Type: Package
Package: ODRF
Title: Oblique Decision Random Forest for Classification and Regression
Version: 0.0.5
Authors@R: c(
    person("Yu", "Liu", , "liuyuchina123@gmail.com", role = c("aut", "cre", "cph")),
    person("Yingcun", "Xia", , "staxyc@nus.edu.sg", role = "aut")
  )
Author: Yu Liu [aut, cre, cph],
  Yingcun Xia [aut]
Maintainer: Yu Liu <liuyuchina123@gmail.com>
Description: The oblique decision tree (ODT) uses linear combinations of predictors as partitioning variables in a decision tree. Oblique Decision Random Forest (ODRF) is an ensemble of multiple ODTs generated by feature bagging. Oblique Decision Boosting Tree (ODBT) applies feature bagging during the training process of ODT-based boosting trees to ensemble multiple boosting trees. All three methods can be used for classification and regression, and ODT and ODRF serve as supplements to the classical CART of Breiman (1984) <DOI:10.1201/9781315139470> and Random Forest of Breiman (2001) <DOI:10.1023/A:1010933404324> respectively. 
License: GPL (>= 3)
URL: https://liuyu-star.github.io/ODRF/
BugReports: https://github.com/liuyu-star/ODRF/issues
Depends: partykit, R (>= 3.5.0)
Imports: doParallel, foreach, glue, graphics, grid, lifecycle,
        magrittr, nnet, parallel, Pursuit, Rcpp, rlang (>= 0.4.11),
        stats, rpart, methods, glmnet
Suggests: knitr, rmarkdown, spelling, testthat (>= 3.0.0)
LinkingTo: Rcpp, RcppArmadillo, RcppEigen
VignetteBuilder: knitr
Config/testthat/edition: 3
Encoding: UTF-8
Language: en-US
LazyData: yes
NeedsCompilation: yes
RoxygenNote: 7.2.3
Packaged: 2025-04-25 15:19:10 UTC; Administrator
Repository: CRAN
Date/Publication: 2025-04-25 23:20:21 UTC
Built: R 4.6.0; x86_64-w64-mingw32; 2026-05-20 02:25:10 UTC; windows
Archs: x64
