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  "Package": "RobustPrediction",
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  "Title": "Robust Tuning and Training for Cross-Source Prediction",
  "Version": "0.1.6",
  "Authors@R": "c(\nperson(\"Yuting\", \"He\", email = \"yutingh19@gmail.com\", role = c(\"aut\", \"cre\")),\nperson(\"Nicole\", \"Ellenbach\", role = \"ctb\"),\nperson(\"Roman\", \"Hornung\", role = \"ctb\"))",
  "Maintainer": "Yuting He <yutingh19@gmail.com>",
  "Description": "Provides robust parameter tuning and model training for\npredictive models applied across data sources where the data\ndistribution varies slightly from source to source. This\npackage implements three primary tuning methods:\ncross-validation-based internal tuning, external tuning, and\nthe 'RobustTuneC' method. External tuning includes a\nconservative option where parameters are tuned internally on\nthe training data and validating on an external dataset,\nproviding a slightly pessimistic AUC estimate. It supports\nLasso, Ridge, Random Forest, Boosting, and Support Vector\nMachine classifiers. Currently, only binary classification is\nsupported. The response variable must be the first column of\nthe dataset and a factor with exactly two levels. The tuning\nmethods are based on the paper by Nicole Ellenbach, Anne-Laure\nBoulesteix, Bernd Bischl, Kristian Unger, and Roman Hornung\n(2021) \"Improved Outcome Prediction Across Data Sources Through\nRobust Parameter Tuning\" <doi:10.1007/s00357-020-09368-z>.",
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  "Repository": "https://yuting-he.r-universe.dev",
  "Date/Publication": "2024-12-16 15:58:13 UTC",
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      "title": "Package Title: Robust Tuning and Training for Cross-Source Prediction",
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        "RobustPrediction"
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