tipr: An R package for sensitivity analyses forunmeasured confounders

The strength of evidence provided by epidemiological and observational studies is inherently limited by the potential for unmeasured confounding.We focus on three key quantities: the observed bound of the confidence interval closest to the null, the relationship between an unmeasured confounder and the outcome, for example a plausible residual effect size for an unmeasured continuous or binary confounder, and the relationship between an unmeasured confounder and the exposure, for example a realistic mean difference or prevalence difference for this hypothetical confounder between exposure groups.Building on the methods put forth by Cornfield et al.