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999a17c
feat: add stats/base/dists/halfnormal/skewness
Lokeshranjan8 Jan 15, 2026
39a35d7
fix: updated the return values
Lokeshranjan8 Jan 15, 2026
1b4d59d
fix: updated the data json
Lokeshranjan8 Jan 15, 2026
a62449a
fix: updated expected value
Lokeshranjan8 Jan 15, 2026
3b21577
fix: js tests
Lokeshranjan8 Jan 15, 2026
8902539
fix: updated the readme
Lokeshranjan8 Jan 15, 2026
9c82c7b
Update package.json
Lokeshranjan8 Jan 15, 2026
26233a9
Update parameter description from 'Standard deviation' to 'scale para…
Lokeshranjan8 Jan 15, 2026
94707c5
Update data.json
Lokeshranjan8 Jan 15, 2026
2342e7b
Update runner.jl
Lokeshranjan8 Jan 15, 2026
fbea6ba
Update documentation for scale parameter description
Lokeshranjan8 Jan 15, 2026
5f62fb5
Remove blank line in runner.jl
Lokeshranjan8 Jan 15, 2026
cbf8f29
Update lib/node_modules/@stdlib/stats/base/dists/halfnormal/skewness/…
Lokeshranjan8 Jan 16, 2026
f88bf49
Update lib/node_modules/@stdlib/stats/base/dists/halfnormal/skewness/…
Lokeshranjan8 Jan 16, 2026
c1b2dd5
Update lib/node_modules/@stdlib/stats/base/dists/halfnormal/skewness/…
Lokeshranjan8 Jan 16, 2026
64d8a03
Update lib/node_modules/@stdlib/stats/base/dists/halfnormal/skewness/…
Lokeshranjan8 Jan 16, 2026
150b440
Update lib/node_modules/@stdlib/stats/base/dists/halfnormal/skewness/…
Lokeshranjan8 Jan 16, 2026
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Update lib/node_modules/@stdlib/stats/base/dists/halfnormal/skewness/…
Lokeshranjan8 Jan 16, 2026
bafd8b7
Update lib/node_modules/@stdlib/stats/base/dists/halfnormal/skewness/…
Lokeshranjan8 Jan 16, 2026
87f0c54
Update lib/node_modules/@stdlib/stats/base/dists/halfnormal/skewness/…
Lokeshranjan8 Jan 16, 2026
efac689
fix: done requested changes
Lokeshranjan8 Jan 16, 2026
4b1609d
Merge remote-tracking branch 'origin/develop' into skewness-halfnormal
Lokeshranjan8 Jan 25, 2026
6a68d97
fix: updated the expected value
Lokeshranjan8 Jan 25, 2026
f8e0c77
fix: fix the lint errors
Lokeshranjan8 Jan 25, 2026
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Original file line number Diff line number Diff line change
@@ -0,0 +1,233 @@
<!--

@license Apache-2.0

Copyright (c) 2026 The Stdlib Authors.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

-->

# Skewness

> [Half-normal][half-normal-distribution] distribution [skewness][skewness].

<!-- 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">

The [skewness][skewness] for a [halfnormal][half-normal-distribution] random variable is

<!-- <equation class="equation" label="eq:half-normal_skewness" align="center" raw="\frac{\sqrt{2\pi}(\pi - 3)}{(\pi - 2)^{3/2}}" alt="Skewness for a half-normal distribution."> -->

```math
\mathop{\mathrm{skew}}\left( X \right) \approx 0.995
```

<!-- <div class="equation" align="center" data-raw-text="\operatorname{skew}\left( X \right) = 0.995" data-equation="eq:half-normal_skewness"> alt="Skewness for a half-normal distribution.">
<br>
</div> -->

<!-- </equation> -->

</section>

<!-- /.intro -->

<!-- Package usage documentation. -->

<section class="usage">

## Usage

```javascript
var skewness = require( '@stdlib/stats/base/dists/halfnormal/skewness' );
```

#### skewness( sigma )

Returns the [skewness][skewness] for a [half-normal][half-normal-distribution] distribution with scale parameter `sigma`.

```javascript
var y = skewness( 1.0 );
// returns 0.9952717

y = skewness( 5.0 );
// returns 0.9952717
```

If provided `NaN` as any argument, the function returns `NaN`.

```javascript
var y = skewness( NaN );
// returns NaN
```

If provided `sigma <= 0`, the function returns `NaN`.

```javascript
var y = skewness( 0.0 );
// returns NaN

y = skewness( -1.0 );
// returns NaN
```

</section>

<!-- /.usage -->

<!-- Package usage notes. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->

<section class="notes">

</section>

<!-- /.notes -->

<!-- Package usage examples. -->

<section class="examples">

## Examples

<!-- eslint no-undef: "error" -->

```javascript
var uniform = require( '@stdlib/random/array/uniform' );
var logEachMap = require( '@stdlib/console/log-each-map' );
var skewness = require( '@stdlib/stats/base/dists/halfnormal/skewness' );

var opts = {
'dtype': 'float64'
};
var sigma = uniform( 10, 0.0, 20.0, opts );

logEachMap( 'σ: %0.4f, skew(X;σ): %0.4f', sigma, skewness );
```

</section>

<!-- /.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/dists/halfnormal/skewness.h"
```

#### stdlib_base_dists_halfnormal_skewness( sigma )

Returns the skewness for a [half-normal][half-normal-distribution] distribution with scale parameter `sigma`.

