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  "Title": "Conformal Prediction and Uncertainty Quantification",
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  "Authors@R": "person(\"Charles\", \"Coverdale\", email = \"charlesfcoverdale@gmail.com\",\nrole = c(\"aut\", \"cre\", \"cph\"))",
  "Description": "Implements conformal prediction methods for constructing\nprediction intervals (regression) and prediction sets\n(classification) with finite-sample coverage guarantees.\nMethods include split conformal, 'CV+' and 'Jackknife+' (Barber\net al. 2021) <doi:10.1214/20-AOS1965>, 'Conformalized Quantile\nRegression' (Romano et al. 2019)\n<doi:10.48550/arXiv.1905.03222>, 'Adaptive Prediction Sets'\n(Romano, Sesia, Candes 2020) <doi:10.48550/arXiv.2006.02544>,\n'Regularized Adaptive Prediction Sets' (Angelopoulos et al.\n2021) <doi:10.48550/arXiv.2009.14193>, Mondrian conformal\nprediction for group-conditional coverage (Vovk, Gammerman, and\nShafer 2005) <doi:10.1007/b106715>, weighted conformal\nprediction for covariate shift (Tibshirani et al. 2019)\n<doi:10.48550/arXiv.1904.06019>, and adaptive conformal\ninference for sequential prediction (Gibbs and Candes 2021)\n<doi:10.48550/arXiv.2106.00170>. All methods are\ndistribution-free and provide calibrated uncertainty\nquantification without parametric assumptions. Works with any\nmodel that can produce predictions from new data, including\n'lm', 'glm', 'ranger', 'xgboost', and custom user-defined\nmodels.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "Language": "en-US",
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