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.
- Version0.2.79
- R version≥ 3.3.0
- LicenseBSD_3_clause
- LicenseLICENSE
- Needs compilation?Yes
- mashr citation info
- Last release10/18/2023
Documentation
- Vignetteusing mashr for eQTL studies
- Vignettemashr intro with correlations
- Vignettemashr intro with data-driven covariances
- Vignettemashr intro
- Vignettemashnocommonbaseline intro
- Vignettemashcommonbaseline intro
- VignetteSample from mash posteriors
- Vignettemashr simulation with non-canonical matrices
- MaterialREADME
Team
Peter Carbonetto
Matthew Stephens
Show author detailsRolesAuthorGao Wang
Show author detailsRolesAuthorSarah Urbut
Show author detailsRolesAuthorYuxin Zou
Show author detailsRolesAuthorYunqi Yang
Show author detailsRolesContributorSam Roweis
Show author detailsRolesCopyright holderDavid Hogg
Show author detailsRolesCopyright holderJo Bovy
Show author detailsRolesCopyright holder
Insights
Last 30 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Depends1 package
- Imports7 packages
- Suggests10 packages
- Linking To3 packages
- Reverse Imports1 package