get_output

named vector \(\tau\) which defines the variable transformation.
Must have at least <code>'mu_x'</code> and <code>'sigma_x'</code> element; see
<code><a rd-options="" href="/link/complete_tau?package=LambertW&version=0.6.4" data-mini-rdoc="LambertW::complete_tau">complete_tau</a></code> for details.

should the shifted and scaled output also be returned?
Default: <code>FALSE</code>.

return.z

<code>get_output</code> transforms the input \(\boldsymbol x\) to the observed
 data \(\boldsymbol y\) given the transformation vector \(\tau =
 (\mu_x(\boldsymbol \beta), \sigma_x(\boldsymbol \beta), \gamma, \alpha,
 \delta)\).
This is the inverse of <code><a rd-options="" href="/link/get_input?package=LambertW&version=0.6.4" data-mini-rdoc="LambertW::get_input">get_input</a></code>.

manip

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. Probably the most important 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

get_output function

named vector \(\tau\) which defines the variable transformation.
Must have at least <code>'mu_x'</code> and <code>'sigma_x'</code> element; see
<code><a rd-options='' href='complete_tau'>complete_tau</a></code> for details.

<code>get_output</code> transforms the input \(\boldsymbol x\) to the observed
 data \(\boldsymbol y\) given the transformation vector \(\tau =
 (\mu_x(\boldsymbol \beta), \sigma_x(\boldsymbol \beta), \gamma, \alpha,
 \delta)\).
This is the inverse of <code><a rd-options='' href='get_input'>get_input</a></code>.

get_output: Transform input X to output Y

Description

Usage

Arguments

Value

See Also