Kurtosis

Geometric distribution excess kurtosis.

The excess kurtosis for a geometric random variable is

where p is the success probability.

Installation

npm install @stdlib/stats-base-dists-geometric-kurtosis

Usage

var kurtosis = require( '@stdlib/stats-base-dists-geometric-kurtosis' );

kurtosis( p )

Returns the excess kurtosis of a geometric distribution with success probability p.

var v = kurtosis( 0.1 );
// returns ~6.011

v = kurtosis( 0.5 );
// returns 6.5

If provided a success probability p outside of [0,1], the function returns NaN.

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

v = kurtosis( 1.5 );
// returns NaN

v = kurtosis( -1.0 );
// returns NaN

Examples

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

var opts = {
    'dtype': 'float64'
};
var p = uniform( 10, 0.0, 1.0, opts );

logEachMap( 'p: %0.4f, Kurt(X;p): %0.4f', p, kurtosis );

C APIs

Usage

#include "stdlib/stats/base/dists/geometric/kurtosis.h"

stdlib_base_dists_geometric_kurtosis( p )

Returns the excess kurtosis of a geometric distribution with success probability p.

double out = stdlib_base_dists_geometric_kurtosis( 0.5 );
// returns 6.5

The function accepts the following arguments:

• p: [in] double success probability.

double stdlib_base_dists_geometric_kurtosis( const double p );

Examples

#include "stdlib/stats/base/dists/geometric/kurtosis.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 p;
    double y;
    int i;

    for ( i = 0; i < 25; i++ ) {
        p = random_uniform( 0.0, 1.0 );
        y = stdlib_base_dists_geometric_kurtosis( p );
        printf( "p: %lf, Kurt(X;p): %lf\n", p, 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.