Last data update: 2014.03.03

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Classification

Results 1 - 2 of 2 found.
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LogicForest : Logic Forest

Package: LogicForest
Type: Package
Title: Logic Forest
Version: 2.1.0
Date: 2014-09-18
Author: Bethany Wolf
Maintainer: Bethany Wolf <wolfb@musc.edu>
Depends: R (>= 2.10), LogicReg, CircStats
Imports: gtools, plotrix
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.
License: GPL-2
Packaged: 2014-09-18 18:56:11 UTC; wolfb
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-19 00:46:31

● Data Source: CranContrib
● Cran Task View: MachineLearning
13 images, 32 functions, 4 datasets
● Reverse Depends: 0

OrdLogReg : Ordinal Logic Regression

Package: OrdLogReg
Type: Package
Title: Ordinal Logic Regression
Version: 1.1
Date: 2014-09-05
Author: Bethany Wolf
Maintainer: Bethany Wolf <wolfb@musc.edu>
Depends: LogicReg
Description: Method for develops a classification model for ordinal responses based on logic regression.
License: GPL-2
Packaged: 2014-09-19 16:02:04 UTC; wolfb
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-20 07:18:07

● Data Source: CranContrib
3 images, 5 functions, 3 datasets
● Reverse Depends: 0