predictset: Conformal Prediction and Uncertainty Quantification

Implements conformal prediction methods for constructing prediction intervals (regression) and prediction sets (classification) with finite-sample coverage guarantees. Methods include split conformal, 'CV+' and 'Jackknife+' (Barber et al. 2021) <doi:10.1214/20-AOS1965>, 'Conformalized Quantile Regression' (Romano et al. 2019) <doi:10.48550/arXiv.1905.03222>, 'Adaptive Prediction Sets' (Romano, Sesia, Candes 2020) <doi:10.48550/arXiv.2006.02544>, 'Regularized Adaptive Prediction Sets' (Angelopoulos et al. 2021) <doi:10.48550/arXiv.2009.14193>, Mondrian conformal prediction for group-conditional coverage (Vovk, Gammerman, and Shafer 2005) <doi:10.1007/b106715>, weighted conformal prediction for covariate shift (Tibshirani et al. 2019) <doi:10.48550/arXiv.1904.06019>, and adaptive conformal inference for sequential prediction (Gibbs and Candes 2021) <doi:10.48550/arXiv.2106.00170>. All methods are distribution-free and provide calibrated uncertainty quantification without parametric assumptions. Works with any model that can produce predictions from new data, including 'lm', 'glm', 'ranger', 'xgboost', and custom user-defined models.

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