Functions for Bayesian Data Analysis, with datasets from
        the book "Bayesian data Analysis (second edition)" by Gelman,
        Carlin, Stern and Rubin. Not all datasets yet, hopefully
        completed soon.

Bayesian estimation of dynamic conditional correlation GARCH model for multivariate time series volatility (Fioruci, J.A., Ehlers, R.S. and Andrade-Filho, M.G., (2014). <doi:10.1080/02664763.2013.839635>.

Estimates the density of a variable in a measurement error setup, potentially with an excess of zero values. For more details see Sarkar (2022) <doi:10.1080/01621459.2020.1782220>.

The proposed event-driven approach for Bayesian two-stage single-arm phase II trial design is a novel clinical trial design and can be regarded as an extension of the Simon’s two-stage design with the time-to-event endpoint. This design is motivated by cancer clinical trials with immunotherapy and molecularly targeted therapy, in which time-to-event endpoint is often a desired endpoint.

Implements Bayesian dynamic factor analysis with 'Stan'. Dynamic 
    factor analysis is a dimension reduction tool for multivariate time series.
    'bayesdfa' extends conventional dynamic factor models in several ways. 
    First, extreme events may be estimated in the latent trend by modeling
    process error with a student-t distribution. Second, alternative constraints
    (including proportions are allowed). Third, the estimated
    dynamic factors can be analyzed with hidden Markov models to evaluate
    support for latent regimes.

Provides comprehensive tools for Bayesian model diagnostics 
    and comparison. Includes prior sensitivity analysis, posterior predictive 
    checks (Gelman et al. (2013) <doi:10.1201/b16018>), advanced model 
    comparison using Pareto-smoothed importance sampling leave-one-out 
    cross-validation (Vehtari et al. (2017) <doi:10.1007/s11222-016-9696-4>), 
    convergence diagnostics, and prior elicitation tools. Integrates with 
    'brms' (Burkner (2017) <doi:10.18637/jss.v080.i01>), 'rstan', and 
    'rstanarm' packages for comprehensive Bayesian workflow diagnostics.