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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.

Type Size Name Uploaded Downloads Labels
conda 666.8 kB | linux-64/r-mashr-0.2.50-r41hbad8821_0.tar.bz2  3 years and 4 months ago 23 main
conda 340.8 kB | linux-64/r-mashr-0.2.21-r351hf484d3e_0.tar.bz2  5 years and 10 months ago 16 main

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