Dynamic forecasting is a common prediction tool after fitting multivariate time-series models, such as vector autoregressive (VAR) models. You use fcast to compute dynamic forecasts after fitting a classical var model. You can now use bayesfcast to compute Bayesian dynamic forecasts after fitting a Bayesian VAR model using bayes: var.
Bayesian dynamic forecasts produce an entire sample of predicted values instead of a single prediction as in classical analysis. This sample can be used to answer various modeling questions, such as how well the model predicts future observations without making the asymptotic normality assumption when estimating forecast uncertainty. This is particularly appealing for small datasets for which the asymptotic normality assumption may be suspect.