R : Copyright 2005, The R Foundation for Statistical Computing
Version 2.1.1  (2005-06-20), ISBN 3-900051-07-0

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> ### * <HEADER>
> ###
> attach(NULL, name = "CheckExEnv")
> assign(".CheckExEnv", as.environment(2), pos = length(search())) # base
> ## add some hooks to label plot pages for base and grid graphics
> setHook("plot.new", ".newplot.hook")
> setHook("persp", ".newplot.hook")
> setHook("grid.newpage", ".gridplot.hook")
> 
> assign("cleanEx",
+        function(env = .GlobalEnv) {
+ 	   rm(list = ls(envir = env, all.names = TRUE), envir = env)
+            RNGkind("default", "default")
+ 	   set.seed(1)
+    	   options(warn = 1)
+ 	   delayedAssign("T", stop("T used instead of TRUE"),
+ 		  assign.env = .CheckExEnv)
+ 	   delayedAssign("F", stop("F used instead of FALSE"),
+ 		  assign.env = .CheckExEnv)
+ 	   sch <- search()
+ 	   newitems <- sch[! sch %in% .oldSearch]
+ 	   for(item in rev(newitems))
+                eval(substitute(detach(item), list(item=item)))
+ 	   missitems <- .oldSearch[! .oldSearch %in% sch]
+ 	   if(length(missitems))
+ 	       warning("items ", paste(missitems, collapse=", "),
+ 		       " have been removed from the search path")
+        },
+        env = .CheckExEnv)
> assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now
> assign("ptime", proc.time(), env = .CheckExEnv)
> grDevices::postscript("evd-Examples.ps")
> assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv)
> options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly"))
> options(warn = 1)    
> library('evd')
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "abvnonpar"
> 
> ### * abvnonpar
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: abvnonpar
> ### Title: Non-parametric Estimates for Dependence Functions of the
> ###   Bivariate Extreme Value Distribution
> ### Aliases: abvnonpar
> ### Keywords: nonparametric
> 
> ### ** Examples
> 
> bvdata <- rbvevd(100, dep = 0.7, model = "log")
> abvnonpar(seq(0, 1, length = 10), data = bvdata, convex = TRUE)
 [1] 1.0000000 0.9179923 0.8657633 0.8416603 0.8303880 0.8505473 0.8765786
 [8] 0.9026100 0.9286414 1.0000000
> abvnonpar(data = bvdata, method = "d", plot = TRUE)
> 
> M1 <- fitted(fbvevd(bvdata, model = "log"))
> abvpar(dep = M1["dep"], model = "log", plot = TRUE)
> abvnonpar(data = bvdata, add = TRUE, lty = 2)
> 
> 
> 
> cleanEx(); ..nameEx <- "abvpar"
> 
> ### * abvpar
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: abvpar
> ### Title: Parametric Dependence Functions of Bivariate Extreme Value
> ###   Models
> ### Aliases: abvpar
> ### Keywords: distribution
> 
> ### ** Examples
> 
> abvpar(dep = 2.7, model = "hr")
[1] 0.6444467
> abvpar(seq(0,1,0.25), dep = 0.3, asy = c(.7,.9), model = "alog")
[1] 1.0000000 0.8272414 0.7012552 0.7841595 1.0000000
> abvpar(alpha = 0.3, beta = 1.2, model = "negbi", plot = TRUE)
> 
> bvdata <- rbvevd(100, dep = 0.7, model = "log")
> M1 <- fitted(fbvevd(bvdata, model = "log"))
> abvpar(dep = M1["dep"], model = "log", plot = TRUE)
> abvnonpar(data = bvdata, add = TRUE, lty = 2)
> 
> 
> 
> cleanEx(); ..nameEx <- "anova.evd"
> 
> ### * anova.evd
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: anova.evd
> ### Title: Compare Nested EVD Objects
> ### Aliases: anova.evd
> ### Keywords: models
> 
> ### ** Examples
> 
> uvdata <- rgev(100, loc = 0.13, scale = 1.1, shape = 0.2)
> trend <- (-49:50)/100
> M1 <- fgev(uvdata, nsloc = trend)
> M2 <- fgev(uvdata)
> M3 <- fgev(uvdata, shape = 0)
> anova(M1, M2, M3)
Analysis of Deviance Table

   M.Df Deviance Df  Chisq Pr(>chisq)
M1    4   312.34                     
M2    3   312.39  1 0.0470     0.8283
M3    2   313.07  1 0.6777     0.4104
> 
> bvdata <- rbvevd(100, dep = 0.75, model = "log")
> M1 <- fbvevd(bvdata, model = "log")
> M2 <- fbvevd(bvdata, model = "log", dep = 0.75)
> anova(M1, M2)
Analysis of Deviance Table

