CalibrationCurves: assessing the agreement between observed outcomes and predictions.
Package to generate (generalized) calibration curves and related statistics. The function for the logistic/flexible calibration curves are based on the val.prob function from Frank Harrell's rms package.
Installation
On current R (>= 3.0.0)
- You can install the latest development version from Github using the code below
library("devtools")
install_github("BavoDC/CalibrationCurves", dependencies = TRUE, build_vignettes = TRUE, ref = "master")
This requires devtools >= 1.6.1, and installs the "master" branch. This approach builds the package from source.
Documentation
The basic functionality of the package is explained and demonstrated in the vignette, which you can access using
vignette("CalibrationCurves")
or via the homepage of the package.
Contact
If you have questions, remarks or suggestions regarding the package, you can contact me at bavo.campo@kuleuven.be (all emails to bavo.decock@kuleuven.be are
forwarded to this one).
Citation
If you use this package, please cite:
- Barreñada L, De Cock Campo B, Wynants L, Van Calster B. Clustered flexible calibration plots for binary outcomes using random effects modeling. Research Synthesis Methods. Published online 2025:1-22. doi:10.1017/rsm.2025.10046
- De Cock Campo, B. (2023). Towards reliable predictive analytics: a generalized calibration framework.
arXiv:2309.08559, available at [https://arxiv.org/abs/2309.08559](https://arxiv.org/abs/2309.08559).
- De Cock, B., Nieboer, D., Van Calster, B., Steyerberg, E.W., Vergouwe, Y. (2023). _The CalibrationCurves package: assessing the agreement between observed outcomes and predictions_. R package version 2.0.3, [doi:10.32614/CRAN.package.CalibrationCurves](https://doi.org/10.32614/CRAN.package.CalibrationCurves), available at [https://cran.r-project.org/package=CalibrationCurves](https://cran.r-project.org/package=CalibrationCurves)
- Van Calster, B., Nieboer, D., Vergouwe, Y., De Cock, B., Pencina, M.J., Steyerberg, E.W. (2016). A calibration hierarchy for risk models was defined: from utopia to empirical data. _Journal of Clinical Epidemiology_, __74__, pp. 167-176