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:Bethany Wolf [aut], Melica Nikahd [ctb, cre], Madison Hyer [ctb]

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'))

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This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.00 score 3 scripts 246 downloads 8 exports 4 dependencies

Last updated 8 months agofrom:004ed81370. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 09 2024
R-4.5-linuxOKNov 09 2024

Exports:logforestp.combosPermspimp.importpimp.matprime.impproportion.positiveTTab

Dependencies:latticeLogicRegMatrixsurvival

Introduction to Logic Forest

Rendered fromIntro_to_logicforest.Rmdusingknitr::rmarkdownon Nov 09 2024.

Last update: 2024-03-14
Started: 2024-03-14