mobForest: Model based Random Forest analysis

This package implements random forest method for model based recursive partitioning. The mob() function, developed by Zeileis et al (2008), within party package, is modified to construct model-based decision trees based on random forests methodology. The main input function mobForestAnalysis() takes all input parameters to construct trees, compute out-of-bag errors, predictions, and overall accuracy of forest. The algorithm performs parallel computation using clusterApply() function within 'parallel' package.

Version: 1.2
Depends: parallel, party, lattice
Imports: methods, modeltools
Suggests: mlbench
Published: 2013-01-29
Author: Nikhil Garge, Barry Eggleston and Georgiy Bobashev
Maintainer: Nikhil Garge <ngarge at rti.org>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: mobForest results

Downloads:

Reference manual: mobForest.pdf
Package source: mobForest_1.2.tar.gz
MacOS X binary: mobForest_1.2.tgz
Windows binary: mobForest_1.2.zip
Old sources: mobForest archive