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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. Wolf, B.J., Slate, E.H., Hill, E.G. (2010) <doi:10.1093/bioinformatics/btq354>.
| Version: | 2.1.1 |
| Depends: | R (≥ 2.10) |
| Imports: | LogicReg, methods |
| Suggests: | data.table, knitr, rmarkdown |
| Published: | 2024-03-13 |
| Author: | Bethany Wolf [aut], Melica Nikahd [ctb, cre], Madison Hyer [ctb] |
| Maintainer: | Melica Nikahd <melica.nikahd at osumc.edu> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | LogicForest results |
| Reference manual: | LogicForest.pdf |
| Vignettes: |
Introduction to Logic Forest |
| Package source: | LogicForest_2.1.1.tar.gz |
| Windows binaries: | r-devel: LogicForest_2.1.1.zip, r-release: LogicForest_2.1.1.zip, r-oldrel: LogicForest_2.1.1.zip |
| macOS binaries: | r-release (arm64): LogicForest_2.1.1.tgz, r-oldrel (arm64): LogicForest_2.1.1.tgz, r-release (x86_64): LogicForest_2.1.1.tgz, r-oldrel (x86_64): LogicForest_2.1.1.tgz |
| Old sources: | LogicForest archive |
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