mashr - Multivariate Adaptive Shrinkage
Implements the multivariate adaptive shrinkage (mash) method of Urbut et al (2019) <DOI:10.1038/s41588-018-0268-8> for estimating and testing large numbers of effects in many conditions (or many outcomes). Mash takes an empirical Bayes approach to testing and effect estimation; it estimates patterns of similarity among conditions, then exploits these patterns to improve accuracy of the effect estimates. The core linear algebra is implemented in C++ for fast model fitting and posterior computation.
Last updated 11 months ago
87 stars 4.48 score 18 dependencies 3 dependentsebnm - Solve the Empirical Bayes Normal Means Problem
Provides simple, fast, and stable functions to fit the normal means model using empirical Bayes. For available models and details, see function ebnm(). A detailed introduction to the package is provided by Willwerscheid and Stephens (2023) <arXiv:2110.00152>.
Last updated 3 months ago
12 stars 2.44 score 43 dependencies 1 dependents