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konfound  

Quantify the Robustness of Causal Inferences
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("konfound", "1.0.3")



Attach the package and use:
library("konfound")
Maintained by
Joshua M Rosenberg
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-04-06
Latest Update: 2025-05-28
Description:
Statistical methods that quantify the conditions necessary to alter inferences, also known as sensitivity analysis, are becoming increasingly important to a variety of quantitative sciences. A series of recent works, including Frank (2000) and Frank et al. (2013) extend previous sensitivity analyses by considering the characteristics of omitted variables or unobserved cases that would change an inference if such variables or cases were observed. These analyses generate statements such as "an omitted variable would have to be correlated at xx with the predictor of interest (e.g., treatment) and outcome to invalidate an inference of a treatment effect". Or "one would have to replace pp percent of the observed data with null hypothesis cases to invalidate the inference". We implement these recent developments of sensitivity analysis and provide modules to calculate these two robustness indices and generate such statements in R. In particular, the functions konfound(), pkonfound() and mkonfound() allow users to calculate the robustness of inferences for a user's own model, a single published study and multiple studies respectively.
How to cite:
Joshua M Rosenberg (2018). konfound: Quantify the Robustness of Causal Inferences. R package version 1.0.3, https://cran.r-project.org/web/packages/konfound. Accessed 18 Sep. 2026.
Previous versions and publish date:
(2026-07-09 07:51), 0.1.0 (2018-04-06 18:12), 0.1.1 (2019-01-21 17:40), 0.1.2 (2019-04-12 13:52), 0.2.1 (2020-02-26 15:50), 0.2.2 (2020-08-18 09:00), 0.3.0 (2020-12-17 18:30), 0.4.0 (2021-06-01 09:40), 0.5.0 (2024-03-18 15:40), 0.5.1 (2024-04-12 23:10), 1.0.1 (2024-10-08 00:30), 1.0.2 (2024-10-17 18:10)
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