# Examples of Nonstandard Copulas – “Wild Animals”

#### Marius Hofert and Martin Mächler

#### 2026-02-19

## 1 Swiss Alps copulas of Hofert, Vrins (2013)

This example implements the Swiss Alps copulas of Hofert, Vrins (2013, “Sibuya copulas”).

### Lambda and its inverse

    Lambda <- function(t) pmax(log(t), 0)
    LambdaInv <- function(t) (t != 0)*exp(t) ## <=>  ifelse(t == 0, 0, exp(t))

### M and its inverse (for \(M_i, i=1,2\)):

    M1 <- function(t) 0.01*t
    M1Inv <- function(t) t/0.01

    M2 <- function(t) {
        f <- function(t.) {
            ct <- ceiling(t.)
            if(ct %% 2) t. - (ct-1)/2 else ct/2
        }
        unlist(lapply(t, f))
    }
    M2Inv <- function(t) {
        f <- function(t.) {
            if(t. == 0) return(0)
            t. + ceiling(t) - 1
        }
        unlist(lapply(t, f))
    }

### S and its inverse (for \(S_i, i=1,2\))

    S1 <- function(t, H) exp(-(M1(t) + Lambda(t)*(1-exp(-H))))
    S1Inv <- function(u, H, upper=1e6) unlist(lapply(u, function(u.)
        uniroot(function(x) S1(x,H=H)-u., interval=c(0, upper))$root))
    S2 <- function(t, H) exp(-(M2(t) + Lambda(t)*(1-exp(-H))))
    S2Inv <- function(u, H, upper=1e6) unlist(lapply(u, function(u.)
        uniroot(function(x) S2(x,H=H)-u., interval=c(0, upper))$root))

### Wrappers for \(p_1\) and \(p_2\) and their inverses:

p and its inverse (for \(p_i(t_k-)\) and \(p_i(t_k), i = 1,2\)):

    p1 <- function(t, k, H) exp(-M1(t)-H*k)
    p1Inv <- function(u, k, H, upper=1e6) unlist(lapply(u, function(u.)
        uniroot(function(x) p1(x,k=k,H=H)-u., interval=c(0, upper))$root))
    p2 <- function(t, k, H) exp(-M2(t)-H*k)
    p2Inv <- function(u, k, H, upper=1e6) unlist(lapply(u, function(u.)
        uniroot(function(x) p2(x,k=k,H=H)-u., interval=c(0, upper))$root))

and the wrappers, which work with `p1(), p2(), p1Inv(), p2Inv()` as arguments:

    p <- function(t, k, H, I, p1, p2){
        if((lI <- length(I)) == 0){
            stop("error in p")
        }else if(lI==1){
            if(I==1) p1(t, k=k, H=H) else p2(t, k=k, H=H)
        }else{ # lI == 2
            c(p1(t, k=k, H=H), p2(t, k=k, H=H))
        }
    }
    pInv <- function(u, k, H, I, p1Inv, p2Inv){
        if((lI <- length(I)) == 0){
            stop("error in pInv")
        }else if(lI==1){
            if(I==1) p1Inv(u, k=k, H=H) else p2Inv(u, k=k, H=H)
        }else{ # lI == 2
            c(p1Inv(u[1], k=k, H=H), p2Inv(u[2], k=k, H=H))
        }
    }

### Define the copula \(C\)

    C <- function(u, H, Lambda, S1Inv, S2Inv) {
        if(all(u == 0)) 0 else
        u[1]*u[2] * exp(expm1(-H)^2 * Lambda(min(S1Inv(u[1],H), S2Inv(u[2],H))))
    }

Compute the singular component (given \(u_1=\) `u1`, find \(u_2=\) `u2` on the singular component) \(u_2 = S2(S1^{-1}(u_1))\):

    s.comp <- function(u1, H, S1Inv, S2){
        if(u1 == 0) 0 else S2 (S1Inv(u1,H), H)
    }

