Matrix-Variate Non-Gaussian Linear Regression Models // Fits matrix-variate variance-gamma (MVVG) and matrix-variate normal- inverse-Gaussian (MVNIG) linear regression models using expectation-conditional maximization (ECM) algorithms. The models accommodate clustered matrix-valued responses, with unequal numbers of observations across subjects, correlated responses, skewness, and within-subject dependence. Functions are provided for model fitting, prediction, and subject-level influence assessment using approximate generalized Cook's distances. The package also includes motivating periodontal data from Gullah-speaking African Americans with Type-II diabetes. For details on the underlying matrix-variate distributions (MVVG and MVNIG), see Gallaugher and McNicholas (2019, doi:10.1016/j.spl.2018.08.012).