# Kurtosis

Beta distribution excess kurtosis.

The excess kurtosis for a beta random variable is

where α > 0 is the first shape parameter and β > 0 is the second shape parameter.

## Usage

var kurtosis = require( '@stdlib/math/base/dists/beta/kurtosis' );


#### kurtosis( alpha, beta )

Returns the excess kurtosis of a beta distribution with parameters alpha (first shape parameter) and beta (second shape parameter).

var v = kurtosis( 1.0, 1.0 );
// returns -1.2

v = kurtosis( 4.0, 12.0 );
// returns ~0.082

v = kurtosis( 8.0, 2.0 );
// returns ~0.490


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 alpha <= 0, the function returns NaN.

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

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


If provided beta <= 0, the function returns NaN.

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

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


## Examples

var randu = require( '@stdlib/random/base/randu' );
var EPS = require( '@stdlib/constants/math/float64-eps' );
var kurtosis = require( '@stdlib/math/base/dists/beta/kurtosis' );

var alpha;
var beta;
var v;
var i;

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