openEBGM: An R Implementation of the Gamma-Poisson Shrinker Data Mining Model

We introduce the R package openEBGM, an implementation of the Gamma-Poisson Shrinker (GPS) model for identifying unexpected counts in large contingency tables using an empirical Bayes approach. The Empirical Bayes Geometric Mean (EBGM) and quantile scores are obtained from the GPS model estimates. openEBGM provides for the evaluation of counts using a number of different methods, including the model-based disproportionality scores, the relative reporting ratio (RR), and the proportional reporting ratio (PRR). Data squashing for computational efficiency and stratification for confounding variable adjustment are included. Application to adverse event detection is discussed.

Travis Canida , John Ihrie
2017-11-22

Supplementary materials

Supplementary materials are available in addition to this article. It can be downloaded at RJ-2017-063.zip

CRAN packages used

openEBGM, PhViD, mederrRank, tidyr, ggplot2, data.table

CRAN Task Views implied by cited packages

Bayesian, Finance, Graphics, HighPerformanceComputing, Phylogenetics

Reuse

Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".

Citation

For attribution, please cite this work as

Canida & Ihrie, "openEBGM: An R Implementation of the Gamma-Poisson Shrinker Data Mining Model", The R Journal, 2017

BibTeX citation

@article{RJ-2017-063,
  author = {Canida, Travis and Ihrie, John},
  title = {openEBGM: An R Implementation of the Gamma-Poisson Shrinker Data Mining Model},
  journal = {The R Journal},
  year = {2017},
  note = {https://doi.org/10.32614/RJ-2017-063},
  doi = {10.32614/RJ-2017-063},
  volume = {9},
  issue = {2},
  issn = {2073-4859},
  pages = {499-519}
}