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 <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.
|Author||Sam Wilson [aut, cre]|
|Maintainer||Sam Wilson <email@example.com>|
|License||MIT + file LICENSE|
|Package repository||View on CRAN|
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