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.


On current R (>= 3.0.0)

install_github("BavoDC/CalibrationCurves", dependencies = TRUE, build_vignettes = TRUE)

(This requires devtools >= 1.6.1, and installs the “master” (development) branch.) This approach builds the package from source, i.e. make and compilers must be installed on your system – see the R FAQ for your operating system; you may also need to install dependencies manually.


The basic functionality of the package is explained and demonstrated in the vignette, which you can access using


or via the homepage of the package.


If you have questions, remarks or suggestions regarding the package, you can contact me at (all emails to are forwarded to this one).


If you use this package, please cite:
- De Cock Campo, B. (2023). Towards reliable predictive analytics: a generalized calibration framework. arXiv:2309.08559, available at
- 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 2.0.2, doi:10.32614/CRAN.package.CalibrationCurves, available at
- 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