Fully specified Bayes factors for process tracing.
This package implements the methods of Lopez, Bowers, and Gajardo Cooper (2026), “Fully specified Bayes factors for process tracing.” Two generative models for evidence in favor of a working theory against a single rival — a binomial model for open-ended evidence and a hypergeometric urn model for bounded archives — each yield a conservative Bayes factor. Sensitivity analyses vary coding error and observation bias.
The package is named after Dorothy Maud Wrinch (1894–1976), the mathematician whose joint papers with Harold Jeffreys (1919, 1921, 1923) developed the framework that became Jeffreys’s theory of Bayes factors. A companion package, DrBristol, implements p-value methods for the same class of problems.
Interactive app
DrWrinch ships with a Shiny app that exposes the four core functions through a browser UI:
DrWrinch::run_app()A hosted copy lives at https://jakebowers.shinyapps.io/drwrinch/. Defaults reproduce the paper’s running example (y_W = 7, y_R = 3, threshold = 20); the Sensitivity tab shows omega_star and M_star tipping points with plotly curves.