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: | 0.2.1 |
Depends: | R (≥ 3.1.2) |
Imports: | lme4 (≥ 1.1-8), Matrix, R6, Rcpp, methods, stats, utils, numDeriv |
LinkingTo: | Rcpp, RcppEigen, BH |
Suggests: | BradleyTerry2, hglm.data, knitr, rmarkdown, testthat |
Published: | 2018-01-09 |
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 |
Reference manual: | glmmsr.pdf |
Vignettes: |
glmmsr |
Package source: | glmmsr_0.2.1.tar.gz |
Windows binaries: | r-devel: glmmsr_0.2.1.zip, r-release: glmmsr_0.2.1.zip, r-oldrel: glmmsr_0.2.1.zip |
OS X El Capitan binaries: | r-release: glmmsr_0.2.1.tgz |
OS X Mavericks binaries: | r-oldrel: glmmsr_0.2.1.tgz |
Old sources: | glmmsr archive |
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