Sstack: Bootstrap Stacking of Random Forest Models for Heterogeneous Data

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>.

Version: 1.0.1
Depends: R (≥ 2.10)
Imports: randomForest, foreach, dplyr, parallel, doParallel
Published: 2018-05-01
DOI: 10.32614/CRAN.package.Sstack
Author: Kevin Matlock, Raziur Rahman
Maintainer: Kevin Matlock <kevin.matlock at>
License: GPL-3
NeedsCompilation: no
Materials: README
CRAN checks: Sstack results


Reference manual: Sstack.pdf


Package source: Sstack_1.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): Sstack_1.0.1.tgz, r-oldrel (arm64): Sstack_1.0.1.tgz, r-release (x86_64): Sstack_1.0.1.tgz, r-oldrel (x86_64): Sstack_1.0.1.tgz


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