Generates and predicts a set of linearly stacked Random Forest models using bootstrap sampling. Individual datasets may be heterogeneous (not all samples have full sets of features). Contains support for parallelization but the user should register their cores before running. This is an extension of the method found in Matlock (2018) <doi:10.1186/s12859-018-2060-2>.
|Author||Kevin Matlock, Raziur Rahman|
|Maintainer||Kevin Matlock <[email protected]>|
|Package repository||View on CRAN|
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