Running examples in 'BGVAR-Ex.R' failed
The error most likely occurred in:

> ### Name: fevd
> ### Title: Forecast Error Variance Decomposition
> ### Aliases: fevd fevd.bgvar.irf
> 
> ### ** Examples
> 
> ## Don't show: 
> set.seed(123)
> library(BGVAR)
> data(testdata)
> model.eer<-bgvar(Data=testdata,W=W.test,prior="MN",
+                  draws=100,burnin=50,plag=1,eigen=TRUE)


Start estimation of Bayesian Global Vector Autoregression.

Prior: Minnesota prior.
Lag order: 1 (endo.), 1 (w. exog.)
Stochastic volatility: enabled.
Number of cores used: 1.
Thinning factor: 1. This means every draw is saved.
Hyperparameter setup: 
	 No hyperparameters are chosen, default setting applied.

Estimation of country models starts... 

Start estimation of Bayesian Global Vector Autoregression.

Prior: Minnesota prior.
Lag order: 1 (endo.), 1 (w. exog.)
Stochastic volatility: enabled.
Number of cores used: 1.
Thinning factor: 1. This means every draw is saved.
	 No hyperparameters are chosen, default setting applied.

Estimation of country models starts... 
Model:  1 / 3  done. 

Start estimation of Bayesian Global Vector Autoregression.

Prior: Minnesota prior.
Lag order: 1 (endo.), 1 (w. exog.)
Stochastic volatility: enabled.
Number of cores used: 1.
Thinning factor: 1. This means every draw is saved.
	 No hyperparameters are chosen, default setting applied.

Estimation of country models starts... 
Model:  2 / 3  done. 

Start estimation of Bayesian Global Vector Autoregression.

Prior: Minnesota prior.
Lag order: 1 (endo.), 1 (w. exog.)
Stochastic volatility: enabled.
Number of cores used: 1.
Thinning factor: 1. This means every draw is saved.
	 No hyperparameters are chosen, default setting applied.

Estimation of country models starts... 
Model:  3 / 3  done.

Start estimation of Bayesian Global Vector Autoregression.

Prior: Minnesota prior.
Lag order: 1 (endo.), 1 (w. exog.)
Stochastic volatility: enabled.
Number of cores used: 1.
Thinning factor: 1. This means every draw is saved.
	 No hyperparameters are chosen, default setting applied.

Estimation of country models starts...
Estimation done and took 0 mins 4 seconds.
Stacking of global model starts... 

Stacking finished.
Computation of BGVAR yields 76 (76%) draws (active trimming).
 Needed time for estimation of bgvar: 0 mins 4 seconds.
>                       
> # US monetary policy shock
> shockinfo <- get_shockinfo("chol")
> shockinfo$shock <- "US.stir"; shockinfo$scale <- -100
> irf.chol.us.mp<-irf(model.eer,n.ahead=48,shockinfo=shockinfo)
Start computing impulse response functions of Bayesian Global Vector Autoregression.

Identification scheme: Short-run identification via Cholesky decomposition.
Start impulse response analysis on 1 core (76 stable draws in total). 

Impulse response analysis took 0 mins 0 seconds.

Size of irf object:  0.1 Mb
Needed time for impulse response analysis: 0 mins 2 seconds.
> 
> # calculates FEVD for variables US.Dp and EA.y
> fevd.us.mp=fevd(irf.chol.us.mp,var.slct=c("US.Dp","EA.y"))
Start computing forecast error variance decomposition of Bayesian Global Vector Autoregression.

Identification scheme: Short-run restrictions via Cholesky decomposition.
FEVD computed for the following variables: US.Dp, EA.y.
Start computing FEVDs...

Size of FEVD object:  0 Mb
Needed time for computation: 0 mins 0 seconds.
> 
> # US monetary policy shock with sign restrictions
> shockinfo <- get_shockinfo("sign")
> shockinfo <- add_shockinfo(shockinfo, shock="US.stir", 
+                            restriction=c("US.y","US.Dp"), 
+                            sign=c("<","<"), horizon=c(1,1), 1, 100)
> irf.sign.us.mp<-irf(model.eer,n.ahead=24,shockinfo=shockinfo)
Start computing impulse response functions of Bayesian Global Vector Autoregression.

Identification scheme: identification via sign-restriction.
Start impulse response analysis on 1 core (76 stable draws in total). 

Impulse response analysis took 0 mins 0 seconds.
For  74  draws out of  76  draws, a rotation matrix has been found.
Size of irf object:  0.1 Mb
Needed time for impulse response analysis: 0 mins 1 second.> 
> # calculates FEVD for variables US.Dp and EA.y
> fevd.us.mp=fevd(irf.sign.us.mp,var.slct=c("US.Dp","EA.y"))
Start computing forecast error variance decomposition of Bayesian Global Vector Autoregression.

Identification scheme: Sign-restrictions provided.
FEVD computed for the following variables: US.Dp, EA.y.
Error in `colnames<-`(`*tmp*`, value = varNames) : 
  attempt to set 'colnames' on an object with less than two dimensions
Calls: fevd -> fevd.bgvar.irf -> colnames<-
Execution halted