Usage

`javascript
var Gamma = require( '@stdlib/stats-base-dists-gamma-ctor' );
`

#### Gamma( \[alpha, beta] )

Returns a [gamma][gamma-distribution] distribution object.

`javascript
var gamma = new Gamma();

var mode = gamma.mode;
// returns 0.0
`

By default, alpha = 1.0 and beta = 1.0. To create a distribution having a different alpha (shape parameter) and beta (rate parameter), provide the corresponding arguments.

`javascript
var gamma = new Gamma( 2.0, 4.0 );

var mu = gamma.mean;
// returns 0.5
`

*

gamma

A [gamma][gamma-distribution] distribution object has the following properties and methods...

$3

#### gamma.alpha

Shape parameter of the distribution. alpha must be a positive number.

`javascript
var gamma = new Gamma();

var alpha = gamma.alpha;
// returns 1.0

gamma.alpha = 3.0;

alpha = gamma.alpha;
// returns 3.0
`

#### gamma.beta

Rate parameter of the distribution. beta must be a positive number.

`javascript
var gamma = new Gamma( 2.0, 4.0 );

var b = gamma.beta;
// returns 4.0

gamma.beta = 3.0;

b = gamma.beta;
// returns 3.0
`

*

$3

#### Gamma.prototype.entropy

Returns the [differential entropy][entropy].

`javascript
var gamma = new Gamma( 4.0, 12.0 );

var entropy = gamma.entropy;
// returns ~-0.462
`

#### Gamma.prototype.kurtosis

Returns the [excess kurtosis][kurtosis].

`javascript
var gamma = new Gamma( 4.0, 12.0 );

var kurtosis = gamma.kurtosis;
// returns 1.5
`

#### Gamma.prototype.mean

Returns the [expected value][expected-value].

`javascript
var gamma = new Gamma( 4.0, 12.0 );

var mu = gamma.mean;
// returns ~0.333
`

#### Gamma.prototype.mode

Returns the [mode][mode].

`javascript
var gamma = new Gamma( 4.0, 12.0 );

var mode = gamma.mode;
// returns 0.25
`

#### Gamma.prototype.skewness

Returns the [skewness][skewness].

`javascript
var gamma = new Gamma( 4.0, 12.0 );

var skewness = gamma.skewness;
// returns 1.0
`

#### Gamma.prototype.stdev

Returns the [standard deviation][standard-deviation].

`javascript
var gamma = new Gamma( 4.0, 12.0 );

var s = gamma.stdev;
// returns ~0.167
`

#### Gamma.prototype.variance

Returns the [variance][variance].

`javascript
var gamma = new Gamma( 4.0, 12.0 );

var s2 = gamma.variance;
// returns ~0.028
`

*

$3

#### Gamma.prototype.cdf( x )

Evaluates the [cumulative distribution function][cdf] (CDF).

`javascript
var gamma = new Gamma( 2.0, 4.0 );

var y = gamma.cdf( 0.5 );
// returns ~0.594
`

#### Gamma.prototype.logcdf( x )

Evaluates the natural logarithm of the [cumulative distribution function][cdf] (CDF).

`javascript
var gamma = new Gamma( 2.0, 4.0 );

var y = gamma.logcdf( 0.5 );
// returns ~-0.521
`

#### Gamma.prototype.logpdf( x )

Evaluates the natural logarithm of the [probability density function][pdf] (PDF).

`javascript
var gamma = new Gamma( 2.0, 4.0 );

var y = gamma.logpdf( 0.8 );
// returns ~-0.651
`

#### Gamma.prototype.mgf( t )

Evaluates the [moment-generating function][mgf] (MGF).

`javascript
var gamma = new Gamma( 2.0, 4.0 );

var y = gamma.mgf( 0.5 );
// returns ~1.306
`

#### Gamma.prototype.pdf( x )

Evaluates the [probability density function][pdf] (PDF).

`javascript
var gamma = new Gamma( 2.0, 4.0 );

var y = gamma.pdf( 0.8 );
// returns ~0.522
`

#### Gamma.prototype.quantile( p )

Evaluates the [quantile function][quantile-function] at probability p.

`javascript
var gamma = new Gamma( 2.0, 4.0 );

var y = gamma.quantile( 0.5 );
// returns ~0.42

y = gamma.quantile( 1.9 );
// returns NaN
`