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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.
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.
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.
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).
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
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.