Generate one bivariate random vector from `C`:

    rC1 <- function(H, LambdaInv, S1, S2, p1, p2, p1Inv, p2Inv) {
        d <- 2 # dim = 2
        ## (1)
        U <- runif(d) # for determining the default times of all components
        ## (2) -- t_{h,0} := initial value for the occurrence of the homogeneous
        ##                   Poisson process with unit intensity
        t_h <- 0
        t <- 0 # t_0; initial value for the occurrence of the jump process
        k <- 1 # indices for the sets I
        I_ <- list(1:d) # I_k; indices for which tau has to be determined
        tau <- rep(Inf,d) # in the beginning, set all default times to Inf (= "no default")
        ## (3)
        repeat{
            ## (4)
            ## k-th occurrence of a homogeneous Poisson process with unit intensity:
            t_h[k] <- rexp(1) + if(k == 1) 0 else t_h[k-1]
            ## k-th occ. of non-homogeneous Poisson proc. with integrated rate function Lambda:
            t[k] <- LambdaInv(t_h[k])
            ## (5)
            pvec <- p(t[k], k=k, H=H, I=c(1,2), p1=p1, p2=p2) # c(p_1(t_k), p_2(t_k))
            I. <- (1:d)[U >= pvec] # determine all i in I
            I <- intersect(I., I_[[k]]) # determine I
            ## (6)--(10)
            if(length(I) > 0){
                default.at.t <- U[I] <= p(t[k], k=k-1, H=H, I=I, p1=p1, p2=p2)
                tau[I[default.at.t]] <- t[k]
                Ic <- I[!default.at.t] # I complement
                if(length(Ic) > 0) tau[Ic] <- pInv(U[Ic], k=k-1, H=H, I=Ic,
                                                   p1Inv=p1Inv, p2Inv=p2Inv)
            }
            ## (11) --  define I_{k+1} := I_k \ I
            I_[[k+1]] <- setdiff(I_[[k]], I)
            ## (12)
            if(length(I_[[k+1]]) == 0) break else k <- k+1
        }
        ## (14)
        c(S1(tau[1], H=H), S2(tau[2], H=H))
    }

### Draw n vectors of random variates from \(C\)

    rC <- function(n, H, LambdaInv, S1, S2, p1, p2, p1Inv, p2Inv){
        mat <- t(sapply(rep(H, n), rC1, LambdaInv=LambdaInv, S1=S1, S2=S2,
                        p1=p1, p2=p2, p1Inv=p1Inv, p2Inv=p2Inv))
        row.names(mat) <- NULL
        mat
    }

    ## Generate copula data
    n <- 2e4  # <<< use for niceness
    n <- 4000 # (rather use to decrease *.html and final package size)
    H <- 10
    set.seed(271)
    U <- rC(n, H=H, LambdaInv=LambdaInv, S1=S1, S2=S2, p1=p1, p2=p2, p1Inv=p1Inv, p2Inv=p2Inv)

    ## Check margins of U
    par(pty="s")
    hist(U[,1], probability=TRUE, main="Histogram of the first component",
         xlab=expression(italic(U[1])))

    hist(U[,2], probability=TRUE, main="Histogram of the second component",
         xlab=expression(italic(U[2])))

    ## Plot U (copula sample)
    plot(U, pch=".", xlab=expression(italic(U[1])%~%~"U[0,1]"),
                   , ylab=expression(italic(U[2])%~%~"U[0,1]"))

Wireframe plot to incorporate singular component :

