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An R package for multiple imputation using chained random forests. Implemented methods can handle missing data in mixed types of variables by using prediction-based or node-based conditional distributions constructed using random forests. For prediction-based imputation, the method based on the empirical distribution of out-of-bag prediction errors of random forests and the method based on normality assumption for prediction errors of random forests are provided for imputing continuous variables. And the method based on predicted probabilities is provided for imputing categorical variables. For node-based imputation, the method based on the conditional distribution formed by the predicting nodes of random forests, and the method based on proximity measures of random forests are provided. More details of the statistical methods can be found in Hong et al. (2020) <doi:10.48550/arXiv.2004.14823>.
| Version: | 2.1.8 |
| Depends: | R (≥ 3.5.0), mice (≥ 3.9.0), ranger (≥ 0.12.1) |
| Suggests: | testthat (≥ 2.1.0), knitr, rmarkdown |
| Published: | 2022-10-20 |
| DOI: | 10.32614/CRAN.package.RfEmpImp |
| Author: | Shangzhi Hong [aut, cre], Henry S. Lynn [ths] |
| Maintainer: | Shangzhi Hong <shangzhi-hong at hotmail.com> |
| BugReports: | https://github.com/shangzhi-hong/RfEmpImp/issues |
| License: | GPL-3 |
| URL: | https://github.com/shangzhi-hong/RfEmpImp |
| NeedsCompilation: | no |
| Citation: | RfEmpImp citation info |
| Materials: | NEWS |
| In views: | MissingData |
| CRAN checks: | RfEmpImp results |
| Reference manual: | RfEmpImp.pdf |
| Vignettes: |
Introduction to RfEmpImp |
| Package source: | RfEmpImp_2.1.8.tar.gz |
| Windows binaries: | r-devel: RfEmpImp_2.1.8.zip, r-release: RfEmpImp_2.1.8.zip, r-oldrel: RfEmpImp_2.1.8.zip |
| macOS binaries: | r-release (arm64): RfEmpImp_2.1.8.tgz, r-oldrel (arm64): RfEmpImp_2.1.8.tgz, r-release (x86_64): RfEmpImp_2.1.8.tgz, r-oldrel (x86_64): RfEmpImp_2.1.8.tgz |
| Old sources: | RfEmpImp archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.