{ "_id": "6a10417cacfb0bcc41c9d7cd", "Package": "FDRestimation", "Version": "1.0.1", "Date": "2022-03-21", "Title": "Estimate, Plot, and Summarize False Discovery Rates", "Authors@R": "c(\nperson(\"Megan\", \"Murray\", email = \"megan.c.hollister@vanderbilt.edu\", role = c(\"aut\", \"cre\")),\nperson(\"Jeffrey\", \"Blume\", email = \"j.blume@vanderbilt.edu\", role = c(\"aut\"))\n)", "Author": "Megan Murray [aut, cre], Jeffrey Blume [aut]", "Maintainer": "Megan Murray ", "Description": "The user can directly compute and display false discovery\nrates from inputted p-values or z-scores under a variety of\nassumptions. p.fdr() computes FDRs, adjusted p-values and\ndecision reject vectors from inputted p-values or z-values.\nget.pi0() estimates the proportion of data that are truly null.\nplot.p.fdr() plots the FDRs, adjusted p-values, and the raw\np-values points against their rejection threshold lines.", "License": "MIT + file LICENSE", "URL": "", "RoxygenNote": "7.1.2", "Encoding": 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