# @stdlib/stats-base-dists-arcsine-kurtosis

0.2.1 • Public • Published

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# Kurtosis

Arcsine distribution excess kurtosis.

The excess kurtosis for an arcsine random variable with minimum support a and maximum support b is

## Installation

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

## Usage

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

#### kurtosis( a, b )

Returns the excess kurtosis of an arcsine distribution with parameters a (minimum support) and b (maximum support).

var v = kurtosis( 0.0, 1.0 );
// returns -1.5

v = kurtosis( 4.0, 12.0 );
// returns -1.5

v = kurtosis( 2.0, 8.0 );
// returns -1.5

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

var v = kurtosis( NaN, 2.0 );
// returns NaN

v = kurtosis( 2.0, NaN );
// returns NaN

If provided a >= b, the function returns NaN.

var y = kurtosis( 3.0, 2.0 );
// returns NaN

y = kurtosis( 3.0, 3.0 );
// returns NaN

## Examples

var randu = require( '@stdlib/random-base-randu' );
var kurtosis = require( '@stdlib/stats-base-dists-arcsine-kurtosis' );

var a;
var b;
var v;
var i;

for ( i = 0; i < 10; i++ ) {
a = ( randu()*10.0 );
b = ( randu()*10.0 ) + a;
v = kurtosis( a, b );
console.log( 'a: %d, b: %d, Kurt(X;a,b): %d', a.toFixed( 4 ), b.toFixed( 4 ), v.toFixed( 4 ) );
}

## 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.

## Package Sidebar

### Install

npm i @stdlib/stats-base-dists-arcsine-kurtosis

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