ODRF: Oblique Decision Random Forest for Classification and Regression

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. Both can be used for classification and regression 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.

Version: 0.0.4
Depends: partykit, R (≥ 3.5.0)
Imports: doParallel, foreach, glue, graphics, grid, lifecycle, magrittr, nnet, parallel, Pursuit, Rcpp, rlang (≥ 0.4.11), stats
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, spelling, testthat (≥ 3.0.0)
Published: 2023-05-28
Author: Yu Liu [aut, cre, cph], Yingcun Xia [aut]
Maintainer: Yu Liu <liuyuchina123 at gmail.com>
BugReports: https://github.com/liuyu-star/ODRF/issues
License: GPL (≥ 3)
URL: https://liuyu-star.github.io/ODRF/
NeedsCompilation: yes
Language: en-US
Citation: ODRF citation info
Materials: README NEWS
CRAN checks: ODRF results

Documentation:

Reference manual: ODRF.pdf
Vignettes: Oblique Decision Random Forest for Classification and Regression

Downloads:

Package source: ODRF_0.0.4.tar.gz
Windows binaries: r-devel: ODRF_0.0.4.zip, r-release: ODRF_0.0.4.zip, r-oldrel: ODRF_0.0.4.zip
macOS binaries: r-release (arm64): ODRF_0.0.4.tgz, r-oldrel (arm64): ODRF_0.0.4.tgz, r-release (x86_64): ODRF_0.0.4.tgz
Old sources: ODRF archive

Linking:

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