TY - JOUR
AU - Molas, Marek
AU - Lesaffre, Emmanuel
PY - 2011/03/09
Y2 - 2023/12/02
TI - Hierarchical Generalized Linear Models: The R Package HGLMMM
JF - Journal of Statistical Software
JA - J. Stat. Soft.
VL - 39
IS - 13
SE - Articles
DO - 10.18637/jss.v039.i13
UR - https://www.jstatsoft.org/index.php/jss/article/view/v039i13
SP - 1 - 20
AB - 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.
ER -