refactor!: reorder parameters

BREAKING CHANGE: switch order of `mean` and `correction` parameters

To migrate, users should swap `mean` and `correction` arguments.
This change ensures that the `*varm*` function signatures follow
similar conventions as found in binary APIs, such as those for
computing the covariance, where the `mean` parameter immediately
precedes the array argument.

---
type: pre_commit_static_analysis_report
description: Results of running static analysis checks when committing changes.
report:
  - task: lint_filenames
    status: passed
  - task: lint_editorconfig
    status: passed
  - task: lint_markdown
    status: passed
  - task: lint_package_json
    status: na
  - task: lint_repl_help
    status: passed
  - task: lint_javascript_src
    status: passed
  - task: lint_javascript_cli
    status: na
  - task: lint_javascript_examples
    status: passed
  - task: lint_javascript_tests
    status: passed
  - task: lint_javascript_benchmarks
    status: passed
  - task: lint_python
    status: na
  - task: lint_r
    status: na
  - task: lint_c_src
    status: passed
  - task: lint_c_examples
    status: passed
  - task: lint_c_benchmarks
    status: passed
  - task: lint_c_tests_fixtures
    status: na
  - task: lint_shell
    status: na
  - task: lint_typescript_declarations
    status: passed
  - task: lint_typescript_tests
    status: passed
  - task: lint_license_headers
    status: passed
---

Author: kgryte

lib/node_modules/@stdlib/stats/strided/dvarmpn/README.md

@@ -98,7 +98,7 @@ The use of the term `n-1` is commonly referred to as Bessel's correction. Note,
 var dvarmpn = require( '@stdlib/stats/strided/dvarmpn' );
 ```
 
-#### dvarmpn( N, mean, correction, x, strideX )
+#### dvarmpn( N, correction, mean, x, strideX )
 
 Computes the [variance][variance] of a double-precision floating-point strided array provided a known `mean` and using Neely's correction algorithm.
 
@@ -107,15 +107,15 @@ var Float64Array = require( '@stdlib/array/float64' );
 
 var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );
 
-var v = dvarmpn( x.length, 1.0/3.0, 1, x, 1 );
+var v = dvarmpn( x.length, 1, 1.0/3.0, x, 1 );
 // returns ~4.3333
 ```
 
 The function has the following parameters:
 
 -   **N**: number of indexed elements.
--   **mean**: mean.
 -   **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).
+-   **mean**: mean.
 -   **x**: input [`Float64Array`][@stdlib/array/float64].
 -   **strideX**: stride length for `x`.
 
@@ -126,7 +126,7 @@ var Float64Array = require( '@stdlib/array/float64' );
 
 var x = new Float64Array( [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ] );
 
-var v = dvarmpn( 4, 1.25, 1, x, 2 );
+var v = dvarmpn( 4, 1, 1.25, x, 2 );
 // returns 6.25
 ```
 
@@ -140,11 +140,11 @@ 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 ] );
 var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
 
-var v = dvarmpn( 4, 1.25, 1, x1, 2 );
+var v = dvarmpn( 4, 1, 1.25, x1, 2 );
 // returns 6.25
 ```
 
-#### dvarmpn.ndarray( N, mean, correction, x, strideX, offsetX )
+#### dvarmpn.ndarray( N, correction, mean, x, strideX, offsetX )
 
 Computes the [variance][variance] of a double-precision floating-point strided array provided a known `mean` and using Neely's correction algorithm and alternative indexing semantics.
 
@@ -153,7 +153,7 @@ var Float64Array = require( '@stdlib/array/float64' );
 
 var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );
 
-var v = dvarmpn.ndarray( x.length, 1.0/3.0, 1, x, 1, 0 );
+var v = dvarmpn.ndarray( x.length, 1, 1.0/3.0, x, 1, 0 );
 // returns ~4.33333
 ```
 
@@ -168,7 +168,7 @@ var Float64Array = require( '@stdlib/array/float64' );
 
 var x = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
 
-var v = dvarmpn.ndarray( 4, 1.25, 1, x, 2, 1 );
+var v = dvarmpn.ndarray( 4, 1, 1.25, x, 2, 1 );
 // returns 6.25
 ```
 
@@ -202,7 +202,7 @@ var x = discreteUniform( 10, -50, 50, {
 });
 console.log( x );
 
-var v = dvarmpn( x.length, 0.0, 1, x, 1 );
+var v = dvarmpn( x.length, 1, 0.0, x, 1 );
 console.log( v );
 ```
 
@@ -236,14 +236,14 @@ console.log( v );
 #include "stdlib/stats/strided/dvarmpn.h"
 ```
 
-#### stdlib_strided_dvarmpn( N, mean, correction, \*X, strideX )
+#### stdlib_strided_dvarmpn( N, correction, mean, \*X, strideX )
 
 Computes the [variance][variance] of a double-precision floating-point strided array provided a known `mean` and using Neely's correction algorithm.
 
 ```c
 const double x[] = { 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 };
 
-double v = stdlib_strided_dvarmpn( 4, 1.25, 1.0, x, 2 );
+double v = stdlib_strided_dvarmpn( 4, 1.0, 1.25, x, 2 );
 // returns 6.25
 ```
 
