Fit Bayesian models using 'brms'/'Stan' with 'parsnip'/'tidymodels'
via 'bayesian' <doi:10.5281/zenodo.6654386>. '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>.

Bayesian Estimation of the ETAS Model for Earthquake Occurrences
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.

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.

Linear Combinations of Bayesian Posterior Samples
c(person("Josue E.", "Rodriguez", email = "josue.rodriguez594@gmail.com", role = c("aut", "cre")),
person("Donald", "Williams", email = "drwwilliams@ucdavis.edu", role = "aut"))
Computes point estimates, standard deviations, and credible intervals
for linear combinations of posterior samples. Optionally performs region
practical equivalence (ROPE) tests as described in
Kruschke and Liddell (2018) <doi:10.3758/s13423-016-1221-4>.
ggplot2 (>= 3.3.2), methods, stats, lifecycle
Josue E. Rodriguez [aut, cre],
Donald Williams [aut]

Efficient Sampling for Gaussian Linear Regression with Arbitrary
Priors
P. Richard Hahn, Jingyu He and Hedibert Lopes
Efficient sampling for Gaussian linear regression with arbitrary priors.