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 |
Documentation:
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