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.

Using mixed effects models to analyse longitudinal gene expression can highlight differences between sample groups over time. The most widely used differential gene expression tools are unable to fit linear mixed effect models, and are less optimal for analysing longitudinal data. This package provides negative binomial and Gaussian mixed effects models to fit gene expression and other biological data across repeated samples. This is particularly useful for investigating changes in RNA-Sequencing gene expression between groups of individuals over time, as described in: Rivellese, F., Surace, A. E., Goldmann, K., Sciacca, E., Cubuk, C., Giorli, G., ... Lewis, M. J., & Pitzalis, C. (2022) Nature medicine <doi:10.1038/s41591-022-01789-0>.