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)

• Development version from Github:

library("devtools")
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.

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:

• De Cock Campo, B. (2023). Towards reliable predictive analytics: a generalized calibration framework. arXiv:2309.08559, available at 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, available at 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