miceRanger: Multiple Imputation by Chained Equations with Random Forests

Multiple Imputation has been shown to be a flexible method to impute missing values by Van Buuren (2007) <doi:10.1177/0962280206074463>. Expanding on this, random forests have been shown to be an accurate model by Stekhoven and Buhlmann <arXiv:1105.0828> to impute missing values in datasets. They have the added benefits of returning out of bag error and variable importance estimates, as well as being simple to run in parallel.

Version: 1.1.0
Depends: R (≥ 3.5.0)
Imports: ranger, data.table, stats, FNN, ggplot2, gridExtra, crayon, corrplot, ggpubr, DescTools, foreach
Suggests: knitr, rmarkdown, doParallel
Published: 2020-01-19
Author: Sam Wilson [aut, cre]
Maintainer: Sam Wilson <samwilson303 at gmail.com>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: NEWS
CRAN checks: miceRanger results


Reference manual: miceRanger.pdf
Vignettes: miceRanger and the MICE algorithm
Package source: miceRanger_1.1.0.tar.gz
Windows binaries: r-devel: not available, r-devel-gcc8: miceRanger_1.1.0.zip, r-release: not available, r-oldrel: not available
OS X binaries: r-release: not available, r-oldrel: not available


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