mcglm: 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) <doi:10.18637/jss.v084.i04>, for more information
||R (≥ 4.2.0)
||stats, Matrix, assertthat, graphics, Rcpp (≥ 0.12.16)
||testthat, knitr, rmarkdown, MASS, mvtnorm, tweedie, devtools
||Wagner Hugo Bonat [aut, cre]
||Wagner Hugo Bonat <wbonat at ufpr.br>
||GPL-3 | file LICENSE
||mcglm citation info
Please use the canonical form
to link to this page.