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

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