Title: Analyze and Gaussianize skewed, heavy-tailed data
Description: 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.

Package: LambertW
Type: Package
Title: Gaussianize and analyze skewed, heavy-tailed data
Version: 0.2.9
Date: 2011-05-10
Author: Georg M. Goerg <gmg@stat.cmu.edu>
Maintainer: Georg M. Goerg <gmg@stat.cmu.edu>
URL: http://www.stat.cmu.edu/~gmg http://arxiv.org/abs/0912.4554
http://arxiv.org/abs/1010.2265
Description: 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.
Depends: moments, gsl, MASS, nortest, maxLik
License: GPL (>= 2)
LazyLoad: yes
Packaged: 2011-05-11 01:14:32 UTC; root
Repository: CRAN
Date/Publication: 2011-05-11 07:18:37