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) <doi:10.18637/jss.v084.i04>, for more information and examples.
|Depends:||R (≥ 4.1.0)|
|Imports:||stats, Matrix, assertthat, graphics, Rcpp (≥ 0.12.16)|
|Suggests:||testthat, knitr, rmarkdown, MASS, mvtnorm, tweedie, devtools|
|Author:||Wagner Hugo Bonat [aut, cre], Walmes Marques Zeviani [ctb], Fernando de Pol Mayer [ctb]|
|Maintainer:||Wagner Hugo Bonat <wbonat at ufpr.br>|
|License:||GPL-3 | file LICENSE|
|Citation:||mcglm citation info|
|CRAN checks:||mcglm results|
Fitting generalized linear models using the mcglm package
Choosing link, variance and covariance functions
|Windows binaries:||r-devel: mcglm_0.7.0.zip, r-release: mcglm_0.7.0.zip, r-oldrel: not available|
|macOS binaries:||r-release (arm64): mcglm_0.7.0.tgz, r-release (x86_64): mcglm_0.7.0.tgz, r-oldrel: not available|
|Old sources:||mcglm archive|
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