Regularized PQL for Joint Selection in GLMMs
Overview
Performs joint selection in Generalized Linear Mixed Models (GLMMs) using penalized likelihood methods. Specifically, the Penalized Quasi-Likelihood (PQL) is used as a loss function, and penalties are then augmented to perform simultaneous fixed and random effects selection. Regularized PQL avoids the need for integration (or approximations such as the Laplace's method) during the estimation process, and so the full solution path for model selection can be constructed relatively quickly.
Install
Health
- OK13 OK2026-08-04
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-04-22
- ERROR13 OK, 1 ERROR2026-04-18
- OK14 OK2026-03-10
Documentation
- Help pages
- 10includes a package overview page
- Examples that run
- 33%3 run in a plain check · 1 never run · 5 hold no code
- Exported functions with examples
- 89%
- Documented parameters
- 69%
- Return-value docs
- 100%
- Help pages citing references
- 60%
Downloads
Dependencies
Code & Tests
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- 0.8.4Latest
- RR 4.6.0 released · 2026-04-24
- 0.8.32025-12-17 · diff ↗
- 0.8.22025-07-26 · diff ↗
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 0.8.12023-08-20 · diff ↗
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- 0.82020-06-16 · diff ↗
- 0.7.12020-06-05 · diff ↗
- RR 4.0.0 released · 2020-04-24
- RR 3.6.0 released · 2019-04-26
- 0.72018-12-05 · diff ↗
- 0.62018-04-23 · diff ↗
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- RR 3.5.0 released · 2018-04-23
- RR 3.4.0 released · 2017-04-21
- 0.52016-10-07
- RR 3.3.0 released · 2016-05-03
Package metadata
- First published
- 2016-10-07
- Total releases
- 9 / 10 yrs
- License
- GPL-3 OSI
- Download size
- 93 KB
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