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An EM framework is used to update fixed effects, random\neffects, and a single variance component tau^2 for G = tau^2 I,\nwith staged approximations (Laplace -> FEL mean-only -> FEL\nfull) for efficiency and stability. A pseudo-likelihood engine\nglmmFEL_pl() implements the working-response / working-weights\nlinearization approach of Wolfinger and O'Connell (1993)\n, and is adapted from the\nimplementation used in the 'RealVAMS' package (Broatch, Green,\nand Karl (2018)) . The FEL\nimplementation follows Karl, Yang, and Lohr (2014)\n and related work (e.g.,\nTierney, Kass, and Kadane (1989)\n; Rizopoulos, Verbeke, and\nLesaffre (2009) ; Steele\n(1996) ). Package code was drafted with\nassistance from generative AI tools.", "License": "GPL-3", "Encoding": "UTF-8", "Config/testthat/edition": "3", "RoxygenNote": "7.3.3", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-09 07:25:45 UTC", "User": "root" }, "Author": "Andrew T. Karl [cre, aut] (ORCID:\n)", "Maintainer": "Andrew T. 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