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LogicForest
View on CRAN: Click
here
Download and install LogicForest package within the R console
Install from CRAN:
install.packages("LogicForest")
Install from Github:
library("remotes")
install_github("cran/LogicForest")
Install by package version:
library("remotes")
install_version("LogicForest", "2.1.1")
Attach the package and use:
library("LogicForest")
Maintained by
Melica Nikahd
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.LogicForest . LogicForest results . LogicForest.pdf . LogicForest_2.1.1.tar.gz . LogicForest_2.1.1.zip . LogicForest_2.1.1.zip . LogicForest_2.1.1.zip . LogicForest_2.1.1.tgz . LogicForest_2.1.1.tgz . LogicForest_2.1.1.tgz . LogicForest_2.1.1.tgz . LogicForest_2.1.1.tgz . LogicForest_2.1.1.tgz . LogicForest archive . https://CRAN.R-project.org/package=LogicForest .
First Published: 2010-07-25
Latest Update: 2024-03-13
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. Wolf, B.J., Slate, E.H., Hill, E.G. (2010) .
How to cite:
Melica Nikahd (2010). LogicForest: Logic Forest. R package version 2.1.1, https://cran.r-project.org/web/packages/LogicForest. Accessed 17 Feb. 2025.
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