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.

Package details

AuthorMatthew Stephens [aut], Sarah Urbut [aut], Gao Wang [aut], Yuxin Zou [aut], Yunqi Yang [ctb], Sam Roweis [cph], David Hogg [cph], Jo Bovy [cph], Peter Carbonetto [aut, cre]
MaintainerPeter Carbonetto <>
LicenseBSD_3_clause + file LICENSE
Package repositoryView on CRAN
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mashr documentation built on May 24, 2021, 1:06 a.m.