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chore: minor clean up
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‎lib/node_modules/@stdlib/stats/base/dnanmeanpn/README.md‎

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@@ -51,17 +51,16 @@ The [arithmetic mean][arithmetic-mean] is defined as
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var dnanmeanpn = require( '@stdlib/stats/base/dnanmeanpn' );
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```
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#### dnanmeanpn( N, x, stride )
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#### dnanmeanpn( N, x, strideX )
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Computes the [arithmetic mean][arithmetic-mean] of a double-precision floating-point strided array `x`, ignoring `NaN` values and using a two-pass error correction algorithm.
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```javascript
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var Float64Array = require( '@stdlib/array/float64' );
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var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );
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var N = x.length;
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var v = dnanmeanpn( N, x, 1 );
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var v = dnanmeanpn( x.length, x, 1 );
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// returns ~0.3333
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```
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@@ -71,16 +70,14 @@ The function has the following parameters:
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- **x**: input [`Float64Array`][@stdlib/array/float64].
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- **stride**: index increment for `x`.
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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`,
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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`,
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```javascript
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var Float64Array = require( '@stdlib/array/float64' );
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var floor = require( '@stdlib/math/base/special/floor' );
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var x = new Float64Array( [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0, NaN ] );
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var N = floor( x.length / 2 );
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var v = dnanmeanpn( N, x, 2 );
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var v = dnanmeanpn( 4, x, 2 );
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// returns 1.25
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```
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@@ -90,45 +87,39 @@ Note that indexing is relative to the first index. To introduce an offset, use [
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```javascript
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var Float64Array = require( '@stdlib/array/float64' );
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var floor = require( '@stdlib/math/base/special/floor' );
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var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN ] );
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var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
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var N = floor( x0.length / 2 );
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var v = dnanmeanpn( N, x1, 2 );
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var v = dnanmeanpn( 4, x1, 2 );
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// returns 1.25
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```
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#### dnanmeanpn.ndarray( N, x, stride, offset )
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#### dnanmeanpn.ndarray( N, x, strideX, offsetX )
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Computes the [arithmetic mean][arithmetic-mean] of a double-precision floating-point strided array, ignoring `NaN` values and using a two-pass error correction algorithm and alternative indexing semantics.
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```javascript
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var Float64Array = require( '@stdlib/array/float64' );
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var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );
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var N = x.length;
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var v = dnanmeanpn.ndarray( N, x, 1, 0 );
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var v = dnanmeanpn.ndarray( x.length, x, 1, 0 );
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// returns ~0.33333
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```
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The function has the following additional parameters:
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- **offset**: starting index for `x`.
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- **offsetX**: starting index for `x`.
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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
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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 element in `x` starting from the second element
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```javascript
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var Float64Array = require( '@stdlib/array/float64' );
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var floor = require( '@stdlib/math/base/special/floor' );
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var x = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN ] );
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var N = floor( x.length / 2 );
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var v = dnanmeanpn.ndarray( N, x, 2, 1 );
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var v = dnanmeanpn.ndarray( 4, x, 2, 1 );
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// returns 1.25
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```
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@@ -192,12 +183,12 @@ console.log( v );
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#### stdlib_strided_dnanmeanpn( N, \*X, strideX )
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Calculate the mean value of a double-precision floating-point strided array, ignoring `NaN` values.
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Computes arithmetic mean of a double-precision floating-point strided array `x`, ignoring `NaN` values and using a two-pass error correction algorithm.
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```c
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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 };
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double v = stdlib_strided_dnanmeanpn( 5, x, 2 );
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double v = stdlib_strided_dnanmeanpn( 6, x, 2 );
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// returns 1.25
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```
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@@ -211,14 +202,14 @@ The function accepts the following arguments:
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double stdlib_strided_dnanmeanpn( const CBLAS_INT N, const double *X, const CBLAS_INT strideX );
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```
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214-
#### stdlib_strided_dsmeanwd_ndarray( N, \*X, strideX, offsetX )
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#### stdlib_strided_dnanmeanpn_ndarray( N, \*X, strideX, offsetX )
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216-
Computes the mean value of a double-precision floating-point strided array, ignoring `NaN` values and using alternative indexing semantics.
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Computes the aithmetic mean of a double-precision floating-point strided array, ignoring `NaN` values and using a two-pass error correction algorithm and alternative indexing semantics.
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```c
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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 };
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double v = stdlib_strided_dnanmeanpn( 5, x, 2, 0 );
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double v = stdlib_strided_dnanmeanpn( 6, x, 2, 0 );
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// returns 1.25
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```
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‎lib/node_modules/@stdlib/stats/base/dnanmeanpn/docs/repl.txt‎

