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Approximate false positive rate control in selection frequency for random forest using the methods described by Ender Konukoglu and Melanie Ganz (2014) <doi:10.48550/arXiv.1410.2838>. Methods for calculating the selection frequency threshold at false positive rates and selection frequency false positive rate feature selection.
Version: | 0.2.2 |
Imports: | Rcpp, purrr, tibble, magrittr, dplyr |
LinkingTo: | Rcpp |
Suggests: | testthat, randomForest, ranger, parsnip, knitr, rmarkdown |
Published: | 2022-02-09 |
DOI: | 10.32614/CRAN.package.forestControl |
Author: | Tom Wilson |
Maintainer: | Tom Wilson <tpw2 at aber.ac.uk> |
BugReports: | https://github.com/aberHRML/forestControl/issues |
License: | MIT + file LICENSE |
URL: | https://github.com/aberHRML/forestControl |
NeedsCompilation: | yes |
Citation: | forestControl citation info |
Materials: | NEWS |
CRAN checks: | forestControl results |
Reference manual: | forestControl.pdf |
Vignettes: |
Getting_started |
Package source: | forestControl_0.2.2.tar.gz |
Windows binaries: | r-devel: forestControl_0.2.2.zip, r-release: forestControl_0.2.2.zip, r-oldrel: forestControl_0.2.2.zip |
macOS binaries: | r-release (arm64): forestControl_0.2.2.tgz, r-oldrel (arm64): forestControl_0.2.2.tgz, r-release (x86_64): forestControl_0.2.2.tgz, r-oldrel (x86_64): forestControl_0.2.2.tgz |
Old sources: | forestControl archive |
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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.
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