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  "Package": "biotmle",
  "Title": "Targeted Learning with Moderated Statistics for Biomarker\nDiscovery",
  "Version": "1.37.0",
  "Authors@R": "c(\nperson(\"Nima\", \"Hejazi\", email = \"nh@nimahejazi.org\",\nrole = c(\"aut\", \"cre\", \"cph\"),\ncomment = c(ORCID = \"0000-0002-7127-2789\")),\nperson(\"Alan\", \"Hubbard\", email = \"hubbard@berkeley.edu\",\nrole = c(\"aut\", \"ths\"),\ncomment = c(ORCID = \"0000-0002-3769-0127\")),\nperson(\"Mark\", \"van der Laan\", email = \"laan@stat.berkeley.edu\",\nrole = c(\"aut\", \"ths\"),\ncomment = c(ORCID = \"0000-0003-1432-5511\")),\nperson(\"Weixin\", \"Cai\", email = \"wcai@berkeley.edu\",\nrole = \"ctb\",\ncomment = c(ORCID = \"0000-0003-2680-3066\")),\nperson(\"Philippe\", \"Boileau\", email = \"philippe_boileau@berkeley.edu\",\nrole = \"ctb\",\ncomment = c(ORCID = \"0000-0002-4850-2507\"))\n)",
  "Description": "Tools for differential expression biomarker discovery\nbased on microarray and next-generation sequencing data that\nleverage efficient semiparametric estimators of the average\ntreatment effect for variable importance analysis. Estimation\nand inference of the (marginal) average treatment effects of\npotential biomarkers are computed by targeted minimum\nloss-based estimation, with joint, stable inference constructed\nacross all biomarkers using a generalization of moderated\nstatistics for use with the estimated efficient influence\nfunction. The procedure accommodates the use of ensemble\nmachine learning for the estimation of nuisance functions.",
  "License": "MIT + file LICENSE",
  "URL": "https://code.nimahejazi.org/biotmle",
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  "Repository": "https://bioc.r-universe.dev",
  "Date/Publication": "2026-04-28 12:45:34 UTC",
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