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array, ignoring `NaN` values and using a two-pass error correction
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algorithm.
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The `N` and `stride` parameters determine which elements in `x` are accessed
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at runtime.
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The `N` and stride parameters determine which elements in the strided array
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are accessed at runtime.
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Indexing is relative to the first index. To introduce an offset, use a typed
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array view.
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Input array.
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strideX: integer
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Stride Length.
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Stride length.
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Returns
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-------
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> {{alias}}( x.length, x, 1 )
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~0.3333
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// Using `N` and `stride` parameters:
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// Using `N` and stride parameters:
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> x = new {{alias:@stdlib/array/float64}}( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0, NaN ] );
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> {{alias}}( 6, x, 2 )
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NaN
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> {{alias}}( 3, x, 2 )
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~0.3333
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// Using view offsets:
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> var x0 = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN ] );
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> var x1 = new {{alias:@stdlib/array/float64}}( x0.buffer, x0.BYTES_PER_ELEMENT*1 );
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> {{alias}}( 6, x1, 2 )
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NaN
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> {{alias}}( 3, x1, 2 )
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~-0.3333
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{{alias}}.ndarray( N, x, strideX, offsetX )
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Input array.
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strideX: integer
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Stride Length.
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Stride length.
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offsetX: integer
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Starting index.
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// Using offset parameter:
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> var x = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN ] );
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> {{alias}}.ndarray( 6, x, 2, 1 )
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NaN
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> {{alias}}.ndarray( 3, x, 2, 1 )
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~-0.3333
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See Also
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--------

‎lib/node_modules/@stdlib/stats/base/dnanmeanpn/examples/index.js‎

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'use strict';
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var discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
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var randu = require( '@stdlib/random/base/randu' );
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var round = require( '@stdlib/math/base/special/round' );
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var Float64Array = require( '@stdlib/array/float64' );
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var dnanmeanpn = require( './../lib' );
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var x = discreteUniform( 10, -50, 50, {
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'dtype': 'float32'
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});
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var x;
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var i;
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x = new Float64Array( 10 );
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for ( i = 0; i < x.length; i++ ) {
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if ( randu() < 0.2 ) {
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x[ i ] = NaN;
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} else {
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x[ i ] = round( randu() * 10.0 );
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}
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}
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console.log( x );
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var v = dnanmeanpn( x.length, x, 1 );

‎lib/node_modules/@stdlib/stats/base/dnanmeanpn/include/stdlib/stats/base/dnanmeanpn.h‎

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double API_SUFFIX(stdlib_strided_dnanmeanpn)( const CBLAS_INT N, const double *X, const CBLAS_INT strideX );
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/**
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* Computes the maximum absolute value of a double-precision floating-point strided array, ignoring `NaN` values and using alternative indexing semantics .
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* Computes the arithmetic mean of a double-precision floating-point strided array, ignoring `NaN` values and using a two-pass error correction algorithm and alternative indexing semantics.
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*/
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double API_SUFFIX(stdlib_strided_dnanmeanpn_ndarray)( const CBLAS_INT N, const double *X, const CBLAS_INT strideX, const CBLAS_INT offsetX );
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‎lib/node_modules/@stdlib/stats/base/dnanmeanpn/lib/dnanmeanpn.js‎

