The proposed group-sequential trial design is based on Bayesian methods for ordinal endpoints, 
             including three methods, the proportional-odds-model (PO)-based, non-proportional-odds-model (NPO)-based, 
             and PO/NPO switch-model-based designs, which makes our proposed methods generic to be able to deal with 
             various scenarios.
             Richard J. Barker, William A. Link (2013) <doi:10.1080/00031305.2013.791644>.
             Thomas A. Murray, Ying Yuan, Peter F. Thall, Joan H. Elizondo, Wayne L.Hofstetter (2018) <doi:10.1111/biom.12842>.
             Chengxue Zhong, Haitao Pan, Hongyu Miao (2021) <arXiv:2108.06568>.

Fits a piecewise exponential hazard to survival data using a
    Hierarchical Bayesian model with an Intrinsic Conditional Autoregressive
    formulation for the spatial dependency in the hazard rates for each piece.
    This function uses Metropolis- Hastings-Green MCMC to allow the number of split
    points to vary and also uses Stochastic Search Variable Selection to determine
    what covariates drive the risk of the event. This function outputs trace plots
    depicting the number of split points in the hazard and the number of variables
    included in the hazard. The function saves all posterior quantities to the
    desired path.

A lightweight modelling syntax for defining likelihoods and priors
    and for computing Bayes factors for simple one parameter models. It includes
    functionality for computing and plotting priors, likelihoods, and model
    predictions. Additional functionality is included for computing and plotting
    posteriors.

Plotting functions for posterior analysis, MCMC diagnostics,
    prior and posterior predictive checks, and other visualizations
    to support the applied Bayesian workflow advocated in
    Gabry, Simpson, Vehtari, Betancourt, and Gelman (2019) <doi:10.1111/rssa.12378>.
    The package is designed not only to provide convenient functionality
    for users, but also a common set of functions that can be easily used by
    developers working on a variety of R packages for Bayesian modeling,
    particularly (but not exclusively) packages interfacing with 'Stan'.