A fast implementation of Random Forests, particularly suited for high dimensional data. Ensembles of classification, regression, survival and probability prediction trees are supported. Data from genome-wide association studies can be analyzed efficiently. In addition to data frames, datasets of class 'gwaa.data' (R package 'GenABEL') and 'dgCMatrix' (R package 'Matrix') can be directly analyzed.
Version: | 0.12.1 |
Depends: | R (≥ 3.1) |
Imports: | Rcpp (≥ 0.11.2), Matrix |
LinkingTo: | Rcpp, RcppEigen |
Suggests: | survival, testthat |
Published: | 2020-01-10 |
Author: | Marvin N. Wright [aut, cre], Stefan Wager [ctb], Philipp Probst [ctb] |
Maintainer: | Marvin N. Wright <cran at wrig.de> |
BugReports: | https://github.com/imbs-hl/ranger/issues |
License: | GPL-3 |
URL: | https://github.com/imbs-hl/ranger |
NeedsCompilation: | yes |
Citation: | ranger citation info |
Materials: | NEWS |
In views: | MachineLearning, Survival |
CRAN checks: | ranger results |
Reference manual: | ranger.pdf |
Package source: | ranger_0.12.1.tar.gz |
Windows binaries: | r-devel: ranger_0.12.1.zip, r-release: ranger_0.12.1.zip, r-oldrel: ranger_0.12.1.zip |
macOS binaries: | r-release: ranger_0.12.1.tgz, r-oldrel: ranger_0.12.1.tgz |
Old sources: | ranger archive |
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