refactor: add requested changes

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Author: rahulptl165

lib/node_modules/@stdlib/stats/base/nanvariancech/README.md

@@ -119,23 +119,23 @@ The function has the following parameters:
 The `N` and stride parameters determine which elements in the stided array are accessed at runtime. For example, to compute the [variance][variance] of every other element in `x`,
 
 ```javascript
-var x = [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0, NaN ];
+var x = [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0, NaN, NaN ];
 
-var v = nanvariancech( 4, 1, x, 2 );
+var v = nanvariancech( 5, 1, x, 2 );
 // returns 6.25
 ```
 
 Note that indexing is relative to the first index. To introduce an offset, use [`typed array`][mdn-typed-array] views.
 
-<!-- eslint-disable stdlib/capitalized-comments -->
+<!-- eslint-disable stdlib/capitalized-comments, max-len -->
 
 ```javascript
 var Float64Array = require( '@stdlib/array/float64' );
 
-var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN ] );
+var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN, NaN ] );
 var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
 
-var v = nanvariancech( 4, 1, x1, 2 );
+var v = nanvariancech( 5, 1, x1, 2 );
 // returns 6.25
 ```
 
@@ -175,7 +175,7 @@ var v = nanvariancech.ndarray( 5, 1, x, 2, 1 );
 -   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`.
 -   The underlying algorithm is a specialized case of Neely's two-pass algorithm. As the variance is invariant with respect to changes in the location parameter, the underlying algorithm uses the first non-`NaN` strided array element as a trial mean to shift subsequent data values and thus mitigate catastrophic cancellation. Accordingly, the algorithm's accuracy is best when data is **unordered** (i.e., the data is **not** sorted in either ascending or descending order such that the first value is an "extreme" value).
 -   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]).
--   Depending on the environment, the typed versions ([`dnanvariancech`][@stdlib/stats/base/dnanvariancech], [`snanvariancech`][@stdlib/stats/base/snanvariancech], etc.) are likely to be significantly more performant.
+-   Depending on the environment, the typed versions ([`dnanvariancech`][@stdlib/stats/strided/dnanvariancech], [`snanvariancech`][@stdlib/stats/base/snanvariancech], etc.) are likely to be significantly more performant.
 
 </section>
 
@@ -234,7 +234,7 @@ console.log( v );
 
 ## See Also
 
--   <span class="package-name">[`@stdlib/stats/base/dnanvariancech`][@stdlib/stats/base/dnanvariancech]</span><span class="delimiter">: </span><span class="description">calculate the variance of a double-precision floating-point strided array ignoring NaN values and using a one-pass trial mean algorithm.</span>
+-   <span class="package-name">[`@stdlib/stats/strided/dnanvariancech`][@stdlib/stats/strided/dnanvariancech]</span><span class="delimiter">: </span><span class="description">calculate the variance of a double-precision floating-point strided array ignoring NaN values and using a one-pass trial mean algorithm.</span>
 -   <span class="package-name">[`@stdlib/stats/base/nanstdevch`][@stdlib/stats/base/nanstdevch]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a strided array ignoring NaN values and using a one-pass trial mean algorithm.</span>
 -   <span class="package-name">[`@stdlib/stats/base/nanvariance`][@stdlib/stats/base/nanvariance]</span><span class="delimiter">: </span><span class="description">calculate the variance of a strided array ignoring NaN values.</span>
 -   <span class="package-name">[`@stdlib/stats/base/snanvariancech`][@stdlib/stats/base/snanvariancech]</span><span class="delimiter">: </span><span class="description">calculate the variance of a single-precision floating-point strided array ignoring NaN values and using a one-pass trial mean algorithm.</span>
@@ -264,7 +264,7 @@ console.log( v );
 
 <!-- <related-links> -->
 
-[@stdlib/stats/base/dnanvariancech]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/dnanvariancech
+[@stdlib/stats/strided/dnanvariancech]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanvariancech
 
 [@stdlib/stats/base/nanstdevch]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/nanstdevch
 

lib/node_modules/@stdlib/stats/base/nanvariancech/docs/repl.txt

@@ -3,7 +3,7 @@
     Computes the variance of a strided array ignoring `NaN` values and using a
     one-pass trial mean algorithm.
 
