A suite of functions that allow the user to analyze A/B test
    data in a Bayesian framework. Intended to be a drop-in replacement for
    common frequentist hypothesis test such as the t-test and chi-sq test.

Performs Bayesian estimation of the additive main effects and multiplicative interaction (AMMI) model. The method is explained in Crossa, J., Perez-Elizalde, S., Jarquin, D., Cotes, J.M., Viele, K., Liu, G. and Cornelius, P.L. (2011) (<doi:10.2135/cropsci2010.06.0343>).

Provides a Bayesian version of the analysis of variance based on a three-component Gaussian mixture for which a Gibbs sampler produces posterior draws. For details about the Bayesian ANOVA based on Gaussian mixtures, see Kelter (2019) <arXiv:1906.07524>.

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>.

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.