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.
| Version: | 0.2.38 |
| Depends: | R (≥ 3.3.0), ashr (≥ 2.2-22) |
| Imports: | assertthat, utils, stats, plyr, rmeta, Rcpp (≥ 0.12.11), mvtnorm, abind |
| LinkingTo: | Rcpp, RcppArmadillo, RcppGSL |
| Suggests: | MASS, REBayes, corrplot, testthat, kableExtra, knitr, rmarkdown, profmem |
| Published: | 2020-06-19 |
| 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 at gmail.com> |
| BugReports: | http://github.com/stephenslab/mashr/issues |
| License: | BSD_3_clause + file LICENSE |
| Copyright: | file COPYRIGHTS mashr copyright details |
| URL: | http://github.com/stephenslab/mashr |
| NeedsCompilation: | yes |
| SystemRequirements: | C++11 |
| Citation: | mashr citation info |
| Materials: | README |
| CRAN checks: | mashr results |
| Reference manual: | mashr.pdf |
| Vignettes: |
using mashr for eQTL studies mashr intro with correlations mashr intro with data-driven covariances mashr intro mashcommonbaseline intro mashnocommonbaseline intro Sample from mash posteriors mashr simulation with non-canonical matrices |
| Package source: | mashr_0.2.38.tar.gz |
| Windows binaries: | r-devel: mashr_0.2.38.zip, r-release: mashr_0.2.38.zip, r-oldrel: mashr_0.2.38.zip |
| macOS binaries: | r-release: mashr_0.2.38.tgz, r-oldrel: mashr_0.2.38.tgz |
| Old sources: | mashr archive |
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