[aut],
Thomas Rusch [aut],
Kurt Hornik [aut],
Lennart Schneider
[aut]Graphical and computational methods that can be used to assess the stability of results from supervised statistical learning.
| Version: | 0.1-2 |
| Depends: | R (≥ 3.0.0) |
| Imports: | graphics, methods, MASS, e1071, partykit, party, randomForest, ranger |
| Suggests: | utils, Formula, nnet, rpart, knitr, evtree |
| Published: | 2020-04-17 |
| Author: | Michel Philipp [aut, cre],
Carolin Strobl [aut],
Achim Zeileis
[aut],
Thomas Rusch [aut],
Kurt Hornik [aut],
Lennart Schneider
[aut] |
| Maintainer: | Michel Philipp <michel.philipp.mp at gmail.com> |
| License: | GPL-2 | GPL-3 |
| NeedsCompilation: | no |
| Citation: | stablelearner citation info |
| Materials: | NEWS |
| CRAN checks: | stablelearner results |
| Reference manual: | stablelearner.pdf |
| Vignettes: |
Variable Selection and Cutpoint Analysis of Random Forests |
| Package source: | stablelearner_0.1-2.tar.gz |
| Windows binaries: | r-devel: stablelearner_0.1-2.zip, r-release: stablelearner_0.1-2.zip, r-oldrel: stablelearner_0.1-2.zip |
| macOS binaries: | r-release: stablelearner_0.1-2.tgz, r-oldrel: stablelearner_0.1-2.tgz |
| Old sources: | stablelearner archive |
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