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  "Title": "Matrix-Variate Non-Gaussian Linear Regression Models",
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  "Description": "Fits matrix-variate variance-gamma (MVVG) and\nmatrix-variate normal-inverse-Gaussian (MVNIG) linear\nregression models using expectation-conditional maximization\n(ECM) algorithms. The models accommodate clustered\nmatrix-valued responses, with unequal numbers of observations\nacross subjects, correlated responses, skewness, and\nwithin-subject dependence. Functions are provided for model\nfitting, prediction, and subject-level influence assessment\nusing approximate generalized Cook's distances. The package\nalso includes motivating periodontal data from Gullah-speaking\nAfrican Americans with Type-II diabetes. For details on the\nunderlying matrix-variate distributions (MVVG and MVNIG), see\nGallaugher and McNicholas (2019,\n<doi:10.1016/j.spl.2018.08.012>).",
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