    require(lattice)
    wf.plot <- function(grid, val.grid, s.comp, val.s.comp, Lambda, S1Inv, S2Inv){
        wireframe(val.grid ~ grid[,1]*grid[,2], xlim=c(0,1), ylim=c(0,1), zlim=c(0,1),
                  aspect=1, scales = list(arrows=FALSE, col=1), # remove arrows
                  par.settings= list(axis.line = list(col="transparent"), # remove global box
                                     clip = list(panel="off")),
                  pts = cbind(s.comp, val.s.comp), # <- add singular component
                  panel.3d.wireframe = function(x, y, z, xlim, ylim, zlim, xlim.scaled,
                                                ylim.scaled, zlim.scaled, pts, ...) {
                      panel.3dwire(x=x, y=y, z=z, xlim=xlim, ylim=ylim, zlim=zlim,
                                   xlim.scaled=xlim.scaled, ylim.scaled=ylim.scaled,
                                   zlim.scaled=zlim.scaled, ...)
                      xx <- xlim.scaled[1]+diff(xlim.scaled)*(pts[,1]-xlim[1])/diff(xlim)
                      yy <- ylim.scaled[1]+diff(ylim.scaled)*(pts[,2]-ylim[1])/diff(ylim)
                      zz <- zlim.scaled[1]+diff(zlim.scaled)*(pts[,3]-zlim[1])/diff(zlim)
                      panel.3dscatter(x=xx, y=yy, z=zz, xlim=xlim, ylim=ylim, zlim=zlim,
                                      xlim.scaled=xlim.scaled, ylim.scaled=ylim.scaled,
                                      zlim.scaled=zlim.scaled, type="l", col=1, ...)
                  },
                  xlab = expression(italic(u[1])),
                  ylab = expression(italic(u[2])),
                  zlab = list(expression(italic(C(u[1],u[2]))), rot=90))
    }

    ## Copula plot with singular component
    u <- seq(0, 1, length.out=20) # grid points per dimension
    grid <- expand.grid(u1=u, u2=u) # grid
    val.grid <- apply(grid, 1, C, H=H, Lambda=Lambda, S1Inv=S1Inv, S2Inv=S2Inv) # copula values on grid
    s.comp <- cbind(u, sapply(u, s.comp, H=H, S1Inv=S1Inv, S2=S2)) # pairs (u1, u2) on singular component
    val.s.comp <- apply(s.comp, 1, C, H=H, Lambda=Lambda, S1Inv=S1Inv, S2Inv=S2Inv) # corresponding z-values
    wf.plot(grid=grid, val.grid=val.grid, s.comp=s.comp, val.s.comp=val.s.comp,
            Lambda=Lambda, S1Inv=S1Inv, S2Inv=S2Inv)

## 2 An example from Wolfgang Trutschnig and Manuela Schreyer

For more details, see Trutschnig, Fernandez Sanchez (2014) “Copulas with continuous, strictly increasing singular conditional distribution functions”

Roughly, one defines an Iterated Function System whose attractor is the word “Copula” and starts the chaos game.

### Define the Iterated Function System

    IFS <- local({ ## Using `local`, so `n` is part of IFS
        n <- 23
        list(function(x) c(3*x[1]/n,      x[2]/4),
             function(x) c(-(x[2]-1)/n,   x[1]/2+1/4),
             function(x) c(3*x[1]/n,      x[2]/4+3/4),
             function(x) c((3*x[1]+4)/n,  x[2]/4),
             function(x) c(-(x[2]-5)/n,   x[1]/2+1/4),
             function(x) c((3*x[1]+4)/n,  x[2]/4+3/4),
             function(x) c(-(x[2]-7)/n,   x[1]/2+1/4),
             function(x) c(-x[2]/n+9/n,   3*x[1]/4),
             function(x) c((3*x[1]+8)/n,  x[2]/4+3/4),
             function(x) c(x[1]/n+10/n,   x[2]/8+1/2+1/8),
             function(x) c(2*x[1]/n+9/n,  x[2]/4+1/4+1/8),
             function(x) c(-x[2]/n+13/n,  (3*x[1]+1)/4),
             function(x) c((3*x[1]+12)/n, x[2]/4),
             function(x) c((3*x[1]+12)/n, x[2]/4),
             function(x) c(-x[2]/n+15/n,  (3*x[1]+1)/4),
             function(x) c((3*x[1]+16)/n, x[2]/4),
             function(x) c(-(x[2]-21)/n,  3*x[1]/4),
             function(x) c((3*x[1]+20)/n, x[2]/4+3/4),
             function(x) c((x[1]+21)/n,   x[2]/4+1/4+1/8),
             function(x) c(-(x[2]-23)/n,  3*x[1]/4))
    })

