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: | 1.2.0 |
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: | 2020-01-20 |
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 |
Reference manual: | miceRanger.pdf |
Vignettes: |
miceRanger and the MICE algorithm |
Package source: | miceRanger_1.2.0.tar.gz |
Windows binaries: | r-devel: miceRanger_1.2.0.zip, r-devel-gcc8: miceRanger_1.1.0.zip, r-release: miceRanger_1.2.0.zip, r-oldrel: not available |
OS X binaries: | r-release: miceRanger_1.1.0.tgz, r-oldrel: miceRanger_1.1.0.tgz |
Old sources: | miceRanger archive |
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