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| author | Ricardo Wurmus <rekado@elephly.net> | 2017-11-07 15:55:25 +0100 |
|---|---|---|
| committer | Ricardo Wurmus <rekado@elephly.net> | 2017-11-07 23:32:44 +0100 |
| commit | 10e16fa93d09d72302a2c27d94b2975aa8f86174 (patch) | |
| tree | ceba0186cd8e042f858d3126da770a9c2db74039 /gnu/packages | |
| parent | 66c39102e51a6d5915161d29fd8641129520ee35 (diff) | |
| download | patches-10e16fa93d09d72302a2c27d94b2975aa8f86174.tar patches-10e16fa93d09d72302a2c27d94b2975aa8f86174.tar.gz | |
gnu: Add r-mice.
* gnu/packages/cran.scm (r-mice): New variable.
Diffstat (limited to 'gnu/packages')
| -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 609d6648e7..97efca3602 100644 --- a/gnu/packages/cran.scm +++ b/gnu/packages/cran.scm @@ -1313,3 +1313,39 @@ Jaro-Winkler). An implementation of soundex is provided as well. Distances can be computed between character vectors while taking proper care of encoding or between integer vectors representing generic sequences.") (license license:gpl3+))) + +(define-public r-mice + (package + (name "r-mice") + (version "2.46.0") + (source + (origin + (method url-fetch) + (uri (cran-uri "mice" version)) + (sha256 + (base32 + "1gjvlk67zvgipfczsca8zqk97vg3sivv82hblsdwp14s7smhjcax")))) + (build-system r-build-system) + (propagated-inputs + `(("r-lattice" ,r-lattice) + ("r-mass" ,r-mass) + ("r-nnet" ,r-nnet) + ("r-rcpp" ,r-rcpp) + ("r-rpart" ,r-rpart) + ("r-survival" ,r-survival))) + (home-page "https://cran.r-project.org/web/packages/mice/") + (synopsis "Multivariate imputation by chained equations") + (description + "Multiple imputation using @dfn{Fully Conditional Specification} (FCS) +implemented by the MICE algorithm as described in @url{Van Buuren and +Groothuis-Oudshoorn (2011), http://doi.org/10.18637/jss.v045.i03}. Each +variable has its own imputation model. Built-in imputation models are +provided for continuous data (predictive mean matching, normal), binary +data (logistic regression), unordered categorical data (polytomous logistic +regression) and ordered categorical data (proportional odds). MICE can also +impute continuous two-level data (normal model, pan, second-level variables). +Passive imputation can be used to maintain consistency between variables. +Various diagnostic plots are available to inspect the quality of the +imputations.") + ;; Any of these two versions. + (license (list license:gpl2 license:gpl3)))) |