Multivariate Adaptive Shrinkage

Implements the multivariate adaptive shrinkage (mash) method of Urbut et al (2019) 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.


Reference manual

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0.2.38 by Peter Carbonetto, 4 months ago

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Authors: Matthew 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]

Documentation:   PDF Manual  

BSD_3_clause + file LICENSE license

Imports assertthat, utils, stats, plyr, rmeta, Rcpp, mvtnorm, abind

Depends on ashr

Suggests MASS, REBayes, corrplot, testthat, kableExtra, knitr, rmarkdown, profmem

Linking to Rcpp, RcppArmadillo, RcppGSL

System requirements: C++11

See at CRAN