R Under development (unstable) (2024-10-16 r87241 ucrt) -- "Unsuffered Consequences"
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> library(POT)
> set.seed(123)
> 
> x <- rbvgpd(50, model = "log", alpha = 0.5, mar1 = c(0, 1, 0.2))
> f1 <- fitbvgpd(x, c(1,1)/2)
> convassess(f1)
There were 21 warnings (use warnings() to see them)
> str(f1)
List of 22
 $ fitted.values: Named num [1:5] 1.2778 -0.1915 1.1561 -0.0168 0.5815
  ..- attr(*, "names")= chr [1:5] "scale1" "shape1" "scale2" "shape2" ...
 $ std.err      : Named num [1:5] 0.3152 0.2147 0.2846 0.1792 0.0807
  ..- attr(*, "names")= chr [1:5] "scale1" "shape1" "scale2" "shape2" ...
 $ var.cov      : num [1:5, 1:5] 0.099328 -0.059559 0.02436 -0.009211 -0.000853 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:5] "scale1" "shape1" "scale2" "shape2" ...
  .. ..$ : chr [1:5] "scale1" "shape1" "scale2" "shape2" ...
 $ fixed        : NULL
 $ param        : Named num [1:5] 1.2778 -0.1915 1.1561 -0.0168 0.5815
  ..- attr(*, "names")= chr [1:5] "scale1" "shape1" "scale2" "shape2" ...
 $ deviance     : num 246
 $ corr         : NULL
 $ convergence  : chr "successful"
 $ counts       : Named int [1:2] 28 12
  ..- attr(*, "names")= chr [1:2] "function" "gradient"
 $ message      : NULL
 $ threshold    : num [1:2] 0.5 0.5
 $ nat          : Named int [1:4] 41 31 28 44
  ..- attr(*, "names")= chr [1:4] "Exceedance nb marg 1" "Exceedance nb marg 2" "Exceedance nb both marg" "Exceedance nb any marg"
 $ pat          : Named num [1:3] 0.82 0.62 0.56
  ..- attr(*, "names")= chr [1:3] "Exceedance prop marg 1" "Exceedance prop marg 2" "Exceedance prop both marg"
 $ data         : num [1:50, 1:2] 0.7215 0.0495 3.4619 0.6959 1.1167 ...
 $ exceed1      : num [1:41] 0.721 3.462 0.696 1.117 1.692 ...
 $ exceed2      : num [1:31] 3.554 0.63 3.108 5.011 0.901 ...
 $ call         : language fitbvgpd(data = x, threshold = c(1, 1)/2)
 $ est          : chr "MLE"
 $ model        : chr "log"
 $ logLik       : num -123
 $ opt.value    : num -123
 $ chi          : Named num 0.504
  ..- attr(*, "names")= chr "alpha"
 - attr(*, "class")= chr [1:2] "bvpot" "pot"
> f1

Call: fitbvgpd(data = x, threshold = c(1, 1)/2) 
Estimator: MLE 
Dependence Model and Strength:
	Model : Logistic 
	lim_u Pr[ X_1 > u | X_2 > u] = 0.504 
Deviance: 246.4116 
     AIC: 256.4116 

Marginal Threshold: 0.5 0.5 
Marginal Number Above: 41 31 
Marginal Proportion Above: 0.82 0.62 
Joint Number Above: 28 
Joint Proportion Above: 0.56 
Number of events such as {Y1 > u1} U {Y2 > u2}: 44 

Estimates
  scale1    shape1    scale2    shape2     alpha  
 1.27779  -0.19154   1.15610  -0.01678   0.58146  

Standard Errors
 scale1   shape1   scale2   shape2    alpha  
0.31516  0.21469  0.28461  0.17919  0.08066  

Asymptotic Variance Covariance
        scale1     shape1     scale2     shape2     alpha    
scale1   0.099328  -0.059559   0.024360  -0.009211  -0.000853
shape1  -0.059559   0.046093  -0.004446   0.007059  -0.003654
scale2   0.024360  -0.004446   0.081003  -0.037056  -0.003994
shape2  -0.009211   0.007059  -0.037056   0.032107  -0.002424
alpha   -0.000853  -0.003654  -0.003994  -0.002424   0.006505

Optimization Information
	Convergence: successful 
	Function Evaluations: 28 
	Gradient Evaluations: 12 

> 
> proc.time()
   user  system elapsed 
   1.84    0.09    1.90