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* var Float64Array = require( '@stdlib/array/float64' );
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*
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* var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );
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* var N = x.length;
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*
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* var v = dnanmeanpn( x.length, x, 1 );
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* // returns ~0.3333

‎lib/node_modules/@stdlib/stats/base/dnanmeanpn/lib/dnanmeanpn.native.js‎

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@@ -37,7 +37,6 @@ var addon = require( './../src/addon.node' );
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* var Float64Array = require( '@stdlib/array/float64' );
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*
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* var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );
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* var N = x.length;
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*
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* var v = dnanmeanpn( x.length, x, 1 );
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* // returns ~0.3333

‎lib/node_modules/@stdlib/stats/base/dnanmeanpn/lib/index.js‎

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* var dnanmeanpn = require( '@stdlib/stats/base/dnanmeanpn' );
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*
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* var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );
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* var N = 3;
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*
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* var v = dnanmeanpn( 5, x, 1 );
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* var v = dnanmeanpn( x.length, x, 1 );
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* // returns ~0.3333
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*
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* @example
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*
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* var x = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN ] );
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*
42-
* var v = dnanmeanpn.ndarray( 5, x, 2, 1 );
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* var v = dnanmeanpn.ndarray( 4, x, 2, 1 );
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* // returns 1.25
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*/
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‎lib/node_modules/@stdlib/stats/base/dnanmeanpn/lib/ndarray.js‎

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* var Float64Array = require( '@stdlib/array/float64' );
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*
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* var x = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN ] );
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*
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* var v = dnanmeanpn( 4, x, 2, 1 );
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* // returns 1.25
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*/
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function dnanmeanpn( N, x, strideX, offsetX ) {
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var ix;

‎lib/node_modules/@stdlib/stats/base/dnanmeanpn/lib/ndarray.native.js‎

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@@ -38,6 +38,9 @@ var addon = require( './../src/addon.node' );
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* var Float64Array = require( '@stdlib/array/float64' );
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*
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* var x = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN ] );
41+
*
42+
* var v = dnanmeanpn( 4, x, 2, 1 );
43+
* // returns 1.25
4144
*/
4245
function dnanmeanpn( N, x, strideX, offsetX ) {
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return addon.ndarray( N, x, strideX, offsetX );

‎lib/node_modules/@stdlib/stats/base/dnanmeanpn/src/main.c‎

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* - Neely, Peter M. 1966. "Comparison of Several Algorithms for Computation of Means, Standard Deviations and Correlation Coefficients." _Communications of the ACM_ 9 (7). Association for Computing Machinery: 496–99. doi:[10.1145/365719.365958](https://doi.org/10.1145/365719.365958).
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* - Schubert, Erich, and Michael Gertz. 2018. "Numerically Stable Parallel Computation of (Co-)Variance." In _Proceedings of the 30th International Conference on Scientific and Statistical Database Management_. New York, NY, USA: Association for Computing Machinery. doi:[10.1145/3221269.3223036](https://doi.org/10.1145/3221269.3223036).
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*
36-
* @param N number of indexed elements
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* @param X input array
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* @param stride stride length
39-
* @return output value
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* @param N number of indexed elements
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* @param X input array
38+
* @param strideX stride length
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* @return output value
4040
*/
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double API_SUFFIX(stdlib_strided_dnanmeanpn)( const CBLAS_INT N, const double *X, const CBLAS_INT strideX ) {
4242
const CBLAS_INT ox = stdlib_strided_stride2offset( N, strideX );
4343
return API_SUFFIX(stdlib_strided_dnanmeanpn_ndarray)( N, X, strideX, ox );
4444
}
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/**
47-
* Computes the maximum absolute value of a double-precision floating-point strided array, ignoring `NaN` values and using alternative indexing semantics.
47+
* Computes the arithmetic mean of a double-precision floating-point strided array, ignoring `NaN` values and using a two-pass error correction algorithm and alternative indexing semantics.
4848
*
4949
* @param N number of indexed elements
5050
* @param X input array

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