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. For more details see Albu, E., Gao, S., Wynants, L., & Van Calster, B. (2024). missForestPredict–Missing data imputation for prediction settings <doi:10.48550/arXiv.2407.03379>.">

missForestPredict: Missing Value Imputation using Random Forest for Prediction Settings (original) (raw)

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. For more details see Albu, E., Gao, S., Wynants, L., & Van Calster, B. (2024). missForestPredict–Missing data imputation for prediction settings <doi:10.48550/arXiv.2407.03379>.

Version: 1.0.1
Depends: R (≥ 4.0)
Imports: ranger, methods, stats
Suggests: knitr, rmarkdown, ggplot2, dplyr, tidyr
Published: 2025-05-24
DOI: 10.32614/CRAN.package.missForestPredict
Author: Elena Albu ORCID iD [aut, cre] (funding: KU Leuven)
Maintainer: Elena Albu <elenaa.albu at gmail.com>
BugReports: https://github.com/sibipx/missForestPredict/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/sibipx/missForestPredict
NeedsCompilation: no
Citation: missForestPredict citation info
CRAN checks: missForestPredict results

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