mayer79/missRanger: Fast Imputation of Missing Values

Alternative implementation of the beautiful 'MissForest' algorithm used to impute mixed-type data sets by chaining random forests, introduced by Stekhoven, D.J. and Buehlmann, P. (2012) <doi:10.1093/bioinformatics/btr597>. Under the hood, it uses the lightning fast random forest package 'ranger'. Between the iterative model fitting, we offer the option of using predictive mean matching. This firstly avoids imputation with values not already present in the original data (like a value 0.3334 in 0-1 coded variable). Secondly, predictive mean matching tries to raise the variance in the resulting conditional distributions to a realistic level. This would allow, e.g., to do multiple imputation when repeating the call to missRanger(). Out-of-sample application is supported as well.

Getting started

Package details

Maintainer
LicenseGPL (>= 2)
Version2.6.0
URL https://github.com/mayer79/missRanger https://mayer79.github.io/missRanger/
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("remotes")
remotes::install_github("mayer79/missRanger")
mayer79/missRanger documentation built on Aug. 21, 2024, 6:28 p.m.