EnsemblePCReg: Extensible Package for Principal-Component-Regression-Based Heterogeneous Ensemble Meta-Learning

Extends the base classes and methods of 'EnsembleBase' package for Principal-Components-Regression-based (PCR) integration of base learners. Default implementation uses cross-validation error to choose the optimal number of PC components for the final predictor. The package takes advantage of the file method provided in 'EnsembleBase' package for writing estimation objects to disk in order to circumvent RAM bottleneck. Special save and load methods are provided to allow estimation objects to be saved to permanent files on disk, and to be loaded again into temporary files in a later R session. Users and developers can extend the package by extending the generic methods and classes provided in 'EnsembleBase' package as well as this package.

Version: 1.1.1
Depends: EnsembleBase, methods
Imports: parallel
Suggests: R.rsp
Published: 2016-09-14
Author: Mansour T.A. Sharabiani, Alireza S. Mahani
Maintainer: Alireza S. Mahani <alireza.s.mahani at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: ChangeLog
CRAN checks: EnsemblePCReg results


Reference manual: EnsemblePCReg.pdf
Vignettes: Multi-stage heterogeneous ensemble meta-learning with hands-off user-interface and on-demand prediction using principal components regression: The R package EnsemblePCReg
Package source: EnsemblePCReg_1.1.1.tar.gz
Windows binaries: r-devel: EnsemblePCReg_1.1.1.zip, r-release: EnsemblePCReg_1.1.1.zip, r-oldrel: EnsemblePCReg_1.1.1.zip
OS X El Capitan binaries: r-release: EnsemblePCReg_1.1.1.tgz
OS X Mavericks binaries: r-oldrel: EnsemblePCReg_1.1.1.tgz
Old sources: EnsemblePCReg archive


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