R: Basic Sensitivity Analysis of Epidemiological Results

# Basic Sensitivity Analysis of Epidemiological Results

## Documentation for package ‘episensr’ version 2.1.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

%>% | Pipe bias functions
boot_bias | Bootstrap resampling for selection and misclassification bias models.
confounders | Uncontrolled confounding
confounders_array | Sensitivity analysis for unmeasured confounders based on confounding imbalance among exposed and unexposed
confounders_emm | Uncontrolled confounding
confounders_evalue | Compute E-value to assess bias due to unmeasured confounder.
confounders_ext | Sensitivity analysis for unmeasured confounders based on external adjustment
confounders_limit | Bounding the bias limits of unmeasured confounding.
confounders_poly | Uncontrolled confounding
mbias | Sensitivity analysis to correct for selection bias caused by M bias.
misclass | Misclassification of exposure or outcome
misclass_cov | Covariate misclassification
multidimBias | Multidimensional sensitivity analysis for different sources of bias
plot.episensr_booted | Plot of bootstrap simulation output for selection and misclassification bias
plot.episensr_probsens | Plot(s) of probabilistic bias analyses
plot.mbias | Plot DAGs before and after conditioning on collider (M bias)
print.episensr | Print associations for episensr class
print.episensr_booted | Print bootstrapped confidence intervals
print.mbias | Print association corrected for M bias
probsens | Misclassification of exposure or outcome
probsens.conf_legacy | Legacy version of 'probsens.conf()'.
probsens.irr.conf_legacy | Legacy version of 'probsens.irr.conf()'.
probsens.irr_legacy | Legacy version of 'probsens.irr()'.
probsens_conf | Uncontrolled confounding
probsens_irr | Probabilistic sensitivity analysis for exposure misclassification of person-time data and random error.
probsens_irr_conf | Probabilistic sensitivity analysis for unmeasured confounding of person-time data and random error.
probsens_legacy | Legacy version of 'probsens()'.
probsens_sel | Selection bias.
selection | Selection bias.