R Under development (unstable) (2024-12-15 r87442 ucrt) -- "Unsuffered Consequences"
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Platform: x86_64-w64-mingw32/x64

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> ## switch for testing the following (time consuming) examples
> docheck <- FALSE
> 
> if(docheck){
+ 
+ ### tests from excluded examples
+ library(VineCopula)
+ data(daxreturns)
+ 
+ ## Not run:
+ # simulate from a bivariate Clayton copula
+ set.seed(123)
+ simdata <- BiCopSim(300, 3, 2)
+ u1 <- simdata[,1]
+ u2 <- simdata[,2]
+ 
+ # perform Kendall's goodness-of-fit test for the true copula
+ gof <- BiCopGofTest(u1, u2, family = 3, method = "kendall")
+ gof$p.value.CvM
+ gof$p.value.KS
+ 
+ # perform Kendall's goodness-of-fit test for the Frank copula
+ gof <- BiCopGofTest(u1, u2, family = 5, method = "kendall")
+ gof$p.value.CvM
+ gof$p.value.KS
+ # End(Not run)
+ 
+ 
+ ## Not run:
+ # simulate from a t-copula
+ set.seed(123)
+ dat <- BiCopSim(500, 2, 0.7, 5)
+ 
+ # apply the test for families 1-10
+ vcgof <- BiCopVuongClarke(dat[,1], dat[,2], familyset = c(1:10))
+ 
+ # display the Vuong test scores
+ vcgof[1,]
+ # End(Not run)
+ 
+ ## Not run:
+ # select the R-vine structure, families and parameters
+ RVM <- RVineStructureSelect(daxreturns[,1:5], c(1:6))
+ RVM$Matrix
+ RVM$par
+ RVM$par2
+ 
+ # select the C-vine structure, families and parameters
+ CVM <- RVineStructureSelect(daxreturns[,1:5], c(1:6), type = "CVine")
+ CVM$Matrix
+ CVM$par
+ CVM$par2
+ 
+ # compare the two models based on the data
+ clarke <- RVineClarkeTest(daxreturns[,1:5], RVM, CVM)
+ clarke$statistic
+ clarke$statistic.Schwarz
+ clarke$p.value
+ clarke$p.value.Schwarz
+ # End(Not run)
+ 
+ ## Not run:
+ # White test with asymptotic p-value
+ RVineGofTest(daxreturns[,1:5], RVM, B = 0)
+ 
+ # ECP2 test with Cramer-von-Mises test statistic and a bootstrap with 200 replications
+ # for the calculation of the p-value
+ RVineGofTest(daxreturns[,1:5], RVM, method = "ECP2", statistic = "CvM", B = 200)
+ # End(Not run)
+ 
+ ## Not run:
+ # define 5-dimensional R-vine tree structure matrix
+ Matrix <- c(5, 2, 3, 1, 4,
+             0, 2, 3, 4, 1,
+             0, 0, 3, 4, 1,
+             0, 0, 0, 4, 1,
+             0, 0, 0, 0, 1)
+ Matrix <- matrix(Matrix, 5, 5)
+ 
+ # define R-vine pair-copula family matrix
+ family <- c(0, 1, 3, 4, 4,
+             0, 0, 3, 4, 1,
+             0, 0, 0, 4, 1,
+             0, 0, 0, 0, 3,
+             0, 0, 0, 0, 0)
+ family <- matrix(family, 5, 5)
+ 
+ # define R-vine pair-copula parameter matrix
+ par <- c(0, 0.2, 0.9, 1.5, 3.9,
+          0, 0, 1.1, 1.6, 0.9,
+          0, 0, 0, 1.9, 0.5,
+          0, 0, 0, 0, 4.8,
+          0, 0, 0, 0, 0)
+ par <- matrix(par, 5, 5)
+ 
+ # define second R-vine pair-copula parameter matrix
+ par2 <- matrix(0, 5, 5)
+ 
+ # define RVineMatrix object
+ RVM <- RVineMatrix(Matrix = Matrix, family = family, par = par, par2 = par2,
+                    names=c("V1", "V2", "V3", "V4", "V5"))
+ 
+ # simulate a sample of size 300 from the R-vine copula model
+ set.seed(123)
+ simdata <- RVineSim(300, RVM)
+ 
+ # compute the MLE
+ mle <- RVineMLE(simdata, RVM, grad = TRUE)
+ mle$RVM
+ # End(Not run)
+ 
+ ## Not run:
+ # PIT data
+ pit <- RVinePIT(daxreturns[,1:5], RVM)
+ 
+ par(mfrow = c(1,2))
+ plot(daxreturns[,1], daxreturns[,2])  # correlated data
+ plot(pit[,1], pit[,2])	# i.i.d. data
+ 
+ cor(daxreturns[,1:5], method = "kendall")
+ cor(pit, method = "kendall")
+ # End(Not run)
+ 
+ ##TODO shorten this test, takes too long
+ # # Not run:
+ # RVM <- RVineStructureSelect(daxreturns, c(1:6), progress=TRUE)
+ # # End(Not run)
+ #
+ # # specify a C-vine copula model with only Clayton, Gumbel and Frank copulas
+ # # Not run:
+ # CVM <- RVineStructureSelect(daxreturns, c(3,4,5), "CVine")
+ # # End(Not run)
+ # # determine the order of the nodes in a D-vine using the package TSP
+ # # Not run:
+ # library(TSP)
+ # d <- dim(daxreturns)[2]
+ # M <- 1 - abs(TauMatrix(daxreturns))
+ # hamilton <- insert_dummy(TSP(M), label = "cut")
+ # sol <- solve_TSP(hamilton,method = "repetitive_nn")
+ # order <- cut_tour(sol, "cut")
+ # DVM <- D2RVine(order, family = rep(0,d*(d-1)/2), par = rep(0,d*(d-1)/2))
+ # RVineCopSelect(daxreturns, c(1:6), DVM$Matrix)
+ # End(Not run)
+ 
+ ## Not run:
+ RVM <- RVineStructureSelect(daxreturns[,1:5], c(1:6))
+ CVM <- RVineStructureSelect(daxreturns[,1:5], c(1:6), type = "CVine")
+ 
+ # compare the two models based on the data
+ vuong <- RVineVuongTest(daxreturns[,1:5], RVM, CVM)
+ vuong$statistic
+ vuong$statistic.Schwarz
+ vuong$p.value
+ vuong$p.value.Schwarz
+ # End(Not run)
+ 
+ }
> 
> proc.time()
   user  system elapsed 
   0.14    0.03    0.15