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  "Title": "Calibration Curves for Clinical Prediction Models",
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  "Description": "Fit calibrations curves for clinical prediction models and\ncalculate several associated metrics (Eavg, E50, E90, Emax).\nIdeally predicted probabilities from a prediction model should\nalign with observed probabilities. Calibration curves relate\npredicted probabilities (or a transformation thereof) to\nobserved outcomes via a flexible non-linear smoothing function.\n'pmcalibration' allows users to choose between several\nsmoothers (regression splines, generalized additive\nmodels/GAMs, lowess, loess). Both binary and time-to-event\noutcomes are supported. See Van Calster et al. (2016)\n<doi:10.1016/j.jclinepi.2015.12.005>; Austin and Steyerberg\n(2019) <doi:10.1002/sim.8281>; Austin et al. (2020)\n<doi:10.1002/sim.8570>.",
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  "BugReports": "https://github.com/stephenrho/pmcalibration/issues",
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  "Repository": "https://stephenrho.r-universe.dev",
  "Date/Publication": "2025-02-21 22:12:07 UTC",
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      "title": "Extract plot data from 'pmcalibration' object",
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      "page": "pmcalibration",
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        "pmcalibration"
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