htmcglm: Hypothesis Testing for McGLMs

Performs hypothesis testing for multivariate covariance generalized linear models (McGLMs). 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.

Version: 0.0.1
Imports: stats, doBy, Matrix, mcglm, sjmisc, stringr
Published: 2022-07-21
Author: Lineu Alberto Cavazani de Freitas [aut, cre], Wagner Hugo Bonat [ctb], Walmes Marques Zeviani [ctb]
Maintainer: Lineu Alberto Cavazani de Freitas <lineuacf at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README
CRAN checks: htmcglm results


Reference manual: htmcglm.pdf


Package source: htmcglm_0.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): htmcglm_0.0.1.tgz, r-oldrel (arm64): htmcglm_0.0.1.tgz, r-release (x86_64): htmcglm_0.0.1.tgz, r-oldrel (x86_64): htmcglm_0.0.1.tgz


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