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.
Version: | 1.3.6 |
Imports: | ggplot2 (≥ 2.0.0), gridExtra, methods |
Suggests: | knitr, rmarkdown |
Published: | 2017-11-07 |
Author: | Martijn Schuemie, Marc Suchard |
Maintainer: | Martijn Schuemie <schuemie at ohdsi.org> |
BugReports: | https://github.com/OHDSI/EmpiricalCalibration/issues |
License: | Apache License 2.0 |
URL: | https://github.com/OHDSI/EmpiricalCalibration |
NeedsCompilation: | no |
Citation: | EmpiricalCalibration citation info |
Materials: | README NEWS |
CRAN checks: | EmpiricalCalibration results |
Reference manual: | EmpiricalCalibration.pdf |
Vignettes: |
Empirical calibration of confidence intervals Empirical calibration of p-values |
Package source: | EmpiricalCalibration_1.3.6.tar.gz |
Windows binaries: | r-devel: EmpiricalCalibration_1.3.6.zip, r-release: EmpiricalCalibration_1.3.6.zip, r-oldrel: EmpiricalCalibration_1.3.6.zip |
OS X El Capitan binaries: | r-release: EmpiricalCalibration_1.3.6.tgz |
OS X Mavericks binaries: | r-oldrel: EmpiricalCalibration_1.3.6.tgz |
Old sources: | EmpiricalCalibration archive |
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