Multivariate Covariance Generalized Linear Models R package mcglm version 0.9.0 Fitting multivariate covariance generalized linear models (McGLMs) to data. McGLM is a general framework for non-normal multivariate data analysis, designed to handle multivariate response variables, along with a wide range of temporal and spatial correlation structures defined in terms of a covariance link function combined with a matrix linear predictor involving known matrices. The models take non-normality into account in the conventional way by means of a variance function, and the mean structure is modelled by means of a link function and a linear predictor. The models are fitted using an efficient Newton scoring algorithm based on quasi-likelihood and Pearson estimating functions, using only second-moment assumptions. This provides a unified approach to a wide variety of different types of response variables and covariance structures, including multivariate extensions of repeated measures, time series, longitudinal, spatial and spatio-temporal structures. The package offers a user-friendly interface for fitting McGLMs similar to the glm() R function. See Bonat (2018) <doi:10.18637/jss.v084.i04>, for more information and examples. Software Depends: R (>= 4.5.0) Imports: stats, Matrix, assertthat, graphics, Rcpp (>= 0.12.16) LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown, MASS, mvtnorm, tweedie, devtools Wagner Hugo Bonat <wbonat@ufpr.br> Comprehensive R Archive Network (CRAN) Wagner Hugo Bonat [aut, cre] GPL-3 2026-01-08 application/tgz https://CRAN.R-project.org/package=mcglm doi:10.32614/CRAN.package.mcglm