To perform model estimation using MCMC algorithms with Bayesian methods for incomplete longitudinal studies on binary and ordinal outcomes that are measured repeatedly on subjects over time with drop-outs. Details about the method can be found in the vignette or <https://sites.google.com/view/kuojunglee/r-packages/bayesrgmm>.

Computation and visualization of Bayesian Regions of Evidence
    to systematically evaluate the sensitivity of a superiority or
    non-inferiority claim against any prior assumption of its assessors.
    Methodological details are elaborated by Hoefler and Miller
    (<https://osf.io/jxnsv>).  Besides generic functions, the package also
    provides an intuitive 'Shiny' application, that can be run in local R
    environments.

Runs hierarchical linear Bayesian models. Samples from the posterior
    distributions of model parameters in JAGS (Just Another Gibbs Sampler;
	Plummer, 2017, <http://mcmc-jags.sourceforge.net>). Computes Bayes factors for group
	parameters of interest with the Savage-Dickey density ratio (Wetzels,
	Raaijmakers, Jakab, Wagenmakers, 2009, <doi:10.3758/PBR.16.4.752>).

Provides datasets and functions used for analysis 
  and visualizations in the Bayes Rules! book (<https://www.bayesrulesbook.com>). 
  The package contains a set of functions that summarize and plot Bayesian models from some conjugate families 
  and another set of functions for evaluation of some Bayesian models.

Allows the reenactment of the R programs used in 
        the book Bayesian Essentials with R without further programming. 
        R code being available as well, they can be modified by the user
        to conduct one's own simulations. 
	    Marin J.-M. and Robert C. P. (2014) <doi:10.1007/978-1-4614-8687-9>.

In p >> n settings, full posterior sampling using existing Markov chain Monte
    Carlo (MCMC) algorithms is highly inefficient and often not feasible from a practical
    perspective. To overcome this problem, we propose a scalable stochastic search algorithm that is called the Simplified Shotgun Stochastic Search (S5) and aimed at rapidly explore interesting regions of model space and finding the maximum a posteriori(MAP) model. Also, the S5 provides an approximation of posterior probability of each model (including the marginal inclusion probabilities). This algorithm is a part of an article titled "Scalable Bayesian Variable Selection Using Nonlocal Prior Densities in Ultrahigh-dimensional Settings" (2018) by Minsuk Shin, Anirban Bhattacharya, and Valen E. Johnson and "Nonlocal Functional Priors for Nonparametric Hypothesis Testing and High-dimensional Model Selection" (2020+) by Minsuk Shin and Anirban Bhattacharya.