EBGM Scores for Mining Large Contingency Tables

An implementation of DuMouchel's (1999) Bayesian data mining method for the market basket problem. Calculates Empirical Bayes Geometric Mean (EBGM) and quantile scores from the posterior distribution using the Gamma-Poisson Shrinker (GPS) model to find unusually large cell counts in large, sparse contingency tables. Can be used to find unusually high reporting rates of adverse events associated with products. In general, can be used to mine any database where the co-occurrence of two variables or items is of interest. Also calculates relative and proportional reporting ratios. Builds on the work of the 'PhViD' package, from which much of the code is derived. Some of the added features include stratification to adjust for confounding variables and data squashing to improve computational efficiency. Now includes an implementation of the EM algorithm for hyperparameter estimation loosely derived from the 'mederrRank' package.


openEBGM v0.3.0


  • Added confidence intervals to autoHyper() and standard errors to autoHyper() and exploreHypers().
  • processRaw() now returns Inf instead of 99999 when PRR results in division by zero.
  • Fixed minor bug in exploreHypers().

openEBGM v0.2.0


  • Minor aesthetic changes to plot(), summary(), and print() methods.
  • Relaxed convergence requirements for exploreHypers() and autoHyper().

Reference manual

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0.6.0 by John Ihrie, a month ago


Browse source code at https://github.com/cran/openEBGM

Authors: John Ihrie [cre, aut], Travis Canida [aut], Ismaïl Ahmed [ctb] (author of 'PhViD' package (derived code)), Antoine Poncet [ctb] (author of 'PhViD'), Sergio Venturini [ctb] (author of 'mederrRank' package (derived code)), Jessica Myers [ctb] (author of 'mederrRank')

Documentation:   PDF Manual  

Task views: Bayesian Inference

GPL-2 | GPL-3 license

Imports data.table, ggplot2, stats

Suggests dplyr, knitr, rmarkdown, testthat, tidyr

See at CRAN