mcglm: Multivariate Covariance Generalized Linear Models

Fitting multivariate covariance generalized linear models (McGLMs) to data. McGLMs 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.

Version: 0.3.0
Depends: R (≥ 3.2.1)
Imports: stats, Matrix, assertthat, graphics
Suggests: testthat, plyr, lattice, latticeExtra, knitr, rmarkdown, MASS, mvtnorm, tweedie, devtools
Published: 2016-06-09
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: no
CRAN checks: mcglm results


Reference manual: mcglm.pdf
Vignettes: Multivariate Covariance Generalized Linear Models
Multivariate Covariance Generalized Linear Models
Multivariate Covariance Generalized Linear Models
Package source: mcglm_0.3.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: mcglm_0.3.0.tgz
OS X Mavericks binaries: r-oldrel: mcglm_0.3.0.tgz


Please use the canonical form to link to this page.