missForestPredict: Missing Value Imputation using Random Forest for Prediction Settings

Missing data imputation based on the 'missForest' algorithm (Stekhoven, Daniel J (2012) <doi:10.1093/bioinformatics/btr597>) with adaptations for prediction settings. The function missForest() is used to impute a (training) dataset with missing values and to learn imputation models that can be later used for imputing new observations. The function missForestPredict() is used to impute one or multiple new observations (test set) using the models learned on the training data.

Version: 1.0
Depends: R (≥ 4.0)
Imports: ranger, methods, stats
Suggests: knitr, rmarkdown, ggplot2, dplyr, tidyr
Published: 2023-12-12
Author: Elena Albu ORCID iD [aut, cre]
Maintainer: Elena Albu <elena.albu at kuleuven.be>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/sibipx/missForestPredict
NeedsCompilation: no
CRAN checks: missForestPredict results

Documentation:

Reference manual: missForestPredict.pdf
Vignettes: missForestPredict convergence criteria and error monitoring
Using the missForestPredict package

Downloads:

Package source: missForestPredict_1.0.tar.gz
Windows binaries: r-prerel: missForestPredict_1.0.zip, r-release: missForestPredict_1.0.zip, r-oldrel: missForestPredict_1.0.zip
macOS binaries: r-prerel (arm64): missForestPredict_1.0.tgz, r-release (arm64): missForestPredict_1.0.tgz, r-oldrel (arm64): missForestPredict_1.0.tgz, r-prerel (x86_64): missForestPredict_1.0.tgz, r-release (x86_64): missForestPredict_1.0.tgz

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