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.

Allows the user to apply the Bayes Linear approach to finite population with the Simple Random Sampling - BLE_SRS() - and
    the Stratified Simple Random Sampling design - BLE_SSRS() - (both without replacement), to the Ratio estimator (using auxiliary
    information) - BLE_Ratio() - and to categorical data - BLE_Categorical().
    The Bayes linear estimation approach is applied to a general linear regression model for finite population prediction in BLE_Reg()
    and it is also possible to achieve the design based estimators using vague prior distributions.    
    Based on Gonçalves, K.C.M, Moura, F.A.S and  Migon, H.S.(2014) <https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X201400111886>.