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The empirical Bayes normal means (EBNM) model is important to many areas ofstatistics, including (but not limited to) multiple testing, wavelet denoising,multiple linear regression, and matrix factorization. There are severalexisting software packages that can fit EBNM models under different priorassumptions and using different algorithms; however, the differences acrossinterfaces complicate direct comparisons. Further, a number of important priorassumptions do not yet have implementations. Motivated by these issues, wedeveloped the R package ebnm, which provides a unified interface forefficiently fitting EBNM models using a variety of prior assumptions, includingnonparametric approaches. In some cases, we incorporated existingimplementations into ebnm; in others, we implemented new fitting procedureswith a focus on speed and numerical stability. To demonstrate the capabilitiesof the unified interface, we compare results using different prior assumptionsin two extended examples: the shrinkage estimation of baseball statistics; andthe matrix factorization of genetics data (via the new R package flashier). Insummary, ebnm is a convenient and comprehensive package for performing EBNManalyses under a wide range of prior assumptions.

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