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 |
CRAN checks: | LogicForest results |
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 |