Fits Multivariate Generalised Linear Mixed Models (and related models) using Markov chain Monte Carlo techniques (Hadfield 2010 J. Stat. Soft.).

Hybrid Markov chain Monte Carlo (MCMC) for sampling from 
    multimodal target distributions when derivatives are unavailable. 
    A Gaussian process approximation is used to emulate derivatives, 
    enabling efficient exploration with parallel tempering. The method 
    is described in Fielding, Nott and Liong (2011) 
    <doi:10.1198/TECH.2010.09195>. The research was carried out as part 
    of the Singapore-Delft Water Alliance Multi-Objective Multi-Reservoir 
    Management programme (R-264-001-272).

Contains functions to perform Bayesian
        inference using posterior simulation for a number of
        statistical models. Most simulation is done in compiled C++
        written in the Scythe Statistical Library Version 1.0.3. All
        models return 'coda' mcmc objects that can then be summarized
        using the 'coda' package. Some useful
        utility functions such as density functions,
	pseudo-random number generators for statistical
        distributions, a general purpose Metropolis sampling algorithm,
        and tools for visualization are provided.

Estimates the precision of transdimensional Markov chain Monte Carlo 
    (MCMC) output, which is often used for Bayesian analysis of models with different 
    dimensionality (e.g., model selection). Transdimensional MCMC (e.g., reversible 
    jump MCMC) relies on sampling a discrete model-indicator variable to estimate 
    the posterior model probabilities. If only few switches occur between the models, 
    precision may be low and assessment based on the assumption of independent 
    samples misleading. Based on the observed transition matrix of the indicator 
    variable, the method of Heck, Overstall, Gronau, & Wagenmakers (2019, 
    Statistics & Computing, 29, 631-643) <doi:10.1007/s11222-018-9828-0> draws 
    posterior samples of the stationary distribution to (a) assess the uncertainty 
    in the estimated posterior model probabilities and (b) estimate the effective 
    sample size of the MCMC output.