diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/dcovarmtk/README.md b/lib/node_modules/@stdlib/stats/base/ndarray/dcovarmtk/README.md
new file mode 100644
index 000000000000..f9756931994e
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/base/ndarray/dcovarmtk/README.md
@@ -0,0 +1,216 @@
+
+
+# dcovarmtk
+
+> Calculate the [covariance][covariance] of two one-dimensional double-precision floating-point ndarrays provided known means and using a one-pass textbook algorithm.
+
+
+
+The population [covariance][covariance] of two finite size populations of size `N` is given by
+
+
+
+```math
+\mathop{\mathrm{cov_N}} = \frac{1}{N} \sum_{i=0}^{N-1} (x_i - \mu_x)(y_i - \mu_y)
+```
+
+
+
+where the population means are given by
+
+
+
+```math
+\mu_x = \frac{1}{N} \sum_{i=0}^{N-1} x_i
+```
+
+
+
+and
+
+
+
+```math
+\mu_y = \frac{1}{N} \sum_{i=0}^{N-1} y_i
+```
+
+
+
+Often in the analysis of data, the true population [covariance][covariance] is not known _a priori_ and must be estimated from samples drawn from population distributions. If one attempts to use the formula for the population [covariance][covariance], the result is biased and yields a **biased sample covariance**. To compute an **unbiased sample covariance** for samples of size `n`,
+
+
+
+```math
+\mathop{\mathrm{cov_n}} = \frac{1}{n-1} \sum_{i=0}^{n-1} (x_i - \bar{x}_n)(y_i - \bar{y}_n)
+```
+
+
+
+where sample means are given by
+
+
+
+```math
+\bar{x} = \frac{1}{n} \sum_{i=0}^{n-1} x_i
+```
+
+
+
+and
+
+
+
+```math
+\bar{y} = \frac{1}{n} \sum_{i=0}^{n-1} y_i
+```
+
+
+
+The use of the term `n-1` is commonly referred to as Bessel's correction. Depending on the characteristics of the population distributions, other correction factors (e.g., `n-1.5`, `n+1`, etc) can yield better estimators.
+
+
+
+
+
+
+
+## Usage
+
+```javascript
+var dcovarmtk = require( '@stdlib/stats/base/ndarray/dcovarmtk' );
+```
+
+#### dcovarmtk( arrays )
+
+Computes the covariance of two one-dimensional double-precision floating-point ndarrays provided known means and using a one-pass textbook algorithm.
+
+```javascript
+var Float64Array = require( '@stdlib/array/float64' );
+var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
+var ndarray = require( '@stdlib/ndarray/base/ctor' );
+
+var opts = {
+ 'dtype': 'float64'
+};
+
+var xbuf = new Float64Array( [ 1.0, -2.0, 2.0 ] );
+var x = new ndarray( opts.dtype, xbuf, [ 3 ], [ 1 ], 0, 'row-major' );
+
+var ybuf = new Float64Array( [ 2.0, -2.0, 1.0 ] );
+var y = new ndarray( opts.dtype, ybuf, [ 3 ], [ 1 ], 0, 'row-major' );
+
+var correction = scalar2ndarray( 1.0, opts );
+var meanx = scalar2ndarray( 1.0/3.0, opts );
+var meany = scalar2ndarray( 1.0/3.0, opts );
+
+var v = dcovarmtk( [ x, y, correction, meanx, meany ] );
+// returns ~3.8333
+```
+
+The function has the following parameters:
+
+- **arrays**: array-like object containing the following ndarrays in order:
+
+ 1. first one-dimensional input ndarray.
+ 2. second one-dimensional input ndarray.
+ 3. a zero-dimensional ndarray specifying the degrees of freedom adjustment. Setting this parameter to a value other than `0` has the effect of adjusting the divisor during the calculation of the [covariance][covariance] according to `N-c` where `c` corresponds to the provided degrees of freedom adjustment and `N` corresponds to the number of elements in each input ndarray. When computing the population [covariance][covariance], setting this parameter to `0` is the standard choice (i.e., the provided arrays contain data constituting entire populations). When computing the unbiased sample [covariance][covariance], setting this parameter to `1` is the standard choice (i.e., the provided arrays contain data sampled from larger populations; this is commonly referred to as Bessel's correction).
+ 4. a zero-dimensional ndarray specifying the mean of the first one-dimensional ndarray.
