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

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@@ -51,36 +51,33 @@ The [arithmetic mean][arithmetic-mean] is defined as
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var dmeanli = require( '@stdlib/stats/base/dmeanli' );
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```
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#### dmeanli( N, x, stride )
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#### dmeanli( N, x, strideX )
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Computes the [arithmetic mean][arithmetic-mean] of a double-precision floating-point strided array `x` using a one-pass trial mean 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, 2.0 ] );
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var N = x.length;
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var v = dmeanli( N, x, 1 );
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var v = dmeanli( x.length, x, 1 );
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// returns ~0.3333
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```
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The function has the following parameters:
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- **N**: number of indexed elements.
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- **x**: input [`Float64Array`][@stdlib/array/float64].
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- **stride**: index increment for `x`.
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- **strideX**: stride length 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 ] );
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var N = floor( x.length / 2 );
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var v = dmeanli( N, x, 2 );
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var v = dmeanli( 4, x, 2 );
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// returns 1.25
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```
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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 ] );
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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 = dmeanli( N, x1, 2 );
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var v = dmeanli( 4, x1, 2 );
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// returns 1.25
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```
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#### dmeanli.ndarray( N, x, stride, offset )
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#### dmeanli.ndarray( N, x, strideX, offsetX )
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Computes the [arithmetic mean][arithmetic-mean] of a double-precision floating-point strided array using a one-pass trial mean 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, 2.0 ] );
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var N = x.length;
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var v = dmeanli.ndarray( N, x, 1, 0 );
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var v = dmeanli.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 ] );
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var N = floor( x.length / 2 );
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var v = dmeanli.ndarray( N, x, 2, 1 );
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var v = dmeanli.ndarray( 4, x, 2, 1 );
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// returns 1.25
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```
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<!-- eslint no-undef: "error" -->
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```javascript
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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 discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
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var dmeanli = require( '@stdlib/stats/base/dmeanli' );
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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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x[ i ] = round( (randu()*100.0) - 50.0 );
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}
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var x = discreteUniform( 10, -50, 50, {
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'dtype': 'float64'
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});
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console.log( x );
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var v = dmeanli( x.length, x, 1 );
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<!-- /.examples -->
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<!-- C usage documentation. -->
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<section class="usage">
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### Usage
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```c
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#include "stdlib/stats/base/dmeanli.h"
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```
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#### stdlib_strided_dmeanli( N, \*X, strideX )
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Computes the [arithmetic mean][arithmetic-mean] of a double-precision floating-point strided array using a one-pass trial mean algorithm.
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```c
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const double x[] = { 1.0, -2.0, 2.0 };
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double v = stdlib_strided_dmeanli( 3, x, 1 );
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// returns ~0.3333
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```
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The function accepts the following arguments:
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- **N**: `[in] CBLAS_INT` number of indexed elements.
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- **X**: `[in] double*` input array.
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- **strideX**: `[in] CBLAS_INT` stride length for `X`.
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```c
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double stdlib_strided_dmeanli( const CBLAS_INT N, const double *X, const CBLAS_INT strideX );
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```
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#### stdlib_strided_dmeanli_ndarray( N, \*X, strideX, offsetX )
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Computes the [arithmetic mean][arithmetic-mean] of a double-precision floating-point strided array using a one-pass trial mean algorithm and alternative indexing semantics.
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```c
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const double x[] = { 1.0, -2.0, 2.0 };
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double v = stdlib_strided_dmeanli_ndarray( 3, x, 1, 0 );
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// returns ~0.3333
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```
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The function accepts the following arguments:
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- **N**: `[in] CBLAS_INT` number of indexed elements.
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- **X**: `[in] double*` input array.
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- **strideX**: `[in] CBLAS_INT` stride length for `X`.
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- **offsetX**: `[in] CBLAS_INT` starting index for `X`.
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```c
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double stdlib_strided_dmeanli_ndarray( const CBLAS_INT N, const double *X, const CBLAS_INT strideX, const CBLAS_INT offsetX );
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```
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</section>
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<!-- /.usage -->
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<!-- C API usage notes. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
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<section class="notes">
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</section>
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<!-- /.notes -->
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<!-- C API usage examples. -->
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<section class="examples">
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### Examples
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```c
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#include "stdlib/stats/base/dmeanli.h"
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#include <stdio.h>
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int main( void ) {
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// Create a strided array:
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const double x[] = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0 };
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// Specify the number of elements:
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const int N = 4;
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// Specify the stride length:
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const int strideX = 2;
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// Compute the arithmetic mean:
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double v = stdlib_strided_dmeanli( N, x, strideX );
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// Print the result:
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printf( "mean: %lf\n", v );
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}
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```
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</section>
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<!-- /.examples -->
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</section>
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<!-- /.c -->
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* * *
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<section class="references">

