ebnm: An R Package for Solving the Empirical Bayes Normal Means Problem Using a Variety of Prior Families

Abstract

The empirical Bayes normal means (EBNM) model is important to many areas of statistics, including (but not limited to) multiple testing, wavelet denoising, and gene expression analysis. There are several existing software packages that can fit EBNM models under different prior assumptions and using different algorithms. However, the differences across interfaces complicate direct comparisons, and a number of important prior assumptions do not yet have implementations. Motivated by these issues, we developed the R package ebnm, which provides a unified interface for efficiently fitting EBNM models using a variety of prior assumptions, including nonparametric approaches. In some cases, we incorporated existing implementations into ebnm; in others, we implemented new fitting procedures, with an emphasis on speed and numerical stability. We illustrate the use of ebnm in a detailed analysis of baseball statistics. By providing a unified and easily extensible interface, ebnm can facilitate development of new methods in statistics, genetics, and other areas; as an example, we briefly discuss the R package flashier, which harnesses ebnm for flexible and robust matrix factorization.