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Built-in priors include the\nMinnesota, the stochastic search variable selection and\nNormal-Gamma (NG) prior. For a reference see also Crespo\nCuaresma, J., Feldkircher, M. and F. Huber (2016) \"Forecasting\nwith Global Vector Autoregressive Models: a Bayesian Approach\",\nJournal of Applied Econometrics, Vol. 31(7), pp. 1371-1391\n. Post-processing functions allow for\ndoing predictions, structurally identify the model with\nshort-run or sign-restrictions and compute impulse response\nfunctions, historical decompositions and forecast error\nvariance decompositions. Plotting functions are also available.\nThe package has a companion paper: Boeck, M., Feldkircher, M.\nand F. 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