RfEmpImp: Multiple Imputation using Chained Random Forests

An R package for methods of multiple imputation using chained random forests. Implemented methods can handle missing data in mixed types of 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 are provided for continuous variables. And the method based on predicted probabilities is provided for 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) <arXiv:2004.14823>.

Version: 2.1.5
Depends: R (≥ 3.5.0), mice (≥ 3.9.0), ranger (≥ 0.12.1)
Suggests: testthat (≥ 2.1.0), knitr, rmarkdown
Published: 2020-06-25
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: README NEWS
CRAN checks: RfEmpImp results


Reference manual: RfEmpImp.pdf
Vignettes: Introduction to RfEmpImp
Package source: RfEmpImp_2.1.5.tar.gz
Windows binaries: r-devel: RfEmpImp_2.1.5.zip, r-release: RfEmpImp_2.1.5.zip, r-oldrel: RfEmpImp_2.0.3.zip
macOS binaries: r-release: RfEmpImp_2.1.5.tgz, r-oldrel: RfEmpImp_2.0.3.tgz
Old sources: RfEmpImp archive


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