Bayesian MCPMod (Fleischer et al. (2022)
    <doi:10.1002/pst.2193>) is an innovative method that improves the
    traditional MCPMod by systematically incorporating historical data,
    such as previous placebo group data. This package offers functions
    for simulating, analyzing, and evaluating Bayesian MCPMod trials with
    normally and binary distributed endpoints. It enables the assessment of trial
    designs incorporating historical data across various true
    dose-response relationships and sample sizes. Robust mixture prior
    distributions, such as those derived with the Meta-Analytic-Predictive
    approach (Schmidli et al. (2014) <doi:10.1111/biom.12242>), can be
    specified for each dose group.  Resulting mixture posterior
    distributions are used in the Bayesian Multiple Comparison Procedure
    and modeling steps. The modeling step also includes a weighted model
    averaging approach (Pinheiro et al. (2014) <doi:10.1002/sim.6052>).
    Estimated dose-response relationships can be bootstrapped and
    visualized.

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.

Fits Bayesian nonlinear Ornstein-Uhlenbeck models with cubic drift,
    stochastic volatility, and Student-t innovations. The package implements
    hierarchical priors for sector-specific parameters and supports parallel
    MCMC sampling via 'Stan'. Model comparison is performed using Pareto
    Smoothed Importance Sampling Leave-One-Out (PSIS-LOO) cross-validation
    following Vehtari, Gelman, and Gabry (2017)
    <doi:10.1007/s11222-016-9696-4>. Prior specifications follow recommendations
    from Gelman (2006) <doi:10.1214/06-BA117A> for scale parameters.