## 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: |
0.6.2 |

Depends: |
MASS, ggplot2 |

Imports: |
lamW (≥ 1.0.0), stats, graphics, grDevices, RColorBrewer, reshape2, Rcpp |

LinkingTo: |
Rcpp, lamW |

Suggests: |
boot, Rsolnp, nortest, numDeriv, testthat, gsl, moments |

Published: |
2016-02-05 |

Author: |
Georg M. Goerg |

Maintainer: |
Georg M. Goerg <im at gmge.org> |

License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |

URL: |
http://www.gmge.org http://arxiv.org/abs/0912.4554
http://arxiv.org/abs/1010.2265 |

NeedsCompilation: |
yes |

Citation: |
LambertW citation info |

Materials: |
NEWS |

In views: |
Distributions |

CRAN checks: |
LambertW results |

#### Downloads:

#### Reverse dependencies: