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EmpiricalCalibration
View on CRAN: Click
here
Download and install EmpiricalCalibration package within the R console
Install from CRAN:
install.packages("EmpiricalCalibration")
Install from Github:
library("remotes")
install_github("cran/EmpiricalCalibration")
Install by package version:
library("remotes")
install_version("EmpiricalCalibration", "3.1.4")
Attach the package and use:
library("EmpiricalCalibration")
Maintained by
Martijn Schuemie
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.EmpiricalCalibration . https://github.com/OHDSI/EmpiricalCalibration/issues . https://ohdsi.github.io/EmpiricalCalibration/ . https://github.com/OHDSI/EmpiricalCalibration . EmpiricalCalibration citation info . EmpiricalCalibration results . EmpiricalCalibration.pdf . EmpiricalCalibration_3.1.4.tar.gz . EmpiricalCalibration_3.1.4.zip . EmpiricalCalibration_3.1.4.zip . EmpiricalCalibration_3.1.4.zip . EmpiricalCalibration_3.1.4.tgz . EmpiricalCalibration_3.1.4.tgz . EmpiricalCalibration_3.1.4.tgz . EmpiricalCalibration_3.1.4.tgz . EmpiricalCalibration_3.1.4.tgz . EmpiricalCalibration_3.1.4.tgz . EmpiricalCalibration archive . https://CRAN.R-project.org/package=EmpiricalCalibration .
First Published: 2016-02-15
Latest Update: 2023-12-21
Description:
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) and
Schuemie et al. (2018) .
How to cite:
Martijn Schuemie (2016). EmpiricalCalibration: Routines for Performing Empirical Calibration of Observational Study Estimates. R package version 3.1.4, https://cran.r-project.org/web/packages/EmpiricalCalibration. Accessed 27 Feb. 2025.
Previous versions and publish date:
1.1.0 (2016-02-15 13:46), 1.2.0 (2016-08-16 19:47), 1.3.1 (2017-05-16 09:33), 1.3.6 (2017-11-07 17:33), 1.4.0 (2018-11-24 21:30), 2.0.0 (2019-07-08 17:30), 2.0.1 (2020-01-13 10:10), 2.0.2 (2020-04-07 11:30), 2.1.0 (2021-03-04 09:20), 3.0.0 (2021-09-28 10:50), 3.1.0 (2022-07-01 16:30), 3.1.1 (2022-08-09 16:40), 3.1.2 (2023-12-21 17:10), 3.1.3 (2024-09-30 08:50)
Other packages that cited EmpiricalCalibration R package
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Other R packages that EmpiricalCalibration depends,
imports, suggests or enhances
Complete documentation for EmpiricalCalibration
Functions, R codes and Examples using
the EmpiricalCalibration R package
Some associated functions: EmpiricalCalibration-package . calibrateConfidenceInterval . calibrateLlr . calibrateP . caseControl . cohortMethod . compareEase . computeCvBinomial . computeCvPoisson . computeCvPoissonRegression . computeExpectedAbsoluteSystematicError . computeTraditionalCi . computeTraditionalP . convertNullToErrorModel . evaluateCiCalibration . fitMcmcNull . fitNull . fitNullNonNormalLl . fitSystematicErrorModel . grahamReplication . plotCalibration . plotCalibrationEffect . plotCiCalibration . plotCiCalibrationEffect . plotCiCoverage . plotErrorModel . plotExpectedType1Error . plotForest . plotMcmcTrace . plotTrueAndObserved . sccs . simulateControls . simulateMaxSprtData . southworthReplication .
Some associated R codes: ConfidenceIntervalCalibration.R . Data.R . EmpiricalCalibration.R . EmpiricalCalibrationUsingAsymptotics.R . EmpiricalCalibrationUsingMcmc.R . Evaluation.R . ExpectedSystematicError.R . Likelihoods.R . LlrCalibration.R . MaxSprtCriticalValues.R . Plots.R . RcppExports.R . Simulation.R . Full EmpiricalCalibration package functions and examples
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