Multivariate Covariance Generalized Linear Models

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.

mcglm 0.9.0

The mcglm package fits multivariate covariance generalized linear models (Bonat and Jorgensen, 2016).

Introduction

mcglm is an R package designed to fit Multivariate Covariance Generalized Linear Models. It allows you to specify a distinct linear predictor for each response variable, offering exceptional flexibility for analyses involving multiple outcomes.

With mcglm, you can model a wide range of response types — continuous, discrete (such as counts and binary), limited, and even zero inflated responses, whether continuous or mixed.

Its main strength lies in the ability to capture complex relationships between variables through multiple covariance structures, enabling more realistic and robust multivariate modeling.

This package was developed as part of the Wagner’s Ph.D. thesis, combining academic rigor with practical value for the statistical modeling community.

Download and install

Linux/Mac

Use the devtools package (available from CRAN) to install automatically from this GitHub repository:

library(devtools)
install_github("bonatwagner/mcglm")

Authors

Wagner Hugo Bonat (author and main developer)

Contributing

This R package is develop using roxygen2 for documentation and devtools to check and build. Also, we adopt the Gitflow worflow in this repository.