Probabilistic Models to Analyze and Gaussianize Heavy-Tailed, Skewed Data
Analyze convergence of Lambert W estimators
Utilities for parameter vector beta of the input distribution
Bootstrap Lambert W x F estimates
Common arguments for several functions
Datasets
Input parameters to get zero mean, unit variance output given delta
Estimate delta
Estimate of delta by Taylor approximation
List of deprecated functions
Utilities for distributions supported in this package
Skewness and kurtosis
Heavy tail transformation for Lambert W random variables
Input parameters to get a zero mean, unit variance output for a given ...
Estimate gamma
Estimate gamma by Taylor approximation
Gaussianize matrix-like objects
Get bounds for gamma
Back-transform Y to X
Transform input X to output Y
Computes support for skewed Lambert W x F distributions
H transformation with gamma
Iterative Generalized Method of Moments -- IGMM
One-sample Kolmogorov-Smirnov test for student-t distribution
R package for Lambert W F distributions
Do-it-yourself toolkit for Lambert W F distribution
Utilities for Lambert W F Random Variables
Methods for Lambert W F estimates
Methods for Lambert W input and output objects
Log-Likelihood for Lambert W F RVs
lp norm of a vector
MedCouple Estimator
Maximum Likelihood Estimation for Lambert W F distributions
Non-principal branch probability
Utilities for transformation vector tau
Visual and statistical Gaussianity check
Test symmetry based on Lambert W heavy tail(s)
Utilities for the parameter vector of Lambert W F distribution...
Zero-mean, unit-variance version of standard distributions
Lambert W function, its logarithm and derivative
Inverse transformation for heavy-tail Lambert W RVs
Inverse transformation for skewed Lambert W RVs
Transformation that defines the Lambert W function and its derivative
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.