feat: add C ndarray interface and refactor implementation for stats/base/dmeanvarpn

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

@@ -102,7 +102,7 @@ var dmeanvarpn = require( '@stdlib/stats/base/dmeanvarpn' );
 
 #### dmeanvarpn( N, correction, x, strideX, out, strideOut )
 
-Computes the [mean][arithmetic-mean] and [variance][variance] of a double-precision floating-point strided array `x` using a two-pass algorithm.
+Computes the [mean][arithmetic-mean] and [variance][variance] of a double-precision floating-point strided array using a two-pass algorithm.
 
 ```javascript
 var Float64Array = require( '@stdlib/array/float64' );
@@ -122,21 +122,19 @@ 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 [`Float64Array`][@stdlib/array/float64].
--   **strideX**: index increment for `x`.
+-   **strideX**: stride length for `x`.
 -   **out**: output [`Float64Array`][@stdlib/array/float64] for storing results.
--   **strideOut**: index increment for `out`.
+-   **strideOut**: stride length for `out`.
 
-The `N` and `stride` parameters determine which elements 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 arrays are accessed at runtime. For example, to compute the [variance][variance] of every other element in `x`,
 
 ```javascript
 var Float64Array = require( '@stdlib/array/float64' );
-var floor = require( '@stdlib/math/base/special/floor' );
 
 var x = new Float64Array( [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ] );
 var out = new Float64Array( 2 );
-var N = floor( x.length / 2 );
 
-var v = dmeanvarpn( N, 1, x, 2, out, 1 );
+var v = dmeanvarpn( 4, 1, x, 2, out, 1 );
 // returns <Float64Array>[ 1.25, 6.25 ]
 ```
 
@@ -146,17 +144,14 @@ 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 out0 = new Float64Array( 4 );
 var out1 = new Float64Array( out0.buffer, out0.BYTES_PER_ELEMENT*2 ); // start at 3rd element
 
-var N = floor( x0.length / 2 );
-
-var v = dmeanvarpn( N, 1, x1, 2, out1, 1 );
+var v = dmeanvarpn( 4, 1, x1, 2, out1, 1 );
 // returns <Float64Array>[ 1.25, 6.25 ]
 ```
 
@@ -179,17 +174,15 @@ The function has the following additional parameters:
 -   **offsetX**: starting index for `x`.
 -   **offsetOut**: starting index for `out`.
 
-While [`typed array`][mdn-typed-array] views mandate a view offset based on the underlying `buffer`, the `offset` parameters support indexing semantics based on a starting index. For example, to calculate the [mean][arithmetic-mean] and [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 parameters support indexing semantics based on a starting index. For example, to calculate the [mean][arithmetic-mean] and [variance][variance] for every other element in `x` starting from the second element
 
 ```javascript
 var Float64Array = require( '@stdlib/array/float64' );
-var floor = require( '@stdlib/math/base/special/floor' );
 
 var x = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
 var out = new Float64Array( 4 );
-var N = floor( x.length / 2 );
 
-var v = dmeanvarpn.ndarray( N, 1, x, 2, 1, out, 2, 1 );
+var v = dmeanvarpn.ndarray( 4, 1, x, 2, 1, out, 2, 1 );
 // returns <Float64Array>[ 0.0, 1.25, 0.0, 6.25 ]
 ```
 
@@ -215,22 +208,16 @@ var v = dmeanvarpn.ndarray( N, 1, x, 2, 1, out, 2, 1 );
 <!-- eslint no-undef: "error" -->
 
 ```javascript
-var randu = require( '@stdlib/random/base/randu' );
-var round = require( '@stdlib/math/base/special/round' );
+var discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
 var Float64Array = require( '@stdlib/array/float64' );
 var dmeanvarpn = require( '@stdlib/stats/base/dmeanvarpn' );
 
-var out;
-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 );
 
-out = new Float64Array( 2 );
+var out = new Float64Array( 2 );
 dmeanvarpn( x.length, 1, x, 1, out, 1 );
 console.log( out );
 ```
@@ -239,6 +226,135 @@ console.log( out );
 
