glmmsr: glmmsr: fit GLMMs with various approximation methods
In glmmsr: Fit a Generalized Linear Mixed Model

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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 lme4, including 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

glmmsr documentation built on May 2, 2019, 2:12 p.m.