LambertW: 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.

Version: 0.6.0
Depends: moments, MASS
Imports: lamW, stats, graphics
Suggests: Rsolnp, nortest, numDeriv, testthat (≥ 0.9.0), gsl
Published: 2015-09-08
Author: Georg M. Goerg
Maintainer: Georg M. Goerg <im at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Citation: LambertW citation info
Materials: NEWS
In views: Distributions
CRAN checks: LambertW results


Reference manual: LambertW.pdf
Package source: LambertW_0.6.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X Snow Leopard binaries: r-release: LambertW_0.6.0.tgz, r-oldrel: LambertW_0.5.1.tgz
OS X Mavericks binaries: r-release: LambertW_0.6.0.tgz
Old sources: LambertW archive

Reverse dependencies:

Reverse imports: CryptRndTest