LambertW: 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.

Version: 0.6.4
Depends: MASS, ggplot2
Imports: lamW (≥ 1.0.0), stats, graphics, grDevices, RColorBrewer, reshape2, Rcpp
LinkingTo: Rcpp, lamW
Suggests: boot, Rsolnp, nortest, numDeriv, testthat, moments
Published: 2016-03-29
Author: Georg M. Goerg
Maintainer: Georg M. Goerg <im at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Citation: LambertW citation info
Materials: NEWS
In views: Distributions
CRAN checks: LambertW results


Reference manual: LambertW.pdf
Package source: LambertW_0.6.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: LambertW_0.6.4.tgz
OS X Mavericks binaries: r-oldrel: LambertW_0.6.4.tgz
Old sources: LambertW archive

Reverse dependencies:

Reverse imports: CryptRndTest, optband


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