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

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

@@ -51,78 +51,67 @@ The [arithmetic mean][arithmetic-mean] is defined as
 var nanmeanors = require( '@stdlib/stats/base/nanmeanors' );
 ```
 
-#### nanmeanors( N, x, stride )
+#### nanmeanors( N, x, strideX )
 
 Computes the [arithmetic mean][arithmetic-mean] of a strided array `x`, ignoring `NaN` values and using ordinary recursive summation.
 
 ```javascript
 var x = [ 1.0, -2.0, NaN, 2.0 ];
-var N = x.length;
 
-var v = nanmeanors( N, x, 1 );
+var v = nanmeanors( 4, x, 1 );
 // returns ~0.3333
 ```
 
 The function has the following parameters:
 
 -   **N**: number of indexed elements.
 -   **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 [arithmetic mean][arithmetic-mean] 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 [arithmetic mean][arithmetic-mean] 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, NaN ];
 
-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 = nanmeanors( N, x, 2 );
+var v = nanmeanors( 5, x, 2 );
 // returns 1.25
 ```
 
 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 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 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 N = floor( x0.length / 2 );
-
-var v = nanmeanors( N, x1, 2 );
+var v = nanmeanors( 5, x1, 2 );
 // returns 1.25
 ```
 
-#### nanmeanors.ndarray( N, x, stride, offset )
+#### nanmeanors.ndarray( N, x, strideX, offsetX )
 
 Computes the [arithmetic mean][arithmetic-mean] of a strided array, ignoring `NaN` values and using ordinary recursive summation and alternative indexing semantics.
 
 ```javascript
 var x = [ 1.0, -2.0, NaN, 2.0 ];
-var N = x.length;
 
-var v = nanmeanors.ndarray( N, x, 1, 0 );
+var v = nanmeanors.ndarray( 4, x, 1, 0 );
 // returns ~0.33333
 ```
 
 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 [arithmetic mean][arithmetic-mean] 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 [arithmetic mean][arithmetic-mean] for every other value in `x` starting from the second value
 
 ```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, NaN, NaN ];
 
