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


Author  Wagner Hugo Bonat [aut, cre], Walmes Marques Zeviani [ctb], Fernando de Pol Mayer [ctb] 
Date of publication  20160609 20:23:56 
Maintainer  Wagner Hugo Bonat <wbonat@ufpr.br> 
License  GPL3  file LICENSE 
Version  0.3.0 
URL  https://github.com/wbonat/mcglm 
Package repository  View on CRAN 
Installation 
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