Package: tipr
Type: Package
Title: Tipping Point Analyses
Version: 1.0.1
Authors@R: c(
    person("Lucy", "D'Agostino McGowan", , "lucydagostino@gmail.com", 
    role = c("aut", "cre"), comment = c(ORCID = "0000-0002-6983-2759"))
           )
Description: 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.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.1.2
BugReports: https://github.com/LucyMcGowan/tipr/issues
Suggests: testthat, broom, dplyr, MASS
Imports: glue, tibble, purrr, sensemakr
Depends: R (>= 2.10)
LazyData: true
NeedsCompilation: no
Packaged: 2022-09-05 12:23:08 UTC; lucymcgowan
Author: Lucy D'Agostino McGowan [aut, cre]
    (<https://orcid.org/0000-0002-6983-2759>)
Maintainer: Lucy D'Agostino McGowan <lucydagostino@gmail.com>
Repository: CRAN
Date/Publication: 2022-09-05 12:50:02 UTC
Built: R 4.1.2; ; 2022-09-06 11:16:00 UTC; unix
