diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancewd/README.md b/lib/node_modules/@stdlib/stats/base/nanvariancewd/README.md
index 35df01d49a2b..1ae7c8e30d24 100644
--- a/lib/node_modules/@stdlib/stats/base/nanvariancewd/README.md
+++ b/lib/node_modules/@stdlib/stats/base/nanvariancewd/README.md
@@ -98,7 +98,7 @@ The use of the term `n-1` is commonly referred to as Bessel's correction. Note,
 var nanvariancewd = require( '@stdlib/stats/base/nanvariancewd' );
 ```
 
-#### nanvariancewd( N, correction, x, stride )
+#### nanvariancewd( N, correction, x, strideX )
 
 Computes the [variance][variance] of a strided array `x` ignoring `NaN` values and using Welford's algorithm.
 
@@ -114,17 +114,14 @@ The function has the following parameters:
 -   **N**: number of indexed elements.
 -   **correction**: 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 [variance][variance] according to `n-c` where `c` corresponds to the provided degrees of freedom adjustment and `n` corresponds to the number of non-`NaN` indexed elements. When computing the [variance][variance] of a population, setting this parameter to `0` is the standard choice (i.e., the provided array contains data constituting an entire population). When computing the unbiased sample [variance][variance], setting this parameter to `1` is the standard choice (i.e., the provided array contains data sampled from a larger population; this is commonly referred to as Bessel's correction).
 -   **x**: input [`Array`][mdn-array] or [`typed array`][mdn-typed-array].
--   **stride**: index increment for `x`.
+-   **strideX**: stride length for `x`.
 
-The `N` and `stride` parameters determine which elements in `x` are accessed at runtime. For example, to compute the [variance][variance] of every other element in `x`,
+The `N` and stride parameters determine which elements in the strided array are accessed at runtime. For example, to compute the [variance][variance] of every other element in `x`,
 
 ```javascript
-var floor = require( '@stdlib/math/base/special/floor' );
-
 var x = [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0, NaN ];
-var N = floor( x.length / 2 );
 
-var v = nanvariancewd( N, 1, x, 2 );
+var v = nanvariancewd( 4, 1, x, 2 );
 // returns 6.25
 ```
 
@@ -134,18 +131,15 @@ Note that indexing is relative to the first index. To introduce an offset, use [
 
 ```javascript
 var Float64Array = require( '@stdlib/array/float64' );
-var floor = require( '@stdlib/math/base/special/floor' );
 
 var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN ] );
 var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
 
-var N = floor( x0.length / 2 );
-
-var v = nanvariancewd( N, 1, x1, 2 );
+var v = nanvariancewd( 4, 1, x1, 2 );
 // returns 6.25
 ```
 
-#### nanvariancewd.ndarray( N, correction, x, stride, offset )
+#### nanvariancewd.ndarray( N, correction, x, strideX, offsetX )
 
 Computes the [variance][variance] of a strided array ignoring `NaN` values and using Welford's algorithm and alternative indexing semantics.
 
@@ -158,17 +152,14 @@ var v = nanvariancewd.ndarray( x.length, 1, x, 1, 0 );
 
 The function has the following additional parameters:
 
--   **offset**: starting index for `x`.
+-   **offsetX**: starting index for `x`.
 
-While [`typed array`][mdn-typed-array] views mandate a view offset based on the underlying `buffer`, the `offset` parameter supports indexing semantics based on a starting index. For example, to calculate the [variance][variance] for every other value in `x` starting from the second value
+While [`typed array`][mdn-typed-array] views mandate a view offset based on the underlying buffer, the offset parameter supports indexing semantics based on a starting index. For example, to calculate the [variance][variance] for every other element in the strided array starting from the second element
 
 ```javascript
-var floor = require( '@stdlib/math/base/special/floor' );
-
 var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ];
-var N = floor( x.length / 2 );
 
-var v = nanvariancewd.ndarray( N, 1, x, 2, 1 );
+var v = nanvariancewd.ndarray( 4, 1, x, 2, 1 );
 // returns 6.25
 ```
 
@@ -181,6 +172,7 @@ var v = nanvariancewd.ndarray( N, 1, x, 2, 1 );
 ## Notes
 
 -   If `N <= 0`, both functions return `NaN`.
+-   Both functions support array-like objects having getter and setter accessors for array element access (e.g., [`@stdlib/array/base/accessor`][@stdlib/array/base/accessor]).
 -   If `n - c` is less than or equal to `0` (where `c` corresponds to the provided degrees of freedom adjustment and `n` corresponds to the number of non-`NaN` indexed elements), both functions return `NaN`.
 -   Depending on the environment, the typed versions ([`dnanvariancewd`][@stdlib/stats/base/dnanvariancewd], [`snanvariancewd`][@stdlib/stats/base/snanvariancewd], etc.) are likely to be significantly more performant.
 
@@ -195,18 +187,12 @@ var v = nanvariancewd.ndarray( N, 1, x, 2, 1 );
 <!-- eslint no-undef: "error" -->
 
 ```javascript
-var randu = require( '@stdlib/random/base/randu' );
-var round = require( '@stdlib/math/base/special/round' );
-var Float64Array = require( '@stdlib/array/float64' );
+var discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
 var nanvariancewd = require( '@stdlib/stats/base/nanvariancewd' );
 
-var x;
-var i;
-
-x = new Float64Array( 10 );
-for ( i = 0; i < x.length; i++ ) {
-    x[ i ] = round( (randu()*100.0) - 50.0 );
-}
+var x = discreteUniform( 10, -50, 50, {
+    'dtype': 'float64'
+});
 console.log( x );
 
 var v = nanvariancewd( x.length, 1, x, 1 );
@@ -258,6 +244,8 @@ console.log( v );
 
 [mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray
 
+[@stdlib/array/base/accessor]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/array/base/accessor
+
 [@welford:1962a]: https://doi.org/10.1080/00401706.1962.10490022
 
 [@vanreeken:1968a]: https://doi.org/10.1145/362929.362961
diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancewd/benchmark/benchmark.js b/lib/node_modules/@stdlib/stats/base/nanvariancewd/benchmark/benchmark.js
index 814189c6687f..43a094abe75f 100644
--- a/lib/node_modules/@stdlib/stats/base/nanvariancewd/benchmark/benchmark.js
+++ b/lib/node_modules/@stdlib/stats/base/nanvariancewd/benchmark/benchmark.js
@@ -21,11 +21,18 @@
 // MODULES //
 
 var bench = require( '@stdlib/bench' );
-var randu = require( '@stdlib/random/base/randu' );
+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 pkg = require( './../package.json' ).name;
-var nanvariancewd = require( './../lib/nanvariancewd.js' );
+var nanvariancewd = require( './../lib/main.js' );
+
+
+// VARIABLES //
+
+var options = {
+	'dtype': 'generic'
+};
 
 
 // FUNCTIONS //
@@ -38,17 +45,7 @@ var nanvariancewd = require( './../lib/nanvariancewd.js' );
 * @returns {Function} benchmark function
 */
 function createBenchmark( len ) {
-	var x;
-	var i;
-
-	x = [];
-	for ( i = 0; i < len; i++ ) {
-		if ( randu() < 0.2 ) {
-			x.push( NaN );
-		} else {
-			x.push( ( randu()*20.0 ) - 10.0 );
-		}
-	}
+	var x = uniform( len, -10, 10, options );
 	return benchmark;
 
 	function benchmark( b ) {
diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancewd/benchmark/benchmark.ndarray.js b/lib/node_modules/@stdlib/stats/base/nanvariancewd/benchmark/benchmark.ndarray.js
index 339ed2b68ef0..65b3f668c5f7 100644
--- a/lib/node_modules/@stdlib/stats/base/nanvariancewd/benchmark/benchmark.ndarray.js
+++ b/lib/node_modules/@stdlib/stats/base/nanvariancewd/benchmark/benchmark.ndarray.js
@@ -21,13 +21,20 @@
 // MODULES //
 
 var bench = require( '@stdlib/bench' );
-var randu = require( '@stdlib/random/base/randu' );
+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 pkg = require( './../package.json' ).name;
 var nanvariancewd = require( './../lib/ndarray.js' );
 
 
+// VARIABLES //
+
+var options = {
+	'dtype': 'generic'
+};
+
+
 // FUNCTIONS //
 
 /**
@@ -38,17 +45,7 @@ var nanvariancewd = require( './../lib/ndarray.js' );
 * @returns {Function} benchmark function
 */
 function createBenchmark( len ) {
-	var x;
-	var i;
-
-	x = [];
-	for ( i = 0; i < len; i++ ) {
-		if ( randu() < 0.2 ) {
-			x.push( NaN );
-		} else {
-			x.push( ( randu()*20.0 ) - 10.0 );
-		}
-	}
+	var x = uniform( len, -10, 10, options );
 	return benchmark;
 
 	function benchmark( b ) {
diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancewd/docs/repl.txt b/lib/node_modules/@stdlib/stats/base/nanvariancewd/docs/repl.txt
index a3b5d9dcb638..538bbf4cb923 100644
--- a/lib/node_modules/@stdlib/stats/base/nanvariancewd/docs/repl.txt
+++ b/lib/node_modules/@stdlib/stats/base/nanvariancewd/docs/repl.txt
@@ -1,10 +1,10 @@
 
-{{alias}}( N, correction, x, stride )
+{{alias}}( N, correction, x, strideX )
     Computes the variance of a strided array ignoring `NaN` values and using
     Welford's algorithm.
 
-    The `N` and `stride` parameters determine which elements in `x` are accessed
-    at runtime.
+    The `N` and `stride` parameters determine which elements
+    in the strided array are accessed at runtime.
 
     Indexing is relative to the first index. To introduce an offset, use a typed
     array view.
@@ -34,8 +34,8 @@
     x: Array<number>|TypedArray
         Input array.
 
-    stride: integer
-        Index increment.
+    strideX: integer
+        Stride length.
 
     Returns
     -------
@@ -49,22 +49,19 @@
     > {{alias}}( x.length, 1, x, 1 )
     ~4.3333
 
-    // Using `N` and `stride` parameters:
+    // Using `N` and stride parameters:
     > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ];
-    > var N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 );
-    > var stride = 2;
-    > {{alias}}( N, 1, x, stride )
+    > {{alias}}( 3, 1, x, 2 )
     ~4.3333
 
     // Using view offsets:
     > var x0 = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );
     > var x1 = new {{alias:@stdlib/array/float64}}( x0.buffer, x0.BYTES_PER_ELEMENT*1 );
-    > N = {{alias:@stdlib/math/base/special/floor}}( x0.length / 2 );
-    > stride = 2;
-    > {{alias}}( N, 1, x1, stride )
+    > {{alias}}( 3, 1, x1, 2 )
     ~4.3333
 
-{{alias}}.ndarray( N, correction, x, stride, offset )
+
+{{alias}}.ndarray( N, correction, x, strideX, offsetX )
     Computes the variance of a strided array ignoring `NaN` values and using
     Welford's algorithm and alternative indexing semantics.
 
