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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. The most useful 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.
Getting started
Browse package contents
Package details |
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| Author | Georg M. Goerg [aut, cre] |
| Maintainer | Georg M. Goerg <im@gmge.org> |
| License | GPL (>= 2) |
| Version | 0.6.9 |
| URL | https://github.com/gmgeorg/LambertW https://arxiv.org/abs/0912.4554 https://arxiv.org/abs/1010.2265 https://arxiv.org/abs/1602.02200 |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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