The glmmsr package provides functions to conduct inference about generalized linear mixed models, giving the user 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.
The main function of the glmmsr package is
glmm, which is
used to fit the GLMM. Its interface allows a larger class of models than
those allowed by
structured pairwise comparison models.
Helen E. Ogden (2015). A sequential reduction method for inference in generalized linear mixed models. Electronic Journal of Statistics 9: 135-152. doi: 10.1214/15-EJS991
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