@@ -93,10 +90,10 @@
     x: Array<number>|TypedArray
         Input array.
 
-    stride: integer
-        Index increment.
+    strideX: integer
+        Stride length.
 
-    offset: integer
+    offsetX: integer
         Starting index.
 
     Returns
@@ -113,8 +110,7 @@
 
     // Using offset parameter:
     > var x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];
-    > var N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 );
-    > {{alias}}.ndarray( N, 1, x, 2, 1 )
+    > {{alias}}.ndarray( 3, 1, x, 2, 1 )
     ~4.3333
 
     See Also
diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancewd/docs/types/index.d.ts b/lib/node_modules/@stdlib/stats/base/nanvariancewd/docs/types/index.d.ts
index e1f49c0edb18..694ca8f637c6 100644
--- a/lib/node_modules/@stdlib/stats/base/nanvariancewd/docs/types/index.d.ts
+++ b/lib/node_modules/@stdlib/stats/base/nanvariancewd/docs/types/index.d.ts
@@ -20,7 +20,12 @@
 
 /// <reference types="@stdlib/types"/>
 
-import { NumericArray } from '@stdlib/types/array';
+import { NumericArray, Collection, AccessorArrayLike } from '@stdlib/types/array';
+
+/**
+* Input array.
+*/
+type InputArray = NumericArray | Collection<number> | AccessorArrayLike<number>;
 
 /**
 * Interface describing `nanvariancewd`.
@@ -32,7 +37,7 @@ interface Routine {
 	* @param N - number of indexed elements
 	* @param correction - degrees of freedom adjustment
 	* @param x - input array
-	* @param stride - stride length
+	* @param strideX - stride length
 	* @returns variance
 	*
 	* @example
@@ -41,7 +46,7 @@ interface Routine {
 	* var v = nanvariancewd( x.length, 1, x, 1 );
 	* // returns ~4.3333
 	*/
-	( N: number, correction: number, x: NumericArray, stride: number ): number;
+	( N: number, correction: number, x: InputArray, strideX: number ): number;
 
 	/**
 	* Computes the variance of a strided array ignoring `NaN` values and using Welford's algorithm and alternative indexing semantics.
@@ -49,8 +54,8 @@ interface Routine {
 	* @param N - number of indexed elements
 	* @param correction - degrees of freedom adjustment
 	* @param x - input array
-	* @param stride - stride length
-	* @param offset - starting index
+	* @param strideX - stride length
+	* @param offsetX - starting index
 	* @returns variance
 	*
 	* @example
@@ -59,7 +64,7 @@ interface Routine {
 	* var v = nanvariancewd.ndarray( x.length, 1, x, 1, 0 );
 	* // returns ~4.3333
 	*/
-	ndarray( N: number, correction: number, x: NumericArray, stride: number, offset: number ): number;
+	ndarray( N: number, correction: number, x: InputArray, strideX: number, offsetX: number ): number;
 }
 
 /**
@@ -68,7 +73,7 @@ interface Routine {
 * @param N - number of indexed elements
 * @param correction - degrees of freedom adjustment
 * @param x - input array
-* @param stride - stride length
+* @param strideX - stride length
 * @returns variance
 *
 * @example
diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancewd/docs/types/test.ts b/lib/node_modules/@stdlib/stats/base/nanvariancewd/docs/types/test.ts
index 4f1e6251e3cc..1152305688f8 100644
--- a/lib/node_modules/@stdlib/stats/base/nanvariancewd/docs/types/test.ts
+++ b/lib/node_modules/@stdlib/stats/base/nanvariancewd/docs/types/test.ts
@@ -16,6 +16,7 @@
 * limitations under the License.
 */
 
+import AccessorArray = require( '@stdlib/array/base/accessor' );
 import nanvariancewd = require( './index' );
 
 
@@ -26,6 +27,7 @@ import nanvariancewd = require( './index' );
 	const x = new Float64Array( 10 );
 
 	nanvariancewd( x.length, 1, x, 1 ); // $ExpectType number
+	nanvariancewd( x.length, 1, new AccessorArray( x ), 1 ); // $ExpectType number
 }
 
 // The compiler throws an error if the function is provided a first argument which is not a number...
@@ -101,6 +103,7 @@ import nanvariancewd = require( './index' );
 	const x = new Float64Array( 10 );
 
 	nanvariancewd.ndarray( x.length, 1, x, 1, 0 ); // $ExpectType number
+	nanvariancewd.ndarray( x.length, 1, new AccessorArray( x ), 1, 0 ); // $ExpectType number
 }
 
 // The compiler throws an error if the `ndarray` method is provided a first argument which is not a number...
diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancewd/examples/index.js b/lib/node_modules/@stdlib/stats/base/nanvariancewd/examples/index.js
index fa0f9a94187e..f08423a1755a 100644
--- a/lib/node_modules/@stdlib/stats/base/nanvariancewd/examples/index.js
+++ b/lib/node_modules/@stdlib/stats/base/nanvariancewd/examples/index.js
@@ -18,22 +18,12 @@
 
 'use strict';
 
-var randu = require( '@stdlib/random/base/randu' );
-var round = require( '@stdlib/math/base/special/round' );
-var Float64Array = require( '@stdlib/array/float64' );
+var discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
 var nanvariancewd = require( './../lib' );
 
-var x;
-var i;
-
-x = new Float64Array( 10 );
-for ( i = 0; i < x.length; i++ ) {
-	if ( randu() < 0.2 ) {
-		x[ i ] = NaN;
-	} else {
-		x[ i ] = round( (randu()*100.0) - 50.0 );
-	}
-}
+var x = discreteUniform( 10, -50, 50, {
+	'dtype': 'float64'
+});
 console.log( x );
 
 var v = nanvariancewd( x.length, 1, x, 1 );
diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancewd/lib/nanvariancewd.js b/lib/node_modules/@stdlib/stats/base/nanvariancewd/lib/accessors.js
similarity index 66%
rename from lib/node_modules/@stdlib/stats/base/nanvariancewd/lib/nanvariancewd.js
rename to lib/node_modules/@stdlib/stats/base/nanvariancewd/lib/accessors.js
index 5efb419f6cc5..8d4fb794433b 100644
--- a/lib/node_modules/@stdlib/stats/base/nanvariancewd/lib/nanvariancewd.js
+++ b/lib/node_modules/@stdlib/stats/base/nanvariancewd/lib/accessors.js
@@ -1,7 +1,7 @@
 /**
 * @license Apache-2.0
 *
-* Copyright (c) 2020 The Stdlib Authors.
+* 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.
@@ -28,20 +28,29 @@
 * -   Welford, B. P. 1962. "Note on a Method for Calculating Corrected Sums of Squares and Products." _Technometrics_ 4 (3). Taylor & Francis: 419–20. doi:[10.1080/00401706.1962.10490022](https://doi.org/10.1080/00401706.1962.10490022).
 * -   van Reeken, A. J. 1968. "Letters to the Editor: Dealing with Neely's Algorithms." _Communications of the ACM_ 11 (3): 149–50. doi:[10.1145/362929.362961](https://doi.org/10.1145/362929.362961).
 *
+* @private
 * @param {PositiveInteger} N - number of indexed elements
 * @param {number} correction - degrees of freedom adjustment
-* @param {NumericArray} x - input array
-* @param {integer} stride - stride length
+* @param {Object} x - input array object
+* @param {Collection} x.data - input array data
+* @param {Array<Function>} x.accessors - array element accessors
+* @param {integer} strideX - stride length
+* @param {NonNegativeInteger} offsetX - starting index
 * @returns {number} variance
 *
 * @example
-* var x = [ 1.0, -2.0, NaN, 2.0 ];
+* var toAccessorArray = require( '@stdlib/array/base/to-accessor-array' );
+* var arraylike2object = require( '@stdlib/array/base/arraylike2object' );
 *
-* var v = nanvariancewd( x.length, 1, x, 1 );
+* var x = toAccessorArray( [ 1.0, -2.0, NaN, 2.0 ] );
+*
+* var v = nanvariancewd( x.length, 1, arraylike2object( x ), 1, 0 );
 * // returns ~4.3333
 */
-function nanvariancewd( N, correction, x, stride ) {
+function nanvariancewd( N, correction, x, strideX, offsetX ) {
 	var delta;
+	var xbuf;
+	var get;
 	var mu;
 	var M2;
 	var ix;
@@ -50,33 +59,33 @@ function nanvariancewd( N, correction, x, stride ) {
 	var n;
 	var i;
 
-	if ( N <= 0 ) {
-		return NaN;
-	}
-	if ( N === 1 || stride === 0 ) {
-		v = x[ 0 ];
+	// Cache reference to array data:
+	xbuf = x.data;
+
+	// Cache a reference to the element accessor:
+	get = x.accessors[ 0 ];
+
+	if ( N === 1 || strideX === 0 ) {
+		v = get( xbuf, offsetX );
 		if ( v === v && N-correction > 0.0 ) {
 			return 0.0;
 		}
 		return NaN;
 	}
-	if ( stride < 0 ) {
-		ix = (1-N) * stride;
-	} else {
-		ix = 0;
-	}
+
+	ix = offsetX;
 	M2 = 0.0;
 	mu = 0.0;
 	n = 0;
 	for ( i = 0; i < N; i++ ) {
-		v = x[ ix ];
+		v = get( xbuf, ix );
 		if ( v === v ) {
 			delta = v - mu;
 			n += 1;
 			mu += delta / n;
 			M2 += delta * ( v - mu );
 		}
-		ix += stride;
+		ix += strideX;
 	}
 	nc = n - correction;
 	if ( nc <= 0.0 ) {
diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancewd/lib/index.js b/lib/node_modules/@stdlib/stats/base/nanvariancewd/lib/index.js
index b51d8cf6749b..6985b6c7bab3 100644
--- a/lib/node_modules/@stdlib/stats/base/nanvariancewd/lib/index.js
+++ b/lib/node_modules/@stdlib/stats/base/nanvariancewd/lib/index.js
@@ -32,19 +32,24 @@
 * // returns ~4.3333
 *
 * @example
-* var floor = require( '@stdlib/math/base/special/floor' );
 * var nanvariancewd = require( '@stdlib/stats/base/nanvariancewd' );
 *
 * var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN, NaN ];
-* var N = floor( x.length / 2 );
 *
-* var v = nanvariancewd.ndarray( N, 1, x, 2, 1 );
+* var v = nanvariancewd.ndarray( 5, 1, x, 2, 1 );
 * // returns 6.25
 */
 
