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.

The Epidemic Type Aftershock Sequence  (ETAS) model is one of the best-performing methods for modeling and forecasting earthquake occurrences. This package implements Bayesian estimation routines to draw samples from the full posterior distribution of the model parameters, given an earthquake catalog. The paper on which this package is based is Gordon J. Ross - Bayesian Estimation of the ETAS Model for Earthquake Occurrences (2016), available from the below URL.

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>.