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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.
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Package details |
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| Author | 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] |
| Maintainer | Peter Carbonetto <peter.carbonetto@gmail.com> |
| License | BSD_3_clause + file LICENSE |
| Version | 0.2.79 |
| URL | https://github.com/stephenslab/mashr |
| Package repository | View on CRAN |
| Installation |
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