#### Run chaos game B times

    B <- 20 # replications
    n.steps <- 20000 # number of steps
    AA <- vector("list", length=B)
    set.seed(271)
    for(i in 1:B) {
        ind <- sample(length(IFS), size=n.steps, replace=TRUE) # (randomly) 'bootstrap' functions
        res <- matrix(0, nrow=n.steps+1, ncol=2) # result matrix (for each i)
        pt <- c(0, 0) # initial point
        for(r in seq_len(n.steps)) {
            res[r+1,] <- IFS[[ind[r]]](pt) # evaluate randomly chosen functions at pt
            pt <- res[r+1,] # redefine point
        }
        AA[[i]] <- res # keep this matrix
    }

    A <- do.call(rbind, AA) # rbind (n.steps+1, 2)-matrices
    n <- nrow(A)
    stopifnot(ncol(A) == 2, n == B*(n.steps+1)) # sanity check

\(X :=\) Rotate \(A\) by \(-45^{o} = -\pi/4\) :

    phi <- -pi/4
    X <- cbind(cos(phi)*A[,1] - sin(phi)*A[,2]/3,
               sin(phi)*A[,1] + cos(phi)*A[,2]/3)
    stopifnot(identical(dim(X), dim(A)))

Now transform the margings by their marginal ECDF’s so we get uniform margins. Note that, it is equivalent but faster to use `rank(*, ties.method="max")`:

    U <- apply(X, 2, function(x) ecdf(x)(x))
    ## Prove equivalence:
    stopifnot(all.equal(U,
                        apply(X, 2, rank, ties.method="max") / n,
                        tolerance = 1e-14))

Now, visually check the margins of `U`; they are perfectly uniform:

    par(pty="s")
    sfsmisc::mult.fig(mfcol = c(1,2), main = "Margins are uniform")
    hist(U[,1], probability=TRUE, main="Histogram of U[,1]", xlab=quote(italic(U[1])))
    hist(U[,2], probability=TRUE, main="Histogram of U[,2]", xlab=quote(italic(U[2])))

whereas `U`, the copula sample, indeed is peculiar and contains the word “COPULA” many times if you look closely (well, the “L” is defect …):

    par(pty="s")
    plot(U, pch=".", xlab = quote(italic(U[1]) %~% ~ "U[0,1]"),
         asp = 1,    ylab = quote(italic(U[2]) %~% ~ "U[0,1]"))

## 3 Sierpinski tetrahedron

This is an implementation of Example 2.3 in https://arxiv.org/pdf/0906.4853

    library(abind) # for merging arrays via abind()
    library(lattice) # for cloud()
    library(sfsmisc) # for polyn.eval()

    ##
    ## Attaching package: 'sfsmisc'

    ## The following objects are masked _by_ '.GlobalEnv':
    ##
    ##     Sys.memGB, relErr, relErrV

Implement the random number generator:

    ##' @title Generate samples from the Sierpinski tetrahedron
    ##' @param n sample size
    ##' @param N digits in the base-2 expansion
    ##' @return (n, 3)-matrix
    ##' @author Marius Hofert
    rSierpinskyTetrahedron <- function(n, N)
    {
        stopifnot(n >= 1, N >= 1)
        ## Build coefficients in the base-2 expansion
        U12coeff <- array(sample(0:1, size = 2*n*N, replace = TRUE),
                          dim = c(2, n, N), dimnames = list(U12 = c("U1", "U2"),
                                                            sample = 1:n,
                                                            base2digit = 1:N)) # (2, n, N)-array
        U3coeff <- apply(U12coeff, 2:3, function(x) sum(x) %% 2) # (n, N)-matrix
        Ucoeff <- abind(U12coeff, U3 = U3coeff, along = 1)
        ## Convert to U's
        t(apply(Ucoeff, 1:2, function(x)
            polyn.eval(coef = rev(x), x = 2))/2^N) # see sfsmisc::bi2int
    }

