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tipr  

Tipping Point Analyses
View on CRAN: Click here


Download and install tipr package within the R console
Install from CRAN:
install.packages("tipr")

Install from Github:
library("remotes")
install_github("cran/tipr")

Install by package version:
library("remotes")
install_version("tipr", "1.0.2")



Attach the package and use:
library("tipr")
Maintained by
Lucy D'Agostino McGowan
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-11-28
Latest Update: 2022-09-05
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.
How to cite:
Lucy D'Agostino McGowan (2017). tipr: Tipping Point Analyses. R package version 1.0.2, https://cran.r-project.org/web/packages/tipr. Accessed 05 Jan. 2025.
Previous versions and publish date:
0.1.1 (2017-11-28 19:33), 0.2.0 (2020-11-16 19:50), 0.3.0 (2021-09-10 10:00), 0.4.0 (2022-04-17 00:20), 0.4.1 (2022-05-06 01:20), 1.0.0 (2022-08-06 21:10), 1.0.1 (2022-09-05 14:50)
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