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  "Title": "Cumulative Calibration Assessment for Prediction Models",
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  "Description": "Tools for visualization of, and inference on, the\ncalibration of prediction models on the cumulative domain. This\nprovides a method for evaluating calibration of risk prediction\nmodels without having to group the data or use tuning\nparameters (e.g., loess bandwidth). This package implements the\nmethodology described in Sadatsafavi and Patkau (2024)\n<doi:10.1002/sim.10138>. The core of the package is\ncumulcalib(), which takes in vectors of binary responses and\npredicted risks. The plot() and summary() methods are\nimplemented for the results returned by cumulcalib().",
  "License": "MIT + file LICENSE",
  "URL": "https://github.com/resplab/cumulcalib",
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  "Date/Publication": "2026-02-05 17:07:31 UTC",
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      "title": "A tutorial on the cumulative calibration assessment and the cumulcalib package",
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        "Setup: A running example based on GUSTO data",
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        "Using cumulcalib package for calibration assessment",
        "Graphical assessment of calibration using cumulcalib package",
        "How do different forms of miscalibrations look on the cumulative calibration plot?",
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