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 models, including the prior 
    ICA (independent components analysis) model proposed in Mejia et al. (2020) 
    <doi:10.1080/01621459.2019.1679638> and the spatial prior ICA model 
    proposed in proposed in Mejia et al. (2022) 
    <doi:10.1080/10618600.2022.2104289>. Both models estimate subject-level 
    brain as deviations from known population-level networks, which are 
    estimated using standard ICA algorithms. Both models employ an 
    expectation-maximization algorithm for estimation of the latent brain 
    networks and unknown model parameters. Includes direct support for 'CIFTI',
    'GIFTI', and 'NIFTI' neuroimaging file formats.