Analyze and Gaussianize skewed, heavy-tailed data
The Lambert W framework is a new generalized way to analyze skewed, heavy-tailed data. Lambert W random variables (RV) are based on an input/output framework where the input is a RV X with distribution F(x), and the output Y = func(X) has similar properties as X (but slightly skewed or heavy-tailed). Then this transformed RV Y has a Lambert W x F distribution - for details see References. This package contains functions to perform a Lambert W analysis of skewed and heavy-tailed data: data can be simulated, parameters can be estimated from real world data, quantiles can be computed, and results plotted/printed in a 'nice' way. Probably the most important function is 'Gaussianize', which works the same way as the R function 'scale' but actually makes your data Gaussian. An optional modular toolkit implementation allows users to define their own Lambert W x 'my favorite distribution' and use it for their analysis.

Analyze and Gaussianize Heavy-Tailed, Skewed Data
Lambert W x F distributions are a generalized framework to analyze
skewed, heavy-tailed data. They are based on an input/output system, where
the input random variable (RV) X ~ F, and the output Y is a non-linearly
transformed version of X with similar properties, but slightly skewed and/or
heavy-tailed. This 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.

Analyze and Gaussianize heavy-tailed, skewed data

Rsolnp, nortest, numDeriv, testthat (>= 0.9.0), gsl

Probabilistic Models to Analyze and Gaussianize Heavy-Tailed,
Skewed 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. 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.
lamW (>= 1.0.0), stats, graphics, grDevices, RColorBrewer,
reshape2, Rcpp
boot, Rsolnp, nortest, numDeriv, testthat, gsl, moments,