   M.Df Deviance Df  Chisq Pr(>chisq)
M1    7   589.37                     
M2    6   590.79  1 1.4215     0.2332
> 
> 
> 
> cleanEx(); ..nameEx <- "atvnonpar"
> 
> ### * atvnonpar
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: atvnonpar
> ### Title: Non-parametric Estimates for Dependence Functions of the
> ###   Trivariate Extreme Value Distribution
> ### Aliases: atvnonpar
> ### Keywords: nonparametric
> 
> ### ** Examples
> 
> s3pts <- matrix(rexp(30), nrow = 10, ncol = 3)
> s3pts <- s3pts/rowSums(s3pts)
> sdat <- rmvevd(100, dep = 0.6, model = "log", d = 3)
> atvnonpar(s3pts, sdat)
 [1] 0.7370036 0.7415682 0.8081422 0.8921579 0.7591557 0.8272619 0.7325760
 [8] 0.8058710 0.7273751 0.7863050
> 
> ## Not run: atvnonpar(data = sdat, plot = TRUE)
> ## Not run: atvnonpar(data = sdat, plot = TRUE, ord = c(2,3,1), lab = LETTERS[1:3])
> ## Not run: atvpar(dep = 0.6, model = "log", plot = TRUE)
> ## Not run: atvpar(dep = 0.6, model = "log", plot = TRUE, blty = 1)
> 
> 
> 
> cleanEx(); ..nameEx <- "atvpar"
> 
> ### * atvpar
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: atvpar
> ### Title: Parametric Dependence Functions of Trivariate Extreme Value
> ###   Models
> ### Aliases: atvpar
> ### Keywords: distribution
> 
> ### ** Examples
> 
> atvpar(dep = 0.5, model = "log")
[1] 0.5773503
> s3pts <- matrix(rexp(30), nrow = 10, ncol = 3)
> s3pts <- s3pts/rowSums(s3pts)
> atvpar(s3pts, dep = 0.5, model = "log")
 [1] 0.6308126 0.5977948 0.7633046 0.8698869 0.7177790 0.7707636 0.6287513
 [8] 0.7856694 0.6286950 0.6735762
> ## Not run: atvpar(dep = 0.05, model = "log", plot = TRUE, blty = 1)
> atvpar(dep = 0.95, model = "log", plot = TRUE, lower = 0.94)
> 
> asy <- list(.4, .1, .6, c(.3,.2), c(.1,.1), c(.4,.1), c(.2,.3,.2))
> atvpar(s3pts, dep = 0.15, asy = asy, model = "alog")
 [1] 0.7626463 0.7621025 0.8953048 0.9505544 0.8376848 0.8646419 0.7806575
 [8] 0.8559940 0.7756313 0.7962283
> atvpar(dep = 0.15, asy = asy, model = "al", plot = TRUE, lower = 0.7)
> 
> 
> 
> cleanEx(); ..nameEx <- "bvevd"
> 
> ### * bvevd
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: bvevd
> ### Title: Parametric Bivariate Extreme Value Distributions
> ### Aliases: dbvevd pbvevd rbvevd
> ### Keywords: distribution
> 
> ### ** Examples
> 
> pbvevd(matrix(rep(0:4,2), ncol=2), dep = 0.7, model = "log")
[1] 0.1970092 0.5501183 0.8026369 0.9223050 0.9706844
> pbvevd(c(2,2), dep = 0.7, asy = c(0.6,0.8), model = "alog")
[1] 0.7899611
> pbvevd(c(1,1), dep = 1.7, model = "hr")
[1] 0.5879695
> 
> margins <- cbind(0, 1, seq(-0.5,0.5,0.1))
> rbvevd(11, dep = 1.7, model = "hr", mar1 = margins)
            [,1]        [,2]
 [1,] -0.4254010  0.01150467
 [2,]  1.3751118  1.51970181
 [3,]  0.7563853  1.62772995
 [4,]  1.7945053  0.80787193
 [5,] -0.3046369 -1.07817137
 [6,] -0.8011408 -0.55049599
 [7,]  0.5100566  0.04421376
 [8,]  1.0386106  0.37314890
 [9,] 10.7654622 10.78886330
[10,]  0.8422491  1.84067903
[11,]  1.4112151 -0.39381881
> rbvevd(10, dep = 1.2, model = "neglog", mar1 = c(10, 1, 1))
           [,1]      [,2]
 [1,]  9.913010  9.481925
 [2,]  9.703035 10.050837
 [3,]  9.260082 10.040237
 [4,] 11.611403  9.927830
 [5,] 10.605372 10.954845
 [6,]  9.772099  9.594941
 [7,] 13.678683 11.482815
 [8,] 10.226106  9.449207
 [9,] 10.865629 10.125499
[10,] 13.288596 11.297182
> rbvevd(10, alpha = 0.7, beta = 0.52, model = "bilog")
            [,1]       [,2]
 [1,]  1.1077395  0.5236886
 [2,]  0.9589463  1.4416483
 [3,] -1.2007599  0.3014341
 [4,]  1.2222786  1.0020863
 [5,]  1.1548124  1.9010108
 [6,] -0.2123668 -0.3416906
 [7,] -1.2814224 -0.8363544
 [8,] -0.1202873  0.4207468
 [9,]  0.5038912  0.1060725
[10,]  1.4298580 -0.2033697
> 
> dbvevd(c(0,0), dep = 1.2, asy = c(0.5,0.9), model = "aneglog")
[1] 0.163507
> dbvevd(c(0,0), alpha = 0.75, beta = 0.5, model = "ct", log = TRUE)
[1] -1.798680
> dbvevd(c(0,0), alpha = 0.7, beta = 1.52, model = "negbilog")
[1] 0.1759396
> 
> 
> 
> cleanEx(); ..nameEx <- "chiplot"
> 
> ### * chiplot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: chiplot
> ### Title: Dependence Measure Plots
> ### Aliases: chiplot
> ### Keywords: hplot
> 
> ### ** Examples
> 
> par(mfrow = c(1,2))
> smdat1 <- rbvevd(1000, dep = 0.6, model = "log")
> smdat2 <- rbvevd(1000, dep = 1, model = "log")
> chiplot(smdat1)
> chiplot(smdat2)
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "clusters"
> 
> ### * clusters
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: clusters
> ### Title: Identify Clusters of Exceedences
> ### Aliases: clusters
> ### Keywords: manip
> 
> ### ** Examples
> 
> data(portpirie)
> clusters(portpirie, 4.2, 3)
$cluster1
   9   10   11   12 
4.36 3.96 3.98 4.69 

