miceRanger: Multiple Imputation by Chained Equations with Random Forests (original) (raw)
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 |
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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 |
BugReports: | https://github.com/FarrellDay/miceRanger/issues |
License: | MIT + file |
URL: | https://github.com/FarrellDay/miceRanger |
NeedsCompilation: | no |
Materials: | NEWS |
In views: | MissingData |
CRAN checks: | miceRanger results |
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