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>.

Implements Bayesian Lasso regression using efficient Gibbs sampling
               algorithms, including modified versions of the Hans and Park–Casella (PC) samplers.
               Includes functions for working with the Lasso distribution, such as its density,
               cumulative distribution, quantile, and random generation functions, along with moment
               calculations. Also includes a function to compute the Mills ratio. Designed for sparse
               linear models and suitable for high-dimensional regression problems.

Functional differences between the cerebral hemispheres 
    are a fundamental characteristic of the human brain. Researchers 
    interested in studying these differences often infer underlying 
    hemispheric dominance for a certain function (e.g., language) from 
    laterality indices calculated from observed performance or brain 
    activation measures . However, any inference from observed measures 
    to latent (unobserved) classes has to consider the prior probability 
    of class membership in the population. The provided functions 
    implement a Bayesian model for predicting hemispheric dominance from
    observed laterality indices (Sorensen and Westerhausen, Laterality: 
    Asymmetries of Body, Brain and Cognition, 2020, <doi:10.1080/1357650X.2020.1769124>).