Bayesian inferences on nonparametric regression via Gaussian Processes with a modified exponential square kernel using a basis expansion approach.

Estimate fish length-at-age models using MCMC analysis with 
    'rstan' models. This package allows a multimodel approach to growth fitting
    to be applied to length-at-age data and is supported by further analyses to 
    determine model selection and result presentation. The core methods of this 
    package are presented in Smart and Grammer (2021) "Modernising fish and 
    shark growth curves with Bayesian length-at-age models". PLOS ONE 16(2):
    e0246734 <doi:10.1371/journal.pone.0246734>.

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>.

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.