The Autoregressive Integrated Moving Average (ARIMA) model is very popular univariate time series model. Its application has been widened by the incorporation of exogenous variable(s) (X) in the model and modified as ARIMAX by Bierens (1987) <doi:10.1016/0304-4076(87)90086-8>. In this package we estimate the ARIMAX model using Bayesian framework.

Computes Bayesian assurance under various 
    settings characterized by different assumptions and objectives, including 
    precision-based conditions, credible intervals, and goal functions. 
    All simulation-based functions included in this package rely on a two-stage 
    Bayesian method that assigns two distinct priors to evaluate the 
    probability of observing a positive outcome, which addresses subtle 
    limitations that take place when using the standard single-prior approach. 
    For more information, please refer to Pan and Banerjee (2021)
    <arXiv:2112.03509>.

Bayesian adaptive trial algorithm implements multiple-stage interim analysis. Package includes data generating function, and Bayesian hypothesis testing function.

The Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH) models are used for modelling the volatile multivariate data sets. In this package a variant of MGARCH called BEKK (Baba, Engle, Kraft, Kroner) proposed by Engle and Kroner (1995) <http://www.jstor.org/stable/3532933> has been used to estimate the bivariate time series data using Bayesian technique.

Fully Bayesian inference for estimating the number of clusters and related parameters to heterogeneous binary data.

A hodgepodge of hopefully helpful functions. Two of these perform
    shrinkage estimation: one using a simple weighted method where the user can
    specify the degree of shrinkage required, and one using James-Stein shrinkage
    estimation for the case of unequal variances.

Functions for performing the Bayesian bootstrap as introduced by
    Rubin (1981) <doi:10.1214/aos/1176345338> and for summarizing the result.
    The implementation can handle both summary statistics that works on a
    weighted version of the data and summary statistics that works on a
    resampled data set.