We perform general mediation analysis in the Bayesian setting using the methods described in Yu and Li (2022, ISBN:9780367365479). With the package, the mediation analysis can be performed on different types of outcomes (e.g., continuous, binary, categorical, or time-to-event), with default or user-defined priors and predictive models. The Bayesian estimates and credible sets of mediation effects are reported as analytic results.

A "Shiny"" web application for creating interactive Bayesian Network models,
    learning the structure and parameters of Bayesian networks, and utilities for classic
    network analysis.

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>.