Functions to plot and help understand positive and negative
    predictive values (PPV and NPV), and their relationship with
    sensitivity, specificity, and prevalence. See Akobeng, A.K. (2007)
    <doi:10.1111/j.1651-2227.2006.00180.x> for a theoretical overview of
    the technical concepts and Navarrete et al. (2015) for a practical
    explanation about the importance of their understanding
    <doi:10.3389/fpsyg.2015.01327>.

Implements Bayesian Surprise methodology for data visualization,
    based on Correll and Heer (2017) <doi:10.1109/TVCG.2016.2598839>
    "Surprise! Bayesian Weighting for De-Biasing Thematic Maps". Provides
    tools to weight event data relative to spatio-temporal models,
    highlighting unexpected patterns while de-biasing against known factors
    like population density or sampling variation. Integrates seamlessly with
    'sf' for spatial data and 'ggplot2' for visualization. Supports
    temporal/streaming data analysis.

General-purpose MCMC and SMC samplers, as well as plots and
    diagnostic functions for Bayesian statistics, with a particular focus on
    calibrating complex system models. Implemented samplers include various
    Metropolis MCMC variants (including adaptive and/or delayed rejection MH), the
    T-walk, two differential evolution MCMCs, two DREAM MCMCs, and a sequential
    Monte Carlo (SMC) particle filter.

Performs Bayesian t Regression where mean and scale parameters are modeling by lineal regression structures, and the degrees of freedom parameters are estimated.

Efficient Markov Chain Monte Carlo (MCMC) algorithms for the
    fully Bayesian estimation of vectorautoregressions (VARs) featuring
    stochastic volatility (SV). Implements state-of-the-art shrinkage
    priors following Gruber & Kastner (2025) <doi:10.1016/j.ijforecast.2025.02.001>.
    Efficient equation-per-equation estimation following Kastner & Huber
    (2020) <doi:10.1002/for.2680> and Carrerio et al. (2021)
    <doi:10.1016/j.jeconom.2021.11.010>.