konfound

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) doi:10.1177/0049124100029002001 and Frank et al. (2013) doi:10.3102/0162373713493129 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., the treatment) and outcome to invalidate an inference of a treatment effect". Or "one would have to replace pp percent of the observed data with nor which the treatment had no effect 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.

Last 30 days

This package has been downloaded 967 times in the last 30 days. Not bad! The download count is somewhere between 'small-town buzz' and 'moderate academic conference'. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 5 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 11,261 times in the last 365 days. The downloads are officially high enough to crash an underfunded departmental server. Quite an accomplishment! The day with the most downloads was Feb 20, 2025 with 97 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.