+ 5. a zero-dimensional ndarray specifying the mean of the second one-dimensional ndarray.
+
+
+
+
+
+
+
+## Notes
+
+- Both input ndarrays should have the same number of elements.
+- If provided empty one-dimensional ndarrays, the function returns `NaN`.
+
+
+
+
+
+
+
+## Examples
+
+
+
+```javascript
+var discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
+var ndarray = require( '@stdlib/ndarray/base/ctor' );
+var ndarray2array = require( '@stdlib/ndarray/to-array' );
+var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
+var dcovarmtk = require( '@stdlib/stats/base/ndarray/dcovarmtk' );
+
+// Define array options:
+var opts = {
+ 'dtype': 'float64'
+};
+
+// Create one-dimensional ndarrays containing pseudorandom numbers:
+var xbuf = discreteUniform( 10, -50, 50, opts );
+var x = new ndarray( opts.dtype, xbuf, [ xbuf.length ], [ 1 ], 0, 'row-major' );
+console.log( ndarray2array( x ) );
+
+var ybuf = discreteUniform( 10, -50, 50, opts );
+var y = new ndarray( opts.dtype, ybuf, [ ybuf.length ], [ 1 ], 0, 'row-major' );
+console.log( ndarray2array( y ) );
+
+// Specify the degrees of freedom adjustment:
+var correction = scalar2ndarray( 1.0, opts );
+
+// Specify the known means:
+var meanx = scalar2ndarray( 0.0, opts );
+var meany = scalar2ndarray( 0.0, opts );
+
+// Calculate the sample covariance:
+var v = dcovarmtk( [ x, y, correction, meanx, meany ] );
+console.log( v );
+```
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+[covariance]: https://en.wikipedia.org/wiki/Covariance
+
+
+
+
diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/dcovarmtk/benchmark/benchmark.js b/lib/node_modules/@stdlib/stats/base/ndarray/dcovarmtk/benchmark/benchmark.js
new file mode 100644
index 000000000000..b73104a7edfa
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/base/ndarray/dcovarmtk/benchmark/benchmark.js
@@ -0,0 +1,115 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+// MODULES //
+
+var bench = require( '@stdlib/bench' );
+var uniform = require( '@stdlib/random/array/uniform' );
+var isnan = require( '@stdlib/math/base/assert/is-nan' );
+var pow = require( '@stdlib/math/base/special/pow' );
+var ndarray = require( '@stdlib/ndarray/base/ctor' );
+var scalar2ndarray = require( '@stdlib/ndarray/base/from-scalar' );
+var pkg = require( './../package.json' ).name;
+var dcovarmtk = require( './../lib' );
+
+
+// VARIABLES //
+
+var options = {
+ 'dtype': 'float64'
+};
+
+
+// FUNCTIONS //
+
+/**
+* Creates a benchmark function.
+*
+* @private
+* @param {PositiveInteger} len - array length
+* @returns {Function} benchmark function
+*/
+function createBenchmark( len ) {
+ var correction;
+ var meanx;
+ var meany;
+ var xbuf;
+ var ybuf;
+ var x;
+ var y;
+
+ xbuf = uniform( len, -10.0, 10.0, options );
+ x = new ndarray( options.dtype, xbuf, [ len ], [ 1 ], 0, 'row-major' );
+
+ ybuf = uniform( len, -10.0, 10.0, options );
+ y = new ndarray( options.dtype, ybuf, [ len ], [ 1 ], 0, 'row-major' );
+
+ correction = scalar2ndarray( 1.0, options.dtype, 'row-major' );
+ meanx = scalar2ndarray( 0.0, options.dtype, 'row-major' );
+ meany = scalar2ndarray( 0.0, options.dtype, 'row-major' );
+
+ return benchmark;
+
+ function benchmark( b ) {
+ var v;
+ var i;
+
+ b.tic();
+ for ( i = 0; i < b.iterations; i++ ) {
+ v = dcovarmtk( [ x, y, correction, meanx, meany ] );
+ if ( isnan( v ) ) {
+ b.fail( 'should not return NaN' );
+ }
+ }
+ b.toc();
+ if ( isnan( v ) ) {
+ b.fail( 'should not return NaN' );
+ }
+ b.pass( 'benchmark finished' );
+ b.end();
+ }
+}
+
+
+// MAIN //
+
+/**
+* Main execution sequence.
