feat: added did_d function

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Author: 0PrashantYadav0

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

@@ -98,7 +98,7 @@ The use of the term `n-1` is commonly referred to as Bessel's correction. Note,
 var dnanvariancepn = require( '@stdlib/stats/base/dnanvariancepn' );
 ```
 
-#### dnanvariancepn( N, correction, x, strideX )
+#### dnanvariancepn( N, correction, x, stride )
 
 Computes the [variance][variance] of a double-precision floating-point strided array `x` ignoring `NaN` values and using a two-pass algorithm.
 
@@ -116,16 +116,18 @@ 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 [`Float64Array`][@stdlib/array/float64].
--   **strideX**: stride length for `x`.
+-   **stride**: index increment for `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`,
+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`,
 
 ```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, NaN, NaN ] ); // eslint-disable-line max-len
+var x = new Float64Array( [ 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 = dnanvariancepn( 5, 1, x, 2 );
+var v = dnanvariancepn( N, 1, x, 2 );
 // returns 6.25
 ```
 
@@ -135,15 +137,18 @@ 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, NaN ] ); // eslint-disable-line max-len
+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 v = dnanvariancepn( 5, 1, x1, 2 );
+var N = floor( x0.length / 2 );
+
+var v = dnanvariancepn( N, 1, x1, 2 );
 // returns 6.25
 ```
 
-#### dnanvariancepn.ndarray( N, correction, x, strideX, offsetX )
+#### dnanvariancepn.ndarray( N, correction, x, stride, offset )
 
 Computes the [variance][variance] of a double-precision floating-point strided array ignoring `NaN` values and using a two-pass algorithm and alternative indexing semantics.
 
@@ -158,16 +163,18 @@ var v = dnanvariancepn.ndarray( x.length, 1, x, 1, 0 );
 
 The function has the following additional parameters:
 
--   **offsetX**: starting index for `x`.
+-   **offset**: 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 element in `x` starting from the second element
+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
 
 ```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, NaN, NaN ] ); // eslint-disable-line max-len
+var x = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
+var N = floor( x.length / 2 );
 
-var v = dnanvariancepn.ndarray( 5, 1, x, 2, 1 );
+var v = dnanvariancepn.ndarray( N, 1, x, 2, 1 );
 // returns 6.25
 ```
 
