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  [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-devel: missForestPredict_1.0.zip, r-release: missForestPredict_1.0.zip, r-oldrel: missForestPredict_1.0.zip |
| macOS binaries: | r-release (arm64): missForestPredict_1.0.tgz, r-oldrel (arm64): missForestPredict_1.0.tgz, r-release (x86_64): missForestPredict_1.0.tgz, r-oldrel (x86_64): missForestPredict_1.0.tgz |
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