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) .


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2.1.5 by Shangzhi Hong, 2 years ago

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Authors: Shangzhi Hong [aut, cre] , Henry S. Lynn [ths]

Documentation:   PDF Manual  

GPL-3 license

Depends on mice, ranger

Suggests testthat, knitr, rmarkdown

See at CRAN