 <!-- /.examples -->
 
+<!-- C interface documentation. -->
+
+* * *
+
+<section class="c">
+
+## C APIs
+
+<!-- Section to include introductory text. Make sure to keep an empty line after the intro `section` element and another before the `/section` close. -->
+
+<section class="intro">
+
+</section>
+
+<!-- /.intro -->
+
+<!-- C usage documentation. -->
+
+<section class="usage">
+
+### Usage
+
+```c
+#include "stdlib/stats/base/dmeanvarpn.h"
+```
+
+#### stdlib_strided_dmeanvarpn( N, correction, \*X, strideX, \*Out, strideOut )
+
+Computes the [mean][arithmetic-mean] and [variance][variance] of a double-precision floating-point strided array using a two-pass algorithm.
+
+```c
+const double x[] = { 1.0, -2.0, 2.0 };
+double out[] = { 0.0, 0.0 }
+
+stdlib_strided_dmeanvarpn( 3, 1.0, x, 1, out, 1 );
+```
+
+The function accepts the following arguments:
+
+-   **N**: `[in] CBLAS_INT` number of indexed elements.
+-   **correction**: `[in] double` 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**: `[in] double*` input array.
+-   **strideX**: `[in] CBLAS_INT` stride length for `X`.
+-   **Out**: `[out] double*` output array.
+-   **strideOut**: `[in] CBLAS_INT` stride length for `Out`.
+
+```c
+double stdlib_strided_dmeanvarpn( const CBLAS_INT N, const double correction, const double *X, const CBLAS_INT strideX, double *Out, const CBLAS_INT strideOut );
+```
+
+#### stdlib_strided_dmeanvarpn( N, correction, \*X, strideX, offsetX, \*Out, strideOut, offsetOut )
+
+Computes the [mean][arithmetic-mean] and [variance][variance] of a double-precision floating-point strided array using a two-pass algorithm and alternative indexing semantics.
+
+```c
+const double x[] = { 1.0, -2.0, 2.0 };
+double out[] = { 0.0, 0.0 }
+
+stdlib_strided_dmeanvarpn_ndarray( 3, 1.0, x, 1, 0, out, 1, 0 );
+```
+
+The function accepts the following arguments:
+
+-   **N**: `[in] CBLAS_INT` number of indexed elements.
+-   **correction**: `[in] double` 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**: `[in] double*` input array.
+-   **strideX**: `[in] CBLAS_INT` stride length for `X`.
+-   **offsetX**: `[in] CBLAS_INT` starting index for `X`.
+-   **Out**: `[out] double*` output array.
+-   **strideOut**: `[in] CBLAS_INT` stride length for `Out`.
+-   **offsetOut**: `[in] CBLAS_INT` starting index for `Out`.
+
+```c
+double stdlib_strided_dmeanvarpn_ndarray( const CBLAS_INT N, const double correction, const double *X, const CBLAS_INT strideX, const CBLAS_INT offsetX, double *Out, const CBLAS_INT strideOut, const CBLAS_INT offsetOut );
+```
+
+</section>
+
+<!-- /.usage -->
+
+<!-- C API usage notes. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
+
+<section class="notes">
+
+</section>
+
+<!-- /.notes -->
+
+<!-- C API usage examples. -->
+
+<section class="examples">
+
+### Examples
+
+```c
+#include "stdlib/stats/base/dmeanvarpn.h"
+#include <stdio.h>
+
+int main( void ) {
+    // Create a strided array:
+    const double x[] = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0 };
+
+    // Create an output array:
+    double out[] = { 0.0, 0.0 };
+
+    // Specify the number of elements:
+    const int N = 4;
+
+    // Specify the stride lengths:
+    const int strideX = 2;
+    const int strideOut = 1;
+
+    // Compute the mean and variance:
+    stdlib_strided_dmeanvarpn( N, 1.0, x, strideX, out, strideOut );
+
+    // Print the result:
+    printf( "sample mean: %lf\n", out[ 0 ] );
+    printf( "sample variance: %lf\n", out[ 1 ] );
+}
+```
+
+</section>
+
+<!-- /.examples -->
+
+</section>
+
+<!-- /.c -->
+
 * * *
 
 <section class="references">

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

@@ -21,14 +21,21 @@
 // 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 Float64Array = require( '@stdlib/array/float64' );
 var pkg = require( './../package.json' ).name;
 var dmeanvarpn = require( './../lib/dmeanvarpn.js' );
 
