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: | 0.23 |
Depends: | R (≥ 3.5.0) |
Imports: | Rcpp (≥ 1.0.10), randomForestSRC, gbm, methods, data.tree |
LinkingTo: | Rcpp |
Published: | 2024-05-24 |
DOI: | 10.32614/CRAN.package.ranktreeEnsemble |
Author: | Ruijie Yin [aut], Chen Ye [aut], Min Lu [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 |
Citation: | ranktreeEnsemble citation info |
Materials: | NEWS |
CRAN checks: | ranktreeEnsemble results |
Reference manual: | ranktreeEnsemble.pdf |
Package source: | ranktreeEnsemble_0.23.tar.gz |
Windows binaries: | r-devel: ranktreeEnsemble_0.23.zip, r-release: ranktreeEnsemble_0.23.zip, r-oldrel: ranktreeEnsemble_0.23.zip |
macOS binaries: | r-release (arm64): ranktreeEnsemble_0.23.tgz, r-oldrel (arm64): ranktreeEnsemble_0.23.tgz, r-release (x86_64): ranktreeEnsemble_0.23.tgz, r-oldrel (x86_64): ranktreeEnsemble_0.23.tgz |
Old sources: | ranktreeEnsemble archive |
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