$cluster2
  20   21   22   23   24   25   26 
4.25 3.97 4.05 4.24 4.22 3.73 4.37 

$cluster3
  31 
4.55 

$cluster4
  38   39   40   41   42   43 
4.21 4.01 4.11 4.24 3.96 4.21 

$cluster5
  58   59 
4.33 4.55 

$cluster6
  65 
4.33 

attr(,"acs")
[1] 2.166667
> clusters(portpirie, 4.2, 3, cmax = TRUE)
  12   26   31   41   59   65 
4.69 4.37 4.55 4.24 4.55 4.33 
attr(,"acs")
[1] 2.166667
> clusters(portpirie, 4.2, 3, 3.8, plot = TRUE)
> clusters(portpirie, 4.2, 3, 3.8, plot = TRUE, lvals = FALSE)
> tvu <- c(rep(4.2, 20), rep(4.1, 25), rep(4.2, 20))
> clusters(portpirie, tvu, 3, plot = TRUE)
> 
> 
> 
> cleanEx(); ..nameEx <- "evmc"
> 
> ### * evmc
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: evmc
> ### Title: Simulate Markov Chains With Extreme Value Dependence Structures
> ### Aliases: evmc
> ### Keywords: distribution
> 
> ### ** Examples
> 
> evmc(100, alpha = 0.1, beta = 0.1, model = "bilog")
  [1] 0.2655087 0.2493311 0.2614518 0.3454474 0.2964023 0.3769529 0.4796189
  [8] 0.5018306 0.5186875 0.4197970 0.3698886 0.3140964 0.3436225 0.3270035
 [15] 0.3718841 0.3716701 0.4059425 0.5723884 0.5544245 0.5927964 0.6684819
 [22] 0.6284096 0.6445051 0.5826650 0.5476211 0.5302293 0.3737576 0.3563103
 [29] 0.4260592 0.4013080 0.3984068 0.4129188 0.4115486 0.3571454 0.4148297
 [36] 0.4398502 0.4872760 0.4096966 0.4442778 0.4304718 0.4843178 0.5042983
 [43] 0.5464635 0.5517890 0.5539393 0.5945228 0.4646595 0.4603661 0.4949906
 [50] 0.5217597 0.5171183 0.5760992 0.5660847 0.5264390 0.4331993 0.3519331
 [57] 0.3243236 0.3278534 0.3531216 0.3397654 0.4262481 0.3935740 0.3873886
 [64] 0.3617582 0.3848413 0.3461516 0.3435172 0.3875241 0.3008859 0.3729991
 [71] 0.3487023 0.4097061 0.3861262 0.3607424 0.3575624 0.4350805 0.5001975
 [78] 0.4830857 0.5251852 0.6252280 0.6151365 0.6398744 0.6256938 0.6012378
 [85] 0.6333760 0.5890504 0.6148575 0.5491226 0.5090381 0.4438883 0.4010058
 [92] 0.3001417 0.3224205 0.3948346 0.4404866 0.4885489 0.4809422 0.4668053
 [99] 0.5166757 0.5296978
> evmc(100, dep = 10, model = "hr", margins = "exp")
  [1] 0.4235416 0.4608016 0.5243798 0.3274644 0.3101703 0.3674429 0.4639559
  [8] 0.4736111 0.3611857 0.3480652 0.2384190 0.2141353 0.2337807 0.2454883
 [15] 0.3060496 0.4804770 0.4340566 0.5614075 0.5808711 0.5458954 0.3435640
 [22] 0.3486165 0.3556667 0.4329307 0.3822891 0.3956244 0.3979452 0.4720024
 [29] 0.5508679 0.5298255 0.5151115 0.6856548 0.9765001 0.9105399 0.6873832
 [36] 0.6587334 0.6433208 0.6392263 0.4207667 0.4237464 0.3902786 0.3753681
 [43] 0.4364527 0.5006024 0.4484915 0.4637566 0.5618934 0.4977375 0.6411323
 [50] 0.5210076 0.4965995 0.4873643 0.5363050 0.5533407 0.5576959 0.6709124
 [57] 0.6651221 0.8828558 0.9893672 1.1501960 1.2755854 1.0000088 1.0252507
 [64] 0.8839541 0.7058467 0.7399824 0.9960942 1.0789504 0.9618150 1.0414859
 [71] 0.9762565 0.8065645 0.6591158 0.6992525 0.7452191 0.5869092 0.5503058
 [78] 0.4892170 0.4687821 0.3670632 0.4131233 0.4953980 0.3948404 0.3986918
 [85] 0.3210746 0.3866802 0.3409739 0.3070887 0.2275040 0.2255305 0.2050093
 [92] 0.2201386 0.2875949 0.2186991 0.2486509 0.2411302 0.3115681 0.2594161
 [99] 0.2888665 0.2510050
> 
> 
> 
> cleanEx(); ..nameEx <- "exi"
> 
> ### * exi
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: exi
> ### Title: Estimates of the Extremal Index
> ### Aliases: exi
> ### Keywords: manip
> 
> ### ** Examples
> 
> data(portpirie)
> exi(portpirie, 4.2, 3, 3.8)
[1] 0.5384615
> us <- seq(3.9, 4.2, len = 10)
> exi(portpirie, us, 3)
 [1] 0.1388889 0.1764706 0.2413793 0.2692308 0.2608696 0.3500000 0.3888889
 [8] 0.4666667 0.4666667 0.4615385
> exi(portpirie, us, 1:3)
           [,1]      [,2]      [,3]
 [1,] 0.4722222 0.2500000 0.1388889
 [2,] 0.4411765 0.2647059 0.1764706
 [3,] 0.5517241 0.3448276 0.2413793
 [4,] 0.6153846 0.3846154 0.2692308
 [5,] 0.6956522 0.3913043 0.2608696
 [6,] 0.8000000 0.5000000 0.3500000
 [7,] 0.8333333 0.5555556 0.3888889
 [8,] 0.8666667 0.6666667 0.4666667
 [9,] 0.8666667 0.6666667 0.4666667
[10,] 0.8461538 0.6923077 0.4615385
> tvu <- c(rep(4.2, 20), rep(4.1, 25), rep(4.2, 20))
> exi(portpirie, as.matrix(tvu), 1:3)
[1] 0.8 0.6 0.4
> 
> us <- seq(3.9, 4.2, len = 100)
> eis <- exi(portpirie, us, 1:3)
> matplot(us, eis, type = "l", xlab = "Threshold", ylab = "Ext. Index")
> 
> 
> 
> cleanEx(); ..nameEx <- "extreme"
> 
> ### * extreme
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: extreme
> ### Title: Distributions of Maxima and Minima
> ### Aliases: dextreme pextreme qextreme rextreme
> ### Keywords: distribution
> 
> ### ** Examples
> 
> dextreme(2:4, dnorm, pnorm, mean = 0.5, sd = 1.2, mlen = 5)
[1] 0.48689660 0.17602941 0.02346192
> dextreme(2:4, distn = "norm", mean = 0.5, sd = 1.2, mlen = 5)
[1] 0.48689660 0.17602941 0.02346192
> dextreme(2:4, distn = "exp", mlen = 2, largest = FALSE)
[1] 0.0366312778 0.0049575044 0.0006709253
> pextreme(2:4, distn = "exp", rate = 1.2, mlen = 2)
[1] 0.8267938 0.9460991 0.9836082
> qextreme(seq(0.9, 0.6, -0.1), distn = "exp", rate = 1.2, mlen = 2)
[1] 2.474783 1.873629 1.509935 1.241553
> rextreme(5, qgamma, shape = 1, mlen = 10)
[1] 1.529282 2.668003 5.146046 2.888284 1.279281
> p <- (1:9)/10
> pexp(qextreme(p, distn = "exp", rate = 1.2, mlen = 1), rate = 1.2)
[1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
> ## [1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
> 
> 
> 
> cleanEx(); ..nameEx <- "fbvevd"
> 
> ### * fbvevd
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: fbvevd
> ### Title: Maximum-likelihood Fitting of Bivariate Extreme Value
> ###   Distributions
> ### Aliases: fbvevd print.bvevd
> ### Keywords: models
> 
> ### ** Examples
> 
> bvdata <- rbvevd(100, dep = 0.6, model = "log", mar1 = c(1.2,1.4,0.4))
> M1 <- fbvevd(bvdata, model = "log")
> M2 <- fbvevd(bvdata, model = "log", dep = 0.75)
> anova(M1, M2)
Analysis of Deviance Table

   M.Df Deviance Df  Chisq Pr(>chisq)   
M1    7   809.29                        
M2    6   819.10  1 9.8085   0.001737 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
> par(mfrow = c(2,2))
> plot(M1)
> plot(M1, mar = 1)
> plot(M1, mar = 2)
> ## Not run: par(mfrow = c(1,1))
> ## Not run: M1P <- profile(M1, which = "dep")
> ## Not run: plot(M1P)
> 
> trend <- (-49:50)/100
> rnd <- runif(100, min = -.5, max = .5)
> fbvevd(bvdata, model = "log", nsloc1 = trend)

Call: fbvevd(x = bvdata, model = "log", nsloc1 = trend) 
Deviance: 809.2756 
AIC: 825.2756 