@@ -193,19 +200,18 @@ var v = dnanvariancepn.ndarray( 5, 1, x, 2, 1 );
 <!-- eslint no-undef: "error" -->
 
 ```javascript
-var uniform = require( '@stdlib/random/base/uniform' );
-var filledarrayBy = require( '@stdlib/array/filled-by' );
-var bernoulli = require( '@stdlib/random/base/bernoulli' );
+var randu = require( '@stdlib/random/base/randu' );
+var round = require( '@stdlib/math/base/special/round' );
+var Float64Array = require( '@stdlib/array/float64' );
 var dnanvariancepn = require( '@stdlib/stats/base/dnanvariancepn' );
 
-function rand() {
-    if ( bernoulli( 0.8 ) < 1 ) {
-        return NaN;
-    }
-    return uniform( -50.0, 50.0 );
-}
+var x;
+var i;
 
-var x = filledarrayBy( 10, 'float64', rand );
+x = new Float64Array( 10 );
+for ( i = 0; i < x.length; i++ ) {
+    x[ i ] = round( (randu()*100.0) - 50.0 );
+}
 console.log( x );
 
 var v = dnanvariancepn( x.length, 1, x, 1 );
@@ -216,125 +222,6 @@ console.log( v );
 
 <!-- /.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/dnanvariancepn.h"
-```
-
-#### stdlib_strided_dnanvariancepn( N, correction, \*X, strideX )
-
-Computes the [variance][variance] of a double-precision floating-point strided array `x` ignoring `NaN` values and using a two-pass algorithm.
-
-```c
-const double x[] = { 1.0, -2.0, 0.0/0.0, 2.0 };
-
-double v = stdlib_strided_dnanvariancepn( 4, 1.0, x, 1 );
-// returns ~4.3333
-```
-
-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 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**: `[in] double*` input array.
--   **strideX**: `[in] CBLAS_INT` stride length for `X`.
-
-```c
-double stdlib_strided_dnanvariancepn( const CBLAS_INT N, const double correction, const double *X, const CBLAS_INT strideX );
-```
-
-#### stdlib_strided_dnanvariancepn_ndarray( N, correction, \*X, strideX, offsetX )
-
-Computes the [variance][variance] of a double-precision floating-point strided array ignoring `NaN` values and using a two-pass algorithm and alternative indexing semantics.
-
-```c
-const double x[] = { 1.0, -2.0, 0.0/0.0, 2.0 };
-
-double v = stdlib_strided_dnanvariancepn_ndarray( 4, 1.0, x, 1, 0 );
-// returns ~4.3333
-```
-
-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 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**: `[in] double*` input array.
--   **strideX**: `[in] CBLAS_INT` stride length for `X`.
--   **offsetX**: `[in] CBLAS_INT` starting index for `X`.
-
-```c
-double stdlib_strided_dnanvariancepn_ndarray( const CBLAS_INT N, const double correction, const double *X, const CBLAS_INT strideX, const CBLAS_INT offsetX );
-```
-
-</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/dnanvariancepn.h"
-#include <stdio.h>
-
-int main( void ) {
-    // Create a strided array:
-    const double x[] = { 1.0, 2.0, 0.0/0.0, 3.0, 0.0/0.0, 4.0, 5.0, 6.0, 0.0/0.0, 7.0, 8.0, 0.0/0.0 };
-
-    // Specify the number of elements:
-    const int N = 6;
-
-    // Specify the stride length:
-    const int strideX = 2;
-
-    // Compute the variance:
-    double v = stdlib_strided_dnanvariancepn( N, 1.0, x, strideX );
-
-    // Print the result:
-    printf( "sample variance: %lf\n", v );
-}
-```
-
-</section>
-
-<!-- /.examples -->
-
-</section>
-
-<!-- /.c -->
-
 * * *
 
 <section class="references">

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

@@ -21,30 +21,16 @@
 // MODULES //
 
 var bench = require( '@stdlib/bench' );
-var uniform = require( '@stdlib/random/base/uniform' );
-var bernoulli = require( '@stdlib/random/base/bernoulli' );
-var filledarrayBy = require( '@stdlib/array/filled-by' );
+var randu = require( '@stdlib/random/base/randu' );
 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 dnanvariancepn = require( './../lib/dnanvariancepn.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.
 *
@@ -53,7 +39,17 @@ function rand() {
 * @returns {Function} benchmark function
 */
 function createBenchmark( len ) {
-	var x = filledarrayBy( len, 'float64', rand );
+	var x;
+	var i;
+
+	x = new Float64Array( len );
+	for ( i = 0; i < x.length; i++ ) {
+		if ( randu() < 0.2 ) {
+			x[ i ] = NaN;
+		} else {
+			x[ i ] = ( randu()*20.0 ) - 10.0;
+		}
+	}
 	return benchmark;
 
 	function benchmark( b ) {

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

@@ -22,11 +22,10 @@
 
 var resolve = require( 'path' ).resolve;
 var bench = require( '@stdlib/bench' );
-var uniform = require( '@stdlib/random/base/uniform' );
-var bernoulli = require( '@stdlib/random/base/bernoulli' );
-var filledarrayBy = require( '@stdlib/array/filled-by' );
+var randu = require( '@stdlib/random/base/randu' );
 var isnan = require( '@stdlib/math/base/assert/is-nan' );
 var pow = require( '@stdlib/math/base/special/pow' );
+var Float64Array = require( '@stdlib/array/float64' );
 var tryRequire = require( '@stdlib/utils/try-require' );
 var pkg = require( './../package.json' ).name;
 
@@ -41,19 +40,6 @@ var opts = {
 
 // 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.
 *
@@ -62,7 +48,17 @@ function rand() {
 * @returns {Function} benchmark function
 */
 function createBenchmark( len ) {
-	var x = filledarrayBy( len, 'float64', rand );
+	var x;
+	var i;
+
+	x = new Float64Array( len );
+	for ( i = 0; i < x.length; i++ ) {
+		if ( randu() < 0.2 ) {
+			x[ i ] = NaN;
+		} else {
+			x[ i ] = ( randu()*20.0 ) - 10.0;
+		}
+	}
 	return benchmark;
 
 	function benchmark( b ) {

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

@@ -21,30 +21,16 @@
 // MODULES //
 
 var bench = require( '@stdlib/bench' );
-var uniform = require( '@stdlib/random/base/uniform' );
-var bernoulli = require( '@stdlib/random/base/bernoulli' );
-var filledarrayBy = require( '@stdlib/array/filled-by' );
+var randu = require( '@stdlib/random/base/randu' );
 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 dnanvariancepn = 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.
 *
@@ -53,7 +39,17 @@ function rand() {
 * @returns {Function} benchmark function
 */
 function createBenchmark( len ) {
-	var x = filledarrayBy( len, 'float64', rand );
+	var x;
+	var i;
+
+	x = new Float64Array( len );
+	for ( i = 0; i < x.length; i++ ) {
+		if ( randu() < 0.2 ) {
+			x[ i ] = NaN;
+		} else {
+			x[ i ] = ( randu()*20.0 ) - 10.0;
+		}
+	}
 	return benchmark;
 
 	function benchmark( b ) {