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 jungle 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: 2.1.0
Depends: R (≥ 3.5.0)
Imports: stats, FNN (≥ 1.1), ranger (≥ 0.10)
Suggests: mice, dplyr, survival, ggplot2, knitr, rmarkdown
Published: 2019-06-30
Author: Michael Mayer [aut, cre, cph]
Maintainer: Michael Mayer <mayermichael79 at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: README NEWS
CRAN checks: missRanger results


Reference manual: missRanger.pdf
Vignettes: missRanger
Package source: missRanger_2.1.0.tar.gz
Windows binaries: r-devel: missRanger_2.1.0.zip, r-release: missRanger_2.1.0.zip, r-oldrel: missRanger_2.1.0.zip
macOS binaries: r-release: missRanger_2.1.0.tgz, r-oldrel: missRanger_2.1.0.tgz
Old sources: missRanger archive

Reverse dependencies:

Reverse imports: NADIA, outForest, wiseR
Reverse suggests: worcs


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