@@ -256,31 +256,31 @@ The function accepts the following arguments:
 -   **strideX**: `[in] CBLAS_INT` stride length for `X`.
 
 ```c
-double stdlib_strided_dvarmpn( const CBLAS_INT N, const double mean, const double correction, const double *X, const CBLAS_INT strideX );
+double stdlib_strided_dvarmpn( const CBLAS_INT N, const double correction, const double mean, const double *X, const CBLAS_INT strideX );
 ```
 
-#### stdlib_strided_dvarmpn_ndarray( N, mean, correction, \*X, strideX, offsetX )
+#### stdlib_strided_dvarmpn_ndarray( N, correction, mean, \*X, strideX, offsetX )
 
 Computes the [variance][variance] of a double-precision floating-point strided array provided a known `mean` and using Neely's correction algorithm and alternative indexing semantics.
 
 ```c
 const double x[] = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0 };
 
-double v = stdlib_strided_dvarmpn_ndarray( 4, 1.25, 1.0, x, 2, 0 );
+double v = stdlib_strided_dvarmpn_ndarray( 4, 1.0, 1.25, x, 2, 0 );
 // returns 6.25
 ```
 
 The function accepts the following arguments:
 
 -   **N**: `[in] CBLAS_INT` number of indexed elements.
--   **mean**: `[in] double` mean.
 -   **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).
+-   **mean**: `[in] double` mean.
 -   **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_dvarmpn_ndarray( const CBLAS_INT N, const double mean, const double correction, const double *X, const CBLAS_INT strideX, const CBLAS_INT offsetX );
+double stdlib_strided_dvarmpn_ndarray( const CBLAS_INT N, const double correction, const double mean, const double *X, const CBLAS_INT strideX, const CBLAS_INT offsetX );
 ```
 
 </section>
@@ -316,7 +316,7 @@ int main( void ) {
     const int strideX = 2;
 
     // Compute the variance:
-    double v = stdlib_strided_dvarmpn( N, 4.5, 1, x, strideX );
+    double v = stdlib_strided_dvarmpn( N, 1, 4.5, x, strideX );
 
     // Print the result:
     printf( "sample variance: %lf\n", v );

lib/node_modules/@stdlib/stats/strided/dvarmpn/benchmark/benchmark.js

@@ -54,7 +54,7 @@ function createBenchmark( len ) {
 
 		b.tic();
 		for ( i = 0; i < b.iterations; i++ ) {
-			v = dvarmpn( x.length, 0.0, 1, x, 1 );
+			v = dvarmpn( x.length, 1, 0.0, x, 1 );
 			if ( isnan( v ) ) {
 				b.fail( 'should not return NaN' );
 			}

lib/node_modules/@stdlib/stats/strided/dvarmpn/benchmark/benchmark.native.js

@@ -59,7 +59,7 @@ function createBenchmark( len ) {
 
 		b.tic();
 		for ( i = 0; i < b.iterations; i++ ) {
-			v = dvarmpn( x.length, 0.0, 1, x, 1 );
+			v = dvarmpn( x.length, 1, 0.0, x, 1 );
 			if ( isnan( v ) ) {
 				b.fail( 'should not return NaN' );
 			}

lib/node_modules/@stdlib/stats/strided/dvarmpn/benchmark/benchmark.ndarray.js

@@ -54,7 +54,7 @@ function createBenchmark( len ) {
 
 		b.tic();
 		for ( i = 0; i < b.iterations; i++ ) {
-			v = dvarmpn( x.length, 0.0, 1, x, 1, 0 );
+			v = dvarmpn( x.length, 1, 0.0, x, 1, 0 );
 			if ( isnan( v ) ) {
 				b.fail( 'should not return NaN' );
 			}

lib/node_modules/@stdlib/stats/strided/dvarmpn/benchmark/benchmark.ndarray.native.js

@@ -59,7 +59,7 @@ function createBenchmark( len ) {
 
 		b.tic();
 		for ( i = 0; i < b.iterations; i++ ) {
-			v = dvarmpn( x.length, 0.0, 1, x, 1, 0 );
+			v = dvarmpn( x.length, 1, 0.0, x, 1, 0 );
 			if ( isnan( v ) ) {
 				b.fail( 'should not return NaN' );
 			}

lib/node_modules/@stdlib/stats/strided/dvarmpn/benchmark/c/benchmark.length.c

@@ -94,7 +94,7 @@ static double rand_double( void ) {
 * @param len          array length
 * @return             elapsed time in seconds
 */
-static double benchmark( int iterations, int len ) {
+static double benchmark1( int iterations, int len ) {
 	double elapsed;
 	double x[ len ];
 	double v;
@@ -107,7 +107,42 @@ static double benchmark( int iterations, int len ) {
 	v = 0.0;
 	t = tic();
 	for ( i = 0; i < iterations; i++ ) {
-		v = stdlib_strided_dvarmpn( len, 0.0, 1, x, 1 );
+		// cppcheck-suppress uninitvar
+		v = stdlib_strided_dvarmpn( len, 1, 0.0, x, 1 );
+		if ( v != v ) {
+			printf( "should not return NaN\n" );
+			break;
+		}
+	}
+	elapsed = tic() - t;
+	if ( v != v ) {