 // MODULES //
 
+var setReadOnly = require( '@stdlib/utils/define-nonenumerable-read-only-property' );
 var main = require( './main.js' );
+var ndarray = require( './ndarray.js' );
+
+
+// MAIN //
+
+setReadOnly( main, 'ndarray', ndarray );
 
 
 // EXPORTS //
diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancewd/lib/main.js b/lib/node_modules/@stdlib/stats/base/nanvariancewd/lib/main.js
index f5a006d685fe..e52e00d53ae0 100644
--- a/lib/node_modules/@stdlib/stats/base/nanvariancewd/lib/main.js
+++ b/lib/node_modules/@stdlib/stats/base/nanvariancewd/lib/main.js
@@ -20,14 +20,31 @@
 
 // MODULES //
 
-var setReadOnly = require( '@stdlib/utils/define-nonenumerable-read-only-property' );
-var nanvariancewd = require( './nanvariancewd.js' );
+var stride2offset = require( '@stdlib/strided/base/stride2offset' );
 var ndarray = require( './ndarray.js' );
 
 
 // MAIN //
 
-setReadOnly( nanvariancewd, 'ndarray', ndarray );
+/**
+* Computes the variance of a strided array ignoring `NaN` values and using Welford's algorithm.
+*
+* @param {PositiveInteger} N - number of indexed elements
+* @param {number} correction - degrees of freedom adjustment
+* @param {NumericArray} x - input array
+* @param {integer} strideX - stride length
+* @returns {number} variance
+*
+* @example
+* var x = [ 1.0, -2.0, NaN, 2.0 ];
+* var N = x.length;
+*
+* var v = nanvariancewd( N, 1, x, 1 );
+* // returns ~4.3333
+*/
+function nanvariancewd( N, correction, x, strideX ) {
+	return ndarray( N, correction, x, strideX, stride2offset( N, strideX ) );
+}
 
 
 // EXPORTS //
diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancewd/lib/ndarray.js b/lib/node_modules/@stdlib/stats/base/nanvariancewd/lib/ndarray.js
index abe3ebc8ce28..4ba4ef5fa8d5 100644
--- a/lib/node_modules/@stdlib/stats/base/nanvariancewd/lib/ndarray.js
+++ b/lib/node_modules/@stdlib/stats/base/nanvariancewd/lib/ndarray.js
@@ -18,6 +18,12 @@
 
 'use strict';
 
+// MODULES //
+
+var arraylike2object = require( '@stdlib/array/base/arraylike2object' );
+var accessors = require( './accessors.js' );
+
+
 // MAIN //
 
 /**
@@ -31,25 +37,23 @@
 * @param {PositiveInteger} N - number of indexed elements
 * @param {number} correction - degrees of freedom adjustment
 * @param {NumericArray} x - input array
-* @param {integer} stride - stride length
-* @param {NonNegativeInteger} offset - starting index
+* @param {integer} strideX - stride length
+* @param {NonNegativeInteger} offsetX - starting index
 * @returns {number} variance
 *
 * @example
-* var floor = require( '@stdlib/math/base/special/floor' );
-*
 * var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN, NaN ];
-* var N = floor( x.length / 2 );
 *
-* var v = nanvariancewd( N, 1, x, 2, 1 );
+* var v = nanvariancewd( 5, 1, x, 2, 1 );
 * // returns 6.25
 */
-function nanvariancewd( N, correction, x, stride, offset ) {
+function nanvariancewd( N, correction, x, strideX, offsetX ) {
 	var delta;
 	var mu;
 	var M2;
 	var ix;
 	var nc;
+	var o;
 	var v;
 	var n;
 	var i;
@@ -57,14 +61,18 @@ function nanvariancewd( N, correction, x, stride, offset ) {
 	if ( N <= 0 ) {
 		return NaN;
 	}
-	if ( N === 1 || stride === 0 ) {
-		v = x[ offset ];
+	o = arraylike2object( x );
+	if ( o.accessorProtocol ) {
+		return accessors( N, correction, o, strideX, offsetX );
+	}
+	if ( N === 1 || strideX === 0 ) {
+		v = x[ offsetX ];
 		if ( v === v && N-correction > 0.0 ) {
 			return 0.0;
 		}
 		return NaN;
 	}
-	ix = offset;
+	ix = offsetX;
 	M2 = 0.0;
 	mu = 0.0;
 	n = 0;
@@ -76,7 +84,7 @@ function nanvariancewd( N, correction, x, stride, offset ) {
 			mu += delta / n;
 			M2 += delta * ( v - mu );
 		}
-		ix += stride;
+		ix += strideX;
 	}
 	nc = n - correction;
 	if ( nc <= 0.0 ) {
diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancewd/test/test.nanvariancewd.js b/lib/node_modules/@stdlib/stats/base/nanvariancewd/test/test.main.js
similarity index 59%
rename from lib/node_modules/@stdlib/stats/base/nanvariancewd/test/test.nanvariancewd.js
rename to lib/node_modules/@stdlib/stats/base/nanvariancewd/test/test.main.js
index 8344438f7d40..e73cfa7603a9 100644
--- a/lib/node_modules/@stdlib/stats/base/nanvariancewd/test/test.nanvariancewd.js
+++ b/lib/node_modules/@stdlib/stats/base/nanvariancewd/test/test.main.js
@@ -21,10 +21,10 @@
 // MODULES //
 
 var tape = require( 'tape' );
-var floor = require( '@stdlib/math/base/special/floor' );
 var isnan = require( '@stdlib/math/base/assert/is-nan' );
+var toAccessorArray = require( '@stdlib/array/base/to-accessor-array' );
 var Float64Array = require( '@stdlib/array/float64' );
-var nanvariancewd = require( './../lib/nanvariancewd.js' );
+var nanvariancewd = require( './../lib/main.js' );
 
 
 // TESTS //
@@ -94,6 +94,60 @@ tape( 'the function calculates the population variance of a strided array (ignor
 	t.end();
 });
 
+tape( 'the function calculates the population variance of a strided array (ignoring `NaN` values) (accessors)', function test( t ) {
+	var x;
+	var v;
+	var i;
+
+	x = [ 1.0, -2.0, -4.0, 5.0, NaN, 0.0, 3.0 ];
+
+	v = nanvariancewd( x.length, 0, toAccessorArray( x ), 1 );
+	t.strictEqual( v, 53.5/(x.length-1), 'returns expected value' );
+
+	x = [ 1.0, NaN, NaN, -2.0, NaN, -4.0, NaN, 5.0, NaN, 0.0, 3.0, NaN ];
+
+	v = nanvariancewd( x.length, 0, toAccessorArray( x ), 1 );
+	t.strictEqual( v, 53.5/(x.length-6), 'returns expected value' );
+
+	x = [ -4.0, NaN ];
+
+	v = nanvariancewd( x.length, 0, toAccessorArray( x ), 1 );
+	t.strictEqual( v, 0.0, 'returns expected value' );
+
+	x = [ NaN, NaN ];
+
+	v = nanvariancewd( x.length, 0, toAccessorArray( x ), 1 );
+	t.strictEqual( isnan( v ), true, 'returns expected value' );
+
+	x = [ NaN ];
+	v = nanvariancewd( x.length, 0, toAccessorArray( x ), 1 );
+	t.strictEqual( isnan( v ), true, 'returns expected value' );
+
+	x = [ 4.0 ];
+	v = nanvariancewd( x.length, 0, toAccessorArray( x ), 1 );
+	t.strictEqual( v, 0.0, 'returns expected value' );
+
+	x = [];
+	for ( i = 0; i < 1e3; i++ ) {
+		x.push( 100.0 );
+	}
+	v = nanvariancewd( x.length, 0, toAccessorArray( x ), 1 );
+	t.strictEqual( v, 0.0, 'returns expected value' );
+
+	x = [ NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN ];
+	v = nanvariancewd( x.length, 0, toAccessorArray( x ), 1 );
+	t.strictEqual( isnan( v ), true, 'returns expected value' );
+
+	x = [];
+	for ( i = 0; i < 1e3; i++ ) {
+		x.push( NaN );
+	}
+	v = nanvariancewd( x.length, 0, toAccessorArray( x ), 1 );
+	t.strictEqual( isnan( v ), true, 'returns expected value' );
+
+	t.end();
+});
+
 tape( 'the function calculates the sample variance of a strided array (ignoring `NaN` values)', function test( t ) {
 	var x;
 	var v;
@@ -148,6 +202,60 @@ tape( 'the function calculates the sample variance of a strided array (ignoring
 	t.end();
 });
 
+tape( 'the function calculates the sample variance of a strided array (ignoring `NaN` values) (accessors)', function test( t ) {
+	var x;
+	var v;
+	var i;
+
+	x = [ 1.0, -2.0, -4.0, 5.0, NaN, 0.0, 3.0 ];
+
+	v = nanvariancewd( x.length, 1, toAccessorArray( x ), 1 );
+	t.strictEqual( v, 53.5/(x.length-2), 'returns expected value' );
+
+	x = [ 1.0, NaN, NaN, -2.0, NaN, -4.0, NaN, 5.0, NaN, 0.0, 3.0, NaN ];
+
+	v = nanvariancewd( x.length, 1, toAccessorArray( x ), 1 );
+	t.strictEqual( v, 53.5/(x.length-7), 'returns expected value' );
+
+	x = [ -4.0, NaN ];
+
+	v = nanvariancewd( x.length, 1, toAccessorArray( x ), 1 );
+	t.strictEqual( isnan( v ), true, 'returns expected value' );
+
+	x = [ NaN, NaN ];
+
+	v = nanvariancewd( x.length, 1, toAccessorArray( x ), 1 );
+	t.strictEqual( isnan( v ), true, 'returns expected value' );
+
+	x = [ NaN ];
+	v = nanvariancewd( x.length, 1, toAccessorArray( x ), 1 );
+	t.strictEqual( isnan( v ), true, 'returns expected value' );
+
+	x = [ 4.0 ];
+	v = nanvariancewd( x.length, 1, toAccessorArray( x ), 1 );
+	t.strictEqual( isnan( v ), true, 'returns expected value' );
+
+	x = [];
+	for ( i = 0; i < 1e3; i++ ) {
+		x.push( 100.0 );
+	}
+	v = nanvariancewd( x.length, 1, toAccessorArray( x ), 1 );
+	t.strictEqual( v, 0.0, 'returns expected value' );
+
+	x = [ NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN ];
+	v = nanvariancewd( x.length, 1, toAccessorArray( x ), 1 );
+	t.strictEqual( isnan( v ), true, 'returns expected value' );
+
+	x = [];
+	for ( i = 0; i < 1e3; i++ ) {
+		x.push( NaN );
+	}
+	v = nanvariancewd( x.length, 1, toAccessorArray( x ), 1 );
+	t.strictEqual( isnan( v ), true, 'returns expected value' );
+
+	t.end();
+});
+
 tape( 'if provided an `N` parameter less than or equal to `0`, the function returns `NaN`', function test( t ) {
 	var x;
 	var v;
@@ -213,7 +321,6 @@ tape( 'if provided a `correction` parameter yielding a correction term less than
 });
 
 tape( 'the function supports a `stride` parameter', function test( t ) {
-	var N;
 	var x;
 	var v;
 
