Functions for data augmentation using the Bayesian discount prior 
    method for single arm and two-arm clinical trials, as described in Haddad 
    et al. (2017) <doi:10.1080/10543406.2017.1300907>. The discount power prior 
    methodology was developed in collaboration with the The Medical Device 
    Innovation Consortium (MDIC) Computer Modeling & Simulation Working Group.

A  Bayesian smoothing method for post-processing of remote 
    sensing image classification which refines the
    labelling in a classified image in order to enhance its classification accuracy.
    Combines pixel-based classification methods with a spatial post-processing
    method to remove outliers and misclassified pixels.

Determines effective sample size of a parametric prior distribution
    in Bayesian models. For a web-based Shiny application related to this package, see <https://implement.shinyapps.io/bayesess/>.

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 (2024) 
    <doi:10.48550/arXiv.2401.06082>, and the software paper is in Axillus et al. 
    (2024) <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.