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.

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.

Smoothed lexis diagrams with Bayesian method specifically tailored to cancer 
             incidence data. Providing to calculating slope and constructing credible interval.
             LC Chien et al. (2015) <doi:10.1080/01621459.2015.1042106>. 
             LH Chien et al. (2017) <doi:10.1002/cam4.1102>.

Implements Bayesian brain mapping with population-derived priors, 
    including the original model described in Mejia et al. (2020) 
    <doi:10.1080/01621459.2019.1679638>, the model with spatial priors described
    in Mejia et al. (2022) <doi:10.1080/10618600.2022.2104289>, and the model 
    with population-derived priors on functional connectivity described in Mejia
    et al. (2025) <doi:10.1093/biostatistics/kxaf022>. Population-derived priors
    are based on templates representing established brain network maps, for 
    example derived from independent component analysis (ICA), parcellations, 
    or other methods.  Model estimation is based on expectation-maximization or 
    variational Bayes algorithms. Includes direct support for 'CIFTI', 'GIFTI', 
    and 'NIFTI' neuroimaging file formats.