+*
+* @private
+*/
+function main() {
+ var len;
+ var min;
+ var max;
+ var f;
+ var i;
+
+ min = 1; // 10^min
+ max = 6; // 10^max
+
+ for ( i = min; i <= max; i++ ) {
+ len = pow( 10, i );
+ f = createBenchmark( len );
+ bench( pkg+':len='+len, f );
+ }
+}
+
+main();
diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/dcovarmtk/docs/repl.txt b/lib/node_modules/@stdlib/stats/base/ndarray/dcovarmtk/docs/repl.txt
new file mode 100644
index 000000000000..8ce72f1b7055
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/base/ndarray/dcovarmtk/docs/repl.txt
@@ -0,0 +1,66 @@
+
+{{alias}}( arrays )
+ Computes the covariance of two one-dimensional double-precision floating-
+ point ndarrays provided known means and using a one-pass textbook algorithm.
+
+ Both input ndarrays should have the same number of elements.
+
+ If provided empty one-dimensional ndarrays, the function returns `NaN`.
+
+ Parameters
+ ----------
+ arrays: ArrayLikeObject
+ The function expects the following ndarrays in order:
+
+ - first one-dimensional input ndarray.
+ - second one-dimensional input ndarray.
+ - a zero-dimensional ndarray specifying the degrees of freedom
+ adjustment. Setting this parameter to a value other than `0` has the
+ effect of adjusting the divisor during the calculation of the
+ covariance according to `N-c` where `c` corresponds to the provided
+ degrees of freedom adjustment and `N` corresponds to the number of
+ elements in each input ndarray. When computing the population
+ covariance, setting this parameter to `0` is the standard choice (i.e.,
+ the provided arrays contain data constituting entire populations). When
+ computing the unbiased sample covariance, setting this parameter to `1`
+ is the standard choice (i.e., the provided arrays contain data sampled
+ from larger populations; this is commonly referred to as Bessel's
+ correction).
+ - a zero-dimensional ndarray specifying the mean of the first one-
+ dimensional ndarray.
+ - a zero-dimensional ndarray specifying the mean of the second one-
+ dimensional ndarray.
+
+ Returns
+ -------
+ out: number
+ The covariance.
+
+ Examples
+ --------
+ // Create the input ndarrays:
+ > var xbuf = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 2.0 ] );
+ > var ybuf = new {{alias:@stdlib/array/float64}}( [ 2.0, -2.0, 1.0 ] );
+ > var dt = 'float64';
+ > var sh = [ xbuf.length ];
+ > var st = [ 1 ];
+ > var oo = 0;
+ > var ord = 'row-major';
+ > var x = new {{alias:@stdlib/ndarray/ctor}}( dt, xbuf, sh, st, oo, ord );
+ > var y = new {{alias:@stdlib/ndarray/ctor}}( dt, ybuf, sh, st, oo, ord );
+
+ // Specify the degrees of freedom adjustment:
+ > var opts = { 'dtype': dt };
+ > var correction = new {{alias:@stdlib/ndarray/from-scalar}}( 1.0, opts );
+
+ // Specify the known means:
+ > var meanx = new {{alias:@stdlib/ndarray/from-scalar}}( 1.0/3.0, opts );
+ > var meany = new {{alias:@stdlib/ndarray/from-scalar}}( 1.0/3.0, opts );
+
+ // Calculate the sample covariance:
+ > {{alias}}( [ x, y, correction, meanx, meany ] )
+ ~3.8333
+
+ See Also
+ --------
+
diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/dcovarmtk/docs/types/index.d.ts b/lib/node_modules/@stdlib/stats/base/ndarray/dcovarmtk/docs/types/index.d.ts
new file mode 100644
index 000000000000..eeadddfde502
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/base/ndarray/dcovarmtk/docs/types/index.d.ts
@@ -0,0 +1,68 @@
+/*
+* @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 { float64ndarray } from '@stdlib/types/ndarray';
+
+/**
+* Computes the covariance of two one-dimensional double-precision floating-point ndarrays provided known means and using a one-pass textbook algorithm.
+*
+* ## Notes
+*
+* - The function expects the following ndarrays in order:
+*
+* - first one-dimensional input ndarray.
+* - second one-dimensional input ndarray.