@@ -230,15 +337,36 @@ tape( 'the function supports a `stride` parameter', function test( t ) {
 		NaN
 	];
 
-	N = floor( x.length / 2 );
-	v = nanvariancewd( N, 1, x, 2 );
+	v = nanvariancewd( 5, 1, x, 2 );
+
+	t.strictEqual( v, 6.25, 'returns expected value' );
+	t.end();
+});
+
+tape( 'the function supports a `stride` parameter (accessors)', function test( t ) {
+	var x;
+	var v;
+
+	x = [
+		1.0,  // 0
+		2.0,
+		2.0,  // 1
+		-7.0,
+		-2.0, // 2
+		3.0,
+		4.0,  // 3
+		2.0,
+		NaN,  // 4
+		NaN
+	];
+
+	v = nanvariancewd( 5, 1, toAccessorArray( x ), 2 );
 
 	t.strictEqual( v, 6.25, 'returns expected value' );
 	t.end();
 });
 
 tape( 'the function supports a negative `stride` parameter', function test( t ) {
-	var N;
 	var x;
 	var v;
 	var i;
@@ -255,9 +383,8 @@ tape( 'the function supports a negative `stride` parameter', function test( t )
 		4.0,  // 0
 		2.0
 	];
-	N = floor( x.length / 2 );
 
-	v = nanvariancewd( N, 1, x, -2 );
+	v = nanvariancewd( 5, 1, x, -2 );
 	t.strictEqual( v, 6.25, 'returns expected value' );
 
 	x = [];
@@ -270,6 +397,37 @@ tape( 'the function supports a negative `stride` parameter', function test( t )
 	t.end();
 });
 
+tape( 'the function supports a negative `stride` parameter (accessors)', function test( t ) {
+	var x;
+	var v;
+	var i;
+
+	x = [
+		NaN,  // 4
+		NaN,
+		1.0,  // 3
+		2.0,
+		2.0,  // 2
+		-7.0,
+		-2.0, // 1
+		3.0,
+		4.0,  // 0
+		2.0
+	];
+
+	v = nanvariancewd( 5, 1, toAccessorArray( x ), -2 );
+	t.strictEqual( v, 6.25, 'returns expected value' );
+
+	x = [];
+	for ( i = 0; i < 1e3; i++ ) {
+		x.push( 100.0 );
+	}
+	v = nanvariancewd( x.length, 1, toAccessorArray( x ), -1 );
+	t.strictEqual( v, 0.0, 'returns expected value' );
+
+	t.end();
+});
+
 tape( 'if provided a `stride` parameter equal to `0`, the function returns `0` provided the correction term is not less than `0` and the first element is not `NaN`', function test( t ) {
 	var x;
 	var v;
@@ -292,10 +450,31 @@ tape( 'if provided a `stride` parameter equal to `0`, the function returns `0` p
 	t.end();
 });
 
+tape( 'if provided a `stride` parameter equal to `0`, the function returns `0` provided the correction term is not less than `0` and the first element is not `NaN` (accessors)', function test( t ) {
+	var x;
+	var v;
+
+	x = [ 1.0, -2.0, -4.0, 5.0, 3.0 ];
+
+	v = nanvariancewd( x.length, 1, toAccessorArray( x ), 0 );
+	t.strictEqual( v, 0.0, 'returns expected value' );
+
+	x = [ NaN, 1.0, -2.0, -4.0, 5.0, 3.0 ];
+
+	v = nanvariancewd( x.length, 1, toAccessorArray( x ), 0 );
+	t.strictEqual( isnan( v ), true, 'returns expected value' );
+
+	x = [ 1.0, -2.0, -4.0, 5.0, 3.0 ];
+
+	v = nanvariancewd( x.length, x.length, toAccessorArray( x ), 0 );
+	t.strictEqual( isnan( v ), true, 'returns expected value' );
+
+	t.end();
+});
+
 tape( 'the function supports view offsets', function test( t ) {
 	var x0;
 	var x1;
-	var N;
 	var v;
 
 	x0 = new Float64Array([
@@ -313,9 +492,8 @@ tape( 'the function supports view offsets', function test( t ) {
 	]);
 
 	x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
-	N = floor(x1.length / 2);
 
-	v = nanvariancewd( N, 1, x1, 2 );
+	v = nanvariancewd( 5, 1, x1, 2 );
 	t.strictEqual( v, 6.25, 'returns expected value' );
 
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancewd/test/test.ndarray.js b/lib/node_modules/@stdlib/stats/base/nanvariancewd/test/test.ndarray.js
index 91885e4015ad..478609352e0a 100644
--- a/lib/node_modules/@stdlib/stats/base/nanvariancewd/test/test.ndarray.js
+++ b/lib/node_modules/@stdlib/stats/base/nanvariancewd/test/test.ndarray.js
@@ -21,8 +21,8 @@
 // MODULES //
 
 var tape = require( 'tape' );
-var floor = require( '@stdlib/math/base/special/floor' );
 var isnan = require( '@stdlib/math/base/assert/is-nan' );
+var toAccessorArray = require( '@stdlib/array/base/to-accessor-array' );
 var nanvariancewd = require( './../lib/ndarray.js' );
 
 
@@ -93,6 +93,60 @@ tape( 'the function calculates the population variance of a strided array (ignor
 	t.end();
 });
 
+tape( 'the function calculates the population variance of a strided array (ignoring `NaN` values) (accessors)', function test( t ) {
+	var x;
+	var v;
+	var i;
+
+	x = [ 1.0, -2.0, -4.0, 5.0, NaN, 0.0, 3.0 ];
+
+	v = nanvariancewd( x.length, 0, toAccessorArray( x ), 1, 0 );
+	t.strictEqual( v, 53.5/(x.length-1), 'returns expected value' );
+
+	x = [ 1.0, NaN, NaN, -2.0, NaN, -4.0, NaN, 5.0, NaN, 0.0, 3.0, NaN ];
+
+	v = nanvariancewd( x.length, 0, toAccessorArray( x ), 1, 0 );
+	t.strictEqual( v, 53.5/(x.length-6), 'returns expected value' );
+
+	x = [ -4.0, NaN ];
+
+	v = nanvariancewd( x.length, 0, toAccessorArray( x ), 1, 0 );
+	t.strictEqual( v, 0.0, 'returns expected value' );
+
+	x = [ NaN, NaN ];
+
+	v = nanvariancewd( x.length, 0, toAccessorArray( x ), 1, 0 );
+	t.strictEqual( isnan( v ), true, 'returns expected value' );
+
+	x = [ NaN ];
+	v = nanvariancewd( x.length, 0, toAccessorArray( x ), 1, 0 );
+	t.strictEqual( isnan( v ), true, 'returns expected value' );
+
+	x = [ 4.0 ];
+	v = nanvariancewd( x.length, 0, toAccessorArray( x ), 1, 0 );
+	t.strictEqual( v, 0.0, 'returns expected value' );
+
+	x = [];
+	for ( i = 0; i < 1e3; i++ ) {
+		x.push( 100.0 );
+	}
+	v = nanvariancewd( x.length, 0, toAccessorArray( x ), 1, 0 );
+	t.strictEqual( v, 0.0, 'returns expected value' );
+
+	x = [ NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN ];
+	v = nanvariancewd( x.length, 0, toAccessorArray( x ), 1, 0 );
+	t.strictEqual( isnan( v ), true, 'returns expected value' );
+
+	x = [];
+	for ( i = 0; i < 1e3; i++ ) {
+		x.push( NaN );
+	}
+	v = nanvariancewd( x.length, 0, toAccessorArray( x ), 1, 0 );
+	t.strictEqual( isnan( v ), true, 'returns expected value' );
+
+	t.end();
+});
+
 tape( 'the function calculates the sample variance of a strided array (ignoring `NaN` values)', function test( t ) {
 	var x;
 	var v;
@@ -147,6 +201,60 @@ tape( 'the function calculates the sample variance of a strided array (ignoring
 	t.end();
 });
 
+tape( 'the function calculates the sample variance of a strided array (ignoring `NaN` values) (accessors)', function test( t ) {
+	var x;
+	var v;
+	var i;
+
+	x = [ 1.0, -2.0, -4.0, 5.0, NaN, 0.0, 3.0 ];
+
+	v = nanvariancewd( x.length, 1, toAccessorArray( x ), 1, 0 );
+	t.strictEqual( v, 53.5/(x.length-2), 'returns expected value' );
+
+	x = [ 1.0, NaN, NaN, -2.0, NaN, -4.0, NaN, 5.0, NaN, 0.0, 3.0, NaN ];
+
+	v = nanvariancewd( x.length, 1, toAccessorArray( x ), 1, 0 );
+	t.strictEqual( v, 53.5/(x.length-7), 'returns expected value' );
+
+	x = [ -4.0, NaN ];
+
+	v = nanvariancewd( x.length, 1, toAccessorArray( x ), 1, 0 );
+	t.strictEqual( isnan( v ), true, 'returns expected value' );
+
+	x = [ NaN, NaN ];
+
+	v = nanvariancewd( x.length, 1, toAccessorArray( x ), 1, 0 );
+	t.strictEqual( isnan( v ), true, 'returns expected value' );
+
+	x = [ NaN ];
+	v = nanvariancewd( x.length, 1, toAccessorArray( x ), 1, 0 );
+	t.strictEqual( isnan( v ), true, 'returns expected value' );
+
+	x = [ 4.0 ];
+	v = nanvariancewd( x.length, 1, toAccessorArray( x ), 1, 0 );
+	t.strictEqual( isnan( v ), true, 'returns expected value' );
+
+	x = [];
+	for ( i = 0; i < 1e3; i++ ) {
+		x.push( 100.0 );
+	}
+	v = nanvariancewd( x.length, 1, toAccessorArray( x ), 1, 0 );
+	t.strictEqual( v, 0.0, 'returns expected value' );
+
+	x = [ NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN ];
+	v = nanvariancewd( x.length, 1, toAccessorArray( x ), 1, 0 );
+	t.strictEqual( isnan( v ), true, 'returns expected value' );
+
+	x = [];
+	for ( i = 0; i < 1e3; i++ ) {
+		x.push( NaN );
+	}
+	v = nanvariancewd( x.length, 1, toAccessorArray( x ), 1, 0 );
+	t.strictEqual( isnan( v ), true, 'returns expected value' );
+
+	t.end();
+});
+
 tape( 'if provided an `N` parameter less than or equal to `0`, the function returns `NaN`', function test( t ) {
 	var x;
 	var v;
@@ -212,7 +320,6 @@ tape( 'if provided a `correction` parameter yielding a correction term less than
 });
 
 tape( 'the function supports a `stride` parameter', function test( t ) {
-	var N;
 	var x;
 	var v;
 
