Fit a Generalized Linear Mixed Model

Conduct inference about generalized linear mixed models, with a choice about which method to use to approximate the likelihood. In addition to the Laplace and adaptive Gaussian quadrature approximations, which are borrowed from 'lme4', the likelihood may be approximated by the sequential reduction approximation, or an importance sampling approximation. These methods provide an accurate approximation to the likelihood in some situations where it is not possible to use adaptive Gaussian quadrature.


Generalized linear mixed models (GLMMs) are an important and widely-used model class. In R, we can fit these models with the lme4 package, but there are some limitations. First, except in very simple cases, lme4 uses a Laplace approximation to the likelihood for inference, which may be of poor quality in some cases. Second, it is difficult to fit some GLMMs, such as pairwise comparison models, with lme4. The glmmsr package offers progress on both of these problems.

A user must choose which method to use to approximate the likelihood. In addition to the Laplace and adaptive Gaussian quadrature approximations, which are borrowed from lme4, the likelihood may be approximated by the sequential reduction approximation, or an importance sampling approximation. These methods provide an accurate approximation to the likelihood in some situations where it is not possible to use adaptive Gaussian quadrature.

The vignette provides more information about the different approximations.

The interface of glmmsr allows easy fitting of pairwise comparison and many other interesting models, which are difficult to fit with lme4. See the vignette for some examples.

You can install version 0.1.0 of glmmsr from CRAN with

install.packages("glmmsr")

You can install this development version of glmmsr by running

devtools::install_github("heogden/glmmsr")

To view the vignette for glmmsr, use

browseVignettes("glmmsr")

or see here

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Reference manual

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install.packages("glmmsr")

0.1.1 by Helen Ogden, a year ago


http://github.com/heogden/glmmsr


Report a bug at http://github.com/heogden/glmmsr/issues


Browse source code at https://github.com/cran/glmmsr


Authors: Helen Ogden [aut, cre]


Documentation:   PDF Manual  


GPL (>= 2) license


Imports lme4, Matrix, R6, Rcpp, methods, stats, utils

Suggests BradleyTerry2, hglm.data, knitr, rmarkdown, testthat

Linking to Rcpp, RcppEigen, BH


See at CRAN