Estimates
     loc1  loc1trend     scale1     shape1       loc2     scale2     shape2  
  1.28198   -0.03981    1.37525    0.43585    1.29947    1.41485    0.41422  
      dep  
  0.56473  

Standard Errors
     loc1  loc1trend     scale1     shape1       loc2     scale2     shape2  
  0.15306    0.32455    0.14334    0.08051    0.16026    0.14779    0.08598  
      dep  
  0.05488  

Dependence Structure
  Dependence One: 0.5208874 
  Dependence Two: 0.6838701 
  Asymmetry: 0 

Optimization Information
  Convergence: successful 
  Function Evaluations: 51 
  Gradient Evaluations: 15 

> fbvevd(bvdata, model = "log", nsloc1 = trend, nsloc2 = data.frame(trend
+ = trend,  random = rnd))

Call: fbvevd(x = bvdata, model = "log", nsloc1 = trend, nsloc2 = data.frame(trend = trend,      random = rnd)) 
Deviance: 809.2208 
AIC: 829.2208 

Estimates
      loc1   loc1trend      scale1      shape1        loc2   loc2trend  
   1.28138    -0.02438     1.37503     0.43638     1.29627     0.03587  
loc2random      scale2      shape2         dep  
  -0.06761     1.40886     0.41810     0.56557  

Standard Errors
      loc1   loc1trend      scale1      shape1        loc2   loc2trend  
    0.1531      0.3447      0.1433      0.0807      0.1603      0.3294  
loc2random      scale2      shape2         dep  
    0.3516      0.1496      0.0882      0.0551  

Dependence Structure
  Dependence One: 0.5200316 
  Dependence Two: 0.6829166 
  Asymmetry: 0 

Optimization Information
  Convergence: successful 
  Function Evaluations: 56 
  Gradient Evaluations: 15 

> fbvevd(bvdata, model = "log", nsloc1 = trend, nsloc2 = data.frame(trend
+ = trend, random = rnd), loc2random = 0)

Call: fbvevd(x = bvdata, model = "log", loc2random = 0, nsloc1 = trend,      nsloc2 = data.frame(trend = trend, random = rnd)) 
Deviance: 809.2574 
AIC: 827.2574 

Estimates
     loc1  loc1trend     scale1     shape1       loc2  loc2trend     scale2  
  1.28188   -0.02397    1.37538    0.43550    1.29840    0.04441    1.41243  
   shape2        dep  
  0.41525    0.56530  

Standard Errors
     loc1  loc1trend     scale1     shape1       loc2  loc2trend     scale2  
  0.15306    0.34532    0.14333    0.08046    0.16030    0.32800    0.14876  
   shape2        dep  
  0.08661    0.05504  

Dependence Structure
  Dependence One: 0.520307 
  Dependence Two: 0.6832235 
  Asymmetry: 0 

Optimization Information
  Convergence: successful 
  Function Evaluations: 57 
  Gradient Evaluations: 14 

> 
> bvdata <- rbvevd(100, dep = 1, asy = c(0.5,0.5), model = "anegl")
> anlog <- fbvevd(bvdata, model = "anegl")
> mixed <- fbvevd(bvdata, model = "anegl", dep = 1, sym = TRUE)
> anova(anlog, mixed)
Analysis of Deviance Table

      M.Df Deviance Df  Chisq Pr(>chisq)
anlog    9   616.71                     
mixed    7   618.05  2 1.3402     0.5116
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "fbvpot"
> 
> ### * fbvpot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: fbvpot
> ### Title: Maximum-likelihood Fitting of Bivariate Extreme Value
> ###   Distributions to Threshold Exceedances
> ### Aliases: fbvpot print.bvpot plot.bvpot
> ### Keywords: models
> 
> ### ** Examples
> 
> bvdata <- rbvevd(1000, dep = 0.5, model = "log")
> u <- apply(bvdata, 2,  quantile, probs = 0.9)
> M1 <- fbvpot(bvdata, u, model = "log")
> M2 <- fbvpot(bvdata, u, "log", dep = 0.5)
> anova(M1, M2)
Analysis of Deviance Table

   M.Df Deviance Df  Chisq Pr(>chisq)
M1    5   1411.8                     
M2    4   1412.2  1 0.3234     0.5696
> 
> 
> 
> cleanEx(); ..nameEx <- "fextreme"
> 
> ### * fextreme
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: fextreme
> ### Title: Maximum-likelihood Fitting of Maxima and Minima
> ### Aliases: fextreme
> ### Keywords: models
> 
> ### ** Examples
> 
> uvdata <- rextreme(100, qnorm, mean = 0.56, mlen = 365)
> fextreme(uvdata, list(mean = 0, sd = 1), distn = "norm", mlen = 365)

Call: fextreme(x = uvdata, start = list(mean = 0, sd = 1), distn = "norm",      mlen = 365) 
Deviance: 84.84415 

Estimates
  mean      sd  
0.5076  1.0127  

Standard Errors
   mean       sd  
0.20817  0.07593  

Optimization Information
  Convergence: successful 
  Function Evaluations: 63 

> fextreme(uvdata, list(rate = 1), distn = "exp", mlen = 365)
Warning in optim(start, nllh, x = x, hessian = TRUE, ..., densfun = densfun,  : 
	 one-diml optimization by Nelder-Mead is unreliable: use optimize

Call: fextreme(x = uvdata, start = list(rate = 1), distn = "exp", mlen = 365) 
Deviance: 126.1562 

Estimates
 rate  
1.784  

Standard Errors
   rate  
0.03163  

Optimization Information
  Convergence: successful 
  Function Evaluations: 26 

> fextreme(uvdata, list(scale = 1), shape = 1, distn = "gamma", mlen = 365)
Warning in optim(start, nllh, x = x, hessian = TRUE, ..., densfun = densfun,  : 
	 one-diml optimization by Nelder-Mead is unreliable: use optimize

Call: fextreme(x = uvdata, start = list(scale = 1), shape = 1, distn = "gamma",      mlen = 365) 
Deviance: 126.1562 

Estimates
 scale  
0.5607  

Standard Errors
   scale  
0.009943  

Optimization Information
  Convergence: successful 
  Function Evaluations: 30 

> fextreme(uvdata, list(shape = 1, scale = 1), distn = "gamma", mlen = 365)

Call: fextreme(x = uvdata, start = list(shape = 1, scale = 1), distn = "gamma",      mlen = 365) 
Deviance: 85.27312 