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

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// MODULES //
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var bench = require( '@stdlib/bench' );
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var randu = require( '@stdlib/random/base/randu' );
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var uniform = require( '@stdlib/random/array/uniform' );
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var isnan = require( '@stdlib/math/base/assert/is-nan' );
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var pow = require( '@stdlib/math/base/special/pow' );
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var Float64Array = require( '@stdlib/array/float64' );
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var pkg = require( './../package.json' ).name;
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var dmeanli = require( './../lib/dmeanli.js' );
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// VARIABLES //
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var options = {
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'dtype': 'float64'
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};
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// FUNCTIONS //
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/**
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* @returns {Function} benchmark function
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*/
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function createBenchmark( len ) {
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var x;
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var i;
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x = new Float64Array( len );
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for ( i = 0; i < x.length; i++ ) {
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x[ i ] = ( randu()*20.0 ) - 10.0;
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}
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var x = uniform( len, -10.0, 10.0, options );
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return benchmark;
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function benchmark( b ) {

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

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var resolve = require( 'path' ).resolve;
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var bench = require( '@stdlib/bench' );
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var randu = require( '@stdlib/random/base/randu' );
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var uniform = require( '@stdlib/random/array/uniform' );
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var isnan = require( '@stdlib/math/base/assert/is-nan' );
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var pow = require( '@stdlib/math/base/special/pow' );
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var Float64Array = require( '@stdlib/array/float64' );
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var tryRequire = require( '@stdlib/utils/try-require' );
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var pkg = require( './../package.json' ).name;
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var opts = {
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'skip': ( dmeanli instanceof Error )
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};
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var options = {
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'dtype': 'float64'
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};
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// FUNCTIONS //
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* @returns {Function} benchmark function
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*/
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function createBenchmark( len ) {
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var x;
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var i;
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x = new Float64Array( len );
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for ( i = 0; i < x.length; i++ ) {
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x[ i ] = ( randu()*20.0 ) - 10.0;
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}
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var x = uniform( len, -10.0, 10.0, options );
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return benchmark;
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function benchmark( b ) {

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

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// MODULES //
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var bench = require( '@stdlib/bench' );
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var randu = require( '@stdlib/random/base/randu' );
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var uniform = require( '@stdlib/random/array/uniform' );
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var isnan = require( '@stdlib/math/base/assert/is-nan' );
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var pow = require( '@stdlib/math/base/special/pow' );
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var Float64Array = require( '@stdlib/array/float64' );
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var pkg = require( './../package.json' ).name;
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var dmeanli = require( './../lib/ndarray.js' );
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// VARIABLES //
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var options = {
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'dtype': 'float64'
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};
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// FUNCTIONS //
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/**
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* @returns {Function} benchmark function
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*/
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function createBenchmark( len ) {
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var x;
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var i;
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x = new Float64Array( len );
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for ( i = 0; i < x.length; i++ ) {
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x[ i ] = ( randu()*20.0 ) - 10.0;
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}
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var x = uniform( len, -10.0, 10.0, options );
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return benchmark;
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function benchmark( b ) {

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

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var resolve = require( 'path' ).resolve;
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var bench = require( '@stdlib/bench' );
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var randu = require( '@stdlib/random/base/randu' );
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var uniform = require( '@stdlib/random/array/uniform' );
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var isnan = require( '@stdlib/math/base/assert/is-nan' );
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var pow = require( '@stdlib/math/base/special/pow' );
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var Float64Array = require( '@stdlib/array/float64' );
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var tryRequire = require( '@stdlib/utils/try-require' );
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var pkg = require( './../package.json' ).name;
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var opts = {
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'skip': ( dmeanli instanceof Error )
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};
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var options = {
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'dtype': 'float64'
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};
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// FUNCTIONS //
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* @returns {Function} benchmark function
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*/
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function createBenchmark( len ) {
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var x;
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var i;
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x = new Float64Array( len );
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for ( i = 0; i < x.length; i++ ) {
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x[ i ] = ( randu()*20.0 ) - 10.0;
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}
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var x = uniform( len, -10.0, 10.0, options );
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return benchmark;
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function benchmark( b ) {

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