ranktreeEnsemble: Ensemble Models of Rank-Based Trees with Extracted Decision Rules

Fast computing an ensemble of rank-based trees via boosting or random forest on binary and multi-class problems. It converts continuous gene expression profiles into ranked gene pairs, for which the variable importance indices are computed and adopted for dimension reduction. Decision rules can be extracted from trees.

Version: 0.22
Depends: R (≥ 3.5.0)
Imports: Rcpp (≥ 1.0.10), randomForestSRC, gbm, methods, data.tree
LinkingTo: Rcpp
Published: 2023-08-18
Author: Ruijie Yin [aut], Chen Ye [aut], Min Lu ORCID iD [aut, cre]
Maintainer: Min Lu <luminwin at gmail.com>
BugReports: https://github.com/TransBioInfoLab/ranktreeEnsemble/issues/
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/TransBioInfoLab/ranktreeEnsemble/
NeedsCompilation: yes
CRAN checks: ranktreeEnsemble results

Documentation:

Reference manual: ranktreeEnsemble.pdf

Downloads:

Package source: ranktreeEnsemble_0.22.tar.gz
Windows binaries: r-devel: ranktreeEnsemble_0.22.zip, r-release: ranktreeEnsemble_0.22.zip, r-oldrel: ranktreeEnsemble_0.22.zip
macOS binaries: r-release (arm64): ranktreeEnsemble_0.22.tgz, r-oldrel (arm64): ranktreeEnsemble_0.22.tgz, r-release (x86_64): ranktreeEnsemble_0.22.tgz
Old sources: ranktreeEnsemble archive

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