Multiple Imputation by Chained Equations with Random Forests

Multiple Imputation has been shown to be a flexible method to impute missing values by Van Buuren (2007) . Expanding on this, random forests have been shown to be an accurate model by Stekhoven and Buhlmann 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.


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install.packages("miceRanger")

1.3.5 by Sam Wilson, 7 months ago


https://github.com/FarrellDay/miceRanger


Report a bug at https://github.com/FarrellDay/miceRanger/issues


Browse source code at https://github.com/cran/miceRanger


Authors: Sam Wilson [aut, cre]


Documentation:   PDF Manual  


Task views: Missing Data


MIT + file LICENSE license


Imports ranger, data.table, stats, FNN, ggplot2, crayon, corrplot, ggpubr, DescTools, foreach

Suggests knitr, rmarkdown, doParallel, testthat


See at CRAN