Tipping Point Analyses R package tipr version 1.0.2 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. (1959), Bross (1966), Schlesselman (1978), Rosenbaum & Rubin (1983), Lin et al. (1998), Lash et al. (2009), Rosenbaum (1986), Cinelli & Hazlett (2020), VanderWeele & Ding (2017), and Ding & VanderWeele (2016), we can use these quantities to assess how an unmeasured confounder may tip our result to insignificance. Software Depends: R (>= 2.10) Imports: cli (>= 3.4.1), glue, purrr, rlang (>= 1.0.6), sensemakr, tibble Suggests: broom, dplyr, MASS, testthat Lucy D'Agostino McGowan <lucydagostino@gmail.com> Comprehensive R Archive Network (CRAN) Lucy D'Agostino McGowan [aut, cre] (ORCID: <https://orcid.org/0000-0002-6983-2759>), Malcolm Barrett [aut] (ORCID: <https://orcid.org/0000-0003-0299-5825>) MIT + file LICENSE (https://CRAN.R-project.org/package=tipr/LICENSE) 2024-02-06 application/tgz https://CRAN.R-project.org/package=tipr doi:10.32614/CRAN.package.tipr