nlme, diseasemapping, geoRglm, R2WinBUGS, R2OpenBUGS, R2jags,
rjags
Automates running Generalised Linear Mixed Models, including
spatial models, with WinBUGS, OpenBUGS and JAGS. Models are specified with
formulas, with the package writings model files, arranging unbalanced data
in ragged arrays, and creating starting values. The model is re-parameterized,
and functions are provided for converting model outputs to the original
parameterization.
Compiling vignette requires JAGS
(http://mcmc-jags.sourceforge.net) and OpenBUGS (>= 3.2.2)
(http://www.openbugs.net)

Generalized Linear Mixed Model Analysis via Expectation
Propagation
c(person("Matt P.", "Wand", role = c("aut", "cre"),
email = "matt.wand@uts.edu.au"),
person("James F.C.", "Yu", role = "aut",
email = "james.yu@student.uts.edu.au"))
Approximate frequentist inference for generalized linear mixed model analysis with expectation propagation used to circumvent the need for multivariate integration. In this version, the random effects can be any reasonable dimension. However, only probit mixed models with one level of nesting are supported. The methodology is described in Hall, Johnstone, Ormerod, Wand and Yu (2018) <arXiv:1805.08423v1>.
Matt P. Wand [aut, cre],
James F.C. Yu [aut]

c(person("Matt P.", "Wand", role = c("aut", "cre"),
email = "matt.wand@uts.edu.au"),
person("James C.F.", "Yu", role = "aut",
email = "james.yu@student.uts.edu.au"))
Matt P. Wand [aut, cre],
James C.F. Yu [aut]

Gauss-Seidel Generalized Linear Mixed Model solver
Michele Morara, Louise Ryan, Subharup Guha, Christopher
Paciorek
Solve Generalized Linear Mixed Models using Gauss-Seidel
algorithm

Michele Morara, Louise Ryan, Subharup Guha, Christopher Paciorek
Solve Generalized Linear Mixed Models using Gauss-Seidel algorithm

Variable selection for generalized linear mixed models by
L1-penalized estimation

Variable selection for generalized linear mixed models by L1-penalized estimation

Variable Selection for Generalized Linear Mixed Models by
L1-Penalized Estimation
A variable selection approach for generalized linear mixed models by L1-penalized estimation is provided.