Estimates
 shape   scale  
5.7105  0.2272  

Standard Errors
  shape    scale  
1.18200  0.02957  

Optimization Information
  Convergence: successful 
  Function Evaluations: 123 

> 
> 
> 
> cleanEx(); ..nameEx <- "fgev"
> 
> ### * fgev
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: fgev
> ### Title: Maximum-likelihood Fitting of the Generalized Extreme Value
> ###   Distribution
> ### Aliases: fgev fitted.evd std.errors std.errors.evd print.evd logLik.evd
> ### Keywords: models
> 
> ### ** Examples
> 
> uvdata <- rgev(100, loc = 0.13, scale = 1.1, shape = 0.2)
> trend <- (-49:50)/100
> M1 <- fgev(uvdata, nsloc = trend, control = list(trace = 1))
initial  value 156.827545 
final  value 156.171376 
converged
> M2 <- fgev(uvdata)
> M3 <- fgev(uvdata, shape = 0)
> M4 <- fgev(uvdata, scale = 1, shape = 0)
> anova(M1, M2, M3, M4)
Analysis of Deviance Table

   M.Df Deviance Df  Chisq Pr(>chisq)
M1    4   312.34                     
M2    3   312.39  1 0.0470     0.8283
M3    2   313.07  1 0.6777     0.4104
M4    1   313.21  1 0.1402     0.7081
> par(mfrow = c(2,2))
> plot(M2)
> ## Not run: M2P <- profile(M2)
> ## Not run: plot(M2P)
> 
> rnd <- runif(100, min = -.5, max = .5)
> fgev(uvdata, nsloc = data.frame(trend = trend, random = rnd))

Call: fgev(x = uvdata, nsloc = data.frame(trend = trend, random = rnd)) 
Deviance: 312.3426 

Estimates
      loc   loctrend  locrandom      scale      shape  
 0.089361   0.073210  -0.003946   0.948606   0.061243  

Standard Errors
      loc   loctrend  locrandom      scale      shape  
  0.10745    0.33720    0.30948    0.07989    0.07804  

Optimization Information
  Convergence: successful 
  Function Evaluations: 30 
  Gradient Evaluations: 10 

> fgev(uvdata, nsloc = data.frame(trend = trend, random = rnd), locrandom = 0)

Call: fgev(x = uvdata, locrandom = 0, nsloc = data.frame(trend = trend,      random = rnd)) 
Deviance: 312.3428 

Estimates
     loc  loctrend     scale     shape  
 0.08928   0.07308   0.94853   0.06139  

Standard Errors
     loc  loctrend     scale     shape  
 0.10727   0.33697   0.07962   0.07721  

Optimization Information
  Convergence: successful 
  Function Evaluations: 35 
  Gradient Evaluations: 8 

> 
> uvdata <- rgev(100, loc = 0.13, scale = 1.1, shape = 0.2)
> M1 <- fgev(uvdata, prob = 0.1)
> M2 <- fgev(uvdata, prob = 0.01)
> ## Not run: M1P <- profile(M1, which = "quantile")
> ## Not run: M2P <- profile(M2, which = "quantile")
> ## Not run: plot(M1P)
> ## Not run: plot(M2P)
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "forder"
> 
> ### * forder
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: forder
> ### Title: Maximum-likelihood Fitting of Order Statistics
> ### Aliases: forder
> ### Keywords: models
> 
> ### ** Examples
> 
> uvd <- rorder(100, qnorm, mean = 0.56, mlen = 365, j = 2)
> forder(uvd, list(mean = 0, sd = 1), distn = "norm", mlen = 365, j = 2)

Call: forder(x = uvd, start = list(mean = 0, sd = 1), distn = "norm",      mlen = 365, j = 2) 
Deviance: -5.755266 

Estimates
  mean      sd  
0.7668  0.9038  

Standard Errors
   mean       sd  
0.16546  0.06538  

Optimization Information
  Convergence: successful 
  Function Evaluations: 67 

> forder(uvd, list(rate = 1), distn = "exp", mlen = 365, j = 2)
Warning in optim(start, nllh, x = x, hessian = TRUE, ..., densfun = densfun,  : 
	 one-diml optimization by Nelder-Mead is unreliable: use optimize

Call: forder(x = uvd, start = list(rate = 1), distn = "exp", mlen = 365,      j = 2) 
Deviance: 47.80988 

Estimates
 rate  
1.705  

Standard Errors
   rate  
0.02364  

Optimization Information
  Convergence: successful 
  Function Evaluations: 30 

> forder(uvd, list(scale = 1), shape = 1, distn = "gamma", mlen = 365, j = 2)
Warning in optim(start, nllh, x = x, hessian = TRUE, ..., densfun = densfun,  : 
	 one-diml optimization by Nelder-Mead is unreliable: use optimize

Call: forder(x = uvd, start = list(scale = 1), shape = 1, distn = "gamma",      mlen = 365, j = 2) 
Deviance: 47.80988 

Estimates
 scale  
0.5864  

Standard Errors
   scale  
0.008129  

Optimization Information
  Convergence: successful 
  Function Evaluations: 30 

> forder(uvd, list(shape = 1, scale = 1), distn = "gamma", mlen = 365, j = 2)

Call: forder(x = uvd, start = list(shape = 1, scale = 1), distn = "gamma",      mlen = 365, j = 2) 
Deviance: -4.912165 

Estimates
 shape   scale  
6.4968  0.2073  

Standard Errors
  shape    scale  
1.25585  0.02666  

Optimization Information
  Convergence: successful 
  Function Evaluations: 189 

> 
> 
> 
> cleanEx(); ..nameEx <- "fpot"
> 
> ### * fpot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: fpot
> ### Title: Peaks Over Threshold Modelling using the Generalized Pareto or
> ###   Point Process Representation
> ### Aliases: fpot print.pot
> ### Keywords: models
> 
> ### ** Examples
> 
> uvdata <- rgpd(100, loc = 0, scale = 1.1, shape = 0.2)
> M1 <- fpot(uvdata, 1)
> M2 <- fpot(uvdata, 1, shape = 0)
> anova(M1, M2)
Analysis of Deviance Table

