Routines for Performing Empirical Calibration of Observational Study Estimates R package EmpiricalCalibration version 3.1.4 Routines for performing empirical calibration of observational study estimates. By using a set of negative control hypotheses we can estimate the empirical null distribution of a particular observational study setup. This empirical null distribution can be used to compute a calibrated p-value, which reflects the probability of observing an estimated effect size when the null hypothesis is true taking both random and systematic error into account. A similar approach can be used to calibrate confidence intervals, using both negative and positive controls. For more details, see Schuemie et al. (2013) <doi:10.1002/sim.5925> and Schuemie et al. (2018) <doi:10.1073/pnas.1708282114>. Software Depends: R (>= 3.5.0) Imports: ggplot2 (>= 2.0.0), gridExtra, methods, rlang, Rcpp LinkingTo: Rcpp Suggests: knitr, markdown, rmarkdown, testthat, Cyclops, survival, Sequential Martijn Schuemie <schuemie@ohdsi.org> Comprehensive R Archive Network (CRAN) Martijn Schuemie [aut, cre] (ORCID: <https://orcid.org/0000-0002-0817-5361>), Marc Suchard [aut] (ORCID: <https://orcid.org/0000-0001-9818-479X>) Apache License 2.0 2025-02-14 application/tgz https://CRAN.R-project.org/package=EmpiricalCalibration doi:10.32614/CRAN.package.EmpiricalCalibration