bagRboostR: Ensemble bagging and boosting classifiers

bagRboostR is a set of ensemble classifiers for multinomial classification. The bagging function is the implementation of Breiman's ensemble as described by Opitz & Maclin (1999). The boosting function is the implementation of Stagewise Additive Modeling using a Multi-class Exponential loss function (SAMME) created by Zhu et al (2006). Both bagging and SAMME implementations use randomForest as the weak classifier and expect a character outcome variable. Each ensemble classifier returns a character vector of predictions for the test set.

Version: 0.0.2
Imports: randomForest
Suggests: testthat
Published: 2014-03-05
Author: Shannon Rush
Maintainer: Shannon Rush <shannonmrush at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: bagRboostR results


Reference manual: bagRboostR.pdf
Package source: bagRboostR_0.0.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: bagRboostR_0.0.2.tgz
OS X Mavericks binaries: r-oldrel: bagRboostR_0.0.2.tgz
Old sources: bagRboostR archive


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