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.
- Version1.2.0
- R versionunknown
- LicenseGPL-3
- Needs compilation?No
- Last release08/24/2023
Documentation
Team
Shuangshuang Xu
Marco Ferreira
Show author detailsRolesAuthorErica Porter
Show author detailsRolesAuthorChristopher Franck
Show author detailsRolesAuthor
Insights
Last 30 days
This package has been downloaded 166 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 9 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 2,258 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Jul 21, 2024 with 70 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
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Dependencies
- Suggests2 packages