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>.

Optimal design analysis algorithms for any study design that can be represented or
  modelled as a generalised linear mixed model including cluster randomised trials,
  cohort studies, spatial and temporal epidemiological studies, and split-plot designs.
  See <https://github.com/samuel-watson/glmmrBase/blob/master/README.md> for a
  detailed manual on model specification. A detailed discussion of the methods in this
  package can be found in Watson, Hemming, and Girling (2023) <doi:10.1177/09622802231202379>.

Generalized Linear Mixed Model (GLMM) for Binary Randomized Response Data.
    Includes Cauchit, Compl. Log-Log, Logistic, and Probit link functions for Bernoulli Distributed RR data.
    RR Designs: Warner, Forced Response, Unrelated Question, Kuk, Crosswise, and Triangular. 
    Reference: Fox, J-P, Veen, D. and Klotzke, K. (2018). Generalized Linear Mixed Models for Randomized Responses. Methodology. <doi:10.1027/1614-2241/a000153>.

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 Thompson, Wand, and Wang (2025) <doi:10.48550/arXiv.2506.20425>.

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.