glmmsr
: fit GLMMs with various approximation methodsGeneralized 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.
glmmsr
You can glmmsr
from CRAN with
install.packages("glmmsr")
You can install the development version of glmmsr
from GitHub by running
devtools::install_github("heogden/glmmsr")
To view the vignette for glmmsr
, use
browseVignettes("glmmsr")
or see here
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