( 'uint8', mbuf, [ 2, 2 ], [ 1, 2 ], 0, 'column-major' );
+
+ actual = mskmax( x, mask, {
+ 'dtype': 'float64'
+ });
+ expected = 2.0;
+
+ t.strictEqual( isndarrayLike( actual ), true, 'returns expected value' );
+ t.strictEqual( String( getDType( actual ) ), 'float64', 'returns expected value' );
+ t.deepEqual( getShape( actual ), [], 'returns expected value' );
+ t.strictEqual( getOrder( actual ), getOrder( x ), 'returns expected value' );
+ t.strictEqual( actual.get(), expected, 'returns expected value' );
+
+ t.end();
+});