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.
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[aut, cre],
Marvin N Wright [ctb]