Simulating the sequential multi-arm multi-stage or platform trial with Bayesian approach using the 'rstan' package, which provides the R interface for the Stan. 
    This package supports fixed ratio and Bayesian adaptive randomization approaches for randomization. 
    Additionally, it allows for the study of time trend problems in platform trials. 
    There are demos available for a multi-arm multi-stage trial with two different null scenarios, as well as for Bayesian trial cutoff screening.
    The Bayesian adaptive randomisation approaches are described in:
    Trippa et al. (2012) <doi:10.1200/JCO.2011.39.8420> and
    Wathen et al. (2017) <doi:10.1177/1740774517692302>.
    The randomisation algorithm is described in: 
    Zhao W <doi:10.1016/j.cct.2015.06.008>.
    The analysis methods of time trend effect in platform trial are described in:
    Saville et al. (2022) <doi:10.1177/17407745221112013> and
    Bofill Roig et al. (2022) <doi:10.1186/s12874-022-01683-w>.

A collection of methods to determine the required sample size for
    the evaluation of inequality constrained hypotheses by means of a Bayes 
    factor. Alternatively, for a given sample size, the unconditional error 
    probabilities or the expected conditional error probabilities can be
    determined. Additional material on the methods in this package is 
    available in Klaassen, F., Hoijtink, H. & Gu, X. (2019) 
    <doi:10.31219/osf.io/d5kf3>.

Functions to plot and help understand positive and negative
    predictive values (PPV and NPV), and their relationship with
    sensitivity, specificity, and prevalence. See Akobeng, A.K. (2007)
    <doi:10.1111/j.1651-2227.2006.00180.x> for a theoretical overview of
    the technical concepts and Navarrete et al. (2015) for a practical
    explanation about the importance of their understanding
    <doi:10.3389/fpsyg.2015.01327>.

General-purpose MCMC and SMC samplers, as well as plot and
    diagnostic functions for Bayesian statistics, with a particular focus on
    calibrating complex system models. Implemented samplers include various
    Metropolis MCMC variants (including adaptive and/or delayed rejection MH), the
    T-walk, two differential evolution MCMCs, two DREAM MCMCs, and a sequential
    Monte Carlo (SMC) particle filter.