   M.Df Deviance Df  Chisq Pr(>chisq)
M1    2   94.422                     
M2    1   95.600  1 1.1786     0.2776
> par(mfrow = c(2,2))
> plot(M1)
> ## Not run: M1P <- profile(M1)
> ## Not run: plot(M1P)
> 
> M1 <- fpot(uvdata, 1, mper = 10)
> M2 <- fpot(uvdata, 1, mper = 100)
> ## Not run: M1P <- profile(M1, which = "rlevel", conf=0.975, mesh=0.1)
> ## Not run: M2P <- profile(M2, which = "rlevel", conf=0.975, mesh=0.1)
> ## Not run: plot(M1P)
> ## Not run: plot(M2P)
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "frechet"
> 
> ### * frechet
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: frechet
> ### Title: The Frechet Distribution
> ### Aliases: dfrechet pfrechet qfrechet rfrechet
> ### Keywords: distribution
> 
> ### ** Examples
> 
> dfrechet(2:4, 1, 0.5, 0.8)
[1] 0.25871959 0.09487423 0.05010381
> pfrechet(2:4, 1, 0.5, 0.8)
[1] 0.5630712 0.7190122 0.7878127
> qfrechet(seq(0.9, 0.6, -0.1), 2, 0.5, 0.8)
[1] 10.329571  5.260165  3.813966  3.157788
> rfrechet(6, 1, 0.5, 0.8)
[1] 1.710241 1.405847 6.554187 6.849307 2.410998 1.132408
> p <- (1:9)/10
> pfrechet(qfrechet(p, 1, 2, 0.8), 1, 2, 0.8)
[1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
> ## [1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
> 
> 
> 
> cleanEx(); ..nameEx <- "gev"
> 
> ### * gev
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: gev
> ### Title: The Generalized Extreme Value Distribution
> ### Aliases: dgev pgev qgev rgev
> ### Keywords: distribution
> 
> ### ** Examples
> 
> dgev(2:4, 1, 0.5, 0.8)
[1] 0.17210639 0.06706381 0.03428205
> pgev(2:4, 1, 0.5, 0.8)
[1] 0.7386812 0.8467772 0.8948490
> qgev(seq(0.9, 0.6, -0.1), 2, 0.5, 0.8)
[1] 5.157141 3.449973 2.800811 2.444700
> rgev(6, 1, 0.5, 0.8)
[1] 1.1574179 0.9218788 3.2930742 3.3913800 1.5890488 0.6420330
> p <- (1:9)/10
> pgev(qgev(p, 1, 2, 0.8), 1, 2, 0.8)
[1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
> ## [1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
> 
> 
> 
> cleanEx(); ..nameEx <- "gpd"
> 
> ### * gpd
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: gpd
> ### Title: The Generalized Pareto Distribution
> ### Aliases: dgpd pgpd qgpd rgpd
> ### Keywords: distribution
> 
> ### ** Examples
> 
> dgpd(2:4, 1, 0.5, 0.8)
[1] 0.23299144 0.07919889 0.03831043
> pgpd(2:4, 1, 0.5, 0.8)
[1] 0.6971111 0.8336823 0.8888998
> qgpd(seq(0.9, 0.6, -0.1), 2, 0.5, 0.8)
[1] 5.318483 3.639936 3.012506 2.675864
> rgpd(6, 1, 0.5, 0.8)
[1] 2.180581 1.753256 1.350990 1.050044 2.624813 1.055939
> p <- (1:9)/10
> pgpd(qgpd(p, 1, 2, 0.8), 1, 2, 0.8)
[1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
> ## [1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
> 
> 
> 
> cleanEx(); ..nameEx <- "gumbel"
> 
> ### * gumbel
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: gumbel
> ### Title: The Gumbel Distribution
> ### Aliases: dgumbel pgumbel qgumbel rgumbel
> ### Keywords: distribution
> 
> ### ** Examples
> 
> dgumbel(-1:2, -1, 0.5)
[1] 0.735758882 0.236409903 0.035966459 0.004945231
> pgumbel(-1:2, -1, 0.5)
[1] 0.3678794 0.8734230 0.9818511 0.9975243
> qgumbel(seq(0.9, 0.6, -0.1), 2, 0.5)
[1] 3.125184 2.749970 2.515465 2.335863
> rgumbel(6, -1, 0.5)
[1] -0.85960164 -1.08345283 -0.03692030 -0.01621183 -0.58502218 -1.53148712
> p <- (1:9)/10
> pgumbel(qgumbel(p, -1, 2), -1, 2)
[1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
> ## [1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
> 
> 
> 
> cleanEx(); ..nameEx <- "marma"
> 
> ### * marma
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: marma
> ### Title: Simulate MARMA(p,q) Processes
> ### Aliases: marma mar mma
> ### Keywords: distribution
> 
> ### ** Examples
> 
> marma(100, p = 1, q = 1, psi = 0.75, theta = 0.65)
  [1]  0.8607199  6.8631011  7.1533183  5.3649887  4.0237415  3.0178061
  [7]  2.2633546  1.6975160  6.8005935  5.1004451  3.8253338  2.8690004
 [13]  2.1517503  1.6138127  1.2103595  0.9077696  1.5273083  2.9679449
 [19]  2.2259587  1.6694690  1.5578930  3.3999683  2.5499762  9.4275032
 [25] 16.8239254 12.6179440  9.4634580  7.0975935  5.3231951  3.9923964
 [31] 26.8322925 20.1242194 15.0931645 11.3198734  8.4899050  6.3674288
 [37]  4.7755716  3.5816787  4.2548221  3.1911166  2.3933375  1.7950031
 [43]  1.3462523  1.8029668  3.3191385  2.4893539  1.8670154  1.9448658
 [49]  1.4586493  2.3683076  1.7762307  1.3321730  1.7926629  1.6817530
 [55]  1.2613147  4.7649321  3.5736991  2.6802743  2.0102057 11.1514946
 [61]  8.3636209  6.2727157  4.7045368  3.5284026  2.6463019  1.9847265
 [67]  1.4885448  1.2294554  1.1947339  0.8960504  0.6720378  0.5040283
 [73]  3.5015111  2.6261333 19.2102852 14.4077139 10.8057854  8.1043391
 [79]  6.0782543  4.5586907  3.4190180  2.5642635 16.8744458 12.6558343
 [85]  9.4918757  7.1189068  5.3391801  4.0043851  3.0032888  2.2524666
 [91]  2.2348771  1.6761578  3.8339406  2.8754555  3.7916379  2.8437284
 [97]  4.7481827  7.5431047  5.6573286  4.2429964
> mar(100, psi = 0.85, n.start = 20)
  [1]  7.520366  6.392311  5.433465 49.381709 41.974452 35.678285 30.326542
  [8] 25.777561 21.910926 18.624288 15.830644 13.456048 11.437641  9.721994
 [15]  8.263695  7.024141 10.116786  8.599268  7.309378  6.212971  5.281026
 [22]  4.488872  3.815541  4.549012  3.866661  3.286661  2.793662  2.374613
 [29]  2.018421  1.963051  4.152478  3.529606  3.000165  2.550140  2.167619
 [36]  1.842477  1.566105 18.307791 30.081759 25.569495 21.734071 18.473960
 [43] 15.702866 13.347436 11.345321  9.643523  8.196994  6.967445  5.922328