-var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN ];
-var N = floor( x.length / 2 );
-
-var v = nanmeanors.ndarray( N, x, 2, 1 );
+var v = nanmeanors.ndarray( 5, x, 2, 1 );
 // returns 1.25
 ```
 
@@ -137,6 +126,7 @@ var v = nanmeanors.ndarray( N, x, 2, 1 );
 -   If `N <= 0`, both functions return `NaN`.
 -   If every indexed element is `NaN`, both functions return `NaN`.
 -   Ordinary recursive summation (i.e., a "simple" sum) is performant, but can incur significant numerical error. If performance is paramount and error tolerated, using ordinary recursive summation to compute an arithmetic mean is acceptable; in all other cases, exercise due caution.
+-   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 ([`dnanmeanors`][@stdlib/stats/strided/dnanmeanors], [`snanmeanors`][@stdlib/stats/strided/snanmeanors], etc.) are likely to be significantly more performant.
 
 </section>
@@ -150,22 +140,19 @@ var v = nanmeanors.ndarray( N, 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 uniform = require( '@stdlib/random/base/uniform' );
+var filledarrayBy = require( '@stdlib/array/filled-by' );
+var bernoulli = require( '@stdlib/random/base/bernoulli' );
 var nanmeanors = require( '@stdlib/stats/base/nanmeanors' );
 
-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 );
+function rand() {
+    if ( bernoulli( 0.8 ) < 1 ) {
+        return NaN;
     }
+    return uniform( -50.0, 50.0 );
 }
+
+var x = filledarrayBy( 10, 'generic', rand );
 console.log( x );
 
 var v = nanmeanors( x.length, x, 1 );
@@ -219,6 +206,8 @@ console.log( v );
 
 [@stdlib/stats/strided/snanmeanors]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/snanmeanors
 
+[@stdlib/array/base/accessor]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/array/base/accessor
+
 <!-- </related-links> -->
 
 </section>

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

@@ -21,7 +21,9 @@
 // MODULES //
 
 var bench = require( '@stdlib/bench' );
-var randu = require( '@stdlib/random/base/randu' );
+var uniform = require( '@stdlib/random/base/uniform' );
+var bernoulli = require( '@stdlib/random/base/bernoulli' );
+var filledarrayBy = require( '@stdlib/array/filled-by' );
 var isnan = require( '@stdlib/math/base/assert/is-nan' );
 var pow = require( '@stdlib/math/base/special/pow' );
 var pkg = require( './../package.json' ).name;
@@ -30,6 +32,19 @@ var nanmeanors = require( './../lib/nanmeanors.js' );
 
 // FUNCTIONS //
 
+/**
+* Returns a random value or `NaN`.
+*
+* @private
+* @returns {number} random number or `NaN`
+*/
+function rand() {
+	if ( bernoulli( 0.8 ) < 1 ) {
+		return NaN;
+	}
+	return uniform( -10.0, 10.0 );
+}
+
 /**
 * Creates a benchmark function.
 *
@@ -38,17 +53,7 @@ var nanmeanors = require( './../lib/nanmeanors.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 = filledarrayBy( len, 'generic', rand );
 	return benchmark;
 
 	function benchmark( b ) {

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

@@ -21,7 +21,9 @@
 // MODULES //
 
 var bench = require( '@stdlib/bench' );
-var randu = require( '@stdlib/random/base/randu' );
+var uniform = require( '@stdlib/random/base/uniform' );
+var bernoulli = require( '@stdlib/random/base/bernoulli' );
+var filledarrayBy = require( '@stdlib/array/filled-by' );
 var isnan = require( '@stdlib/math/base/assert/is-nan' );
 var pow = require( '@stdlib/math/base/special/pow' );
 var pkg = require( './../package.json' ).name;
@@ -30,6 +32,19 @@ var nanmeanors = require( './../lib/ndarray.js' );
 
 // FUNCTIONS //
 
+/**
+* Returns a random value or `NaN`.
+*
+* @private
+* @returns {number} random number or `NaN`
+*/
+function rand() {
+	if ( bernoulli( 0.8 ) < 1 ) {
+		return NaN;
+	}
+	return uniform( -10.0, 10.0 );
+}
+
 /**
 * Creates a benchmark function.
 *
@@ -38,17 +53,7 @@ var nanmeanors = 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 = filledarrayBy( len, 'generic', rand );
 	return benchmark;
 
 	function benchmark( b ) {

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

@@ -1,10 +1,10 @@
 
-{{alias}}( N, x, stride )
+{{alias}}( N, x, strideX )
     Computes the arithmetic mean of a strided array, ignoring `NaN` values and
     using ordinary recursive summation.
 
-    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.
@@ -21,8 +21,8 @@
     x: Array<number>|TypedArray
         Input array.
 
-    stride: integer
-        Index increment.
+    strideX: integer
+        Stride length.
 
     Returns
     -------
@@ -33,25 +33,22 @@
     --------
     // Standard Usage:
     > var x = [ 1.0, -2.0, NaN, 2.0 ];
-    > {{alias}}( x.length, x, 1 )
+    > {{alias}}( 4, x, 1 )
     ~0.3333
 
-    // Using `N` and `stride` parameters:
-    > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0, NaN ];
-    > var N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 );
-    > var stride = 2;
-    > {{alias}}( N, x, stride )
+    // Using `N` and stride parameters:
+    > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0, NaN, NaN ];
+    > {{alias}}( 4, x, 2 )
     ~0.3333
 
     // Using view offsets:
-    > var x0 = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN ] );
+    > 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 );
-    > N = {{alias:@stdlib/math/base/special/floor}}( x0.length / 2 );
-    > stride = 2;
-    > {{alias}}( N, x1, stride )
+    > {{alias}}( 4, x1, 2 )
     ~-0.3333
 
-{{alias}}.ndarray( N, x, stride, offset )
+
+{{alias}}.ndarray( N, x, strideX, offsetX )
     Computes the arithmetic mean of a strided array, ignoring `NaN` values and
     using ordinary recursive summation and alternative indexing semantics.
 
@@ -67,10 +64,10 @@
     x: Array<number>|TypedArray
         Input array.
 
-    stride: integer
-        Index increment.
+    strideX: integer
+        Stride length.
 
-    offset: integer
+    offsetX: integer
         Starting index.
 
     Returns
@@ -82,13 +79,12 @@
     --------
     // Standard Usage:
     > var x =[ 1.0, -2.0, NaN, 2.0 ];
-    > {{alias}}.ndarray( x.length, x, 1, 0 )
+    > {{alias}}.ndarray( 4, x, 1, 0 )
     ~0.3333
 
     // Using offset parameter:
-    > var x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN ];
-    > var N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 );
-    > {{alias}}.ndarray( N, x, 2, 1 )
+    > var x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ];
+    > {{alias}}.ndarray( 4, x, 2, 1 )
     ~-0.3333
 
     See Also

lib/node_modules/@stdlib/stats/base/nanmeanors/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 `nanmeanors`.
@@ -31,7 +36,7 @@ interface Routine {
 	*
 	* @param N - number of indexed elements
 	* @param x - input array
-	* @param stride - stride length
+	* @param strideX - stride length
 	* @returns arithmetic mean
 	*
 	* @example
@@ -40,15 +45,15 @@ interface Routine {
 	* var v = nanmeanors( x.length, x, 1 );
 	* // returns ~0.3333
 	*/
-	( N: number, x: NumericArray, stride: number ): number;
+	( N: number, x: InputArray, strideX: number ): number;
 
 	/**
 	* Computes the arithmetic mean of a strided array, ignoring `NaN` values and using ordinary recursive summation and alternative indexing semantics.
 	*
 	* @param N - number of indexed elements
 	* @param x - input array
-	* @param stride - stride length
-	* @param offset - starting index
+	* @param strideX - stride length
+	* @param offsetX - starting index
 	* @returns arithmetic mean
 	*
 	* @example
@@ -57,15 +62,15 @@ interface Routine {
 	* var v = nanmeanors.ndarray( x.length, x, 1, 0 );
 	* // returns ~0.3333
 	*/
-	ndarray( N: number, x: NumericArray, stride: number, offset: number ): number;
+	ndarray( N: number, x: InputArray, strideX: number, offsetX: number ): number;
 }
 
 /**
 * Computes the arithmetic mean of a strided array, ignoring `NaN` values and using ordinary recursive summation.
 *
 * @param N - number of indexed elements
 * @param x - input array
-* @param stride - stride length
+* @param strideX - stride length
 * @returns arithmetic mean
 *
 * @example