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>.">

pmcalibration: Calibration Curves for Clinical Prediction Models (original) (raw)

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: 0.1.0
Imports: Hmisc, MASS, checkmate, chk, mgcv, splines, graphics, stats, methods, survival, pbapply, parallel
Suggests: knitr, rmarkdown, data.table, ggplot2, rms, simsurv
Published: 2023-09-06
DOI: 10.32614/CRAN.package.pmcalibration
Author: Stephen Rhodes [aut, cre, cph]
Maintainer: Stephen Rhodes
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:

Downloads:

Linking:

Please use the canonical formhttps://CRAN.R-project.org/package=pmcalibrationto link to this page.