Fits Bayesian grouped weighted quantile sum (BGWQS) regressions for one or more chemical groups with binary outcomes. Wheeler DC et al. (2019) <doi:10.1016/j.sste.2019.100286>.

Fit Bayesian models using 'brms'/'Stan' with 'parsnip'/'tidymodels'
    via 'bayesian' <doi:10.5281/zenodo.4426836>. 'tidymodels' is a collection of
    packages for machine learning; see Kuhn and Wickham (2020) <https://www.tidymodels.org>).
    The technical details of 'brms' and 'Stan' are described in Bürkner (2017)
    <doi:10.18637/jss.v080.i01>, Bürkner (2018) <doi:10.32614/RJ-2018-017>,
    and Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

Implements a novel Bayesian disaggregation framework that combines 
    Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) 
    dimension reduction of prior weight matrices with deterministic Bayesian 
    updating rules. The method provides Markov Chain Monte Carlo (MCMC) free 
    posterior estimation with built-in diagnostic metrics. While based on 
    established PCA (Jolliffe, 2002) <doi:10.1007/b98835> and Bayesian principles 
    (Gelman et al., 2013) <doi:10.1201/b16018>, the specific integration for 
    economic disaggregation represents an original methodological contribution.

Methods for Bayesian parameter estimation and forecasting in epidemiological models. 
    Functions enable model fitting using Bayesian methods and generate forecasts with uncertainty quantification. 
    Implements approaches described in <doi:10.48550/arXiv.2411.05371> and <doi:10.1002/sim.9164>.

Adjust the Gamma regression models from a Bayesian perspective described by Cepeda and Urdinola (2012) <doi:10.1080/03610918.2011.600500>, modeling the parameters of mean and shape and using different link functions for the parameter associated to the mean. And calculates different adjustment statistics such as the Akaike information criterion and Bayesian information criterion.

Implements a data-augmented block Gibbs sampler for simulating the posterior distribution of concentration matrices for specifying the topology and parameterization of a Gaussian Graphical Model (GGM). This sampler was originally proposed in Wang (2012) <doi:10.1214/12-BA729>.