Package: LogicForest 2.1.1
Melica Nikahd
LogicForest: Logic Forest
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>.
Authors:
LogicForest_2.1.1.tar.gz
LogicForest_2.1.1.tar.gz(r-4.5-noble)LogicForest_2.1.1.tar.gz(r-4.4-noble)
LogicForest_2.1.1.tgz(r-4.4-emscripten)LogicForest_2.1.1.tgz(r-4.3-emscripten)
LogicForest.pdf |LogicForest.html✨
LogicForest/json (API)
# Install 'LogicForest' in R: |
install.packages('LogicForest', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- LF.data - LF.data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 8 months agofrom:004ed81370. Checks:OK: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 10 2024 |
R-4.5-linux | OK | Oct 10 2024 |
Exports:logforestp.combosPermspimp.importpimp.matprime.impproportion.positiveTTab
Readme and manuals
Help Manual
Help page | Topics |
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LF.data | LF.data |
Logic Forest | logforest |