+* - a zero-dimensional ndarray specifying the degrees of freedom adjustment. Setting this parameter to a value other than `0` has the effect of adjusting the divisor during the calculation of the covariance according to `N-c` where `c` corresponds to the provided degrees of freedom adjustment and `N` corresponds to the number of elements in each input ndarray. When computing the population covariance, setting this parameter to `0` is the standard choice (i.e., the provided arrays contain data constituting entire populations). When computing the unbiased sample covariance, setting this parameter to `1` is the standard choice (i.e., the provided arrays contain data sampled from larger populations; this is commonly referred to as Bessel's correction).
+* - a zero-dimensional ndarray specifying the mean of the first one-dimensional ndarray.
+* - a zero-dimensional ndarray specifying the mean of the second one-dimensional ndarray.
+*
+* @param arrays - array-like object containing input ndarrays
+* @returns covariance
+*
+* @example
+* var Float64Array = require( '@stdlib/array/float64' );
+* var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
+* var ndarray = require( '@stdlib/ndarray/base/ctor' );
+*
+* var opts = {
+* 'dtype': 'float64'
+* };
+*
+* var xbuf = new Float64Array( [ 1.0, -2.0, 2.0 ] );
+* var x = new ndarray( opts.dtype, xbuf, [ 3 ], [ 1 ], 0, 'row-major' );
+*
+* var ybuf = new Float64Array( [ 2.0, -2.0, 1.0 ] );
+* var y = new ndarray( opts.dtype, ybuf, [ 3 ], [ 1 ], 0, 'row-major' );
+*
+* var correction = scalar2ndarray( 1.0, opts );
+* var meanx = scalar2ndarray( 1.0/3.0, opts );
+* var meany = scalar2ndarray( 1.0/3.0, opts );
+*
+* var v = dcovarmtk( [ x, y, correction, meanx, meany ] );
+* // returns ~3.8333
+*/
+declare function dcovarmtk( arrays: [ float64ndarray, float64ndarray, float64ndarray, float64ndarray, float64ndarray ] ): number;
+
+
+// EXPORTS //
+
+export = dcovarmtk;
diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/dcovarmtk/docs/types/test.ts b/lib/node_modules/@stdlib/stats/base/ndarray/dcovarmtk/docs/types/test.ts
new file mode 100644
index 000000000000..89d0b12b302f
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/base/ndarray/dcovarmtk/docs/types/test.ts
@@ -0,0 +1,57 @@
+/*
+* @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 dcovarmtk = require( './index' );
+
+
+// TESTS //
+
+// The function returns a number...
+{
+ const x = zeros( [ 10 ], {
+ 'dtype': 'float64'
+ });
+
+ dcovarmtk( [ x, x, x, x, x ] ); // $ExpectType number
+}
+
+// The compiler throws an error if the function is provided a first argument which is not an array of ndarrays...
+{
+ dcovarmtk( '10' ); // $ExpectError
+ dcovarmtk( 10 ); // $ExpectError
+ dcovarmtk( true ); // $ExpectError
+ dcovarmtk( false ); // $ExpectError
+ dcovarmtk( null ); // $ExpectError
+ dcovarmtk( undefined ); // $ExpectError
+ dcovarmtk( [] ); // $ExpectError
+ dcovarmtk( {} ); // $ExpectError
+ dcovarmtk( ( x: number ): number => x ); // $ExpectError
+}
+
+// The compiler throws an error if the function is provided an unsupported number of arguments...
+{
+ const x = zeros( [ 10 ], {
+ 'dtype': 'float64'
+ });
+
+ dcovarmtk(); // $ExpectError
+ dcovarmtk( [ x, x, x, x, x ], {} ); // $ExpectError
+}
diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/dcovarmtk/examples/index.js b/lib/node_modules/@stdlib/stats/base/ndarray/dcovarmtk/examples/index.js
new file mode 100644
index 000000000000..c06e5bc0572c
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/base/ndarray/dcovarmtk/examples/index.js
@@ -0,0 +1,50 @@
+/**
+* @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 ndarray = require( '@stdlib/ndarray/base/ctor' );
+var ndarray2array = require( '@stdlib/ndarray/to-array' );
+var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
+var dcovarmtk = require( './../lib' );
+
+// Define array options:
+var opts = {
+ 'dtype': 'float64'
+};
+
+// Create one-dimensional ndarrays containing pseudorandom numbers:
+var xbuf = discreteUniform( 10, -50, 50, opts );
+var x = new ndarray( opts.dtype, xbuf, [ xbuf.length ], [ 1 ], 0, 'row-major' );
+console.log( ndarray2array( x ) );
+
+var ybuf = discreteUniform( 10, -50, 50, opts );
+var y = new ndarray( opts.dtype, ybuf, [ ybuf.length ], [ 1 ], 0, 'row-major' );
+console.log( ndarray2array( y ) );
+
+// Specify the degrees of freedom adjustment:
+var correction = scalar2ndarray( 1.0, opts );
+
+// Specify the known means:
+var meanx = scalar2ndarray( 0.0, opts );
+var meany = scalar2ndarray( 0.0, opts );
+
+// Calculate the sample covariance:
+var v = dcovarmtk( [ x, y, correction, meanx, meany ] );
+console.log( v );
diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/dcovarmtk/lib/index.js b/lib/node_modules/@stdlib/stats/base/ndarray/dcovarmtk/lib/index.js
new file mode 100644
index 000000000000..2d68c5efe6bf
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/base/ndarray/dcovarmtk/lib/index.js
@@ -0,0 +1,57 @@
+/**
+* @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 covariance of two one-dimensional double-precision floating-point ndarrays provided known means and using a one-pass textbook algorithm.
