As I tried to draw random sample from Hüsler-Reiss density with the following code,

  library(evd)
 margins <- cbind(0, 1, seq(-0.5,0.5,0.1))
  x <- rbvevd(101, dep = 1.7, model = "hr", mar1 = margins)

             [,1]       [,2]
 [1,] -0.56662298  0.8448505
 [2,]  0.55824918  1.0217279
 [3,]  0.24741124  0.6684668
 [4,] -0.31547985  0.1781680
 [5,]  0.69466134  0.2299363
 [6,]  3.58035098  2.3489841
 [7,] -0.07171582  0.6984240
 [8,]  0.54275202  0.7706450
 [9,] -1.01611325 -0.6598119
[10,]  0.01010218  0.5329360
[11,]  2.13074835  0.4344

These are the set of observation I got.

If we look into the R documentation for the function rbvevd( ), for Hüsler-Reiss model, it is clear that the marginals are exponential (see below)


The Husler-Reiss distribution function with parameter dep = 
r is 
G(z1,z2) = exp(-y1 Phi{r^{-1}+r[log(y1/y2)]/2} - y2 
Phi{r^{-1}+r[log(y2/y1)]/2}
where Phi() is the standard normal distribution function and r > 
0. Independence is obtained in the limit as r approaches zero. 
Complete dependence is obtained as r tends to infinity. 

But if we look into a sample set it has negative values too but it cannot be.
And more over exponential margins are obtained after take exponential transformation for Gumbel margins.  But  for these data set we cannot take reverse transformation, that is,  -log  and to get back original model.

Please clarify whether the samples are drawn with Gumbel margins or exponential margins.



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