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.

Functions for convenient plotting and viewing of MCMC output.

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.

Functions and classes to store, manipulate and summarise
    Monte Carlo Markov Chain (MCMC) samples. For more information see
    Brooks et al. (2011) <isbn:978-1-4200-7941-8>.

Fit multi-level models with possibly correlated
    random effects using Markov Chain Monte Carlo simulation.
    Such models allow smoothing over space and time and are useful in,
    for example, small area estimation.

Provides tools for computing Monte Carlo standard
        errors (MCSE) in Markov chain Monte Carlo (MCMC) settings. MCSE
        computation for expectation and quantile estimators is
        supported as well as multivariate estimations. The package also provides 
	functions for computing effective sample size and for plotting
	Monte Carlo estimates versus sample size.