mcglm: Multivariate Covariance Generalized Linear Models

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.

Version: 0.7.0
Depends: R (≥ 4.1.0)
Imports: stats, Matrix, assertthat, graphics, Rcpp (≥ 0.12.16)
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat, knitr, rmarkdown, MASS, mvtnorm, tweedie, devtools
Published: 2021-07-11
Author: Wagner Hugo Bonat [aut, cre], Walmes Marques Zeviani [ctb], Fernando de Pol Mayer [ctb]
Maintainer: Wagner Hugo Bonat <wbonat at>
License: GPL-3 | file LICENSE
NeedsCompilation: yes
Citation: mcglm citation info
CRAN checks: mcglm results


Reference manual: mcglm.pdf
Vignettes: Fitting generalized linear models using the mcglm package
Choosing link, variance and covariance functions


Package source: mcglm_0.7.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel: not available
macOS binaries: r-release (arm64): mcglm_0.7.0.tgz, r-release (x86_64): mcglm_0.7.0.tgz, r-oldrel: not available
Old sources: mcglm archive


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