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.

glmmsr: fit GLMMs with various approximation methods

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.

Installing glmmsr

You can glmmsr from CRAN with

install.packages("glmmsr")

You can install the development version of glmmsr from GitHub by running