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>.

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

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

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.