glmm: Generalized Linear Mixed Models via Monte Carlo Likelihood Approximation

Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approximation. Then maximizes the likelihood approximation to return maximum likelihood estimates, observed Fisher information, and other model information.

Version: 1.4.2
Depends: R (≥ 3.5), trust, mvtnorm, Matrix, parallel, doParallel
Imports: stats, foreach, itertools, utils
Suggests: knitr
Published: 2020-06-21
Author: Christina Knudson [aut, cre], Charles J. Geyer [ctb], Sydney Benson [ctb]
Maintainer: Christina Knudson <knud8583 at stthomas.edu>
License: GPL-2
NeedsCompilation: yes
CRAN checks: glmm results

Documentation:

Reference manual: glmm.pdf

Downloads:

Package source: glmm_1.4.2.tar.gz
Windows binaries: r-devel: glmm_1.4.2.zip, r-devel-UCRT: glmm_1.4.2.zip, r-release: glmm_1.4.2.zip, r-oldrel: glmm_1.4.2.zip
macOS binaries: r-release (arm64): glmm_1.4.2.tgz, r-release (x86_64): glmm_1.4.2.tgz, r-oldrel: glmm_1.4.2.tgz
Old sources: glmm archive

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