Skewness

Planck (discrete exponential) distribution skewness.

The skewness for a Planck random variable is

where λ is the shape parameter.

Installation

npm install @stdlib/stats-base-dists-planck-skewness

Usage

var skewness = require( '@stdlib/stats-base-dists-planck-skewness' );

skewness( lambda )

Returns the skewness of a Planck distribution with shape parameter lambda.

var v = skewness( 0.1 );
// returns ~2.0025

v = skewness( 1.5 );
// returns ~2.5894

If provided a shape parameter lambda which is nonpositive or NaN, the function returns NaN.

var v = skewness( NaN );
// returns NaN

v = skewness( -1.5 );
// returns NaN

Examples

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

var opts = {
    'dtype': 'float64'
};
var lambda = uniform( 10, 0.1, 10.0, opts );

logEachMap( 'λ: %0.4f, skew(X;λ): %0.4f', lambda, skewness );

C APIs

Usage

#include "stdlib/stats/base/dists/planck/skewness.h"

stdlib_base_dists_planck_skewness( lambda )

Returns the skewness of a Planck distribution with shape parameter lambda.

double out = stdlib_base_dists_planck_skewness( 0.1 );
// returns ~2.0025

The function accepts the following arguments:

• lambda: [in] double shape parameter.

double stdlib_base_dists_planck_skewness( const double lambda );

Examples

#include "stdlib/stats/base/dists/planck/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 lambda;
    double y;
    int i;

    for ( i = 0; i < 25; i++ ) {
        lambda = random_uniform( 0.1, 5.0 );
        y = stdlib_base_dists_planck_skewness( lambda );
        printf( "λ: %lf, skew(X;λ): %lf\n", lambda, y );
    }
}

Notice

This package is part of stdlib, a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.

For more information on the project, filing bug reports and feature requests, and guidance on how to develop stdlib, see the main project repository.

Community

License

See LICENSE.

Copyright

Copyright © 2016-2026. The Stdlib Authors.