 
+// VARIABLES //
+
+var options = {
+	'dtype': 'float64'
+};
+
+
 // FUNCTIONS //
 
 /**
@@ -39,15 +46,8 @@ var dmeanvarpn = require( './../lib/dmeanvarpn.js' );
 * @returns {Function} benchmark function
 */
 function createBenchmark( len ) {
-	var out;
-	var x;
-	var i;
-
-	x = new Float64Array( len );
-	for ( i = 0; i < x.length; i++ ) {
-		x[ i ] = ( randu()*20.0 ) - 10.0;
-	}
-	out = new Float64Array( 2 );
+	var out = new Float64Array( 2 );
+	var x = uniform( len, -10.0, 10.0, options );
 	return benchmark;
 
 	function benchmark( b ) {

lib/node_modules/@stdlib/stats/base/dmeanvarpn/benchmark/benchmark.native.js

@@ -22,7 +22,7 @@
 
 var resolve = require( 'path' ).resolve;
 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 Float64Array = require( '@stdlib/array/float64' );
@@ -36,6 +36,9 @@ var dmeanvarpn = tryRequire( resolve( __dirname, './../lib/dmeanvarpn.native.js'
 var opts = {
 	'skip': ( dmeanvarpn instanceof Error )
 };
+var options = {
+	'dtype': 'float64'
+};
 
 
 // FUNCTIONS //
@@ -48,15 +51,8 @@ var opts = {
 * @returns {Function} benchmark function
 */
 function createBenchmark( len ) {
-	var out;
-	var x;
-	var i;
-
-	x = new Float64Array( len );
-	for ( i = 0; i < x.length; i++ ) {
-		x[ i ] = ( randu()*20.0 ) - 10.0;
-	}
-	out = new Float64Array( 2 );
+	var out = new Float64Array( 2 );
+	var x = uniform( len, -10.0, 10.0, options );
 	return benchmark;
 
 	function benchmark( b ) {

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

@@ -21,14 +21,21 @@
 // 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 Float64Array = require( '@stdlib/array/float64' );
 var pkg = require( './../package.json' ).name;
 var dmeanvarpn = require( './../lib/ndarray.js' );
 
 
+// VARIABLES //
+
+var options = {
+	'dtype': 'float64'
+};
+
+
 // FUNCTIONS //
 
 /**
@@ -39,15 +46,8 @@ var dmeanvarpn = require( './../lib/ndarray.js' );
 * @returns {Function} benchmark function
 */
 function createBenchmark( len ) {
-	var out;
-	var x;
-	var i;
-
-	x = new Float64Array( len );
-	for ( i = 0; i < x.length; i++ ) {
-		x[ i ] = ( randu()*20.0 ) - 10.0;
-	}
-	out = new Float64Array( 2 );
+	var out = new Float64Array( 2 );
+	var x = uniform( len, -10.0, 10.0, options );
 	return benchmark;
 
 	function benchmark( b ) {

lib/node_modules/@stdlib/stats/base/dmeanvarpn/benchmark/benchmark.ndarray.native.js

@@ -22,7 +22,7 @@
 
 var resolve = require( 'path' ).resolve;
 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 Float64Array = require( '@stdlib/array/float64' );
@@ -36,6 +36,9 @@ var dmeanvarpn = tryRequire( resolve( __dirname, './../lib/ndarray.native.js' )
 var opts = {
 	'skip': ( dmeanvarpn instanceof Error )
 };
+var options = {
+	'dtype': 'float64'
+};
 
 
 // FUNCTIONS //
@@ -48,15 +51,8 @@ var opts = {
 * @returns {Function} benchmark function
 */
 function createBenchmark( len ) {
-	var out;
-	var x;
-	var i;
-
-	x = new Float64Array( len );
-	for ( i = 0; i < x.length; i++ ) {
-		x[ i ] = ( randu()*20.0 ) - 10.0;
-	}
-	out = new Float64Array( 2 );
+	var out = new Float64Array( 2 );
+	var x = uniform( len, -10.0, 10.0, options );
 	return benchmark;
 
 	function benchmark( b ) {