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 R package offers functions
    for simulating, analyzing, and evaluating Bayesian MCPMod trials with
    normally 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.