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For any changes after the official\nversion, see the commit history and here.\n\n\n## Installation \u0026 usage\n\nTo install **LambertW** run\n\n```{r}\ninstall.packages(\"LambertW\")\ncitation(\"LambertW\")\n```\n\nSee `?LambertW` for examples on how to use the **LambertW** package.\n\nThere is also an [R vignette on CRAN](https://CRAN.R-project.org/package=LambertW/vignettes/lambertw-overview.html) with a brief tutorial on the main functionalities.\n\n## Python implementation\n\nSee https://github.com/gmgeorg/pylambertw for the Python equivalent of the **LambertW** package.\n\n## Tutorials \u0026 posts\n\nSee cross-validated / stackoverflow for [a variety of **LambertW** posts](https://stats.stackexchange.com/search?q=LambertW) on how to normalize/Gaussianize data and model skewed/heavy-tailed distributions.\n\n\n## References\n\nGeorg M. Goerg (2011): [*Lambert W random variables - a new family of generalized skewed distributions with applications to risk estimation*](https://projecteuclid.org/euclid.aoas/1318514301). Annals of Applied Statistics 3(5). 2197-2230.\n\nGeorg M. Goerg (2014): [*The Lambert Way to Gaussianize heavy-tailed data with the inverse of Tukey's h transformation as a special case*](https://downloads.hindawi.com/journals/tswj/2015/909231.pdf). The Scientific World Journal.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgmgeorg%2Flambertw","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgmgeorg%2Flambertw","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgmgeorg%2Flambertw/lists"}