A suite of functions for computing
    various Bayes factors for simple designs, including contingency tables,
    one- and two-sample designs, one-way designs, general ANOVA designs, and
    linear regression.

Allows Bayesian borrowing from a historical dataset for time-to-
    event data. A flexible baseline hazard function is achieved via a piecewise
    exponential likelihood with time varying split points and smoothing prior on the
    historic baseline hazards. The method is described in Scott and Lewin (2026)
    <doi:10.1093/biostatistics/kxag006>, and a paper focused on the software is
    in Scott, Axillus, Lewin and Izmirlian (2026) <doi:10.48550/arXiv.2408.04327>.

Implementation of 'BayesFlux.jl' for R; It extends the famous 
             'Flux.jl' machine learning library to Bayesian Neural Networks. 
             The goal is not to have the fastest production ready 
             library, but rather to allow more people to be able 
             to use and research on Bayesian Neural Networks.

Collection of procedures to perform Bayesian analysis on a variety
    of factor models. Currently, it includes: "Bayesian Exploratory Factor 
    Analysis" (befa) from G. Conti, S. Frühwirth-Schnatter, J.J. Heckman, 
    R. Piatek (2014) <doi:10.1016/j.jeconom.2014.06.008>, an approach to 
    dedicated factor analysis with stochastic search on the structure of the 
    factor loading matrix. The number of latent factors, as well as the 
    allocation of the manifest variables to the factors, are not fixed a priori 
    but determined during MCMC sampling.

Performs a spatial Bayesian general linear model (GLM) for task 
    functional magnetic resonance imaging (fMRI) data on the cortical surface. 
    Additional models include group analysis and inference to detect thresholded
    areas of activation. Includes direct support for the 'CIFTI' neuroimaging 
    file format. For more information see A. F. Mejia, Y. R. Yue, D. Bolin, F. 
    Lindgren, M. A. Lindquist (2020) <doi:10.1080/01621459.2019.1611582> and D. 
    Spencer, Y. R. Yue, D. Bolin, S. Ryan, A. F. Mejia (2022) 
    <doi:10.1016/j.neuroimage.2022.118908>.