varstan: An R package for Bayesian analysis of structured time series models with Stan

Abstract

varstan is an \proglang{R} package for Bayesian analysis of time series models using \proglang{Stan}. The package offers a dynamic way to choose a model, define priors in a wide range of distributions, check model's fit, and forecast with the m-steps ahead predictive distribution. The users can widely choose between implemented models such as \textit{multiplicative seasonal ARIMA, dynamic regression, random walks, GARCH, dynamic harmonic regressions,VARMA, stochastic Volatility Models, and generalized t-student with unknown degree freedom GARCH models}. Every model constructor in \pkg{varstan} defines weakly informative priors, but prior specifications can be changed in a dynamic and flexible way, so the prior distributions reflect the parameter's initial beliefs. For model selection, the package offers the classical information criteria: AIC, AICc, BIC, DIC, Bayes factor. And more recent criteria such as Widely-applicable information criteria (\textit{WAIC}), and the Bayesian leave one out cross-validation (\textit{loo}). In addition, a Bayesian version for automatic order selection in seasonal ARIMA and dynamic regression models can be used as an initial step for the time series analysis.