@@ -229,15 +336,36 @@ tape( 'the function supports a `stride` parameter', function test( t ) {
 		NaN
 	];
 
-	N = floor( x.length / 2 );
-	v = nanvariancewd( N, 1, x, 2, 0 );
+	v = nanvariancewd( 5, 1, x, 2, 0 );
+
+	t.strictEqual( v, 6.25, 'returns expected value' );
+	t.end();
+});
+
+tape( 'the function supports a `stride` parameter (accessors)', function test( t ) {
+	var x;
+	var v;
+
+	x = [
+		1.0,  // 0
+		2.0,
+		2.0,  // 1
+		-7.0,
+		-2.0, // 2
+		3.0,
+		4.0,  // 3
+		2.0,
+		NaN,  // 4
+		NaN
+	];
+
+	v = nanvariancewd( 5, 1, toAccessorArray( x ), 2, 0 );
 
 	t.strictEqual( v, 6.25, 'returns expected value' );
 	t.end();
 });
 
 tape( 'the function supports a negative `stride` parameter', function test( t ) {
-	var N;
 	var x;
 	var v;
 	var i;
@@ -254,9 +382,8 @@ tape( 'the function supports a negative `stride` parameter', function test( t )
 		4.0,  // 0
 		2.0
 	];
-	N = floor( x.length / 2 );
 
-	v = nanvariancewd( N, 1, x, -2, 8 );
+	v = nanvariancewd( 5, 1, x, -2, 8 );
 	t.strictEqual( v, 6.25, 'returns expected value' );
 
 	x = [];
@@ -269,6 +396,37 @@ tape( 'the function supports a negative `stride` parameter', function test( t )
 	t.end();
 });
 
+tape( 'the function supports a negative `stride` parameter (accessors)', function test( t ) {
+	var x;
+	var v;
+	var i;
+
+	x = [
+		NaN,  // 4
+		NaN,
+		1.0,  // 3
+		2.0,
+		2.0,  // 2
+		-7.0,
+		-2.0, // 1
+		3.0,
+		4.0,  // 0
+		2.0
+	];
+
+	v = nanvariancewd( 5, 1, toAccessorArray( x ), -2, 8 );
+	t.strictEqual( v, 6.25, 'returns expected value' );
+
+	x = [];
+	for ( i = 0; i < 1e3; i++ ) {
+		x.push( 100.0 );
+	}
+	v = nanvariancewd( x.length, 1, toAccessorArray( x ), -1, x.length-1 );
+	t.strictEqual( v, 0.0, 'returns expected value' );
+
+	t.end();
+});
+
 tape( 'if provided a `stride` parameter equal to `0`, the function returns `0` provided the correction term is not less than `0` and the first element is not `NaN`', function test( t ) {
 	var x;
 	var v;
@@ -291,8 +449,29 @@ tape( 'if provided a `stride` parameter equal to `0`, the function returns `0` p
 	t.end();
 });
 
+tape( 'if provided a `stride` parameter equal to `0`, the function returns `0` provided the correction term is not less than `0` and the first element is not `NaN`', function test( t ) {
+	var x;
+	var v;
+
+	x = [ 1.0, -2.0, -4.0, 5.0, 3.0 ];
+
+	v = nanvariancewd( x.length, 1, toAccessorArray( x ), 0, 0 );
+	t.strictEqual( v, 0.0, 'returns expected value' );
+
+	x = [ NaN, 1.0, -2.0, -4.0, 5.0, 3.0 ];
+
+	v = nanvariancewd( x.length, 1, toAccessorArray( x ), 0, 0 );
+	t.strictEqual( isnan( v ), true, 'returns expected value' );
+
+	x = [ 1.0, -2.0, -4.0, 5.0, 3.0 ];
+
+	v = nanvariancewd( x.length, x.length, toAccessorArray( x ), 0, 0 );
+	t.strictEqual( isnan( v ), true, 'returns expected value' );
+
+	t.end();
+});
+
 tape( 'the function supports an `offset` parameter', function test( t ) {
-	var N;
 	var x;
 	var v;
 
@@ -308,9 +487,31 @@ tape( 'the function supports an `offset` parameter', function test( t ) {
 		NaN,
 		NaN   // 4
 	];
-	N = floor( x.length / 2 );
 
-	v = nanvariancewd( N, 1, x, 2, 1 );
+	v = nanvariancewd( 5, 1, x, 2, 1 );
+	t.strictEqual( v, 6.25, 'returns expected value' );
+
+	t.end();
+});
+
+tape( 'the function supports an `offset` parameter (accessors)', function test( t ) {
+	var x;
+	var v;
+
+	x = [
+		2.0,
+		1.0,  // 0
+		2.0,
+		-2.0, // 1
+		-2.0,
+		2.0,  // 2
+		3.0,
+		4.0,  // 3
+		NaN,
+		NaN   // 4
+	];
+
+	v = nanvariancewd( 5, 1, toAccessorArray( x ), 2, 1 );
 	t.strictEqual( v, 6.25, 'returns expected value' );
 
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/variancetk/README.md b/lib/node_modules/@stdlib/stats/base/variancetk/README.md
index 6d7e2c24e243..e7c67690eed6 100644
--- a/lib/node_modules/@stdlib/stats/base/variancetk/README.md
+++ b/lib/node_modules/@stdlib/stats/base/variancetk/README.md
@@ -98,7 +98,7 @@ The use of the term `n-1` is commonly referred to as Bessel's correction. Note,
 var variancetk = require( '@stdlib/stats/base/variancetk' );
 ```
 
-#### variancetk( N, correction, x, stride )
+#### variancetk( N, correction, x, strideX )
 
 Computes the [variance][variance] of a strided array `x` using a one-pass textbook algorithm.
 
@@ -114,17 +114,14 @@ The function has the following parameters:
 -   **N**: number of indexed elements.
 -   **correction**: 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 [variance][variance] according to `N-c` where `c` corresponds to the provided degrees of freedom adjustment. When computing the [variance][variance] of a population, setting this parameter to `0` is the standard choice (i.e., the provided array contains data constituting an entire population). When computing the unbiased sample [variance][variance], setting this parameter to `1` is the standard choice (i.e., the provided array contains data sampled from a larger population; this is commonly referred to as Bessel's correction).
 -   **x**: input [`Array`][mdn-array] or [`typed array`][mdn-typed-array].
--   **stride**: index increment for `x`.
+-   **strideX**: stride length for `x`.
 
-The `N` and `stride` parameters determine which elements in `x` are accessed at runtime. For example, to compute the [variance][variance] of every other element in `x`,
+The `N` and stride parameters determine which elements in the strided array are accessed at runtime. For example, to compute the [variance][variance] of every other element in `x`,
 
 ```javascript
-var floor = require( '@stdlib/math/base/special/floor' );
-
 var x = [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ];
-var N = floor( x.length / 2 );
 
-var v = variancetk( N, 1, x, 2 );
+var v = variancetk( 4, 1, x, 2 );
 // returns 6.25
 ```
 
@@ -134,18 +131,15 @@ Note that indexing is relative to the first index. To introduce an offset, use [
 
 ```javascript
 var Float64Array = require( '@stdlib/array/float64' );
-var floor = require( '@stdlib/math/base/special/floor' );
 
 var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
 var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
 
-var N = floor( x0.length / 2 );
-
-var v = variancetk( N, 1, x1, 2 );
+var v = variancetk( 4, 1, x1, 2 );
 // returns 6.25
 ```
 
-#### variancetk.ndarray( N, correction, x, stride, offset )
+#### variancetk.ndarray( N, correction, x, strideX, offsetX )
 
 Computes the [variance][variance] of a strided array using a one-pass textbook algorithm and alternative indexing semantics.
 
@@ -158,17 +152,14 @@ var v = variancetk.ndarray( x.length, 1, x, 1, 0 );
 
 The function has the following additional parameters:
 
--   **offset**: starting index for `x`.
+-   **offsetX**: starting index for `x`.
 
-While [`typed array`][mdn-typed-array] views mandate a view offset based on the underlying `buffer`, the `offset` parameter supports indexing semantics based on a starting index. For example, to calculate the [variance][variance] for every other value in `x` starting from the second value
+While [`typed array`][mdn-typed-array] views mandate a view offset based on the underlying buffer, the offset parameter supports indexing semantics based on a starting index. For example, to calculate the [variance][variance] for every element value in the strided array starting from the second element
 
 ```javascript
-var floor = require( '@stdlib/math/base/special/floor' );
-
 var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ];
-var N = floor( x.length / 2 );
 
-var v = variancetk.ndarray( N, 1, x, 2, 1 );
+var v = variancetk.ndarray( 4, 1, x, 2, 1 );
 // returns 6.25
 ```
 
@@ -181,6 +172,7 @@ var v = variancetk.ndarray( N, 1, x, 2, 1 );
 ## Notes
 
 -   If `N <= 0`, both functions return `NaN`.
+-   Both functions support array-like objects having getter and setter accessors for array element access (e.g., [`@stdlib/array/base/accessor`][@stdlib/array/base/accessor]).
 -   If `N - c` is less than or equal to `0` (where `c` corresponds to the provided degrees of freedom adjustment), both functions return `NaN`.
 -   Some caution should be exercised when using the one-pass textbook algorithm. Literature overwhelmingly discourages the algorithm's use for two reasons: 1) the lack of safeguards against underflow and overflow and 2) the risk of catastrophic cancellation when subtracting the two sums if the sums are large and the variance small. These concerns have merit; however, the one-pass textbook algorithm should not be dismissed outright. For data distributions with a moderately large standard deviation to mean ratio (i.e., **coefficient of variation**), the one-pass textbook algorithm may be acceptable, especially when performance is paramount and some precision loss is acceptable (including a risk of returning a negative variance due to floating-point rounding errors!). In short, no single "best" algorithm for computing the variance exists. The "best" algorithm depends on the underlying data distribution, your performance requirements, and your minimum precision requirements. When evaluating which algorithm to use, consider the relative pros and cons, and choose the algorithm which best serves your needs.
 -   Depending on the environment, the typed versions ([`dvariancetk`][@stdlib/stats/base/dvariancetk], [`svariancetk`][@stdlib/stats/base/svariancetk], etc.) are likely to be significantly more performant.
@@ -196,18 +188,12 @@ var v = variancetk.ndarray( N, 1, x, 2, 1 );
 <!-- eslint no-undef: "error" -->
 
