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', 2009).

Version: 1.1.0
Depends: R (≥ 3.6.0), ggplot2 (≥ 3.3.3)
Imports: triangle, trapezoid, actuar, dagitty, ggdag, boot, magrittr
Suggests: testthat, knitr, rmarkdown, aplore3, directlabels, tidyverse, lattice, covr
Published: 2021-08-20
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
NeedsCompilation: no
Citation: episensr citation info
Materials: README NEWS
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_1.1.0.tar.gz
Windows binaries: r-devel: episensr_1.1.0.zip, r-release: episensr_1.1.0.zip, r-oldrel: episensr_1.1.0.zip
macOS binaries: r-release (arm64): episensr_1.1.0.tgz, r-release (x86_64): episensr_1.1.0.tgz, r-oldrel: episensr_1.1.0.tgz
Old sources: episensr archive

Reverse dependencies:

Reverse depends: apisensr

Linking:

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