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

arXiv: https://arxiv.org/abs/2110.00152 · HTML: https://mlsanity.com/paper/2110.00152/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2021.

## Built on by (most cited first, 4 of 4)

- [Empirical Bayes Covariance Decomposition, and a Solution to the Multiple Tuning Problem in Sparse PCA](https://mlsanity.com/paper/2312.03274/) (2023)
- [Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regression](https://mlsanity.com/paper/2505.11143/) (2025)
- [A Flexible Empirical Bayes Approach to Generalized Linear Models, with Applications to Sparse Logistic Regression](https://mlsanity.com/paper/2601.21217/) (2026)
- [Empirical Bayes for correlated Gaussian sequence model](https://mlsanity.com/paper/2607.03596/) (2026)

## Abstract (arXiv)

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.