Package: mcglm Type: Package Title: Multivariate Covariance Generalized Linear Models Version: 0.9.0 Date: 2025-12-14 Author: Wagner Hugo Bonat [aut, cre] Maintainer: Wagner Hugo Bonat Authors@R: c(person(c("Wagner","Hugo"), "Bonat", role = c("aut", "cre"), email = "wbonat@ufpr.br")) Description: 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) , for more information and examples. Depends: R (>= 4.5.0) Suggests: testthat, knitr, rmarkdown, MASS, mvtnorm, tweedie, devtools Imports: stats, Matrix, assertthat, graphics, Rcpp (>= 0.12.16) License: GPL-3 | file LICENSE LazyData: TRUE URL: https://github.com/bonatwagner/mcglm BugReports: https://github.com/bonatwagner/mcglm/issues Encoding: UTF-8 VignetteBuilder: knitr RoxygenNote: 7.3.3 LinkingTo: Rcpp, RcppArmadillo Repository: https://bonatwagner.r-universe.dev Date/Publication: 2025-12-14 20:57:50 UTC RemoteUrl: https://github.com/bonatwagner/mcglm RemoteRef: HEAD RemoteSha: 6bc460bb25168f4ddd8b1f25abac2a2db818cb88 NeedsCompilation: yes Packaged: 2026-10-21 11:19:58 UTC; root