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>.

Fit Bayesian time series models using 'Stan' for full Bayesian inference. A wide range 
  of distributions and models are supported, allowing users to fit Seasonal ARIMA, ARIMAX, Dynamic 
  Harmonic Regression, GARCH, t-student innovation GARCH models, asymmetric GARCH, Random Walks, stochastic 
  volatility models for univariate time series. Prior specifications are flexible and explicitly encourage 
  users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed 
  and compared with typical visualization methods, information criteria such as loglik, AIC, BIC WAIC, Bayes 
  factor and leave-one-out cross-validation methods. References: Hyndman (2017)
    <doi:10.18637/jss.v027.i03>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.