Package: cumulcalib 0.1.0

cumulcalib: Cumulative Calibration Assessment for Prediction Models

Tools for visualization of, and inference on, the calibration of prediction models on the cumulative domain. This provides a method for evaluating calibration of risk prediction models without having to group the data or use tuning parameters (e.g., loess bandwidth). This package implements the methodology described in Sadatsafavi and Petkau (2024) <doi:10.1002/sim.10138>. The core of the package is cumulcalib(), which takes in vectors of binary responses and predicted risks. The package also implements non-parametric assessment of the calibration of individualized treatment effect (ITE) models using data from a randomized trial, via cumulcalibITE(), as described in Sadatsafavi et al. (2025) <doi:10.48550/arXiv.2512.08140>. The plot() and summary() methods are implemented for the results returned by cumulcalib() and cumulcalibITE().

Authors:Mohsen Sadatsafavi [aut, cre]