Package: mobForest
Type: Package
Title: Model Based Random Forest Analysis
Version: 1.3.0
Date: 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@rti.org>
Description: 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.
License: GPL (>= 2)
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
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-01-03 17:43:56 UTC; krjones
Repository: CRAN
Date/Publication: 2018-01-03 18:45:25
