GLMMselect: Bayesian Model Selection for Generalized Linear Mixed Models

A Bayesian model selection approach for generalized linear mixed models. Currently, 'GLMMselect' can be used for Poisson GLMM and Bernoulli GLMM. 'GLMMselect' can select fixed effects and random effects simultaneously. Covariance structures for the random effects are a product of a unknown scalar and a known semi-positive definite matrix. 'GLMMselect' can be widely used in areas such as longitudinal studies, genome-wide association studies, and spatial statistics. 'GLMMselect' is based on Xu, Ferreira, Porter, and Franck (202X), Bayesian Model Selection Method for Generalized Linear Mixed Models, Biometrics, under review.

Version:	1.2.0
Depends:	R (≥ 3.5.0)
Imports:	stats (≥ 4.2.2)
Suggests:	knitr, rmarkdown
Published:	2023-08-24
Author:	Shuangshuang Xu [aut, cre], Marco Ferreira [aut], Erica Porter [aut], Christopher Franck [aut]
Maintainer:	Shuangshuang Xu <xshuangshuang at vt.edu>
License:	GPL-3
NeedsCompilation:	no
Materials:	README NEWS
CRAN checks:	GLMMselect results

Documentation:

Reference manual:	GLMMselect.pdf
Vignettes:	GLMMselect: Bayesian model selection for generalized linear mixed models

Downloads:

Package source:	GLMMselect_1.2.0.tar.gz
Windows binaries:	r-devel: GLMMselect_1.2.0.zip, r-release: GLMMselect_1.2.0.zip, r-oldrel: GLMMselect_1.2.0.zip
macOS binaries:	r-release (arm64): GLMMselect_1.2.0.tgz, r-oldrel (arm64): GLMMselect_1.2.0.tgz, r-release (x86_64): GLMMselect_1.2.0.tgz, r-oldrel (x86_64): GLMMselect_1.2.0.tgz
Old sources:	GLMMselect archive

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