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author | Ricardo Wurmus <rekado@elephly.net> | 2023-02-18 20:31:57 +0100 |
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committer | Ricardo Wurmus <rekado@elephly.net> | 2023-02-18 21:11:56 +0100 |
commit | 7ec692587a20a248e6b94fffc214ab152e7907d3 (patch) | |
tree | 24636f847e84b2ab80fca7172a989949f9d8677f /gnu/packages/cran.scm | |
parent | 48e5c3bd290da7e796197f972645ae7a19a5677c (diff) | |
download | guix-7ec692587a20a248e6b94fffc214ab152e7907d3.tar.gz guix-7ec692587a20a248e6b94fffc214ab152e7907d3.zip |
gnu: Add r-lambertw.
* gnu/packages/cran.scm (r-lambertw): New variable.
Diffstat (limited to 'gnu/packages/cran.scm')
-rw-r--r-- | gnu/packages/cran.scm | 36 |
1 files changed, 36 insertions, 0 deletions
diff --git a/gnu/packages/cran.scm b/gnu/packages/cran.scm index 174c85a964..1f4f55f038 100644 --- a/gnu/packages/cran.scm +++ b/gnu/packages/cran.scm @@ -5576,6 +5576,42 @@ selection, as well as methods for retrieving coordinates, for subsetting, print, summary, etc.") (license license:gpl2+))) +(define-public r-lambertw + (package + (name "r-lambertw") + (version "0.6.7-1") + (source (origin + (method url-fetch) + (uri (cran-uri "LambertW" version)) + (sha256 + (base32 + "0cdrq2nrvji8l5blswkffymm7cbjk5jzzx16js2a516cm3gjwxk4")))) + (properties `((upstream-name . "LambertW"))) + (build-system r-build-system) + (propagated-inputs (list r-ggplot2 + r-lamw + r-mass + r-rcolorbrewer + r-rcpp + r-reshape2)) + (native-inputs (list r-knitr)) + (home-page "https://cran.r-project.org/package=LambertW") + (synopsis + "Probabilistic models to analyze and Gaussianize heavy-tailed, skewed data") + (description + "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 @code{Gaussianize}, which +works similarly to @code{scale}, but actually makes the data Gaussian. A +do-it-yourself toolkit allows users to define their own Lambert W x +@code{MyFavoriteDistribution} and use it in their analysis right away.") + (license license:gpl2+))) + (define-public r-lamw (package (name "r-lamw") |