A variable selection approach for generalized linear mixed models by L1-penalized estimation is provided, 
	see Groll and Tutz (2014) <doi:10.1007/s11222-012-9359-z>.
	See also Groll and Tutz (2017) <doi:10.1007/s10985-016-9359-y> for discrete survival models including heterogeneity.

Binomial and Poisson regression for clustered data, fixed
        and random effects with bootstrapping.

Fits high dimensional penalized generalized linear 
    mixed models using 
    the Monte Carlo Expectation Conditional Minimization (MCECM) algorithm. 
    The purpose of the package is to perform variable selection on both the fixed and 
    random effects simultaneously for generalized linear mixed models.
    The package supports fitting of Binomial, Gaussian, and Poisson data with canonical links, and
    supports penalization using the MCP, SCAD, or LASSO penalties. The MCECM algorithm
    is described in Rashid et al. (2020) <doi:10.1080/01621459.2019.1671197>.
    The techniques used in the minimization portion of the procedure (the M-step) are
    derived from the procedures of the 'ncvreg' package (Breheny and Huang (2011) 
    <doi:10.1214/10-AOAS388>) and 'grpreg' package (Breheny and Huang (2015)
    <doi:10.1007/s11222-013-9424-2>), with
    appropriate modifications to account for the estimation and penalization of
    the random effects. The 'ncvreg' and 'grpreg' packages also describe the MCP, SCAD, 
    and LASSO penalties.

Specification, analysis, simulation, and fitting of generalised linear mixed models. 
  Includes Markov Chain Monte Carlo Maximum likelihood and Laplace approximation model fitting for a range of models, 
  non-linear fixed effect specifications, a wide range of flexible covariance functions that can be combined arbitrarily,
  robust and bias-corrected standard error estimation, power calculation, data simulation, and more. 
  See <https://samuel-watson.github.io/glmmr-web/> for a detailed manual.

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 and Pan (2022) <arXiv:2207.09183>.