LambertW: Analyze and Gaussianize skewed, heavy-tailed data , 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. (Source:

References in zbMATH (referenced in 13 articles , 1 standard article )

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  1. Cammarota, Camillo; Pinto, Alessandro: Variable selection and importance in presence of high collinearity: an application to the prediction of lean body mass from multi-frequency bioelectrical impedance (2021)
  2. Al-Thani, Hessa; Lee, Jon: An R package for generating covariance matrices for maximum-entropy sampling from precipitation chemistry data (2020)
  3. Batsidis, Apostolos; Jiménez-Gamero, María Dolores; Lemonte, Artur J.: On goodness-of-fit tests for the Bell distribution (2020)
  4. Hessa Al-Thani, Jon Lee: An R Package for generating covariance matrices for maximum-entropy sampling from precipitation chemistry data (2020) arXiv
  5. Lemonte, Artur J.; Moreno-Arenas, Germán; Castellares, Fredy: Zero-inflated Bell regression models for count data (2020)
  6. Belkić, Dževad: All the trinomial roots, their powers and logarithms from the Lambert series, Bell polynomials and Fox-Wright function: illustration for genome multiplicity in survival of irradiated cells (2019)
  7. Zavaleta, Katherine E. C.; Cancho, Vicente G.; Lemonte, Artur J.: Likelihood-based tests in zero-inflated power series models (2019)
  8. Powell, Christopher D.; López, Secundino; Dumas, André; Bureau, Dominique P.; Hook, Sarah E.; France, James: Mathematical descriptions of indeterminate growth (2017)
  9. Rubio, F. J.; Steel, M. F. J.: Bayesian modelling of skewness and kurtosis with two-piece scale and shape distributions (2015)
  10. Stehlík, Milan; Hermann, Philipp: Letter to the editor (2015)
  11. Witkovský, Viktor; Wimmer, Gejza; Duby, Tomy: Logarithmic Lambert (W \times\mathcalF) random variables for the family of chi-squared distributions and their applications (2015)
  12. Goerg, Georg M.: Acknowledgment of priority: Usage of the Lambert (W) function in statistics (2014)
  13. Goerg, Georg M.: Lambert (W) random variables -- a new family of generalized skewed distributions with applications to risk estimation (2011)