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

TL;DR

The paper introduces the R package ebnm, which offers a unified, efficient, and flexible interface for fitting empirical Bayes normal means models with various prior assumptions, enhancing analysis capabilities across multiple statistical fields.

Contribution

The authors developed the ebnm package to unify and extend existing tools for EBNM models, including new methods for speed, stability, and a broad range of prior assumptions.

Findings

ebnm enables efficient fitting of EBNM models with diverse priors

The package demonstrates practical utility through baseball statistics analysis

ebnm facilitates development of new statistical methods and tools

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. Further, 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 a focus 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, the ebnm package can facilitate development of new methods in statistics, genetics, and other areas; as an example, we briefly discuss the R package flashier, which harnesses methods in ebnm to provide a flexible and robust approach to matrix factorization.