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Kumaraswamy's double bounded distribution constructor.
npm install @stdlib/stats-base-dists-kumaraswamy-ctor
Alternatively,
- To load the package in a website via a
script
tag without installation and bundlers, use the ES Module available on theesm
branch (see README). - If you are using Deno, visit the
deno
branch (see README for usage intructions). - For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the
umd
branch (see README).
The branches.md file summarizes the available branches and displays a diagram illustrating their relationships.
To view installation and usage instructions specific to each branch build, be sure to explicitly navigate to the respective README files on each branch, as linked to above.
var Kumaraswamy = require( '@stdlib/stats-base-dists-kumaraswamy-ctor' );
Returns a Kumaraswamy's double bounded distribution object.
var kumaraswamy = new Kumaraswamy();
var mu = kumaraswamy.mean;
// returns 0.5
By default, a = 1.0
and b = 1.0
. To create a distribution having a different a
(first shape parameter) and b
(second shape parameter), provide the corresponding arguments.
var kumaraswamy = new Kumaraswamy( 2.0, 4.0 );
var mu = kumaraswamy.mean;
// returns ~0.406
A Kumaraswamy's double bounded distribution object has the following properties and methods...
First shape parameter of the distribution. a
must be a positive number.
var kumaraswamy = new Kumaraswamy();
var a = kumaraswamy.a;
// returns 1.0
kumaraswamy.a = 3.0;
a = kumaraswamy.a;
// returns 3.0
Second shape parameter of the distribution. b
must be a positive number.
var kumaraswamy = new Kumaraswamy( 2.0, 4.0 );
var b = kumaraswamy.b;
// returns 4.0
kumaraswamy.b = 3.0;
b = kumaraswamy.b;
// returns 3.0
Returns the excess kurtosis.
var kumaraswamy = new Kumaraswamy( 4.0, 12.0 );
var kurtosis = kumaraswamy.kurtosis;
// returns ~2.704
Returns the expected value.
var kumaraswamy = new Kumaraswamy( 4.0, 12.0 );
var mu = kumaraswamy.mean;
// returns ~0.481
Returns the mode.
var kumaraswamy = new Kumaraswamy( 4.0, 12.0 );
var mode = kumaraswamy.mode;
// returns ~0.503
Returns the skewness.
var kumaraswamy = new Kumaraswamy( 4.0, 12.0 );
var skewness = kumaraswamy.skewness;
// returns ~-0.201
Returns the standard deviation.
var kumaraswamy = new Kumaraswamy( 4.0, 12.0 );
var s = kumaraswamy.stdev;
// returns ~0.13
Returns the variance.
var kumaraswamy = new Kumaraswamy( 4.0, 12.0 );
var s2 = kumaraswamy.variance;
// returns ~0.017
Evaluates the cumulative distribution function (CDF).
var kumaraswamy = new Kumaraswamy( 2.0, 4.0 );
var y = kumaraswamy.cdf( 0.5 );
// returns ~0.684
Evaluates the natural logarithm of the cumulative distribution function (CDF).
var kumaraswamy = new Kumaraswamy( 2.0, 4.0 );
var y = kumaraswamy.logcdf( 0.5 );
// returns ~-0.38
Evaluates the natural logarithm of the probability density function (PDF).
var kumaraswamy = new Kumaraswamy( 2.0, 4.0 );
var y = kumaraswamy.logpdf( 0.8 );
// returns ~-1.209
Evaluates the probability density function (PDF).
var kumaraswamy = new Kumaraswamy( 2.0, 4.0 );
var y = kumaraswamy.pdf( 0.8 );
// returns ~0.299
Evaluates the quantile function at probability p
.
var kumaraswamy = new Kumaraswamy( 2.0, 4.0 );
var y = kumaraswamy.quantile( 0.5 );
// returns ~0.399
y = kumaraswamy.quantile( 1.9 );
// returns NaN
var Kumaraswamy = require( '@stdlib/stats-base-dists-kumaraswamy-ctor' );
var kumaraswamy = new Kumaraswamy( 2.0, 4.0 );
var mu = kumaraswamy.mean;
// returns ~0.406
var mode = kumaraswamy.mode;
// returns ~0.378
var s2 = kumaraswamy.variance;
// returns ~0.035
var y = kumaraswamy.cdf( 0.8 );
// returns ~0.983
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