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
Author:	Kevin Matlock, Raziur Rahman
Maintainer:	Kevin Matlock <kevin.matlock at gmail.com>
License:	GPL-3
NeedsCompilation:	no
Materials:	README
CRAN checks:	Sstack results

Documentation:

Reference manual:

Sstack.pdf

Downloads:

Package source:	Sstack_1.0.1.tar.gz
Windows binaries:	r-devel: Sstack_1.0.1.zip, r-release: Sstack_1.0.1.zip, r-oldrel: Sstack_1.0.1.zip
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

Linking:

Please use the canonical form https://CRAN.R-project.org/package=Sstack to link to this page.