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.

Maintainer: Wagner Hugo Bonat <wbonat at ufpr.br>

Author(s): Wagner Hugo Bonat*, Walmes Marques Zeviani*, Fernando de Pol Mayer*

Install package and any missing dependencies by running this line in your R console:

install.packages("mcglm")

Depends R (>= 3.2.1)
Imports stats, Matrix, assertthat, graphics
Suggests testthat, plyr, lattice, latticeExtra, knitr, rmarkdown, MASS, mvtnorm, tweedie, devtools
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Package mcglm
Materials
URL https://github.com/wbonat/mcglm
Task Views
Version 0.3.0
Published 2016-06-09
License GPL-3 | file LICENSE
BugReports https://github.com/wbonat/mcglm/issues
SystemRequirements
NeedsCompilation no
Citation
CRAN checks mcglm check results
Package source mcglm_0.3.0.tar.gz