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: 1.3.1
Depends: R (≥ 3.5.0)
Imports: ranger, data.table, stats, FNN, ggplot2, crayon, corrplot, ggpubr, DescTools, foreach
Suggests: knitr, rmarkdown, doParallel, testthat (≥ 2.1.0)
Published: 2020-02-14
Author: Sam Wilson [aut, cre]
Maintainer: Sam Wilson <samwilson303 at gmail.com>
BugReports: https://github.com/FarrellDay/miceRanger/issues
License: MIT + file LICENSE
URL: https://github.com/FarrellDay/miceRanger
NeedsCompilation: no
Materials: NEWS
CRAN checks: miceRanger results

Downloads:

Reference manual: miceRanger.pdf
Vignettes: Diagnostic Plotting
The MICE Algorithm
Filling in Missing Data with miceRanger
Package source: miceRanger_1.3.1.tar.gz
Windows binaries: r-devel: miceRanger_1.3.1.zip, r-devel-gcc8: miceRanger_1.2.0.zip, r-release: miceRanger_1.3.0.zip, r-oldrel: miceRanger_1.3.0.zip
OS X binaries: r-release: miceRanger_1.3.1.tgz, r-oldrel: miceRanger_1.3.1.tgz
Old sources: miceRanger archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=miceRanger to link to this page.