glmmrBase: Monte Carlo Maximum Likelihood and Analysis of Generalised Linear Mixed Models

Specification, analysis, simulation, and fitting of generalised linear mixed models. Monte Carlo Maximum likelihood model fitting for a range of models, non-linear fixed effect specifications, a wide range of flexible covariance functions including Gaussian Process approximations. Methods described in Watson, Wang, and Giorgi (2026) <doi:10.48550/arXiv.2601.16022>.

Version: 1.4.1
Depends: R (≥ 3.5.0), Matrix (≥ 1.3-1)
Imports: methods, Rcpp (≥ 1.0.11), R6
LinkingTo: Rcpp (≥ 1.0.11), RcppEigen, BH, RcppParallel (≥ 5.0.1)
Suggests: fmesher, lme4
Published: 2026-05-28
DOI: 10.32614/CRAN.package.glmmrBase
Author: Sam Watson [aut, cre]
Maintainer: Sam Watson <S.I.Watson at bham.ac.uk>
BugReports: https://github.com/samuel-watson/glmmrBase/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/samuel-watson/glmmrBase
NeedsCompilation: yes
SystemRequirements: GNU make
CRAN checks: glmmrBase results

Documentation:

Reference manual: glmmrBase.html , glmmrBase.pdf

Downloads:

Package source: glmmrBase_1.4.1.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries: r-release (arm64): glmmrBase_1.4.1.tgz, r-oldrel (arm64): glmmrBase_1.4.1.tgz, r-release (x86_64): glmmrBase_1.4.1.tgz, r-oldrel (x86_64): glmmrBase_1.4.1.tgz
Old sources: glmmrBase archive

Linking:

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