```c
double out = stdlib_base_dists_halfnormal_skewness( 1.0 );
// returns 0.9952717
```

The function accepts the following arguments:

- **sigma**: `[in] double` scale parameter.

```c
double stdlib_base_dists_halfnormal_skewness( const double sigma );
```

</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/dists/halfnormal/skewness.h"
#include <stdlib.h>
#include <stdio.h>

static double random_uniform( const double min, const double max ) {
double v = (double)rand() / ( (double)RAND_MAX + 1.0 );
return min + ( v*(max-min) );
}

int main( void ) {
double sigma;
double y;
int i;

for ( i = 0; i < 10; i++ ) {
sigma = random_uniform( 0.1, 20.0 );
y = stdlib_base_dists_halfnormal_skewness( sigma );
printf( "σ: %.4f, skew(X;σ): %.4f\n", sigma, y );
}
}
```

</section>

<!-- /.examples -->

</section>

<!-- /.c -->

<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->

<section class="related">

</section>

<!-- /.related -->

<!-- Section for all links. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->

<section class="links">

[half-normal-distribution]: https://en.wikipedia.org/wiki/Half-normal_distribution

[skewness]: https://en.wikipedia.org/wiki/Skewness

</section>

<!-- /.links -->
Original file line number Diff line number Diff line change
@@ -0,0 +1,52 @@
/**
* @license Apache-2.0
*
* Copyright (c) 2026 The Stdlib Authors.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/

'use strict';

// MODULES //

var bench = require( '@stdlib/bench' );
var randu = require( '@stdlib/random/base/randu' );
var isnan = require( '@stdlib/math/base/assert/is-nan' );
var EPS = require( '@stdlib/constants/float64/eps' );
var pkg = require( './../package.json' ).name;
var skewness = require( './../lib' );


// MAIN //

bench( pkg, function benchmark( b ) {
var sigma;
var y;
var i;

b.tic();
for ( i = 0; i < b.iterations; i++ ) {
sigma = ( randu()*20.0 ) + EPS;
y = skewness( sigma );
if ( isnan( y ) ) {
b.fail( 'should not return NaN' );
}
}
b.toc();
if ( isnan( y ) ) {
b.fail( 'should not return NaN' );
}
b.pass( 'benchmark finished' );
b.end();
});
Original file line number Diff line number Diff line change
@@ -0,0 +1,67 @@
/**
* @license Apache-2.0
*
* Copyright (c) 2026 The Stdlib Authors.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/

'use strict';

// MODULES //

var resolve = require( 'path' ).resolve;
var bench = require( '@stdlib/bench' );
var Float64Array = require( '@stdlib/array/float64' );
var uniform = require( '@stdlib/random/base/uniform' );
var isnan = require( '@stdlib/math/base/assert/is-nan' );
var tryRequire = require( '@stdlib/utils/try-require' );
var EPS = require( '@stdlib/constants/float64/eps' );
var pkg = require( './../package.json' ).name;


// VARIABLES //

var skewness = tryRequire( resolve( __dirname, './../lib/native.js' ) );
var opts = {
'skip': ( skewness instanceof Error )
};


// MAIN //

bench( pkg+'::native', opts, function benchmark( b ) {

Check warning on line 43 in lib/node_modules/@stdlib/stats/base/dists/halfnormal/skewness/benchmark/benchmark.native.js

View workflow job for this annotation

GitHub Actions / Lint Changed Files

Use `@stdlib/string/format` instead of string concatenation for benchmark descriptions
var sigma;
var len;
var y;
var i;

len = 100;
sigma = new Float64Array( len );
for ( i = 0; i < len; i++ ) {
sigma[ i ] = uniform( EPS, 20.0 );
}
b.tic();
for ( i = 0; i < b.iterations; i++ ) {
y = skewness( sigma[ i % len ] );
if ( isnan( y ) ) {
b.fail( 'should not return NaN' );
}
}
b.toc();
if ( isnan( y ) ) {
b.fail( 'should not return NaN' );
}
b.pass( 'benchmark finished' );
b.end();
});
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