missRanger: Fast Imputation of Missing Values

Alternative implementation of the beautiful 'MissForest' algorithm used to impute mixed-type data sets by chaining random forests, introduced by Stekhoven, D.J. and Buehlmann, P. (2012) <doi:10.1093/bioinformatics/btr597>. Under the hood, it uses the lightning fast random forest package 'ranger'. Between the iterative model fitting, we offer the option of using predictive mean matching. This firstly avoids imputation with values not already present in the original data (like a value 0.3334 in 0-1 coded variable). Secondly, predictive mean matching tries to raise the variance in the resulting conditional distributions to a realistic level. This would allow, e.g., to do multiple imputation when repeating the call to missRanger(). A formula interface allows to control which variables should be imputed by which.

Version: 2.5.0
Depends: R (≥ 3.5.0)
Imports: FNN, ranger, stats, utils
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2024-07-12
DOI: 10.32614/CRAN.package.missRanger
Author: Michael Mayer [aut, cre]
Maintainer: Michael Mayer <mayermichael79 at gmail.com>
BugReports: https://github.com/mayer79/missRanger/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/mayer79/missRanger, https://mayer79.github.io/missRanger/
NeedsCompilation: no
Materials: README NEWS
In views: MissingData
CRAN checks: missRanger results


Reference manual: missRanger.pdf
Vignettes: Using missRanger
Multiple Imputation
Censored Variables


Package source: missRanger_2.5.0.tar.gz
Windows binaries: r-devel: missRanger_2.5.0.zip, r-release: missRanger_2.4.0.zip, r-oldrel: missRanger_2.5.0.zip
macOS binaries: r-release (arm64): missRanger_2.4.0.tgz, r-oldrel (arm64): missRanger_2.4.0.tgz, r-release (x86_64): missRanger_2.4.0.tgz, r-oldrel (x86_64): missRanger_2.4.0.tgz
Old sources: missRanger archive

Reverse dependencies:

Reverse imports: hdImpute, mlim, outForest
Reverse suggests: marginaleffects, worcs


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