swMATH ID: 23203
Software Authors: Wagner Hugo Bonat, Walmes Marques Zeviani, Fernando de Pol Mayer
Description: R package mcglm: 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.
Homepage: https://cran.r-project.org/web/packages/mcglm/index.html
Source Code: https://github.com/cran/mcglm
Dependencies: R
Related Software: R; multcomp; geepack; gcmr; car; Survey; lmtest; htmcglm; nadiv; geoR; nlme; Mcmcpack; R-INLA; MCMCglmm; Stan; spdep; lme4; glimmix; JAGS; STEPCEE
Cited in: 5 Publications

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