Practicals, data sets, helper functions and interactive 'Shiny' apps
    used in the introductory course on Bayesian inference at the
    Valencia International Bayesian Summer School. Installing 'vibass' installs
    all the other packages used during the course and downloads all necessary
    materials for working off line.

The Variable Infiltration Capacity (VIC) model is a macroscale
    hydrologic model that solves full water and energy balances, originally
    developed by Xu Liang at the University of Washington (UW).
    The version of VIC source code used is of 5.0.1 on <https://github.com/UW-Hydro/VIC/>,
    see Hamman et al. (2018).
    Development and maintenance of the current official version
    of the VIC model at present is led by the UW Hydro (Computational Hydrology
    group) in the Department of Civil and Environmental Engineering at UW. VIC is
    a research model and in its various forms it has been applied to most of the
    major river basins around the world, as well as globally 
    <http://vic.readthedocs.io/en/master/Documentation/References/>. 
    References: "Liang, X., D. P. Lettenmaier, E. F. Wood, and
    S. J. Burges (1994), A simple hydrologically based model of land surface water
    and energy fluxes for general circulation models, J. Geophys. Res., 99(D7),
    14415-14428, <doi:10.1029/94JD00483>"; 
    "Hamman, J. J., Nijssen, B., Bohn, T. J., Gergel, D. R., and Mao, Y. (2018), The
    Variable Infiltration Capacity model version 5 (VIC-5): infrastructure improvements
    for new applications and reproducibility, Geosci. Model Dev., 11, 3481-3496, 
    <doi:10.5194/gmd-11-3481-2018>".

A variational Bayesian finite mixture model for the clustering of categorical data, and can implement variable selection and semi-supervised outcome guiding if desired. Incorporates an option to perform model averaging over multiple initialisations to reduce the effects of local optima and improve the automatic estimation of the true number of clusters. For further details, see the paper by Rao and Kirk (2024) <doi:10.48550/arXiv.2406.16227>.