Fitting multivariate covariance generalized linear
models (McGLMs) to data. McGLM is a general framework for nonnormal
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 nonnormality 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 quasilikelihood and Pearson estimating functions, using
only secondmoment 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 spatiotemporal structures.
The package offers a userfriendly interface for fitting McGLMs
similar to the glm() R function.
See Bonat (2018)
Package details 


Author  Wagner Hugo Bonat [aut, cre], Walmes Marques Zeviani [ctb], Fernando de Pol Mayer [ctb] 
Maintainer  Wagner Hugo Bonat <[email protected]> 
License  GPL3  file LICENSE 
Version  0.4.0 
URL  https://github.com/wbonat/mcglm 
Package repository  View on GitHub 
Installation 
Install the latest version of this package by entering the following in R:

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