 [50]  5.033979  4.278882  3.637050  3.091492  2.627769  2.233603  1.898563
 [57]  1.613778  2.273616  1.932574  1.642688  4.009167  3.407792  2.896623
 [64]  2.462130  2.092810  1.778889  1.907294  4.913719  4.176661  3.550162
 [71]  3.017637  8.176131  6.949712  5.907255  5.021167  4.267992  3.627793
 [78]  3.083624 17.542662 14.911263 12.674573 10.773387  9.157379  7.783772
 [85]  6.616207  5.623776  4.780209  4.063178  3.453701  6.504083  5.528470
 [92]  4.699200  8.141871  6.920590  6.214197  5.282067  4.489757  3.816294
 [99]  9.316665  7.919165
> mma(100, q = 2, theta = c(0.75, 0.8))
  [1]   0.8508496   3.1470766   2.3603074   2.5176613   2.2352288   1.6764216
  [7]   1.7881830   1.3344363   4.0365748   3.0274311   3.2292599   2.4288469
 [13]   1.8216351   2.5639415   1.9229562   2.0511532   1.2391831   0.9336225
 [19]   0.7597621   0.7468980   1.7208045  19.9100528  14.9325396  15.9280423
 [25]  10.5638668   2.0864478   1.5648359   1.6691583   1.1505965 587.8982779
 [31] 440.9237084 470.3186223   3.6446823   1.0664628  20.6003120  15.4502340
 [37]  16.4802496   3.1170460  22.2124226  16.6593169  17.7699380   8.8462508
 [43]   6.6346881   7.0770006   2.7244792   2.9061111   1.9207625   1.4405719
 [49]   2.4518151 222.1417303 166.6062977 177.7133842   0.4200503   8.7794384
 [55]   6.5845788   7.0235507   1.4440610   1.0830458   3.1634046   3.0610485
 [61]   2.5307237   2.7087716 177.4869855 133.1152391 141.9895884   1.7561806
 [67]   3.4230802  11.9106379   8.9329784   9.5285103   2.7191898   2.9004692
 [73]   1.7944713   1.9141027   0.8029155   1.9015436   2.3789537   1.7842153
 [79]   1.9031630   0.9609283   0.7206962   4.1059911   3.0794933   4.3885099
 [85]   3.2913824   3.5108079   8.0660652   6.0495489 130.7254223  98.0440667
 [91] 104.5803378   3.0173649   1.5568452   1.5182602   3.2313753   2.4235315
 [97]   2.5851003   2.1610260   5.9719335   4.4789501
> 
> 
> 
> cleanEx(); ..nameEx <- "mrlplot"
> 
> ### * mrlplot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mrlplot
> ### Title: Empirical Mean Residual Life Plot
> ### Aliases: mrlplot
> ### Keywords: hplot
> 
> ### ** Examples
> 
> data(portpirie)
> mrlplot(portpirie)
> 
> 
> 
> cleanEx(); ..nameEx <- "mvevd"
> 
> ### * mvevd
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mvevd
> ### Title: Parametric Multivariate Extreme Value Distributions
> ### Aliases: pmvevd rmvevd dmvevd
> ### Keywords: distribution
> 
> ### ** Examples
> 
> pmvevd(matrix(rep(0:4,5), ncol=5), dep = .7, model = "log", d = 5)
[1] 0.04572229 0.32143165 0.65867024 0.85761368 0.94506001
> pmvevd(rep(4,5), dep = .7, model = "log", d = 5)
[1] 0.94506
> rmvevd(10, dep = .7, model = "log", d = 5)
             [,1]       [,2]         [,3]       [,4]        [,5]
 [1,]  0.76277608  0.7917679 -0.004566544 -1.3296175 -0.73019614
 [2,]  1.63991195  0.0671223  0.488243717  0.1487812 -0.74291554
 [3,] -0.93506965 -0.1982024  0.266851603 -0.1235083 -1.09705836
 [4,]  1.26740216  2.4393649  2.844784385  1.2516877 -0.09435917
 [5,]  0.55018216 -0.1149002 -0.370089258  2.1855896  0.67175161
 [6,]  0.03645401  0.8909146  0.277085707  0.2521231  1.06582113
 [7,] -0.66706290 -0.6455245 -0.074983720  0.3522065 -0.66753023
 [8,] -0.78411600  0.3073459 -0.841310159 -1.1142631  0.11241454
 [9,]  1.49337275  1.2215504  0.329989857  0.5796330  2.08857592
[10,]  0.45597383 -0.1615263  0.165302084  0.5067765 -0.78527294
> dmvevd(rep(-1,20), dep = .7, model = "log", d = 20, log = TRUE)
[1] -15.94830
> 
> asy <- list(.4, .1, .6, c(.3,.2), c(.1,.1), c(.4,.1), c(.2,.3,.2))
> pmvevd(rep(2,3), dep = c(.6,.5,.8,.3), asy = asy, model = "alog", d = 3)
[1] 0.7206158
> asy <- list(.4, .0, .6, c(.3,.2), c(.1,.1), c(.4,.1), c(.2,.4,.2))
> rmvevd(10, dep = c(.6,.5,.8,.3), asy = asy, model = "alog", d = 3)
            [,1]       [,2]       [,3]
 [1,] -0.5177859 -0.7896065 -0.3042543
 [2,] -0.6485173  0.5556615 -0.2928822
 [3,]  1.9463989  0.8921011  0.2899117
 [4,]  1.0359059  0.9633195  0.6959547
 [5,] -0.3182253 -0.9797469  1.3425462
 [6,] -0.2417911 -0.4652638  0.9128796
 [7,] -1.5432141 -0.6532295 -1.2595984
 [8,] -0.3041101  0.2085277 -0.2425012
 [9,] -0.5230009 -0.2366470 -0.2883650
[10,]  0.2242628 -0.5753941  0.2739568
> dmvevd(rep(0,3), dep = c(.6,.5,.8,.3), asy = asy, model = "alog", d = 3)
[1] 0.06688153
> 
> asy <- list(0, 0, 0, 0, c(0,0), c(0,0), c(0,0), c(0,0), c(0,0), c(0,0),
+   c(.2,.1,.2), c(.1,.1,.2), c(.3,.4,.1), c(.2,.2,.2), c(.4,.6,.2,.5))
> rmvevd(10, dep = .7, asy = asy, model = "alog", d = 4)
             [,1]        [,2]       [,3]        [,4]
 [1,]  0.03960371  1.65070704  0.1169132  1.02866031
 [2,] -0.07336469 -1.04614880  0.5198222  0.57585211
 [3,] -0.04739408 -0.59249764  0.6281898 -0.01544431
 [4,]  0.04957722  0.29027643 -0.4341733 -0.58607627
 [5,]  1.41372886  0.08340703  1.0034009  0.99921066
 [6,] -0.55533207  0.85032644 -1.5267018 -0.79288766
 [7,] -0.38224882 -0.12235251 -0.4949282  1.33373167
 [8,] -0.22545362  0.44718478  3.4981291  0.10547575
 [9,] -0.87603526 -0.42115158 -1.1047570  1.29297254
[10,] -0.98209988  1.20687004  0.3790307 -0.40629204
> rmvevd(10, dep = c(rep(1,6), rep(.7,5)), asy = asy, model = "alog", d = 4)
               [,1]       [,2]        [,3]       [,4]
 [1,]  7.2134931221  6.9106500  7.21182122  1.0182006
 [2,]  0.9920807997 -0.1880452 -0.43933165 -0.1885301
 [3,] -0.6722461069  0.3715169 -0.16742472 -0.8321245
 [4,]  0.0007447578 -0.8028974  1.21222317  0.6671544
