mcglm: Multivariate Covariance Generalized Linear Models
Version 0.4.0

Fitting multivariate covariance generalized linear models (McGLMs) to data. McGLM 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. See Bonat (2018) , for more information and examples.

Package details

AuthorWagner Hugo Bonat [aut, cre], Walmes Marques Zeviani [ctb], Fernando de Pol Mayer [ctb]
Date of publication2018-04-10 22:01:49 UTC
MaintainerWagner Hugo Bonat <[email protected]>
LicenseGPL-3 | file LICENSE
Version0.4.0
URL https://github.com/wbonat/mcglm
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("mcglm")

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mcglm documentation built on April 11, 2018, 1:03 a.m.