 ```javascript
-var randu = require( '@stdlib/random/base/randu' );
-var round = require( '@stdlib/math/base/special/round' );
-var Float64Array = require( '@stdlib/array/float64' );
+var discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
 var variancetk = require( '@stdlib/stats/base/variancetk' );
 
-var x;
-var i;
-
-x = new Float64Array( 10 );
-for ( i = 0; i < x.length; i++ ) {
-    x[ i ] = round( (randu()*100.0) - 50.0 );
-}
+var x = discreteUniform( 10, -50, 50, {
+    'dtype': 'float64'
+});
 console.log( x );
 
 var v = variancetk( x.length, 1, x, 1 );
@@ -257,6 +243,8 @@ console.log( v );
 
 [mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray
 
+[@stdlib/array/base/accessor]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/array/base/accessor
+
 [@stdlib/stats/base/svariancetk]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/svariancetk
 
 [@ling:1974a]: https://doi.org/10.2307/2286154
diff --git a/lib/node_modules/@stdlib/stats/base/variancetk/benchmark/benchmark.js b/lib/node_modules/@stdlib/stats/base/variancetk/benchmark/benchmark.js
index e0939f181fbf..a9d37ff8895a 100644
--- a/lib/node_modules/@stdlib/stats/base/variancetk/benchmark/benchmark.js
+++ b/lib/node_modules/@stdlib/stats/base/variancetk/benchmark/benchmark.js
@@ -21,11 +21,18 @@
 // MODULES //
 
 var bench = require( '@stdlib/bench' );
-var randu = require( '@stdlib/random/base/randu' );
+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 pkg = require( './../package.json' ).name;
-var variancetk = require( './../lib/variancetk.js' );
+var variancetk = require( './../lib/main.js' );
+
+
+// VARIABLES //
+
+var options = {
+	'dtype': 'generic'
+};
 
 
 // FUNCTIONS //
@@ -38,13 +45,7 @@ var variancetk = require( './../lib/variancetk.js' );
 * @returns {Function} benchmark function
 */
 function createBenchmark( len ) {
-	var x;
-	var i;
-
-	x = [];
-	for ( i = 0; i < len; i++ ) {
-		x.push( ( randu()*20.0 ) - 10.0 );
-	}
+	var x = uniform( len, -10, 10, options );
 	return benchmark;
 
 	function benchmark( b ) {
diff --git a/lib/node_modules/@stdlib/stats/base/variancetk/benchmark/benchmark.ndarray.js b/lib/node_modules/@stdlib/stats/base/variancetk/benchmark/benchmark.ndarray.js
index cc06ca851296..f6dcfaf8ba5f 100644
--- a/lib/node_modules/@stdlib/stats/base/variancetk/benchmark/benchmark.ndarray.js
+++ b/lib/node_modules/@stdlib/stats/base/variancetk/benchmark/benchmark.ndarray.js
@@ -21,13 +21,20 @@
 // MODULES //
 
 var bench = require( '@stdlib/bench' );
-var randu = require( '@stdlib/random/base/randu' );
+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 pkg = require( './../package.json' ).name;
 var variancetk = require( './../lib/ndarray.js' );
 
 
+// VARIABLES //
+
+var options = {
+	'dtype': 'generic'
+};
+
+
 // FUNCTIONS //
 
 /**
@@ -38,13 +45,7 @@ var variancetk = require( './../lib/ndarray.js' );
 * @returns {Function} benchmark function
 */
 function createBenchmark( len ) {
-	var x;
-	var i;
-
-	x = [];
-	for ( i = 0; i < len; i++ ) {
-		x.push( ( randu()*20.0 ) - 10.0 );
-	}
+	var x = uniform( len, -10, 10, options );
 	return benchmark;
 
 	function benchmark( b ) {
diff --git a/lib/node_modules/@stdlib/stats/base/variancetk/docs/repl.txt b/lib/node_modules/@stdlib/stats/base/variancetk/docs/repl.txt
index 75fe4234ae5a..21fe5ee73a7e 100644
--- a/lib/node_modules/@stdlib/stats/base/variancetk/docs/repl.txt
+++ b/lib/node_modules/@stdlib/stats/base/variancetk/docs/repl.txt
@@ -1,10 +1,10 @@
 
-{{alias}}( N, correction, x, stride )
+{{alias}}( N, correction, x, strideX )
     Computes the variance of a strided array using a one-pass textbook
     algorithm.
 
-    The `N` and `stride` parameters determine which elements in `x` are accessed
-    at runtime.
+    The `N` and stride parameters determine which elements in the strided array
+    are accessed at runtime.
 
     Indexing is relative to the first index. To introduce an offset, use a typed
     array view.
@@ -31,8 +31,8 @@
     x: Array<number>|TypedArray
         Input array.
 
-    stride: integer
-        Index increment.
+    strideX: integer
+        Stride length.
 
     Returns
     -------
@@ -46,11 +46,9 @@
     > {{alias}}( x.length, 1, x, 1 )
     ~4.3333
 
-    // Using `N` and `stride` parameters:
+    // Using `N` and stride parameters:
     > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ];
-    > var N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 );
-    > var stride = 2;
-    > {{alias}}( N, 1, x, stride )
+    > {{alias}}( 3, 1, x, 2 )
     ~4.3333
 
     // Using view offsets:
@@ -58,10 +56,11 @@
     > var x1 = new {{alias:@stdlib/array/float64}}( x0.buffer, x0.BYTES_PER_ELEMENT*1 );
     > N = {{alias:@stdlib/math/base/special/floor}}( x0.length / 2 );
     > stride = 2;
-    > {{alias}}( N, 1, x1, stride )
+    > {{alias}}( 3, 1, x1, 2 )
     ~4.3333
 
-{{alias}}.ndarray( N, correction, x, stride, offset )
+
+{{alias}}.ndarray( N, correction, x, strideX, offsetX )
     Computes the variance of a strided array using a one-pass textbook algorithm
     and alternative indexing semantics.
 
@@ -89,10 +88,10 @@
     x: Array<number>|TypedArray
         Input array.
 
-    stride: integer
-        Index increment.
+    strideX: integer
+        Stride length.
 
-    offset: integer
+    offsetX: integer
         Starting index.
 
     Returns
@@ -109,8 +108,7 @@
 
     // Using offset parameter:
     > var x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];
-    > var N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 );
-    > {{alias}}.ndarray( N, 1, x, 2, 1 )
+    > {{alias}}.ndarray( 3, 1, x, 2, 1 )
     ~4.3333
 
     See Also
diff --git a/lib/node_modules/@stdlib/stats/base/variancetk/docs/types/index.d.ts b/lib/node_modules/@stdlib/stats/base/variancetk/docs/types/index.d.ts
index dbe717e14dc0..552d25b8160e 100644
--- a/lib/node_modules/@stdlib/stats/base/variancetk/docs/types/index.d.ts
+++ b/lib/node_modules/@stdlib/stats/base/variancetk/docs/types/index.d.ts
@@ -20,7 +20,12 @@
 
 /// <reference types="@stdlib/types"/>
 
-import { NumericArray } from '@stdlib/types/array';
+import { NumericArray, Collection, AccessorArrayLike } from '@stdlib/types/array';
+
+/**
+* Input array.
+*/
+type InputArray = NumericArray | Collection<number> | AccessorArrayLike<number>;
 
 /**
 * Interface describing `variancetk`.
@@ -32,7 +37,7 @@ interface Routine {
 	* @param N - number of indexed elements
 	* @param correction - degrees of freedom adjustment
 	* @param x - input array
-	* @param stride - stride length
+	* @param strideX - stride length
 	* @returns variance
 	*
 	* @example
@@ -41,7 +46,7 @@ interface Routine {
 	* var v = variancetk( x.length, 1, x, 1 );
 	* // returns ~4.3333
 	*/
-	( N: number, correction: number, x: NumericArray, stride: number ): number;
+	( N: number, correction: number, x: InputArray, strideX: number ): number;
 
 	/**
 	* Computes the variance of a strided array using a one-pass textbook algorithm and alternative indexing semantics.
@@ -49,8 +54,8 @@ interface Routine {
 	* @param N - number of indexed elements
 	* @param correction - degrees of freedom adjustment
 	* @param x - input array
-	* @param stride - stride length
-	* @param offset - starting index
+	* @param strideX - stride length
+	* @param offsetX - starting index
 	* @returns variance
 	*
 	* @example
@@ -68,7 +73,7 @@ interface Routine {
 * @param N - number of indexed elements
 * @param correction - degrees of freedom adjustment
 * @param x - input array
-* @param stride - stride length
+* @param strideX - stride length
 * @returns variance
 *
 * @example
diff --git a/lib/node_modules/@stdlib/stats/base/variancetk/docs/types/test.ts b/lib/node_modules/@stdlib/stats/base/variancetk/docs/types/test.ts
index 35734d2e48e0..8a654deb2d90 100644
--- a/lib/node_modules/@stdlib/stats/base/variancetk/docs/types/test.ts
+++ b/lib/node_modules/@stdlib/stats/base/variancetk/docs/types/test.ts
@@ -16,6 +16,7 @@
 * limitations under the License.
 */
 
+import AccessorArray = require( '@stdlib/array/base/accessor' );
 import variancetk = require( './index' );
 
 
@@ -26,6 +27,7 @@ import variancetk = require( './index' );
 	const x = new Float64Array( 10 );
 
 	variancetk( x.length, 1, x, 1 ); // $ExpectType number
+	variancetk( x.length, 1, new AccessorArray( x ), 1 ); // $ExpectType number
 }
 
 // The compiler throws an error if the function is provided a first argument which is not a number...
@@ -101,6 +103,7 @@ import variancetk = require( './index' );
 	const x = new Float64Array( 10 );
 
 	variancetk.ndarray( x.length, 1, x, 1, 0 ); // $ExpectType number
+	variancetk.ndarray( x.length, 1, new AccessorArray( x ), 1, 0 ); // $ExpectType number
 }
 