 [5,] -0.2911107207 -0.6922242 -0.04528853 -1.3108664
 [6,] -0.0671249838 -0.6821777 -0.32476913 -1.1036482
 [7,] -0.3866065687  3.2108672 -1.14491191 -0.7340268
 [8,]  1.0105847312  2.7393343  0.55862424  1.0609543
 [9,]  4.4580686597 -0.6178543  3.45306245  1.1977051
[10,]  2.3756933696 -0.8413361  0.57596720  0.7727093
> 
> 
> 
> cleanEx(); ..nameEx <- "order"
> 
> ### * order
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: order
> ### Title: Distributions of Order Statistics
> ### Aliases: dorder porder rorder
> ### Keywords: distribution
> 
> ### ** Examples
> 
> dorder(2:4, dnorm, pnorm, mean = 0.5, sd = 1.2, mlen = 5, j = 2)
[1] 0.2300687782 0.0133524232 0.0001663078
> dorder(2:4, distn = "norm", mean = 0.5, sd = 1.2, mlen = 5, j = 2)
[1] 0.2300687782 0.0133524232 0.0001663078
> dorder(2:4, distn = "exp", mlen = 2, j = 2)
[1] 0.0366312778 0.0049575044 0.0006709253
> porder(2:4, distn = "exp", rate = 1.2, mlen = 2, j = 2)
[1] 0.9917703 0.9992534 0.9999323
> rorder(5, qgamma, shape = 1, mlen = 10, j = 2)
[1] 2.228533 1.563058 2.422454 1.453327 2.408000
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.bvevd"
> 
> ### * plot.bvevd
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.bvevd
> ### Title: Plot Diagnostics for a Bivariate EVD Object
> ### Aliases: plot.bvevd
> ### Keywords: hplot
> 
> ### ** Examples
> 
> bvdata <- rbvevd(100, dep = 0.6, model = "log")
> M1 <- fbvevd(bvdata, model = "log")
> ## Not run: par(mfrow = c(2,2))
> ## Not run: plot(M1)
> ## Not run: plot(M1, mar = 1)
> ## Not run: plot(M1, mar = 2)
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.profile.evd"
> 
> ### * plot.profile.evd
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.profile.evd
> ### Title: Plot Profile Deviances and Calculate Profile Confidence
> ###   Intervals
> ### Aliases: plot.profile.evd
> ### Keywords: hplot
> 
> ### ** Examples
> 
> uvdata <- rgev(100, loc = 0.13, scale = 1.1, shape = 0.2)
> M1 <- fgev(uvdata)
> ## Not run: M1P <- profile(M1)
> ## Not run: par(mfrow = c(2,2))
> ## Not run: cint <- plot(M1P, ci = c(0.95, 0.99))
> ## Not run: cint
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.profile2d.evd"
> 
> ### * plot.profile2d.evd
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.profile2d.evd
> ### Title: Plot Joint Profile Devainces
> ### Aliases: plot.profile2d.evd
> ### Keywords: hplot
> 
> ### ** Examples
> 
> uvdata <- rgev(100, loc = 0.13, scale = 1.1, shape = 0.2)
> M1 <- fgev(uvdata)
> ## Not run: M1P <- profile(M1)
> ## Not run: M1JP <- profile2d(M1, M1P, which = c("scale", "shape"))
> ## Not run: plot(M1JP)
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.uvevd"
> 
> ### * plot.uvevd
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.uvevd
> ### Title: Plot Diagnostics for a Univariate EVD Object
> ### Aliases: plot.uvevd
> ### Keywords: hplot
> 
> ### ** Examples
> 
> uvdata <- rgev(100, loc = 0.13, scale = 1.1, shape = 0.2)
> M1 <- fgev(uvdata)
> ## Not run: par(mfrow = c(2,2))
> ## Not run: plot(M1)
> 
> uvdata <- rgpd(100, loc = 0, scale = 1.1, shape = 0.2)
> M1 <- fpot(uvdata, 1)
> ## Not run: par(mfrow = c(2,2))
> ## Not run: plot(M1)
> 
> 
> 
> cleanEx(); ..nameEx <- "profile.evd"
> 
> ### * profile.evd
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: profile.evd
> ### Title: Method for Profiling EVD Objects
> ### Aliases: profile.evd
> ### Keywords: models
> 
> ### ** Examples
> 
> uvdata <- rgev(100, loc = 0.13, scale = 1.1, shape = 0.2)
> M1 <- fgev(uvdata)
> ## Not run: M1P <- profile(M1)
> ## Not run: par(mfrow = c(2,2))
> ## Not run: cint <- plot(M1P)
> ## Not run: cint
> 
> 
> 
> cleanEx(); ..nameEx <- "profile2d.evd"
> 
> ### * profile2d.evd
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: profile2d.evd
> ### Title: Method for Profiling EVD Objects
> ### Aliases: profile2d profile2d.evd
> ### Keywords: models
> 
> ### ** Examples
> 
> uvdata <- rgev(100, loc = 0.13, scale = 1.1, shape = 0.2)
> M1 <- fgev(uvdata)
> ## Not run: M1P <- profile(M1)
> ## Not run: M1JP <- profile2d(M1, M1P, which = c("scale", "shape"))
> ## Not run: plot(M1JP)
> 
> 
> 
> cleanEx(); ..nameEx <- "rweibull"
> 
> ### * rweibull
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: rweibull
> ### Title: The Reversed Weibull Distribution
> ### Aliases: drweibull prweibull qrweibull rrweibull
> ### Keywords: distribution
> 
> ### ** Examples
> 
> drweibull(-5:-3, -1, 0.5, 0.8)
[1] 0.005386194 0.016885315 0.058502349
> prweibull(-5:-3, -1, 0.5, 0.8)
[1] 0.005102464 0.015101477 0.048246445
> qrweibull(seq(0.9, 0.6, -0.1), 2, 0.5, 0.8)
[1] 1.969986 1.923317 1.862180 1.784071
> rrweibull(6, -1, 0.5, 0.8)
[1] -1.351993 -1.615996 -1.045011 -1.042740 -1.177180 -2.888099
> p <- (1:9)/10
> prweibull(qrweibull(p, -1, 2, 0.8), -1, 2, 0.8)
[1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
> ## [1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
> 
> 
> 
> cleanEx(); ..nameEx <- "tcplot"
> 
> ### * tcplot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: tcplot
> ### Title: Threshold Choice Plot
> ### Aliases: tcplot
> ### Keywords: hplot
> 
> ### ** Examples
> 
> data(portpirie)
> tlim <- c(3.6, 4.2)
> ## Not run: tcplot(portpirie, tlim)
> ## Not run: tcplot(portpirie, tlim, nt = 100, lwd = 3, type = "l")
> ## Not run: tcplot(portpirie, tlim, model = "pp")
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  8.85 0.17 9.11 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')