@article{JSSv039i13,
title={Hierarchical Generalized Linear Models: The R Package HGLMMM},
volume={39},
url={https://www.jstatsoft.org/index.php/jss/article/view/v039i13},
doi={10.18637/jss.v039.i13},
abstract={The <b>R</b> package <b>HGLMMM</b> 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 parameters are estimated using first order adjusted profile likelihood.},
number={13},
journal={Journal of Statistical Software},
author={Molas, Marek and Lesaffre, Emmanuel},
year={2011},
pages={1–20}
}