-    The `N` and stride parameters determine which elements in the strided arrays
+    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
@@ -50,14 +50,14 @@
     ~4.3333
 
     // Using `N` and `stride` parameters:
-    > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ];
-    > {{alias}}( 3, 1, x, 2 )
+    > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0, NaN, NaN ];
+    > {{alias}}( 4, 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 x0 = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ] );
     > var x1 = new {{alias:@stdlib/array/float64}}( x0.buffer, x0.BYTES_PER_ELEMENT*1 );
-    > {{alias}}( 3, 1, x1, 2 )
+    > {{alias}}( 4, 1, x1, 2 )
     ~4.3333
 
 
@@ -66,8 +66,8 @@
     one-pass trial mean algorithm and alternative indexing semantics.
 
     While typed array views mandate a view offset based on the underlying
-    buffer, the offset parameters support indexing semantics based on a
-    starting indices.
+    buffer, the offset parameter supports indexing semantics based on a
+    starting index.
 
     Parameters
     ----------
@@ -109,8 +109,8 @@
     ~4.3333
 
     // Using offset parameter:
-    > var x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];
-    > {{alias}}.ndarray( 3, 1, x, 2, 1 )
+    > var x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ];
+    > {{alias}}.ndarray( 4, 1, x, 2, 1 )
     ~4.3333
 
     See Also

lib/node_modules/@stdlib/stats/base/nanvariancech/test/test.nanvariancech.js

@@ -352,7 +352,7 @@ tape( 'the function supports a `stride` parameter', function test( t ) {
 		NaN
 	];
 
-	v = nanvariancech( 4, 1, x, 2 );
+	v = nanvariancech( 5, 1, x, 2 );
 
 	t.strictEqual( v, 6.25, 'returns expected value' );
 	t.end();
@@ -375,7 +375,7 @@ tape( 'the function supports a `stride` parameter (accessors)', function test( t
 		NaN
 	];
 
-	v = nanvariancech( 4, 1, toAccessorArray( x ), 2 );
+	v = nanvariancech( 5, 1, toAccessorArray( x ), 2 );
 
 	t.strictEqual( v, 6.25, 'returns expected value' );
 	t.end();
@@ -486,7 +486,7 @@ tape( 'the function supports view offsets', function test( t ) {
 
 	x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
 
-	v = nanvariancech( 4, 1, x1, 2 );
+	v = nanvariancech( 5, 1, x1, 2 );
 	t.strictEqual( v, 6.25, 'returns expected value' );
 
 	t.end();

lib/node_modules/@stdlib/stats/base/nanvariancech/test/test.ndarray.js

@@ -351,7 +351,7 @@ tape( 'the function supports a `stride` parameter', function test( t ) {
 		NaN
 	];
 
-	v = nanvariancech( 4, 1, x, 2, 0 );
+	v = nanvariancech( 5, 1, x, 2, 0 );
 
 	t.strictEqual( v, 6.25, 'returns expected value' );
 	t.end();
@@ -375,7 +375,7 @@ tape( 'the function supports a negative `stride` parameter', function test( t )
 		2.0
 	];
 
-	v = nanvariancech( 4, 1, x, -2, 8 );
+	v = nanvariancech( 5, 1, x, -2, 8 );
 	t.strictEqual( v, 6.25, 'returns expected value' );
 
 	x = [];
@@ -405,7 +405,7 @@ tape( 'the function supports a `stride` parameter (accessors)', function test( t
 		NaN
 	];
 
-	v = nanvariancech( 4, 1, toAccessorArray( x ), 2, 0 );
+	v = nanvariancech( 5, 1, toAccessorArray( x ), 2, 0 );
 
 	t.strictEqual( v, 6.25, 'returns expected value' );
 	t.end();
@@ -429,7 +429,7 @@ tape( 'the function supports a negative `stride` parameter (accessors)', functio
 		2.0
 	];
 
-	v = nanvariancech( 4, 1, toAccessorArray( x ), -2, 8 );
+	v = nanvariancech( 5, 1, toAccessorArray( x ), -2, 8 );
 	t.strictEqual( v, 6.25, 'returns expected value' );
 
 	x = [];
@@ -481,7 +481,7 @@ tape( 'the function supports an `offset` parameter', function test( t ) {
 		NaN   // 4
 	];
 
-	v = nanvariancech( 4, 1, x, 2, 1 );
+	v = nanvariancech( 5, 1, x, 2, 1 );
 	t.strictEqual( v, 6.25, 'returns expected value' );
 
 	t.end();
@@ -504,7 +504,7 @@ tape( 'the function supports an `offset` parameter (accessors)', function test(
 		NaN   // 4
 	];
 
-	v = nanvariancech( 4, 1, toAccessorArray( x ), 2, 1 );
+	v = nanvariancech( 5, 1, toAccessorArray( x ), 2, 1 );
 	t.strictEqual( v, 6.25, 'returns expected value' );
 
 	t.end();