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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
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

Documentation:

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

Downloads:

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

Linking:

Please use the canonical form https://CRAN.R-project.org/package=episensr to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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