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>.

Provides comprehensive methods to calculate posterior probabilities,
    posterior predictive probabilities, and Go/NoGo/Gray decision probabilities
    for quantitative decision-making under a Bayesian paradigm in clinical trials.
    The package supports both single and two-endpoint analyses for binary and
    continuous outcomes, with controlled, uncontrolled, and external designs.
    For single continuous endpoints, three calculation methods are
    available: numerical integration (NI), Monte Carlo simulation (MC), and
    Moment-Matching approximation (MM). For two continuous endpoints, a bivariate
    Normal-Inverse-Wishart conjugate model is implemented with MC and MM methods.
    For two binary endpoints, a Dirichlet-multinomial model is implemented.
    External designs incorporate historical data through power priors
    using exact conjugate representations (Normal-Inverse-Chi-squared for single
    continuous, Normal-Inverse-Wishart for two continuous, and Dirichlet for
    binary endpoints), enabling closed-form posterior computation without Markov
    chain Monte Carlo (MCMC) sampling. This approach significantly reduces
    computational burden while preserving complete Bayesian rigor. The package
    also provides grid-search functions to find optimal Go and NoGo thresholds
    that satisfy user-specified operating characteristic criteria for all
    supported endpoint types and study designs. S3 print() and plot() methods
    are provided for all decision probability classes, enabling formatted display
    and visualisation of Go/NoGo/Gray operating characteristics across treatment
    scenarios.
    See Kang, Yamaguchi, and Han (2026) <doi:10.1080/10543406.2026.2655410>
    for the methodological framework.