feat: add accessor protocol support and refactor stats/base/nanvariancech

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

@@ -98,7 +98,7 @@ The use of the term `n-1` is commonly referred to as Bessel's correction. Note,
 var nanvariancech = require( '@stdlib/stats/base/nanvariancech' );
 ```
 
-#### nanvariancech( N, correction, x, stride )
+#### nanvariancech( N, correction, x, strideX )
 
 Computes the [variance][variance] of a strided array `x` ignoring `NaN` values and using a one-pass trial mean 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 stided 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 = nanvariancech( N, 1, x, 2 );
+var v = nanvariancech( 4, 1, x, 2 );
 // returns 6.25
 ```
 
@@ -134,41 +131,35 @@ 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 = nanvariancech( N, 1, x1, 2 );
+var v = nanvariancech( 4, 1, x1, 2 );
 // returns 6.25
 ```
 
-#### nanvariancech.ndarray( N, correction, x, stride, offset )
+#### nanvariancech.ndarray( N, correction, x, strideX, offsetX )
 
 Computes the [variance][variance] of a strided array ignoring `NaN` values and using a one-pass trial mean algorithm and alternative indexing semantics.
 
 ```javascript
 var x = [ 1.0, -2.0, NaN, 2.0 ];
 
-var v = nanvariancech.ndarray( x.length, 1, x, 1, 0 );
+var v = nanvariancech.ndarray( 4, 1, x, 1, 0 );
 // returns ~4.33333
 ```
 
 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 = nanvariancech.ndarray( N, 1, x, 2, 1 );
+var v = nanvariancech.ndarray( 4, 1, x, 2, 1 );
 // returns 6.25
 ```
 
@@ -183,6 +174,7 @@ var v = nanvariancech.ndarray( N, 1, x, 2, 1 );
 -   If `N <= 0`, both functions return `NaN`.
 -   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.
 
 </section>
@@ -196,18 +188,11 @@ var v = nanvariancech.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 normal = require( '@stdlib/random/array/normal' );
 var nanvariancech = require( '@stdlib/stats/base/nanvariancech' );
 
-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 = normal( 10, 0, 1 );
+x[4] = NaN;
 console.log( x );
 
 var v = nanvariancech( x.length, 1, x, 1 );

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

@@ -21,7 +21,7 @@
 // MODULES //
 
 var bench = require( '@stdlib/bench' );
-var randu = require( '@stdlib/random/base/randu' );
+var normal = require( '@stdlib/random/array/normal' );
 var isnan = require( '@stdlib/math/base/assert/is-nan' );
 var pow = require( '@stdlib/math/base/special/pow' );
 var pkg = require( './../package.json' ).name;
@@ -37,18 +37,12 @@ var nanvariancech = require( './../lib/nanvariancech.js' );
 * @param {PositiveInteger} len - array length
 * @returns {Function} benchmark function
 */
-function createBenchmark( len ) {
-	var x;
-	var i;
+function createBenchmark() {
+	var x = normal( 10, 0, 1, {
+		'dtype': 'float64'
+	});
+	x[4] = NaN;
 
-	x = [];
-	for ( i = 0; i < len; i++ ) {
-		if ( randu() < 0.2 ) {
-			x.push( NaN );
-		} else {
-			x.push( ( randu()*20.0 ) - 10.0 );
-		}
-	}
 	return benchmark;
 
 	function benchmark( b ) {

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

@@ -21,7 +21,7 @@
 // MODULES //
 
 var bench = require( '@stdlib/bench' );
-var randu = require( '@stdlib/random/base/randu' );
+var normal = require( '@stdlib/random/array/normal' );
 var isnan = require( '@stdlib/math/base/assert/is-nan' );
 var pow = require( '@stdlib/math/base/special/pow' );
 var pkg = require( './../package.json' ).name;
@@ -37,18 +37,12 @@ var nanvariancech = require( './../lib/ndarray.js' );
 * @param {PositiveInteger} len - array length
 * @returns {Function} benchmark function
 */
-function createBenchmark( len ) {
-	var x;
-	var i;
+function createBenchmark() {
+	var x = normal( 10, 0, 1, {
+		'dtype': 'float64'
+	});
+	x[4] = NaN;
 
-	x = [];
-	for ( i = 0; i < len; i++ ) {
-		if ( randu() < 0.2 ) {
-			x.push( NaN );
-		} else {
-			x.push( ( randu()*20.0 ) - 10.0 );
-		}
-	}
 	return benchmark;
 
 	function benchmark( b ) {

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

@@ -1,9 +1,9 @@
 
-{{alias}}( N, correction, x, stride )
+{{alias}}( N, correction, x, strideX )
     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 `x` are accessed
+    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
@@ -34,8 +34,8 @@
     x: Array<number>|TypedArray
         Input array.
 
-    stride: integer
-        Index increment.
+    strideX: integer
+        stride length.
 
     Returns
     -------
@@ -46,25 +46,23 @@
     --------
     // Standard Usage:
     > var x = [ 1.0, -2.0, NaN, 2.0 ];
-    > {{alias}}( x.length, 1, x, 1 )
+    > {{alias}}( 4, 1, x, 1 )
     ~4.3333
 
     // 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, stride )
     ~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, stride )
     ~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 a
     one-pass trial mean algorithm and alternative indexing semantics.
 
@@ -93,10 +91,10 @@
     x: Array<number>|TypedArray
         Input array.
 
-    stride: integer
-        Index increment.
+    strideX: integer
+        stride length.
 
-    offset: integer
+    offsetX: integer
         Starting index.
 
     Returns
@@ -108,13 +106,12 @@
     --------
     // Standard Usage:
     > var x = [ 1.0, -2.0, NaN, 2.0 ];
-    > {{alias}}.ndarray( x.length, 1, x, 1, 0 )
+    > {{alias}}.ndarray( 4, 1, x, 1, 0 )
     ~4.3333
 
     // 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

lib/node_modules/@stdlib/stats/base/nanvariancech/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 `nanvariancech`.
@@ -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
@@ -41,16 +46,16 @@ interface Routine {
 	* var v = nanvariancech( 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 a one-pass trial mean algorithm and alternative indexing semantics.
 	*
 	* @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 = nanvariancech.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, offset: 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