literanger - Random Forests for Multiple Imputation Based on 'ranger'

An updated implementation of R package 'ranger' by Wright et al, (2017) <doi:10.18637/jss.v077.i01> for training and predicting from random forests, particularly suited to high-dimensional data, and for embedding in 'Multiple Imputation by Chained Equations' (MICE) by van Buuren (2007) <doi:10.1177/0962280206074463>. Ensembles of classification and regression trees are currently supported. Sparse data of class 'dgCMatrix' (R package 'Matrix') can be directly analyzed. Conventional bagged predictions are available alongside an efficient prediction for MICE via the algorithm proposed by Doove et al (2014) <doi:10.1016/j.csda.2013.10.025>. Survival and probability forests are not supported in the update, nor is data of class 'gwaa.data' (R package 'GenABEL'); use the original 'ranger' package for these analyses.