Draw vectors of random numbers following a “Sierpinski tetrahedron copula”:

    set.seed(271)
    U <- rSierpinskyTetrahedron(1e4, N = 6)

Use a scatterplot matrix to check all bivariate margins:

    pairs(U, gap = 0, cex = 0.25, col = "black",
          labels = as.expression( sapply(1:3, function(j) bquote(U[.(j)])) ))

All pairs “look” independent but, of course, they aren’t:

    cloud(U[,3] ~ U[,1] * U[,2], cex = 0.25, col = "black", zoom = 1,
          scales = list(arrows = FALSE, col = "black"), # ticks instead of arrows
          par.settings = list(axis.line = list(col = "transparent"), # to remove box
                              clip = list(panel = "off"),
                              standard.theme(color = FALSE)),
          xlab = expression(U[1]), ylab = expression(U[2]), zlab = expression(U[3]))

### Session information

    ## R version 4.5.2 Patched (2026-02-18 r89435)
    ## Platform: x86_64-pc-linux-gnu
    ## Running under: Fedora Linux 42 (Adams)
    ##
    ## Matrix products: default
    ## BLAS:   /sfs/u/maechler/R/D/r-patched/F42-64-inst/lib/libRblas.so
    ## LAPACK: /usr/lib64/liblapack.so.3.12.0  LAPACK version 3.12.0
    ##
    ## attached base packages:
    ##  [1] splines   parallel  grid      stats4    tools     stats     graphics
    ##  [8] grDevices utils     datasets  methods   base
    ##
    ## other attached packages:
    ##  [1] sfsmisc_1.1-23    abind_1.4-8       randtoolbox_2.0.5 rngWELL_0.10-10
    ##  [5] qrng_0.0-11       gridExtra_2.3     VGAM_1.1-14       rugarch_1.5-4
    ##  [9] gsl_2.1-9         mev_2.1           lattice_0.22-9    bbmle_1.0.25.1
    ## [13] copula_1.1-7
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] gtable_0.3.6                pspline_1.0-21
    ##  [3] xfun_0.56                   bslib_0.10.0
    ##  [5] partitions_1.10-9           ks_1.15.1
    ##  [7] Runuran_0.41                numDeriv_2016.8-1.1
    ##  [9] Rdpack_2.6.6                cluster_2.1.8.2
    ## [11] xts_0.14.1                  Matrix_1.7-4
    ## [13] KernSmooth_2.23-26          DistributionUtils_0.6-2
    ## [15] alabama_2025.1.0            lifecycle_1.0.5
    ## [17] truncnorm_1.0-9             compiler_4.5.2
    ## [19] spd_2.0-1                   codetools_0.2-20
    ## [21] htmltools_0.5.9             SkewHyperbolic_0.4-2
    ## [23] sass_0.4.10                 Rsolnp_2.0.1
    ## [25] yaml_2.3.12                 gmp_0.7-5.1
    ## [27] pracma_2.4.6                nloptr_2.2.1
    ## [29] jquerylib_0.1.4             GeneralizedHyperbolic_0.8-7
    ## [31] MASS_7.3-65                 cachem_1.1.0
    ## [33] mclust_6.1.2                parallelly_1.46.1
    ## [35] fracdiff_1.5-3              bdsmatrix_1.3-7
    ## [37] digest_0.6.39               mvtnorm_1.3-3
    ## [39] future_1.69.0               listenv_0.10.0
    ## [41] pcaPP_2.0-5                 fastmap_1.2.0
    ## [43] cli_3.6.5                   future.apply_1.20.1
    ## [45] rmarkdown_2.30              globals_0.19.0
    ## [47] otel_0.2.0                  zoo_1.8-15
    ## [49] evaluate_1.0.5              knitr_1.51
    ## [51] rbibutils_2.4.1             ADGofTest_0.3
    ## [53] stabledist_0.7-2            nleqslv_3.3.5
    ## [55] rlang_1.1.7                 Rcpp_1.1.1
    ## [57] glue_1.8.0                  polynom_1.4-1
    ## [59] jsonlite_2.0.0              R6_2.6.1