glmmsr: Fit a Generalized Linear Mixed Model

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.

Version: 0.2.2
Depends: R (≥ 3.2.0)
Imports: lme4 (≥ 1.1-8), Matrix, R6, Rcpp, methods, stats, utils, numDeriv
LinkingTo: Rcpp, RcppEigen, BH
Suggests: BradleyTerry2, hglm.data, knitr, rmarkdown, testthat
Published: 2018-04-10
Author: Helen Ogden [aut, cre]
Maintainer: Helen Ogden <heogden12 at gmail.com>
BugReports: http://github.com/heogden/glmmsr/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: http://github.com/heogden/glmmsr
NeedsCompilation: yes
Materials: README
CRAN checks: glmmsr results

Downloads:

Reference manual: glmmsr.pdf
Vignettes: glmmsr
Package source: glmmsr_0.2.2.tar.gz
Windows binaries: r-devel: glmmsr_0.2.2.zip, r-release: glmmsr_0.2.2.zip, r-oldrel: glmmsr_0.2.2.zip
OS X binaries: r-release: glmmsr_0.2.2.tgz, r-oldrel: glmmsr_0.2.2.tgz
Old sources: glmmsr archive

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