LogicForest-package: Logic Forest

In LogicForest: Logic Forest

Description

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.

Details

Package:	LogicForest
Type:	Package
Version:	2.0.0
Date:	2011-09-20
License:	GPL-2
LazyLoad:	yes

Author(s)

Bethany Wolf

Maintainer: Bethany Wolf <[e-mail]>

References

Wolf, B.J., Slate, E.H., Hill, E.G. (2010) Logic Forest: An ensemble classifier for discovering logical combinations of binary markers. Bioinformatics.
Wolf, B.J., Hill, E.G., Slate, E.H., Neumann, C.A., Kistner-Griffin, E. (2012). LBoost: A boosting algorithm with applications for epistasis discovery. PLoS One.

LogicForest documentation built on May 30, 2017, 3:07 a.m.