glmmsel: Generalised Linear Mixed Model Selection

Provides tools for fitting sparse generalised linear mixed models with l0 regularisation. Selects fixed and random effects under the hierarchy constraint that fixed effects must precede random effects. Uses coordinate descent and local search algorithms to rapidly deliver near-optimal estimates. Gaussian and binomial response families are currently supported. For more details see Stroup, Ptukhina, and Garai (2024) <doi:10.1201/9780429092060>.

Version:	1.0.2
Depends:	R (≥ 4.1.0)
Imports:	ggplot2, Rcpp
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	testthat, knitr, rmarkdown, lme4, MASS, nlme
Published:	2025-05-29
DOI:	10.32614/CRAN.package.glmmsel
Author:	Ryan Thompson [aut, cre]
Maintainer:	Ryan Thompson <ryan.thompson-1 at uts.edu.au>
BugReports:	https://github.com/ryan-thompson/glmmsel/issues
License:	GPL-3
URL:	https://github.com/ryan-thompson/glmmsel
NeedsCompilation:	yes
Materials:	README NEWS
In views:	MixedModels
CRAN checks:	glmmsel results

Documentation:

Reference manual:	glmmsel.pdf
Vignettes:	Guide to glmmsel (source, R code)

Downloads:

Package source:	glmmsel_1.0.2.tar.gz
Windows binaries:	r-devel: not available, r-release: glmmsel_1.0.2.zip, r-oldrel: glmmsel_1.0.2.zip
macOS binaries:	r-release (arm64): glmmsel_1.0.2.tgz, r-oldrel (arm64): glmmsel_1.0.2.tgz, r-release (x86_64): glmmsel_1.0.2.tgz, r-oldrel (x86_64): glmmsel_1.0.2.tgz

Linking:

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