mobForest: Model Based Random Forest Analysis

Functions to 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 mobforest.analysis() takes all input parameters to construct trees, compute out-of-bag errors, predictions, and overall accuracy of forest. The algorithm performs parallel computation using cluster functions within 'parallel' package.

Version: 1.3.0
Depends: parallel (≥ 3.4.1), party (≥ 1.2-4), sandwich (≥ 2.4.0), strucchange (≥ 1.5-1), zoo (≥ 1.8-0)
Imports: methods, modeltools, stats, graphics
Suggests: testthat (≥ 1.0.2), mlbench (≥ 2.1), lattice
Published: 2018-01-03
Author: Nikhil Garge [aut], Barry Eggleston [aut], Georgiy Bobashev [aut], Benjamin Carper [cre], Kasey Jones [ctb, cre], Torsten Hothorn [ctb], Kurt Hornik [ctb], Carolin Strobl [ctb], Achim Zeileis [ctb]
Maintainer: Kasey Jones <krjones at rti.org>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: README NEWS
CRAN checks: mobForest results

Downloads:

Reference manual: mobForest.pdf
Package source: mobForest_1.3.0.tar.gz
Windows binaries: r-prerel: mobForest_1.3.0.zip, r-release: mobForest_1.3.0.zip, r-oldrel: not available
OS X binaries: r-prerel: mobForest_1.3.0.tgz, r-release: mobForest_1.3.0.tgz
Old sources: mobForest archive

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