Description: Generate the monotonic binning and
perform the woe (weight of evidence) transformation for the logistic regression
used in the consumer credit scorecard development. The woe transformation is a piecewise
transformation that is linear to the log odds. For a numeric variable, all of its monotonic
functional transformations will converge to the same woe transformation.
Packaged: 2020-02-21 00:19:15 UTC; liuwensui

Packaged: 2020-07-04 22:45:22 UTC; liuwensui

Packaged: 2020-07-05 22:03:08 UTC; liuwensui

Packaged: 2020-11-02 00:13:46 UTC; liuwensui

Packaged: 2021-04-04 22:20:43 UTC; liuwensui

Packaged: 2021-07-31 03:43:44 UTC; liuwensui

Packaged: 2021-02-17 01:52:44 UTC; liuwensui

Title: Model based Random Forest analysis
Author: Nikhil Garge and Barry Eggleston
Description: 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
contruct model-based decesion trees based on random forest
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()

Author: Nikhil Garge, Barry Eggleston and Georgiy Bobashev
construct model-based decision trees based on random forests

Title: Model Based Random Forest Analysis
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]
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.
Imports: methods, modeltools, stats, graphics
Suggests: testthat (>= 1.0.2), mlbench (>= 2.1), lattice