gnu: Add r-tidypredict.
* gnu/packages/cran.scm (r-tidypredict): New variable. Signed-off-by: Ricardo Wurmus <rekado@elephly.net>master
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@ -23899,3 +23899,36 @@ the @dfn{root mean squared error} (RMSE) using 10-fold cross-validation, there
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are 10 paired statistics. These can be used to make comparisons between
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models without involving a test set.")
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(license license:gpl2)))
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(define-public r-tidypredict
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(package
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(name "r-tidypredict")
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(version "0.4.5")
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(source
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(origin
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(method url-fetch)
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(uri (cran-uri "tidypredict" version))
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(sha256
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(base32
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"1i6zl6wjz6wbpkmkc9z9ikp8zgck3qh38lar0r6q2jzl8fxpimg4"))))
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(properties `((upstream-name . "tidypredict")))
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(build-system r-build-system)
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(propagated-inputs
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`(("r-dplyr" ,r-dplyr)
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("r-generics" ,r-generics)
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("r-knitr" ,r-knitr)
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("r-purrr" ,r-purrr)
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("r-rlang" ,r-rlang)
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("r-tibble" ,r-tibble)))
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(native-inputs
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`(("r-knitr" ,r-knitr)))
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(home-page "https://tidypredict.tidymodels.org")
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(synopsis "Run predictions inside the database")
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(description
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"This package parses a fitted R model object, and returns a formula in
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Tidy Eval code that calculates the predictions. It works with several
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database backends because it leverages @code{dplyr} and @code{dbplyr} for the
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final SQL translation of the algorithm. It currently supports @code{lm()},
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@code{glm()}, @code{randomForest()}, @code{ranger()}, @code{earth()},
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@code{xgb.Booster.complete()}, @code{cubist()}, and @code{ctree()} models.")
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(license license:gpl3)))
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