I tried to replicate this demo from here, but get this error:

Error in calcKTau[[j]]: the object of type 'closure' is not subsettable. I used chatGpt to fix the error, but it also did not work. I read some posts similar to my issue but cannot fix mine.

I understand it, but I do not know how to fix it.

Here is the complete code:

`library("VineCopula")
library("spcopula")
data("EU_RB_2005")

## spatio-temporal copula
# binning, using only 90 out of all temporal instances
stBins <- calcBins(EU_RB_2005, "PM10", nbins=40, t.lags=-(0:4),
                   instances=10)
# 

calcKTau <- fitCorFun(stBins, rep(3, 5), tlags=-(0:4))

families <- c(normalCopula(0), tCopula(0), claytonCopula(0), 
              frankCopula(1), gumbelCopula(1), VC2copula::joeBiCopula(), 
              cqsCopula(), asCopula())

loglikTau <- list()

#This lines which produce the error:

 for(j in 1:3) { # j <-1
      tmpBins <- list(meanDists=stBins$meanDists,
                      lagData=lapply(stBins$lagData, function(x) x[,c(2*j-1,2*j)]))
      loglikTau[[j]] <- loglikByCopulasLags(tmpBins, families, calcKTau[[j]])
    }
    
    bestFitTau <- list()
    bestFitTau[[1]] <- apply(apply(loglikTau[[1]]$loglik[,-8], 1, rank),
                             2, which.max)
    bestFitTau[[2]] <- apply(apply(loglikTau[[2]]$loglik, 1, rank),
                             2, which.max)
    bestFitTau[[3]] <- apply(apply(loglikTau[[3]]$loglik, 1, rank),
                             2, which.max)
    
    # gather the fitted copulas and representative distances
    listCops <- NULL
    
    for(t.level in 1:3) {
      listCops[[t.level]] <- sapply(1:35, 
                                    function(i) {
                                      tmpTau <- calcKTau[[t.level]](stBins$meanDists[i])
                                      if (tmpTau < 0.05) {
                                        return(indepCopula(dim=2))
                                      } else {
                                        return(loglikTau[[t.level]]$copulas[[bestFitTau[[t.level]][i]]][[i]])
                                      }})
    }
    
    listDists <- NULL
    listDists[[1]] <- stBins$meanDists[1:35]
    listDists[[2]] <- stBins$meanDists[1:35]
    listDists[[3]] <- stBins$meanDists[1:35]
    
    # build the spatio-temporal copula
    stConvCop <- stCopula(components=listCops, distances=listDists,
                          t.lags=c(0,-1,-2))
    
    # get the neighbours
    stNeigh <- getStNeighbours(EU_RB_2005, var="rtPM10", spSize=4, 
                               t.lags=-(0:2), timeSteps=10, min.dist=10)
    
    
    stVineFit <- fitCopula(stVineCopula(stConvCop,vineCopula(9L)), stNeigh,
                           method="indeptest")
    stVine <- stVineFit@copula
    
    # retrieve the log-likelihood
    stVineFit@loglik`