Package: missForestPredict
Type: Package
Title: Missing Value Imputation using Random Forest for Prediction
        Settings
Version: 1.0.1
Date: 2025-05-23
Authors@R: c(
    person("Elena", "Albu", , "elenaa.albu@gmail.com", c("aut", "cre"),
           comment = c(ORCID = "0000-0003-2602-0918", funding = "KU Leuven"))
           )
Depends: R (>= 4.0)
Imports: ranger, methods, stats
Suggests: knitr, rmarkdown, ggplot2, dplyr, tidyr
Description: 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>.
Encoding: UTF-8
License: GPL (>= 2)
URL: https://github.com/sibipx/missForestPredict
BugReports: https://github.com/sibipx/missForestPredict/issues
RoxygenNote: 7.3.2
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2025-05-24 12:23:00 UTC; cbp
Author: Elena Albu [aut, cre] (ORCID: <https://orcid.org/0000-0003-2602-0918>,
    funding: KU Leuven)
Maintainer: Elena Albu <elenaa.albu@gmail.com>
Repository: CRAN
Date/Publication: 2025-05-24 15:40:05 UTC
Built: R 4.6.0; ; 2026-04-26 04:14:26 UTC; unix