 // The compiler throws an error if the `ndarray` method is provided a first argument which is not a number...
diff --git a/lib/node_modules/@stdlib/stats/base/variancetk/examples/index.js b/lib/node_modules/@stdlib/stats/base/variancetk/examples/index.js
index 5ad74c6fb8ae..50517c278915 100644
--- a/lib/node_modules/@stdlib/stats/base/variancetk/examples/index.js
+++ b/lib/node_modules/@stdlib/stats/base/variancetk/examples/index.js
@@ -18,18 +18,12 @@
 
 'use strict';
 
-var randu = require( '@stdlib/random/base/randu' );
-var round = require( '@stdlib/math/base/special/round' );
-var Float64Array = require( '@stdlib/array/float64' );
+var discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
 var variancetk = require( './../lib' );
 
-var x;
-var i;
-
-x = new Float64Array( 10 );
-for ( i = 0; i < x.length; i++ ) {
-	x[ i ] = round( (randu()*100.0) - 50.0 );
-}
+var x = discreteUniform( 10, -50, 50, {
+	'dtype': 'float64'
+});
 console.log( x );
 
 var v = variancetk( x.length, 1, x, 1 );
diff --git a/lib/node_modules/@stdlib/stats/base/variancetk/lib/variancetk.js b/lib/node_modules/@stdlib/stats/base/variancetk/lib/accessors.js
similarity index 56%
rename from lib/node_modules/@stdlib/stats/base/variancetk/lib/variancetk.js
rename to lib/node_modules/@stdlib/stats/base/variancetk/lib/accessors.js
index a8e405c071eb..b87f53931c13 100644
--- a/lib/node_modules/@stdlib/stats/base/variancetk/lib/variancetk.js
+++ b/lib/node_modules/@stdlib/stats/base/variancetk/lib/accessors.js
@@ -1,7 +1,7 @@
 /**
 * @license Apache-2.0
 *
-* Copyright (c) 2020 The Stdlib Authors.
+* 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.
@@ -23,19 +23,28 @@
 /**
 * Computes the variance of a strided array using a one-pass textbook algorithm.
 *
+* @private
 * @param {PositiveInteger} N - number of indexed elements
 * @param {number} correction - degrees of freedom adjustment
-* @param {NumericArray} x - input array
-* @param {integer} stride - stride length
+* @param {Object} x - input array object
+* @param {Collection} x.data - input array data
+* @param {Array<Function>} x.accessors - array element accessors
+* @param {integer} strideX - stride length
+* @param {NonNegativeInteger} offsetX - starting index
 * @returns {number} variance
 *
 * @example
-* var x = [ 1.0, -2.0, 2.0 ];
+* var toAccessorArray = require( '@stdlib/array/base/to-accessor-array' );
+* var arraylike2object = require( '@stdlib/array/base/arraylike2object' );
 *
-* var v = variancetk( x.length, 1, x, 1 );
+* var x = toAccessorArray( [ 1.0, -2.0, 2.0 ] );
+*
+* var v = variancetk( x.length, 1, arraylike2object( x ), 1, 0 );
 * // returns ~4.3333
 */
-function variancetk( N, correction, x, stride ) {
+function variancetk( N, correction, x, strideX, offsetX ) {
+	var xbuf;
+	var get;
 	var S2;
 	var ix;
 	var S;
@@ -47,21 +56,24 @@ function variancetk( N, correction, x, stride ) {
 	if ( N <= 0 || n <= 0.0 ) {
 		return NaN;
 	}
-	if ( N === 1 || stride === 0 ) {
-		return 0.0;
-	}
-	if ( stride < 0 ) {
-		ix = (1-N) * stride;
-	} else {
-		ix = 0;
+
+	// Cache reference to array data:
+	xbuf = x.data;
+
+	// Cache a reference to the element accessor:
+	get = x.accessors[ 0 ];
+
+	if ( N === 1 || strideX === 0 ) {
+		return get( xbuf, offsetX );
 	}
+	ix = offsetX;
 	S2 = 0.0;
 	S = 0.0;
 	for ( i = 0; i < N; i++ ) {
-		v = x[ ix ];
+		v = get( xbuf, ix );
 		S2 += v * v;
 		S += v;
-		ix += stride;
+		ix += strideX;
 	}
 	return (S2 - ((S/N)*S)) / n;
 }
diff --git a/lib/node_modules/@stdlib/stats/base/variancetk/lib/index.js b/lib/node_modules/@stdlib/stats/base/variancetk/lib/index.js
index 3607e87814a5..c7393ac857a5 100644
--- a/lib/node_modules/@stdlib/stats/base/variancetk/lib/index.js
+++ b/lib/node_modules/@stdlib/stats/base/variancetk/lib/index.js
@@ -27,25 +27,29 @@
 * var variancetk = require( '@stdlib/stats/base/variancetk' );
 *
 * var x = [ 1.0, -2.0, 2.0 ];
-* var N = x.length;
 *
-* var v = variancetk( N, 1, x, 1 );
+* var v = variancetk( x.length, 1, x, 1 );
 * // returns ~4.3333
 *
 * @example
-* var floor = require( '@stdlib/math/base/special/floor' );
 * var variancetk = require( '@stdlib/stats/base/variancetk' );
 *
 * var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ];
-* var N = floor( x.length / 2 );
 *
-* var v = variancetk.ndarray( N, 1, x, 2, 1 );
+* var v = variancetk.ndarray( 4, 1, x, 2, 1 );
 * // returns 6.25
 */
 
 // MODULES //
 
+var setReadOnly = require( '@stdlib/utils/define-nonenumerable-read-only-property' );
 var main = require( './main.js' );
+var ndarray = require( './ndarray.js' );
+
+
+// MAIN //
+
+setReadOnly( main, 'ndarray', ndarray );
 
 
 // EXPORTS //
diff --git a/lib/node_modules/@stdlib/stats/base/variancetk/lib/main.js b/lib/node_modules/@stdlib/stats/base/variancetk/lib/main.js
index 024e93aff02d..8b1e9aa89281 100644
--- a/lib/node_modules/@stdlib/stats/base/variancetk/lib/main.js
+++ b/lib/node_modules/@stdlib/stats/base/variancetk/lib/main.js
@@ -20,14 +20,31 @@
 
 // MODULES //
 
-var setReadOnly = require( '@stdlib/utils/define-nonenumerable-read-only-property' );
-var variancetk = require( './variancetk.js' );
+var stride2offset = require( '@stdlib/strided/base/stride2offset' );
 var ndarray = require( './ndarray.js' );
 
 
 // MAIN //
 
-setReadOnly( variancetk, 'ndarray', ndarray );
+/**
+* Computes the variance of a strided array.
+*
+* @param {PositiveInteger} N - number of indexed elements
+* @param {PositiveInteger} correction - degrees of freedom adjustment
+* @param {NumericArray} x - input array
+* @param {integer} strideX - stride length
+* @returns {number} variance
+*
+* @example
+* var x = [ 1.0, -2.0, 2.0 ];
+* var N = x.length;
+*
+* var v = variancetk( N, 1, x, 1 );
+* // returns ~4.3333
+*/
+function variancetk( N, correction, x, strideX ) {
+	return ndarray( N, correction, x, strideX, stride2offset( N, strideX ) );
+}
 
 
 // EXPORTS //
diff --git a/lib/node_modules/@stdlib/stats/base/variancetk/lib/ndarray.js b/lib/node_modules/@stdlib/stats/base/variancetk/lib/ndarray.js
index 90f1bb04897f..0d7439b12c84 100644
--- a/lib/node_modules/@stdlib/stats/base/variancetk/lib/ndarray.js
+++ b/lib/node_modules/@stdlib/stats/base/variancetk/lib/ndarray.js
@@ -18,6 +18,12 @@
 
 'use strict';
 
+// MODULES //
+
+var arraylike2object = require( '@stdlib/array/base/arraylike2object' );
+var accessors = require( './accessors.js' );
+
+
 // MAIN //
 
 /**
@@ -26,22 +32,20 @@
 * @param {PositiveInteger} N - number of indexed elements
 * @param {number} correction - degrees of freedom adjustment
 * @param {NumericArray} x - input array
-* @param {integer} stride - stride length
-* @param {NonNegativeInteger} offset - starting index
+* @param {integer} strideX - stride length
+* @param {NonNegativeInteger} offsetX - starting index
 * @returns {number} variance
 *
 * @example
-* var floor = require( '@stdlib/math/base/special/floor' );
-*
 * var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ];
-* var N = floor( x.length / 2 );
 *
-* var v = variancetk( N, 1, x, 2, 1 );
+* var v = variancetk( 4, 1, x, 2, 1 );
 * // returns 6.25
 */
-function variancetk( N, correction, x, stride, offset ) {
+function variancetk( N, correction, x, strideX, offsetX ) {
 	var S2;
 	var ix;
+	var o;
 	var S;
 	var v;
 	var n;
@@ -51,17 +55,21 @@ function variancetk( N, correction, x, stride, offset ) {
 	if ( N <= 0 || n <= 0.0 ) {
 		return NaN;
 	}
-	if ( N === 1 || stride === 0 ) {
+	o = arraylike2object( x );
+	if ( N === 1 || strideX === 0 ) {
 		return 0.0;
 	}
-	ix = offset;
+	if ( o.accessorProtocol ) {
+		return accessors( N, correction, o, strideX, offsetX );
+	}
+	ix = offsetX;
 	S2 = 0.0;
 	S = 0.0;
 	for ( i = 0; i < N; i++ ) {
 		v = x[ ix ];
 		S2 += v * v;
 		S += v;
-		ix += stride;
+		ix += strideX;
 	}
 	return (S2 - ((S/N)*S)) / n;
 }
diff --git a/lib/node_modules/@stdlib/stats/base/variancetk/test/test.variancetk.js b/lib/node_modules/@stdlib/stats/base/variancetk/test/test.main.js
similarity index 79%
rename from lib/node_modules/@stdlib/stats/base/variancetk/test/test.variancetk.js
rename to lib/node_modules/@stdlib/stats/base/variancetk/test/test.main.js
index 229197f46ff8..b2bbc12157a6 100644
--- a/lib/node_modules/@stdlib/stats/base/variancetk/test/test.variancetk.js
+++ b/lib/node_modules/@stdlib/stats/base/variancetk/test/test.main.js
@@ -21,10 +21,10 @@
 // MODULES //
 
 var tape = require( 'tape' );
-var floor = require( '@stdlib/math/base/special/floor' );
 var isnan = require( '@stdlib/math/base/assert/is-nan' );
+var toAccessorArray = require( '@stdlib/array/base/to-accessor-array' );
 var Float64Array = require( '@stdlib/array/float64' );
-var variancetk = require( './../lib/variancetk.js' );
+var variancetk = require( './../lib/main.js' );
 
