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>.

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.

Provide early termination phase II trial designs with a
    decreasingly informative prior (DIP) or a regular Bayesian prior
    chosen by the user. The program can determine the minimum planned
    sample size necessary to achieve the user-specified admissible
    designs. The program can also perform power and expected sample size
    calculations for the tests in early termination Phase II trials.
    See Wang C and Sabo RT (2022) <doi:10.18203/2349-3259.ijct20221110>;
    Sabo RT (2014) <doi:10.1080/10543406.2014.888441>.

Fits Bayesian models (amongst others) to dissolution data sets that can be used for dissolution testing. The package was originally constructed to include only the Bayesian models outlined in Pourmohamad et al. (2022) <doi:10.1111/rssc.12535>. However, additional Bayesian and non-Bayesian models (based on bootstrapping and generalized pivotal quanties) have also been added. More models may be added over time.