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 <doi:10.48550/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:	1.5.0
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:	2021-09-06
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
In views:	MissingData
CRAN checks:	miceRanger results

Documentation:

Reference manual:	miceRanger.pdf
Vignettes:	Diagnostic Plotting The MICE Algorithm Filling in Missing Data with miceRanger

Downloads:

Package source:	miceRanger_1.5.0.tar.gz
Windows binaries:	r-devel: miceRanger_1.5.0.zip, r-release: miceRanger_1.5.0.zip, r-oldrel: miceRanger_1.5.0.zip
macOS binaries:	r-release (arm64): miceRanger_1.5.0.tgz, r-oldrel (arm64): miceRanger_1.5.0.tgz, r-release (x86_64): miceRanger_1.5.0.tgz, r-oldrel (x86_64): miceRanger_1.5.0.tgz
Old sources:	miceRanger archive

Linking:

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