The R package HGLMMM has been developed to fit generalized linear models with random effects using the h-likelihood approach. The response variable is allowed to follow a binomial, Poisson, Gaussian or gamma distribution. The distribution of random effects can be specified as Gaussian, gamma, inverse-gamma or beta. Complex structures as multi-membership design or multilevel designs can be handled. Further, dispersion parameters of random components and the residual dispersion (overdispersion) can be modeled as a function of covariates. Overdispersion parameter can be fixed or estimated. Fixed effects in the mean structure can be estimated using extended likelihood or a first order Laplace approximation to the marginal likelihood. Dispersion...
Count and proportion data may present overdispersion, i.e., greater variability than expected by the...
Count and proportion data may present overdispersion, i.e., greater variability than expected by the...
Hierarchical generalized linear models are often used to fit random effects models. However, attenti...
The R package HGLMMM has been developed to fit generalized linear models with random effects using t...
We present the hglm package for fitting hierarchical generalized linear models. It can be used for l...
Abstract: Random-effect models are becoming increasingly popular in the analysis of data. Lee and Ne...
Since their introduction in 1972, generalized linear models (GLMs) have proven useful in the general...
We propose a class of double hierarchical generalized linear models in which random effects can be s...
Abstract We propose a class of hierarchical generalized linear models (HGLMs) with ran-dom dispersio...
Description The hglm package is used to fit hierarchical generalized linear mod-els. It can be used ...
Thesis (M.Sc.)-University of Natal, Durban, 2004.Generalized linear mixed models (GLMMs) accommodate...
Este trabalho teve como objetivo principal estudar e comparar os diferentes ajustes de Modelos Linea...
We present a new version of the hglm package for fittinghierarchical generalized linear models (HGLM...
Over the last twenty years there have been numerous developments in diagnostic pro- cedures for hier...
The R package glmm enables likelihood-based inference for generalized linear mixed models with a can...
Count and proportion data may present overdispersion, i.e., greater variability than expected by the...
Count and proportion data may present overdispersion, i.e., greater variability than expected by the...
Hierarchical generalized linear models are often used to fit random effects models. However, attenti...
The R package HGLMMM has been developed to fit generalized linear models with random effects using t...
We present the hglm package for fitting hierarchical generalized linear models. It can be used for l...
Abstract: Random-effect models are becoming increasingly popular in the analysis of data. Lee and Ne...
Since their introduction in 1972, generalized linear models (GLMs) have proven useful in the general...
We propose a class of double hierarchical generalized linear models in which random effects can be s...
Abstract We propose a class of hierarchical generalized linear models (HGLMs) with ran-dom dispersio...
Description The hglm package is used to fit hierarchical generalized linear mod-els. It can be used ...
Thesis (M.Sc.)-University of Natal, Durban, 2004.Generalized linear mixed models (GLMMs) accommodate...
Este trabalho teve como objetivo principal estudar e comparar os diferentes ajustes de Modelos Linea...
We present a new version of the hglm package for fittinghierarchical generalized linear models (HGLM...
Over the last twenty years there have been numerous developments in diagnostic pro- cedures for hier...
The R package glmm enables likelihood-based inference for generalized linear mixed models with a can...
Count and proportion data may present overdispersion, i.e., greater variability than expected by the...
Count and proportion data may present overdispersion, i.e., greater variability than expected by the...
Hierarchical generalized linear models are often used to fit random effects models. However, attenti...