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Regularized Greedy Forest wrapper of the 'Regularized Greedy Forest' <https://github.com/RGF-team/rgf/tree/master/python-package> 'python' package, which also includes a Multi-core implementation (FastRGF) <https://github.com/RGF-team/rgf/tree/master/FastRGF>.
Version: | 1.1.1 |
Depends: | R (≥ 3.2.0) |
Imports: | reticulate, R6, Matrix |
Suggests: | testthat, covr, knitr, rmarkdown |
Published: | 2022-09-12 |
DOI: | 10.32614/CRAN.package.RGF |
Author: | Lampros Mouselimis [aut, cre], Ryosuke Fukatani [cph] (Author of the python wrapper of the 'Regularized Greedy Forest' machine learning algorithm), Nikita Titov [cph] (Author of the python wrapper of the 'Regularized Greedy Forest' machine learning algorithm), Tong Zhang [cph] (Author of the 'Regularized Greedy Forest' and of the Multi-core implementation of Regularized Greedy Forest machine learning algorithm), Rie Johnson [cph] (Author of the 'Regularized Greedy Forest' machine learning algorithm) |
Maintainer: | Lampros Mouselimis <mouselimislampros at gmail.com> |
BugReports: | https://github.com/RGF-team/rgf/issues |
License: | MIT + file LICENSE |
URL: | https://github.com/RGF-team/rgf/tree/master/R-package |
NeedsCompilation: | no |
SystemRequirements: | Python (>= 3.7), rgf_python, scikit-learn (>= 0.18.0), scipy, numpy. Detailed installation instructions for each operating system can be found in the README file. |
Citation: | RGF citation info |
Materials: | README NEWS |
In views: | MachineLearning |
CRAN checks: | RGF results |
Reference manual: | RGF.pdf |
Vignettes: |
Regularized Greedy Forest in R |
Package source: | RGF_1.1.1.tar.gz |
Windows binaries: | r-devel: RGF_1.1.1.zip, r-release: RGF_1.1.1.zip, r-oldrel: RGF_1.1.1.zip |
macOS binaries: | r-release (arm64): RGF_1.1.1.tgz, r-oldrel (arm64): RGF_1.1.1.tgz, r-release (x86_64): RGF_1.1.1.tgz, r-oldrel (x86_64): RGF_1.1.1.tgz |
Old sources: | RGF archive |
Please use the canonical form https://CRAN.R-project.org/package=RGF to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.