+*
+* @module @stdlib/stats/base/ndarray/dcovarmtk
+*
+* @example
+* var Float64Array = require( '@stdlib/array/float64' );
+* var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
+* var ndarray = require( '@stdlib/ndarray/base/ctor' );
+* var dcovarmtk = require( '@stdlib/stats/base/ndarray/dcovarmtk' );
+*
+* var opts = {
+* 'dtype': 'float64'
+* };
+*
+* var xbuf = new Float64Array( [ 1.0, -2.0, 2.0 ] );
+* var x = new ndarray( opts.dtype, xbuf, [ 3 ], [ 1 ], 0, 'row-major' );
+*
+* var ybuf = new Float64Array( [ 2.0, -2.0, 1.0 ] );
+* var y = new ndarray( opts.dtype, ybuf, [ 3 ], [ 1 ], 0, 'row-major' );
+*
+* var correction = scalar2ndarray( 1.0, opts );
+* var meanx = scalar2ndarray( 1.0/3.0, opts );
+* var meany = scalar2ndarray( 1.0/3.0, opts );
+*
+* var v = dcovarmtk( [ x, y, correction, meanx, meany ] );
+* // returns ~3.8333
+*/
+
+// MODULES //
+
+var main = require( './main.js' );
+
+
+// EXPORTS //
+
+module.exports = main;
diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/dcovarmtk/lib/main.js b/lib/node_modules/@stdlib/stats/base/ndarray/dcovarmtk/lib/main.js
new file mode 100644
index 000000000000..66f89996c544
--- /dev/null
+++ b/lib/node_modules/@stdlib/stats/base/ndarray/dcovarmtk/lib/main.js
@@ -0,0 +1,91 @@
+/**
+* @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 numelDimension = require( '@stdlib/ndarray/base/numel-dimension' );
+var getStride = require( '@stdlib/ndarray/base/stride' );
+var getOffset = require( '@stdlib/ndarray/base/offset' );
+var getData = require( '@stdlib/ndarray/base/data-buffer' );
+var ndarraylike2scalar = require( '@stdlib/ndarray/base/ndarraylike2scalar' );
+var strided = require( '@stdlib/stats/strided/dcovarmtk' ).ndarray;
+
+
+// MAIN //
+
+/**
+* Computes the covariance of two one-dimensional double-precision floating-point ndarrays provided known means and using a one-pass textbook algorithm.
+*
+* ## Notes
+*
+* - The function expects the following ndarrays in order:
+*
+* - first one-dimensional input ndarray.
+* - second one-dimensional input ndarray.
+* - a zero-dimensional ndarray specifying the degrees of freedom adjustment. Setting this parameter to a value other than `0` has the effect of adjusting the divisor during the calculation of the covariance according to `N-c` where `c` corresponds to the provided degrees of freedom adjustment and `N` corresponds to the number of elements in each input ndarray. When computing the population covariance, setting this parameter to `0` is the standard choice (i.e., the provided arrays contain data constituting entire populations). When computing the unbiased sample covariance, setting this parameter to `1` is the standard choice (i.e., the provided arrays contain data sampled from larger populations; this is commonly referred to as Bessel's correction).
+* - a zero-dimensional ndarray specifying the mean of the first one-dimensional ndarray.
+* - a zero-dimensional ndarray specifying the mean of the second one-dimensional ndarray.
+*
+* @param {ArrayLikeObject