H_gamma

Skewed Lambert W\(\times\) F RV transformation: \(H_{\gamma}(u) = u
 \exp(\gamma u)\).

math

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

H_gamma function

<dl><dt>u</dt>
<dd>a numeric vector of real values.</dd>
<dt>gamma</dt>
<dd>skewness parameter; default <code>gamma = 0</code>, which implies
<code>H_gamma(u) = u</code>.</dd></dl>

Arguments

H transformation with gamma — H_gamma

<dl>

<dt>u</dt>
<dd>a numeric vector of real values.</dd>


<dt>gamma</dt>
<dd>skewness parameter; default <code>gamma = 0</code>, which implies
<code>H_gamma(u) = u</code>.</dd>

</dl>

H_gamma: H transformation with gamma

Description

Usage

Value

Arguments

See Also