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Provides robust parameter tuning and model training for predictive models across data sources. This package implements three primary tuning methods: cross-validation-based internal tuning, external tuning, and the 'RobustTuneC' method. It supports Lasso, Ridge, Random Forest, Boosting, and Support Vector Machine classifiers. The tuning methods are based on the paper by Nicole Ellenbach, Anne-Laure Boulesteix, Bernd Bischl, Kristian Unger, and Roman Hornung (2021) "Improved Outcome Prediction Across Data Sources Through Robust Parameter Tuning" <doi:10.1007/s00357-020-09368-z>.
| Version: | 0.1.4 |
| Depends: | R (≥ 3.5.0) |
| Imports: | glmnet, mboost, mlr, ranger, e1071, pROC |
| Published: | 2024-11-14 |
| DOI: | 10.32614/CRAN.package.RobustPrediction |
| Author: | Yuting He [aut, cre], Nicole Ellenbach [ctb], Roman Hornung [ctb] |
| Maintainer: | Yuting He <Yuting.He at campus.lmu.de> |
| License: | GPL-3 |
| URL: | https://github.com/Yuting-He/RobustPrediction |
| NeedsCompilation: | no |
| CRAN checks: | RobustPrediction results |
| Reference manual: | RobustPrediction.pdf |
| Package source: | RobustPrediction_0.1.4.tar.gz |
| Windows binaries: | r-devel: RobustPrediction_0.1.4.zip, r-release: RobustPrediction_0.1.4.zip, r-oldrel: RobustPrediction_0.1.4.zip |
| macOS binaries: | r-release (arm64): RobustPrediction_0.1.4.tgz, r-oldrel (arm64): RobustPrediction_0.1.4.tgz, r-release (x86_64): RobustPrediction_0.1.4.tgz, r-oldrel (x86_64): RobustPrediction_0.1.4.tgz |
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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.