Multiple Imputation has been shown to be a flexible method to impute missing values by Van Buuren (2007) <doi:10.1177/0962280206074463>. Expanding on this, random forests have been shown to be an accurate model by Stekhoven and Buhlmann <doi:10.48550/arXiv.1105.0828> to impute missing values in datasets. They have the added benefits of returning out of bag error and variable importance estimates, as well as being simple to run in parallel.
Version: 1.5.0 Depends: R (≥ 3.5.0) Imports: ranger, data.table, stats, FNN, ggplot2, crayon, corrplot, ggpubr, DescTools, foreach Suggests: knitr, rmarkdown, doParallel, testthat (≥ 2.1.0) Published: 2021-09-06 DOI: 10.32614/CRAN.package.miceRanger Author: Sam Wilson [aut, cre] Maintainer: Sam Wilson <samwilson303 at gmail.com> BugReports: https://github.com/FarrellDay/miceRanger/issues License: MIT + file LICENSE URL: https://github.com/FarrellDay/miceRanger NeedsCompilation: no Materials: NEWS In views: MissingData CRAN checks: miceRanger results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=miceRanger to link to this page.
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