Depends: R (>= 2.10), LogicReg, CircStats, gtools, plotrix
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.

Depends: R (>= 2.10), LogicReg, CircStats
Description: 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.

Description: Access to powerful logic minimization algorithms and data structures that operate on a sum-of-products truth table.  The core algorithms are built on Espresso Version 2.3 developed at UC Berkeley for digital logic synthesis purposes.  Enhancements have been made to integrate within the R framework and support additional logic optimization use cases such as those needed by Qualitative Comparative Analysis (QCA) and Genetic Programming.  There are no expressed or implied warranties.
Authors@R: person("William", "Stiehl", email = "wwstiehl@gmail.com", role = c("aut", "cre", "cph"))
Packaged: 2016-05-06 14:49:58 UTC; stiehlmacpro

Author: Charles Kooperberg <clk@fhcrc.org> and Ingo Ruczinski <ingo@jhu.edu>
Description: Routines for Logic Regression

Author: Charles Kooperberg <clk@fhcrc.org> and Ingo Ruczinski

Packaged: Fri Sep 29 22:59:43 2006; hornik

Author: Charles Kooperberg <clk@fredhutch.org> and Ingo Ruczinski <ingo@jhu.edu>
Description: Routines for fitting Logic Regression models.