A set of functions to allow analysis of count data (such
        as faecal egg count data) using Bayesian MCMC methods.  Returns
        information on the possible values for mean count, coefficient
        of variation and zero inflation (true prevalence) present in
        the data.  A complete faecal egg count reduction test (FECRT)
        model is implemented, which returns inference on the true
        efficacy of the drug from the pre- and post-treatment data
        provided, using non-parametric bootstrapping as well as using
        Bayesian MCMC.  Functions to perform power analyses for faecal
        egg counts (including FECRT) are also provided.

Propose a parametric fit for censored linear regression models based on SMSN distributions, from a Bayesian perspective. Also, generates SMSN random variables.

Simulation and analysis of Bayesian adaptive clinical trials for binomial, Gaussian, and time-to-event data types, 
      incorporates historical data and allows early stopping for futility or early success. The package uses novel 
      and efficient Monte Carlo methods for estimating Bayesian posterior probabilities, evaluation of loss to follow up, 
      and imputation of incomplete data. The package has the functionality for dynamically incorporating historical data 
      into the analysis via the power prior or non-informative priors.

Algorithms for computing and generating plots with and without error bars for Bayesian cluster validity index (BCVI) (N. Wiroonsri, O. Preedasawakul (2024) <arXiv:2402.02162>) based on several underlying cluster validity indexes (CVIs) including Calinski-Harabasz, Chou-Su-Lai, Davies-Bouldin, Dunn,  Pakhira-Bandyopadhyay-Maulik, Point biserial correlation, the score function, Starczewski, and Wiroonsri indices for hard clustering, and Correlation Cluster Validity, the generalized C, HF, KWON, KWON2, Modified Pakhira-Bandyopadhyay-Maulik, Pakhira-Bandyopadhyay-Maulik, Tang, Wiroonsri-Preedasawakul, Wu-Li, and Xie-Beni indices for soft clustering. The package is compatible with K-means, fuzzy C means, EM clustering, and hierarchical clustering (single, average, and complete linkage). Though BCVI is compatible with any underlying existing CVIs, we recommend users to use either WI or WP as the underlying CVI.