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Runs generalized and multinominal logistic (GLM and MLM) models, as well as random forest (RF), Bagging (BAG), and Boosting (BOOST). This package prints out to predictive outcomes easy for the selected data and data splits.
| Version: | 3.8.0 |
| Depends: | R (≥ 2.10) |
| Imports: | tibble, caret, gbm, stats, randomForest, pROC, ipred |
| Suggests: | knitr, rmarkdown |
| Published: | 2022-03-26 |
| Author: | Seyma Kalay |
| Maintainer: | Seyma Kalay <seymakalay at hotmail.com> |
| BugReports: | https://github.com/seymakalay/pomodoro/issues |
| License: | GPL-3 |
| URL: | https://github.com/seymakalay/pomodoro, https://seymakalay.github.io/pomodoro/ |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | pomodoro results |
| Reference manual: | pomodoro.pdf |
| Vignettes: |
Pomodoro_Vignette |
| Package source: | pomodoro_3.8.0.tar.gz |
| Windows binaries: | r-devel: pomodoro_3.8.0.zip, r-release: pomodoro_3.8.0.zip, r-oldrel: pomodoro_3.8.0.zip |
| macOS binaries: | r-release (arm64): pomodoro_3.8.0.tgz, r-oldrel (arm64): pomodoro_3.8.0.tgz, r-release (x86_64): pomodoro_3.8.0.tgz, r-oldrel (x86_64): pomodoro_3.8.0.tgz |
| Old sources: | pomodoro 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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