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  "Title": "Cumulative Calibration Assessment for Prediction Models",
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  "Authors@R": "person(\"Mohsen\", \"Sadatsafavi\", , \"mohsen.sadatsafavi@ubc.ca\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-0419-7862\"))",
  "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 Petkau (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 package also implements non-parametric\nassessment of the calibration of individualized treatment\neffect (ITE) models using data from a randomized trial, via\ncumulcalibITE(), as described in Sadatsafavi et al. (2025)\n<doi:10.48550/arXiv.2512.08140>. The plot() and summary()\nmethods are implemented for the results returned by\ncumulcalib() and cumulcalibITE().",
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