mcglm (0.3.0)

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.

Maintainer: Wagner Hugo Bonat
Author(s): Wagner Hugo Bonat [aut, cre], Walmes Marques Zeviani [ctb], Fernando de Pol Mayer [ctb]

License: GPL-3 | file LICENSE

Uses: assertthat, Matrix, latticeExtra, lattice, mvtnorm, tweedie, plyr, MASS, testthat, devtools, knitr, rmarkdown

Released over 3 years ago.