RobustPrediction: Robust Tuning and Training for Cross-Source Prediction

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
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

Documentation:

Reference manual:

RobustPrediction.pdf

Downloads:

Package source:	RobustPrediction_0.1.4.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available

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