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  "Title": "Matrix-Variate Non-Gaussian Linear Regression Models",
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  "Authors@R": "c(person(\"Samuel\", \"Soon\", , \"samksoon2@gmail.com\", role = c(\"aut\", \"cre\"),\ncomment = c()),\nperson(\"Dipankar\", \"Bandyopadhyay\", , \"dbandyop@vcu.edu\", role = c(\"aut\"),\ncomment = c()),\nperson(\"Qingyang\", \"Liu\", , \"qliu432@wisc.edu\", role = c(\"aut\"),\ncomment = c()))",
  "Maintainer": "Samuel Soon <samksoon2@gmail.com>",
  "Description": "An implementation of the expectation conditional\nmaximization (ECM) algorithm for matrix-variate variance gamma\n(MVVG) and normal-inverse Gaussian (MVNIG) linear models. These\nmodels are designed for settings of multivariate analysis with\nclustered non-uniform observations and correlated responses.\nThe package includes fitting and prediction functions for both\nmodels, and an example dataset from a periodontal on\nGullah-speaking African Americans, with responses in\n'gaad_res', and covariates in 'gaad_cov'. For more details on\nthe matrix-variate distributions used, see Gallaugher &\nMcNicholas (2019) <doi:10.1016/j.spl.2018.08.012>.",
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  "Date/Publication": "2026-02-18 21:30:13 UTC",
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