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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_2.5.0.zip, r-oldrel: missRanger_2.5.0.zip |
macOS binaries: | r-release (arm64): missRanger_2.5.0.tgz, r-oldrel (arm64): missRanger_2.5.0.tgz, r-release (x86_64): missRanger_2.5.0.tgz, r-oldrel (x86_64): missRanger_2.5.0.tgz |
Old sources: | missRanger archive |
Reverse imports: | hdImpute, mlim, outForest |
Reverse suggests: | marginaleffects, worcs |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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