GLMMselect
1.2.0Bayesian Model Selection for Generalized Linear Mixed Models
Overview
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.
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- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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- Documented parameters
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- Return-value docs
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3 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 1.2.0Latest
- 1.1.02023-08-23 · diff ↗
- RR 4.3.0 released · 2023-04-21
- 1.0.02023-04-20
- RR 4.2.0 released · 2022-04-22
Package metadata
- First published
- 2023-04-20
- Total releases
- 3 / 3 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 51 KB / 9 files
- Download size
- 69 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet