LogicForest: Logic Forest

Two classification ensemble methods based on logic regression models. Logforest uses a bagging approach to contruct an ensemble of logic regression models. LBoost uses a combination of boosting and cross-validation to construct and 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: 2.0.0
Depends: R (≥ 2.10), LogicReg, CircStats, gtools, plotrix
Published: 2012-07-23
Author: Bethany Wolf
Maintainer: Bethany Wolf <wolfb at musc.edu>
License: GPL-2
NeedsCompilation: no
In views: MachineLearning
CRAN checks: LogicForest results

Downloads:

Package source: LogicForest_2.0.0.tar.gz
MacOS X binary: LogicForest_2.0.0.tgz
Windows binary: LogicForest_2.0.0.zip
Reference manual: LogicForest.pdf
Old sources: LogicForest archive

Reverse dependencies:

Reverse suggests: caret, fscaret