LogicForest: Logic Forest

Two classification ensemble methods based on logic regression models. LogForest uses a bagging approach to construct an ensemble of logic regression models. LBoost uses a combination of boosting and cross-validation to construct an ensemble of logic regression models. Both methods are used for classification of binary responses based on binary predictors and for identification of important variables and variable interactions predictive of a binary outcome.

Version: 2.1.0
Depends: R (≥ 2.10), LogicReg, CircStats
Imports: gtools, plotrix
Published: 2014-09-19
Author: Bethany Wolf
Maintainer: Bethany Wolf <wolfb at musc.edu>
License: GPL-2
NeedsCompilation: no
Citation: NA
Materials: NA
In views: MachineLearning
CRAN checks: LogicForest results

Downloads:

Reference manual: LogicForest.pdf
Package source: LogicForest_2.1.0.tar.gz
Windows binaries: r-devel: LogicForest_2.1.0.zip, r-release: LogicForest_2.1.0.zip, r-oldrel: LogicForest_2.1.0.zip
OS X Snow Leopard binaries: r-release: LogicForest_2.1.0.tgz, r-oldrel: LogicForest_2.1.0.tgz
OS X Mavericks binaries: r-release: LogicForest_2.1.0.tgz
Old sources: LogicForest archive