#Simulating from a three-dimensional frank copula with
#kendell's tau equal to 0.25, sample size N.set=100.
#Please enlarge N.set for further studies.
#require(copula)
#N.set<-100
#cop <- archmCopula(family = "frank", dim = 3, param =2.39)
#parMarg<-list(list(min=0,max=1),list(min=0,max=1),list(min=0,max=1))
#distr.cop <- mvdc(cop, margins=rep("unif",3), paramMargins = parMarg,marginsIdentical=TRUE)
#c.X <- rMvdc(mvdc=distr.cop, n=N.set)
#Y <- punif(c.X)
#vine.copula<-vine(Y,K=8,base="B-spline",q=2,m=2,pen=1,cores=1,lambda=12500)
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