 
 // TESTS //
@@ -59,20 +59,20 @@ tape( 'the function calculates the population variance of a strided array', func
 	t.end();
 });
 
-tape( 'the function calculates the sample variance of a strided array', function test( t ) {
+tape( 'the function calculates the sample variance of a strided array (accessors)', function test( t ) {
 	var x;
 	var v;
 
 	x = [ 1.0, -2.0, -4.0, 5.0, 0.0, 3.0 ];
-	v = variancetk( x.length, 1, x, 1 );
+	v = variancetk( x.length, 1, toAccessorArray( x ), 1 );
 	t.strictEqual( v, 53.5/(x.length-1), 'returns expected value' );
 
 	x = [ -4.0, -4.0 ];
-	v = variancetk( x.length, 1, x, 1 );
+	v = variancetk( x.length, 1, toAccessorArray( x ), 1 );
 	t.strictEqual( v, 0.0, 'returns expected value' );
 
 	x = [ NaN, 4.0 ];
-	v = variancetk( x.length, 1, x, 1 );
+	v = variancetk( x.length, 1, toAccessorArray( x ), 1 );
 	t.strictEqual( isnan( v ), true, 'returns expected value' );
 
 	t.end();
@@ -121,7 +121,6 @@ tape( 'if provided a `correction` parameter yielding `N-correction` less than or
 });
 
 tape( 'the function supports a `stride` parameter', function test( t ) {
-	var N;
 	var x;
 	var v;
 
@@ -136,15 +135,34 @@ tape( 'the function supports a `stride` parameter', function test( t ) {
 		2.0
 	];
 
-	N = floor( x.length / 2 );
-	v = variancetk( N, 1, x, 2 );
+	v = variancetk( 4, 1, x, 2 );
+
+	t.strictEqual( v, 6.25, 'returns expected value' );
+	t.end();
+});
+
+tape( 'the function supports a `stride` parameter (accessors)', function test( t ) {
+	var x;
+	var v;
+
+	x = [
+		1.0,  // 0
+		2.0,
+		2.0,  // 1
+		-7.0,
+		-2.0, // 2
+		3.0,
+		4.0,  // 3
+		2.0
+	];
+
+	v = variancetk( 4, 1, toAccessorArray( x ), 2 );
 
 	t.strictEqual( v, 6.25, 'returns expected value' );
 	t.end();
 });
 
 tape( 'the function supports a negative `stride` parameter', function test( t ) {
-	var N;
 	var x;
 	var v;
 
@@ -159,8 +177,28 @@ tape( 'the function supports a negative `stride` parameter', function test( t )
 		2.0
 	];
 
-	N = floor( x.length / 2 );
-	v = variancetk( N, 1, x, -2 );
+	v = variancetk( 4, 1, x, -2 );
+
+	t.strictEqual( v, 6.25, 'returns expected value' );
+	t.end();
+});
+
+tape( 'the function supports a negative `stride` parameter (accessors)', function test( t ) {
+	var x;
+	var v;
+
+	x = [
+		1.0,  // 3
+		2.0,
+		2.0,  // 2
+		-7.0,
+		-2.0, // 1
+		3.0,
+		4.0,  // 0
+		2.0
+	];
+
+	v = variancetk( 4, 1, toAccessorArray( x ), -2 );
 
 	t.strictEqual( v, 6.25, 'returns expected value' );
 	t.end();
@@ -181,7 +219,6 @@ tape( 'if provided a `stride` parameter equal to `0`, the function returns `0`',
 tape( 'the function supports view offsets', function test( t ) {
 	var x0;
 	var x1;
-	var N;
 	var v;
 
 	x0 = new Float64Array([
@@ -197,9 +234,8 @@ tape( 'the function supports view offsets', function test( t ) {
 	]);
 
 	x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
-	N = floor(x1.length / 2);
 
-	v = variancetk( N, 1, x1, 2 );
+	v = variancetk( 4, 1, x1, 2 );
 	t.strictEqual( v, 6.25, 'returns expected value' );
 
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/variancetk/test/test.ndarray.js b/lib/node_modules/@stdlib/stats/base/variancetk/test/test.ndarray.js
index fa2e93cce0bd..cd1527fa6ea8 100644
--- a/lib/node_modules/@stdlib/stats/base/variancetk/test/test.ndarray.js
+++ b/lib/node_modules/@stdlib/stats/base/variancetk/test/test.ndarray.js
@@ -21,8 +21,8 @@
 // MODULES //
 
 var tape = require( 'tape' );
-var floor = require( '@stdlib/math/base/special/floor' );
 var isnan = require( '@stdlib/math/base/assert/is-nan' );
+var toAccessorArray = require( '@stdlib/array/base/to-accessor-array' );
 var variancetk = require( './../lib/ndarray.js' );
 
 
@@ -58,6 +58,25 @@ tape( 'the function calculates the population variance of a strided array', func
 	t.end();
 });
 
+tape( 'the function calculates the population variance of a strided array (accessors)', function test( t ) {
+	var x;
+	var v;
+
+	x = [ 1.0, -2.0, -4.0, 5.0, 0.0, 3.0 ];
+	v = variancetk( x.length, 0, toAccessorArray( x ), 1, 0 );
+	t.strictEqual( v, 53.5/x.length, 'returns expected value' );
+
+	x = [ -4.0, -4.0 ];
+	v = variancetk( x.length, 0, toAccessorArray( x ), 1, 0 );
+	t.strictEqual( v, 0.0, 'returns expected value' );
+
+	x = [ NaN, 4.0 ];
+	v = variancetk( x.length, 0, toAccessorArray( x ), 1, 0 );
+	t.strictEqual( isnan( v ), true, 'returns expected value' );
+
+	t.end();
+});
+
 tape( 'the function calculates the sample variance of a strided array', function test( t ) {
 	var x;
 	var v;
@@ -77,6 +96,25 @@ tape( 'the function calculates the sample variance of a strided array', function
 	t.end();
 });
 
+tape( 'the function calculates the sample variance of a strided array (accessors)', function test( t ) {
+	var x;
+	var v;
+
+	x = [ 1.0, -2.0, -4.0, 5.0, 0.0, 3.0 ];
+	v = variancetk( x.length, 1, toAccessorArray( x ), 1, 0 );
+	t.strictEqual( v, 53.5/(x.length-1), 'returns expected value' );
+
+	x = [ -4.0, -4.0 ];
+	v = variancetk( x.length, 1, toAccessorArray( x ), 1, 0 );
+	t.strictEqual( v, 0.0, 'returns expected value' );
+
+	x = [ NaN, 4.0 ];
+	v = variancetk( x.length, 1, toAccessorArray( x ), 1, 0 );
+	t.strictEqual( isnan( v ), true, 'returns expected value' );
+
+	t.end();
+});
+
 tape( 'if provided an `N` parameter less than or equal to `0`, the function returns `NaN`', function test( t ) {
 	var x;
 	var v;
@@ -120,7 +158,6 @@ tape( 'if provided a `correction` parameter yielding `N-correction` less than or
 });
 
 tape( 'the function supports a `stride` parameter', function test( t ) {
-	var N;
 	var x;
 	var v;
 
@@ -135,15 +172,34 @@ tape( 'the function supports a `stride` parameter', function test( t ) {
 		2.0
 	];
 
-	N = floor( x.length / 2 );
-	v = variancetk( N, 1, x, 2, 0 );
+	v = variancetk( 4, 1, x, 2, 0 );
+
+	t.strictEqual( v, 6.25, 'returns expected value' );
+	t.end();
+});
+
+tape( 'the function supports a `stride` parameter (accessors)', function test( t ) {
+	var x;
+	var v;
+
+	x = [
+		1.0,  // 0
+		2.0,
+		2.0,  // 1
+		-7.0,
+		-2.0, // 2
+		3.0,
+		4.0,  // 3
+		2.0
+	];
+
+	v = variancetk( 4, 1, toAccessorArray( x ), 2, 0 );
 
 	t.strictEqual( v, 6.25, 'returns expected value' );
 	t.end();
 });
 
 tape( 'the function supports a negative `stride` parameter', function test( t ) {
-	var N;
 	var x;
 	var v;
 
@@ -158,8 +214,28 @@ tape( 'the function supports a negative `stride` parameter', function test( t )
 		2.0
 	];
 
-	N = floor( x.length / 2 );
-	v = variancetk( N, 1, x, -2, 6 );
+	v = variancetk( 4, 1, x, -2, 6 );
+
+	t.strictEqual( v, 6.25, 'returns expected value' );
+	t.end();
+});
+
+tape( 'the function supports a negative `stride` parameter (accessors)', function test( t ) {
+	var x;
+	var v;
+
+	x = [
+		1.0,  // 3
+		2.0,
+		2.0,  // 2
+		-7.0,
+		-2.0, // 1
+		3.0,
+		4.0,  // 0
+		2.0
+	];
+
+	v = variancetk( 4, 1, toAccessorArray( x ), -2, 6 );
 
 	t.strictEqual( v, 6.25, 'returns expected value' );
 	t.end();
@@ -177,8 +253,19 @@ tape( 'if provided a `stride` parameter equal to `0`, the function returns `0`',
 	t.end();
 });
 
+tape( 'if provided a `stride` parameter equal to `0`, the function returns `0` (accessors)', function test( t ) {
+	var x;
+	var v;
+
+	x = [ 1.0, -2.0, -4.0, 5.0, 3.0 ];
+
+	v = variancetk( x.length, 1, toAccessorArray( x ), 0, 0 );
+	t.strictEqual( v, 0.0, 'returns expected value' );
+
+	t.end();
+});
+
 tape( 'the function supports an `offset` parameter', function test( t ) {
-	var N;
 	var x;
 	var v;
 
@@ -192,9 +279,29 @@ tape( 'the function supports an `offset` parameter', function test( t ) {
 		3.0,
 		4.0   // 3
 	];
-	N = floor( x.length / 2 );
 
-	v = variancetk( N, 1, x, 2, 1 );
+	v = variancetk( 4, 1, x, 2, 1 );
+	t.strictEqual( v, 6.25, 'returns expected value' );
+
+	t.end();
+});
+
+tape( 'the function supports an `offset` parameter (accessors)', function test( t ) {
+	var x;
+	var v;
+
+	x = [
+		2.0,
+		1.0,  // 0
+		2.0,
+		-2.0, // 1
+		-2.0,
+		2.0,  // 2
+		3.0,
+		4.0   // 3
+	];
+
+	v = variancetk( 4, 1, toAccessorArray( x ), 2, 1 );
 	t.strictEqual( v, 6.25, 'returns expected value' );
 
 	t.end();