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| -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 d65ff97d1c..64c7f08096 100644 --- a/gnu/packages/cran.scm +++ b/gnu/packages/cran.scm @@ -23259,6 +23259,42 @@ by providing functionality for preprocessing, predicting, and validating input.") (license license:expat))) +(define-public r-lightgbm + (package + (name "r-lightgbm") + (version "3.1.1") + (source + (origin + (method url-fetch) + (uri (cran-uri "lightgbm" version)) + (sha256 + (base32 + "1pwsh6j9ksahh58b15j5ij56bsc6syy3z4k4a5zhy5n7829rz555")))) + (properties `((upstream-name . "lightgbm"))) + (build-system r-build-system) + (propagated-inputs + `(("r-data-table" ,r-data-table) + ("r-jsonlite" ,r-jsonlite) + ("r-matrix" ,r-matrix) + ("r-r6" ,r-r6))) + (home-page "https://github.com/Microsoft/LightGBM") + (synopsis "Light gradient boosting machine") + (description + "Tree based algorithms can be improved by introducing boosting +frameworks. LightGBM is one such framework, based on Ke, Guolin et +al. (2017). This package offers an R interface to work with it. It is +designed to be distributed and efficient with the following goals: + +@enumerate +@item Faster training speed and higher efficiency; +@item lower memory usage; +@item better accuracy; +@item parallel learning supported; and +@item capable of handling large-scale data. +@end enumerate +") + (license license:expat))) + (define-public r-shapforxgboost (package (name "r-shapforxgboost") |