Description: Exploratory Bayesian factor analysis of continuous, mixed-type, and bounded continuous variables using the mode-jumping algorithm of Man and Culpepper (2020) <doi:10.1080/01621459.2020.1773833>.
Maintainer: Steven Andrew Culpepper <sculpepp@illinois.edu>

Title: Bayesian Time Series Modeling with Stan
Authors@R: c(person("Asael","Alonzo Matamoros", role = c("aut", "cre"),email = "asael.alonzo@gmail.com"),
person("Cristian","Cruz Torres", role = 'aut', email = "cristian.cruz@unah.edu.hn"),
person("Rob", "Hyndman", email="Rob.Hyndman@monash.edu", role = "ctb"),
person("Mitchell", "O'Hara-Wild", role = "ctb")
Description: Fit Bayesian time series models using 'Stan' for full Bayesian inference. A wide range
of distributions and models are supported, allowing users to fit Seasonal ARIMA, ARIMAX, Dynamic
Harmonic Regression, GARCH, t-student innovation GARCH models, asymmetric GARCH, Random Walks, stochastic
volatility models for univariate time series.  Prior specifications are flexible and explicitly encourage
users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed
and compared with typical visualization methods, information criteria such as loglik, AIC, BIC WAIC, Bayes
factor and leave-one-out cross-validation methods. References: Hyndman (2017)
Imports: bayesplot (>= 1.5.0), methods, gridExtra, ggplot2, forecast,
'garch.R' 'get_params.R' 'ipc.R' 'log_lik.R' 'Misc.R' 'model.R'
'predictive_error.R' 'print.R' 'prior.R' 'report.R' 'Sarima.R'
'summary.R' 'ssm.R' 'stanmodels.R' 'SVM.R' 'varstan.R'
Author: Asael Alonzo Matamoros [aut, cre],
Maintainer: Asael Alonzo Matamoros <asael.alonzo@gmail.com>

person("Andres", "Dala", role = "ctb",email = "andresht6@hotmail.com"),
'garch.R' 'get_params.R' 'log_lik.R' 'Misc.R' 'model.R'

Description: Fit Bayesian models using 'brms'/'Stan' with 'parsnip'/'tidymodels'.
'tidymodels' is a collection of packages for machine learning, developed
The technical details of 'brms' and 'Stan' are described in Bürkner (2017)