R package mcglm: 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.
Keywords for this software
References in zbMATH (referenced in 5 articles , 1 standard article )
Showing results 1 to 5 of 5.
- Bonat, Wagner H.; Petterle, Ricardo R.; Hinde, John; Demétrio, Clarice G. B.: Flexible quasi-beta regression models for continuous bounded data (2019)
- Petterle, Ricardo Rasmussen; Bonat, Wagner Hugo; Scarpin, Cassius Tadeu: Quasi-beta longitudinal regression model applied to water quality index data (2019)
- Bonat, Wagner H.; Jørgensen, Bent; Kokonendji, Célestin C.; Hinde, John; Demétrio, Clarice G. B.: Extended Poisson-Tweedie: properties and regression models for count data (2018)
- Wagner Bonat: Multiple Response Variables Regression Models in R: The mcglm Package (2018) not zbMATH
- Bonat, W. H.; Olivero, J.; Grande-Vega, M.; Farfán, M. A.; Fa, J. E.: Modelling the covariance structure in marginal multivariate count models: hunting in Bioko Island (2017)