{ "_id": "6a49f6cb8408e3d40c0b405d", "Package": "templateICAr", "Type": "Package", "Title": "Estimate Brain Networks and Connectivity with ICA and Empirical\nPriors", "Version": "0.10.0", "Authors@R": "c(\nperson(given = \"Amanda\",\nfamily = \"Mejia\",\nemail = \"mandy.mejia@gmail.com\",\nrole = c(\"aut\", \"cre\")),\nperson(given = \"Damon\",\nfamily = \"Pham\",\nemail = \"damondpham@gmail.com\",\nrole = c(\"aut\"),\ncomment = c(ORCID = \"0000-0001-7563-4727\")),\nperson(given = \"Daniel\",\nfamily = \"Spencer\",\nemail = \"danieladamspencer@gmail.com\",\nrole = c(\"ctb\"),\ncomment = c(ORCID = \"0000-0002-9705-3605\")),\nperson(given = \"Mary Beth\",\nfamily = \"Nebel\",\nemail = \"Nebel@kennedykrieger.org\",\nrole = c(\"ctb\"))\n)", "Maintainer": "Amanda Mejia ", "Description": "Implements the template ICA (independent components\nanalysis) model proposed in Mejia et al. (2020)\n and the spatial template\nICA model proposed in proposed in Mejia et al. (2022)\n. Both models estimate\nsubject-level brain as deviations from known population-level\nnetworks, which are estimated using standard ICA algorithms.\nBoth models employ an expectation-maximization algorithm for\nestimation of the latent brain networks and unknown model\nparameters. 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