missForest: Nonparametric Missing Value Imputation using Random Forest

The function 'missForest' in this package is used to impute missing values particularly in the case of mixed-type data. It uses a random forest trained on the observed values of a data matrix to predict the missing values. It can be used to impute continuous and/or categorical data including complex interactions and non-linear relations. It yields an out-of-bag (OOB) imputation error estimate without the need of a test set or elaborate cross-validation. It can be run in parallel to save computation time.

Version: 1.5
Imports: randomForest, foreach, itertools, iterators, doRNG
Suggests: doParallel
Published: 2022-04-14
DOI: 10.32614/CRAN.package.missForest
Author: Daniel J. Stekhoven
Maintainer: Daniel J. Stekhoven <stekhoven at stat.math.ethz.ch>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://www.r-project.org, https://github.com/stekhoven/missForest
NeedsCompilation: no
Citation: missForest citation info
Materials: README
In views: MissingData
CRAN checks: missForest results


Reference manual: missForest.pdf
Vignettes: missForest_1.5


Package source: missForest_1.5.tar.gz
Windows binaries: r-devel: missForest_1.5.zip, r-release: missForest_1.5.zip, r-oldrel: missForest_1.5.zip
macOS binaries: r-release (arm64): missForest_1.5.tgz, r-oldrel (arm64): missForest_1.5.tgz, r-release (x86_64): missForest_1.5.tgz, r-oldrel (x86_64): missForest_1.5.tgz
Old sources: missForest archive

Reverse dependencies:

Reverse depends: bartMachine, imp4p
Reverse imports: ADAPTS, funspace, highMLR, imanr, KarsTS, longit, MAI, MERO, missCompare, MSPrep, obliqueRSF, pmp, promor, simputation, speaq
Reverse suggests: CALIBERrfimpute, DepInfeR, hdImpute, MsCoreUtils, qmtools, tidyLPA


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