+		printf( "should not return NaN\n" );
+	}
+	return elapsed;
+}
+
+/**
+* Runs a benchmark.
+*
+* @param iterations   number of iterations
+* @param len          array length
+* @return             elapsed time in seconds
+*/
+static double benchmark2( int iterations, int len ) {
+	double elapsed;
+	double x[ len ];
+	double v;
+	double t;
+	int i;
+
+	for ( i = 0; i < len; i++ ) {
+		x[ i ] = ( rand_double() * 20000.0 ) - 10000.0;
+	}
+	v = 0.0;
+	t = tic();
+	for ( i = 0; i < iterations; i++ ) {
+		// cppcheck-suppress uninitvar
+		v = stdlib_strided_dvarmpn_ndarray( len, 1, 0.0, x, 1, 0 );
 		if ( v != v ) {
 			printf( "should not return NaN\n" );
 			break;
@@ -142,7 +177,18 @@ int main( void ) {
 		for ( j = 0; j < REPEATS; j++ ) {
 			count += 1;
 			printf( "# c::%s:len=%d\n", NAME, len );
-			elapsed = benchmark( iter, len );
+			elapsed = benchmark1( iter, len );
+			print_results( iter, elapsed );
+			printf( "ok %d benchmark finished\n", count );
+		}
+	}
+	for ( i = MIN; i <= MAX; i++ ) {
+		len = pow( 10, i );
+		iter = ITERATIONS / pow( 10, i-1 );
+		for ( j = 0; j < REPEATS; j++ ) {
+			count += 1;
+			printf( "# c::%s:ndarray:len=%d\n", NAME, len );
+			elapsed = benchmark2( iter, len );
 			print_results( iter, elapsed );
 			printf( "ok %d benchmark finished\n", count );
 		}

lib/node_modules/@stdlib/stats/strided/dvarmpn/docs/repl.txt

@@ -1,5 +1,5 @@
 
-{{alias}}( N, mean, correction, x, strideX )
+{{alias}}( N, correction, mean, x, strideX )
     Computes the variance of a double-precision floating-point strided array
     provided a known mean and using Neely's correction algorithm.
 
@@ -16,9 +16,6 @@
     N: integer
         Number of indexed elements.
 
-    mean: number
-        Mean.
-
     correction: number
         Degrees of freedom adjustment. Setting this parameter to a value other
         than `0` has the effect of adjusting the divisor during the calculation
@@ -31,6 +28,9 @@
         sampled from a larger population; this is commonly referred to as
         Bessel's correction).
 
+    mean: number
+        Mean.
+
     x: Float64Array
         Input array.
 
@@ -46,22 +46,22 @@
     --------
     // Standard Usage:
     > var x = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 2.0 ] );
-    > {{alias}}( x.length, 1.0/3.0, 1, x, 1 )
+    > {{alias}}( x.length, 1, 1.0/3.0, x, 1 )
     ~4.3333
 
     // Using `N` and stride parameters:
     > x = new {{alias:@stdlib/array/float64}}( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );
-    > {{alias}}( 3, 1.0/3.0, 1, x, 2 )
+    > {{alias}}( 3, 1, 1.0/3.0, 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 x1 = new {{alias:@stdlib/array/float64}}( x0.buffer, x0.BYTES_PER_ELEMENT*1 );
-    > {{alias}}( 3, 1.0/3.0, 1, x1, 2 )
+    > {{alias}}( 3, 1, 1.0/3.0, x1, 2 )
     ~4.3333
 
 
-{{alias}}.ndarray( N, mean, correction, x, strideX, offsetX )
+{{alias}}.ndarray( N, correction, mean, x, strideX, offsetX )
     Computes the variance of a double-precision floating-point strided array
     provided a known mean and using Neely's correction algorithm and alternative
     indexing semantics.
@@ -75,9 +75,6 @@
     N: integer
         Number of indexed elements.
 
-    mean: number
-        Mean.
-
     correction: number
         Degrees of freedom adjustment. Setting this parameter to a value other
         than `0` has the effect of adjusting the divisor during the calculation
@@ -90,6 +87,9 @@
         sampled from a larger population; this is commonly referred to as
         Bessel's correction).
 
+    mean: number
+        Mean.
+
     x: Float64Array
         Input array.
 
@@ -108,12 +108,12 @@
     --------
     // Standard Usage:
     > var x = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 2.0 ] );
-    > {{alias}}.ndarray( x.length, 1.0/3.0, 1, x, 1, 0 )
+    > {{alias}}.ndarray( x.length, 1, 1.0/3.0, x, 1, 0 )
     ~4.3333
 
     // Using offset parameter:
     > x = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 3.0, 2.0, 5.0, 1.0 ] );
-    > {{alias}}.ndarray( 3, 1.0/3.0, 1, x, 2, 1 )
+    > {{alias}}.ndarray( 3, 1, 1.0/3.0, x, 2, 1 )
     ~4.3333
 
     See Also