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.

Contains the functions to use the econometric methods in the paper Bryzgalova, Huang, and Julliard (2023) <doi:10.1111/jofi.13197>. In this package, we provide a novel Bayesian framework for analyzing linear asset pricing models: simple, robust, and applicable to high-dimensional problems. For a stand-alone model, we provide functions including BayesianFM() and BayesianSDF() to deliver reliable price of risk estimates for both tradable and nontradable factors. For competing factors and possibly nonnested models, we provide functions including continuous_ss_sdf(), continuous_ss_sdf_v2(), and dirac_ss_sdf_pvalue() to analyze high-dimensional models. If you use this package, please cite the paper. We are thankful to Yunan Ding and Jingtong Zhang for their research assistance. Any errors or omissions are the responsibility of the authors.

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>.

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>).