kurtosis

skewness

<code>kurtosis</code> estimates the fourth central, normalized moment from data.
<code>skewness</code> estimates the third central, normalized moment from data.

Lambert W x F distributions are a generalized framework to analyze
skewed, heavy-tailed data. It is based on an input/output system, where the
output random variable (RV) Y is a non-linearly transformed version of an input
RV X ~ F with similar properties as X, but slightly skewed (heavy-tailed).
The transformed RV Y has a Lambert W x F distribution. This package contains
functions to model and analyze skewed, heavy-tailed data the Lambert Way:
simulate random samples, estimate parameters, compute quantiles, and plot/
print results nicely. The most useful function is 'Gaussianize',
which works similarly to 'scale', but actually makes the data Gaussian.
A do-it-yourself toolkit allows users to define their own Lambert W x
'MyFavoriteDistribution' and use it in their analysis right away.

Georg M Goerg

LambertW

Probabilistic Models to Analyze and Gaussianize Heavy-Tailed,
Skewed Data

Georg M. Goerg

kurtosis function

<dl><dt>x</dt>
<dd>a numeric vector.</dd></dl>

Arguments

Skewness and kurtosis — kurtosis

<dl>

<dt>x</dt>
<dd>a numeric vector.</dd>

</dl>

kurtosis: Skewness and kurtosis

Description

Usage

Arguments

See Also