literanger: Fast Serializable Random Forests 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>. Trained
forests can be written to and read from storage. 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]