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.


Reference manual

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0.3.0 by Wagner Hugo Bonat, 10 months ago

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Authors: Wagner Hugo Bonat [aut, cre], Walmes Marques Zeviani [ctb], Fernando de Pol Mayer [ctb]

Documentation:   PDF Manual  

GPL-3 | file LICENSE license

Imports stats, Matrix, assertthat, graphics

Suggests testthat, plyr, lattice, latticeExtra, knitr, rmarkdown, MASS, mvtnorm, tweedie, devtools

See at CRAN