Package: episensr 2.2.0

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 Fox M.P., MacLehose R.F., and Lash T.L. "Applying Quantitative Bias Analysis to Epidemiologic Data, second ed.", ('Springer', 2021).

Authors:Denis Haine [aut, cre]

episensr_2.2.0.tar.gz
episensr_2.2.0.zip(r-4.7-any)episensr_2.2.0.zip(r-4.6-any)episensr_2.2.0.zip(r-4.5-any)
episensr_2.2.0.tgz(r-4.6-any)episensr_2.2.0.tgz(r-4.5-any)
episensr_2.2.0.tar.gz(r-4.7-any)episensr_2.2.0.tar.gz(r-4.6-any)
episensr_2.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
episensr/json (API)

# Install 'episensr' in R:
install.packages('episensr', repos = c('https://dhaine.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://codeberg.org/dhaine/episensr

Pkgdown/docs site:https://dhaine.codeberg.page

On CRAN:

Conda:

5.40 score 1 packages 21 scripts 772 downloads 1 mentions 23 exports 61 dependencies

Last updated from:af869fbf90. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK293
source / vignettesOK301
linux-releaseOK292
macos-releaseOK171
macos-oldrelOK164
windows-develOK165
windows-releaseOK163
windows-oldrelOK160
wasm-releaseOK166

Exports:%>%boot_biasconfoundersconfounders_arrayconfounders_emmconfounders_evalueconfounders_extconfounders_limitconfounders_polymbiasmisclassmisclass_covmultidimBiasprobsensprobsens_confprobsens_irrprobsens_irr_confprobsens_legacyprobsens_selprobsens.conf_legacyprobsens.irr_legacyprobsens.irr.conf_legacyselection

Dependencies:actuarbase64encbootcachemclicpp11curldagittydplyrexpintfarverfastmapforcatsgenericsggdagggforceggplot2ggraphggrepelgluegraphlayoutsgridExtragtableigraphisobandjsonlitelabelinglatticelifecyclemagrittrMASSMatrixmemoisepillarpkgconfigpolyclippurrrR6RColorBrewerRcppRcppArmadillorlangS7scalesstringistringrsystemfontstibbletidygraphtidyrtidyselecttrapezoidtriangletruncnormtweenrutf8V8vctrsviridisviridisLitewithr

Additional Sensitivity Analyses
Array Approach for Unmeasured Confounders | Simple Sensitivity Analyses in the Absence of External Information: Array Approach | Using Additional Information: External Adjustment | Unmeasured Confounding and E-value
Rendered fromd_other_sens.Rmdusingknitr::rmarkdown

Last update: 2026-09-30
Started: 2020-10-16

Probabilistic Sensitivity Analysis
Probabilistic Sensitivity Analysis for Exposure Misclassification | Probabilistic Sensitivity Analysis for Uncontrolled Confounding
Rendered fromb_probabilistic.Rmdusingknitr::rmarkdown

Last update: 2026-09-30
Started: 2020-10-16

Quantitative Bias Analysis for Epidemiologic Data
Selection Bias | Misclassification Bias | Uncontrolled Confounders | Bootstrapping
Rendered fromepisensr.Rmdusingknitr::rmarkdown

Last update: 2026-09-30
Started: 2017-01-03

Multiple Bias Modeling
Rendered fromc_multiple_bias.Rmdusingknitr::rmarkdown

Last update: 2025-11-04
Started: 2020-10-16

Readme and manuals

Help Manual

Help pageTopics
Bootstrap resampling for selection and misclassification bias models.boot_bias
Uncontrolled confoundingconfounders confounders_emm confounders_poly probsens_conf
Sensitivity analysis for unmeasured confounders based on confounding imbalance among exposed and unexposedconfounders_array
Compute E-value to assess bias due to unmeasured confounder.confounders_evalue
Sensitivity analysis for unmeasured confounders based on external adjustmentconfounders_ext
Bounding the bias limits of unmeasured confounding.confounders_limit
Sensitivity analysis to correct for selection bias caused by M bias.mbias
Misclassification of exposure or outcomemisclass probsens
Covariate misclassificationmisclass_cov
Multidimensional sensitivity analysis for different sources of biasmultidimBias
Plot of bootstrap simulation output for selection and misclassification biasplot.episensr_booted
Plot(s) of probabilistic bias analysesplot.episensr_probsens
Plot DAGs before and after conditioning on collider (M bias)plot.mbias
Print associations for episensr classprint.episensr
Print bootstrapped confidence intervalsprint.episensr_booted
Print association corrected for M biasprint.mbias
Probabilistic sensitivity analysis for exposure misclassification of person-time data and random error.probsens_irr
Probabilistic sensitivity analysis for unmeasured confounding of person-time data and random error.probsens_irr_conf
Legacy version of 'probsens()'.probsens_legacy
Legacy version of 'probsens.conf()'.probsens.conf_legacy
Legacy version of 'probsens.irr()'.probsens.irr_legacy
Legacy version of 'probsens.irr.conf()'.probsens.irr.conf_legacy
Selection bias.probsens_sel selection