mashr
Multivariate Adaptive Shrinkage
Peter Carbonetto, Gao Wang, Sarah M. Urbut, Yuxin Zou, Matthew Stephens
Last update: 18 October 2023
Copyright license: 3-clause BSD License, File License
Software version identifier: 0.2.69, 0.2.28, 0.2.38, 0.2.50, 0.2.57, 0.2.79
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.
