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)
|Author||Wagner Hugo Bonat [aut, cre], Walmes Marques Zeviani [ctb], Fernando de Pol Mayer [ctb]|
|Date of publication||2018-04-10 22:01:49 UTC|
|Maintainer||Wagner Hugo Bonat <[email protected]>|
|License||GPL-3 | file LICENSE|
|Package repository||View on CRAN|
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