episensr: Basic Sensitivity Analysis of Epidemiological Results

Basic sensitivity analysis of the observed relative risks adjusting for unmeasured confounding and misclassification of the exposure/outcome, or both. It follows the bias analysis methods and examples from the book by Lash T.L, Fox M.P, and Fink A.K. "Applying Quantitative Bias Analysis to Epidemiologic Data", ('Springer', 2021).

Version: 1.3.0
Depends: R (≥ 4.0.0), ggplot2 (≥ 3.4.0)
Imports: triangle, trapezoid, actuar, dagitty, ggdag, boot, magrittr
Suggests: testthat, knitr, rmarkdown, aplore3, directlabels, tidyr, lattice, covr
Published: 2023-08-30
DOI: 10.32614/CRAN.package.episensr
Author: Denis Haine ORCID iD [aut, cre]
Maintainer: Denis Haine <denis.haine at gmail.com>
BugReports: https://github.com/dhaine/episensr/issues
License: GPL-2
URL: https://github.com/dhaine/episensr, https://dhaine.github.io/episensr/
NeedsCompilation: no
Citation: episensr citation info
Materials: README NEWS
In views: Epidemiology
CRAN checks: episensr results


Reference manual: episensr.pdf
Vignettes: Probabilistic Sensitivity Analysis
Multiple Bias Modeling
Additional Sensitivity Analyses
Quantitative Bias Analysis for Epidemiologic Data


Package source: episensr_1.3.0.tar.gz
Windows binaries: r-devel: episensr_1.3.0.zip, r-release: episensr_1.3.0.zip, r-oldrel: episensr_1.3.0.zip
macOS binaries: r-release (arm64): episensr_1.3.0.tgz, r-oldrel (arm64): episensr_1.3.0.tgz, r-release (x86_64): episensr_1.3.0.tgz, r-oldrel (x86_64): episensr_1.3.0.tgz
Old sources: episensr archive

Reverse dependencies:

Reverse depends: apisensr


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