Package: RobustPrediction Type: Package Title: Robust Tuning and Training for Cross-Source Prediction Version: 0.1.6 Authors@R: c( person("Yuting", "He", email = "yutingh19@gmail.com", role = c("aut", "cre")), person("Nicole", "Ellenbach", role = "ctb"), person("Roman", "Hornung", role = "ctb")) Maintainer: Yuting He Description: Provides robust parameter tuning and model training for predictive models applied across data sources where the data distribution varies slightly from source to source. This package implements three primary tuning methods: cross-validation-based internal tuning, external tuning, and the 'RobustTuneC' method. External tuning includes a conservative option where parameters are tuned internally on the training data and validating on an external dataset, providing a slightly pessimistic AUC estimate. It supports Lasso, Ridge, Random Forest, Boosting, and Support Vector Machine classifiers. Currently, only binary classification is supported. The response variable must be the first column of the dataset and a factor with exactly two levels. 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" . License: GPL-3 Encoding: UTF-8 RoxygenNote: 7.3.2 NeedsCompilation: no LazyData: true Depends: R (>= 3.5.0) Imports: glmnet, mboost, mlr, ranger, e1071, pROC URL: https://github.com/Yuting-He/RobustPrediction Config/pak/sysreqs: libgdal-dev gdal-bin libgeos-dev libglu1-mesa-dev libgmp3-dev libgsl0-dev jags libicu-dev libxml2-dev libmpfr-dev libopenmpi-dev libproj-dev Repository: https://yuting-he.r-universe.dev Date/Publication: 2024-12-16 15:58:13 UTC RemoteUrl: https://github.com/yuting-he/robustprediction RemoteRef: HEAD RemoteSha: 81945018403de574e12f754d196c177b5d25a9d1 Packaged: 2026-06-18 08:40:57 UTC; root Author: Yuting He [aut, cre], Nicole Ellenbach [ctb], Roman Hornung [ctb]