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.

Documentation

Manual: glmmsr.pdf
Vignette: glmmsr

Maintainer: Helen Ogden <heogden12 at gmail.com>

Author(s): Helen Ogden*

Install package and any missing dependencies by running this line in your R console:

install.packages("glmmsr")

Depends R (>= 3.1.2)
Imports lme4(>=1.1-8), Matrix, R6, Rcpp, methods, stats, utils, numDeriv
Suggests BradleyTerry2, hglm.data, knitr, rmarkdown, testthat
Enhances
Linking to Rcpp, RcppEigen, BH
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Package glmmsr
Materials
URL http://github.com/heogden/glmmsr
Task Views
Version 0.2.0
Published 2017-08-31
License GPL (>= 2)
BugReports http://github.com/heogden/glmmsr/issues
SystemRequirements
NeedsCompilation yes
Citation
CRAN checks glmmsr check results
Package source glmmsr_0.2.0.tar.gz