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author | Ricardo Wurmus <rekado@elephly.net> | 2018-06-13 11:40:17 +0200 |
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committer | Ricardo Wurmus <rekado@elephly.net> | 2018-06-13 11:51:28 +0200 |
commit | e83841a296ef7f25a4847aa0e02456bec774a461 (patch) | |
tree | 372af85ee3d680a274df81570edbe6850d3404f7 /gnu/packages/cran.scm | |
parent | cbf6017d83e094dda7f0ed96706c15997c9b0660 (diff) | |
download | guix-e83841a296ef7f25a4847aa0e02456bec774a461.tar guix-e83841a296ef7f25a4847aa0e02456bec774a461.tar.gz |
gnu: Add r-factominer.
* gnu/packages/cran.scm (r-factominer): New variable.
Diffstat (limited to 'gnu/packages/cran.scm')
-rw-r--r-- | gnu/packages/cran.scm | 34 |
1 files changed, 34 insertions, 0 deletions
diff --git a/gnu/packages/cran.scm b/gnu/packages/cran.scm index 6bf278bf7d..f3662ef14c 100644 --- a/gnu/packages/cran.scm +++ b/gnu/packages/cran.scm @@ -4316,3 +4316,37 @@ Analysis and its Applications.") "This package provides a fast implementation of hierarchical clustering.") (license license:gpl2+))) + +(define-public r-factominer + (package + (name "r-factominer") + (version "1.41") + (source + (origin + (method url-fetch) + (uri (cran-uri "FactoMineR" version)) + (sha256 + (base32 + "1h20hydav6l2b7bngqw1av4l5rrh0wk58nhailga1f4qw9lrv259")))) + (properties `((upstream-name . "FactoMineR"))) + (build-system r-build-system) + (propagated-inputs + `(("r-car" ,r-car) + ("r-cluster" ,r-cluster) + ("r-ellipse" ,r-ellipse) + ("r-flashclust" ,r-flashclust) + ("r-lattice" ,r-lattice) + ("r-leaps" ,r-leaps) + ("r-mass" ,r-mass) + ("r-scatterplot3d" ,r-scatterplot3d))) + (home-page "http://factominer.free.fr") + (synopsis "Multivariate exploratory data analysis and data mining") + (description + "This package provides exploratory data analysis methods to summarize, +visualize and describe datasets. The main principal component methods are +available, those with the largest potential in terms of applications: +principal component analysis (PCA) when variables are quantitative, +correspondence analysis (CA) and multiple correspondence analysis (MCA) when +variables are categorical, Multiple Factor Analysis when variables are +structured in groups, etc. and hierarchical cluster analysis.") + (license license:gpl2+))) |