Fit calibrations curves for clinical prediction models and calculate several associated metrics (Eavg, E50, E90, Emax). Ideally predicted probabilities from a prediction model should align with observed probabilities. Calibration curves relate predicted probabilities (or a transformation thereof) to observed outcomes via a flexible non-linear smoothing function. 'pmcalibration' allows users to choose between several smoothers (regression splines, generalized additive models/GAMs, lowess, loess). Both binary and time-to-event outcomes are supported. See Van Calster et al. (2016) <doi:10.1016/j.jclinepi.2015.12.005>; Austin and Steyerberg (2019) <doi:10.1002/sim.8281>; Austin et al. (2020) <doi:10.1002/sim.8570>.
Version: 0.2.0 Imports: Hmisc, MASS, mgcv, splines, graphics, stats, methods, survival, pbapply, parallel, grDevices Suggests: rmarkdown, data.table, ggplot2, rms, simsurv Published: 2025-02-21 DOI: 10.32614/CRAN.package.pmcalibration Author: Stephen Rhodes [aut, cre, cph] Maintainer: Stephen Rhodes <steverho89 at gmail.com> BugReports: https://github.com/stephenrho/pmcalibration/issues License: GPL-3 URL: https://github.com/stephenrho/pmcalibration NeedsCompilation: no Citation: pmcalibration citation info Materials: README NEWS CRAN checks: pmcalibration results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=pmcalibration to link to this page.
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