pmcalibration: Calibration Curves for Clinical Prediction Models
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 |
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