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# CITATION file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "RobustPrediction" in publications use:'
type: software
license: GPL-3.0-only
title: 'RobustPrediction: Robust Tuning and Training for Cross-Source Prediction'
version: 0.1.5
doi: 10.32614/CRAN.package.RobustPrediction
abstract: '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" <https://doi.org/10.1007/s00357-020-09368-z>.'
authors:
- family-names: He
  given-names: Yuting
  email: Yuting.He@campus.lmu.de
repository: https://yuting-he.r-universe.dev
repository-code: https://github.com/Yuting-He/RobustPrediction
commit: 3358e520229cf9327d17d0ade8724c1cacc71bde
url: https://github.com/Yuting-He/RobustPrediction
contact:
- family-names: He
  given-names: Yuting
  email: Yuting.He@campus.lmu.de