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: |
0.2.3 |
| Depends: |
R (≥ 3.2.0) |
| Imports: |
lme4 (≥ 1.1-8), Matrix, R6, Rcpp, methods, stats, utils, numDeriv |
| LinkingTo: |
Rcpp, RcppEigen, BH |
| Suggests: |
BradleyTerry2, knitr, mdhglm, rmarkdown, testthat |
| Published: |
2019-02-04 |
| 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:
Reverse dependencies:
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