{ "_id": "6a631ea5d3420cf5dfdb75f7", "Package": "mashr", "Type": "Package", "Encoding": "UTF-8", "Title": "Multivariate Adaptive Shrinkage", "Version": "0.2.81", "Date": "2024-11-06", "Authors@R": "c(person(\"Matthew\",\"Stephens\",role=\"aut\"),\nperson(\"Sarah\",\"Urbut\",role=\"aut\"),\nperson(\"Gao\",\"Wang\",role=\"aut\"),\nperson(\"Yuxin\",\"Zou\",role=\"aut\"),\nperson(\"Yunqi\",\"Yang\",role=\"ctb\"),\nperson(\"Sam\",\"Roweis\",role=\"cph\"),\nperson(\"David\",\"Hogg\",role=\"cph\"),\nperson(\"Jo\",\"Bovy\",role=\"cph\"),\nperson(\"Peter\",\"Carbonetto\",role=c(\"aut\",\"cre\"),\nemail=\"peter.carbonetto@gmail.com\"))", "Description": "Implements the multivariate adaptive shrinkage (mash)\nmethod of Urbut et al (2019) \nfor estimating and testing large numbers of effects in many\nconditions (or many outcomes). Mash takes an empirical Bayes\napproach to testing and effect estimation; it estimates\npatterns of similarity among conditions, then exploits these\npatterns to improve accuracy of the effect estimates. 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