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("distr-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('distr')
Loading required package: setRNG
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "AbscontDistribution-class"
> 
> ### * AbscontDistribution-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: AbscontDistribution-class
> ### Title: Class "AbscontDistribution"
> ### Aliases: AbscontDistribution-class
> ###   initialize,AbscontDistribution-method
> ### Keywords: distribution
> 
> ### ** Examples
> 
> N = Norm() # N is a normal distribution with mean=0 and sd=1.
> E = Exp() # E is an exponential distribution with rate=1.
> A1 = E+1 # a new absolutely continuous distributions with exact slots d, p, q
> A2 = A1*3 # a new absolutely continuous distributions with exact slots d, p, q
> A3 = N*0.9 + E*0.1 # a new absolutely continuous distribution with approximated slots d, p, q
> r(A3)(1) # one random number generated from this distribution, e.g. -0.7150937
[1] -0.5492378
> d(A3)(0) # The (approximated) density for x=0 is 0.4379882.
[1] 0.4379965
> p(A3)(0) # The (approximated) probability that x <= 0 is 0.4562021.
[1] 0.4561991
> q(A3)(.1) # The (approximated) 10 percent quantile is 0.1.
[1] -1.060143
> 
> 
> 
> cleanEx(); ..nameEx <- "Beta-class"
> 
> ### * Beta-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Beta-class
> ### Title: Class "Beta"
> ### Aliases: Beta-class Beta initialize,Beta-method
> ### Keywords: distribution
> 
> ### ** Examples
> 
> B <- Beta(shape1 = 1, shape2 = 1)
> # B is a beta distribution with shape1 = 1 and shape2 = 1.
> r(B)(1) # one random number generated from this distribution, e.g. 0.6979795
[1] 0.7344913
> d(B)(1) # Density of this distribution is 1 for x=1.
[1] 1
> p(B)(1) # Probability that x < 1 is 1.
[1] 1
> q(B)(.1) # Probability that x < 0.1 is 0.1.
[1] 0.1
> shape1(B) # shape1 of this distribution is 1.
[1] 1
> shape1(B) <- 2 # shape1 of this distribution is now 2.
> 
> 
> 
> cleanEx(); ..nameEx <- "BetaParameter-class"
> 
> ### * BetaParameter-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: BetaParameter-class
> ### Title: Class "BetaParameter"
> ### Aliases: BetaParameter-class initialize,BetaParameter-method
> 
> 
> ### ** Examples
> 
> W <- new("BetaParameter", shape1 = 1, shape2 = 1)
> shape2(W) # shape2 of this distribution is 1.
[1] 1
> shape2(W) <- 2 # shape2 of this distribution is now 2.
> 
> 
> 
> cleanEx(); ..nameEx <- "Binom-class"
> 
> ### * Binom-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Binom-class
> ### Title: Class "Binom"
> ### Aliases: Binom-class Binom initialize,Binom-method
> ### Keywords: distribution
> 
> ### ** Examples
> 
> B=Binom(prob=0.5,size=1) # B is a binomial distribution with prob=0.5 and size=1.
> r(B)(1) # # one random number generated from this distribution, e.g. 1
[1] 0
> d(B)(1) # Density of this distribution is  0.5 for x=1.
[1] 0.5
> p(B)(0.4) # Probability that x<0.4 is 0.5.
[1] 0.5
> q(B)(.1) # x=0 is the smallest value x such that p(B)(x)>=0.1.
[1] 0
> size(B) # size of this distribution is 1.
[1] 1
> size(B)=2 # size of this distribution is now 2.
> C=Binom(prob=0.5,size=1) # C is a binomial distribution with prob=0.5 and size=1.
> D=Binom(prob=0.6,size=1) # D is a binomial distribution with prob=0.6 and size=1.
> E=B+C # E is a binomial distribution with prob=0.5 and size=3.
> F=B+D # F is an object of class DiscreteDistribution.
> 
> 
> 
> cleanEx(); ..nameEx <- "BinomParameter-class"
> 
> ### * BinomParameter-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: BinomParameter-class
> ### Title: Class "BinomParameter"
> ### Aliases: BinomParameter-class initialize,BinomParameter-method
> 
> 
> ### ** Examples
> 
> W=new("BinomParameter",prob=0.5,size=1)
> size(W) # size of this distribution is 1.
[1] 1
> size(W)=2 # size of this distribution is now 2.
> 
> 
> 
> cleanEx(); ..nameEx <- "Cauchy-class"
> 
> ### * Cauchy-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Cauchy-class
> ### Title: Class "Cauchy"
> ### Aliases: Cauchy-class Cauchy initialize,Cauchy-method
> ### Keywords: distribution
> 
> ### ** Examples
> 
> C=Cauchy(location=1,scale=1) # C is a Cauchy distribution with location=1 and scale=1.
> r(C)(1) # one random number generated from this distribution, e.g. 4.104603
[1] 2.10252
> d(C)(1) # Density of this distribution is 0.3183099 for x=1.
[1] 0.3183099
> p(C)(1) # Probability that x<1 is 0.5.
[1] 0.5
> q(C)(.1) # Probability that x<-2.077684 is 0.1.
[1] -2.077684
> location(C) # location of this distribution is 1.
[1] 1
> location(C)=2 # location of this distribution is now 2.
> 
> 
> 
> cleanEx(); ..nameEx <- "CauchyParameter-class"
> 
> ### * CauchyParameter-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: CauchyParameter-class
> ### Title: Class "CauchyParameter"
> ### Aliases: CauchyParameter-class initialize,CauchyParameter-method
> 
> 
> ### ** Examples
> 
> W=new("CauchyParameter",location=1,scale=1)
> location(W) # location of this distribution is 1.
[1] 1
> location(W)=2 # location of this distribution is now 2.
> 
> 
> 
> cleanEx(); ..nameEx <- "Chisq-class"
> 
> ### * Chisq-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Chisq-class
> ### Title: Class "Chisq"
> ### Aliases: Chisq-class Chisq initialize,Chisq-method
> ### Keywords: distribution
> 
> ### ** Examples
> 
> C <- Chisq(df = 1, ncp = 1) # C is a chi-squared distribution with df=1 and ncp=1.
> r(C)(1) # one random number generated from this distribution, e.g. 0.2557184
[1] 3.052466
> d(C)(1) # Density of this distribution is 0.2264666 for x = 1.
[1] 0.2264666
> p(C)(1) # Probability that x < 1 is 0.4772499.
[1] 0.4772499
> q(C)(.1) # Probability that x < 0.04270125 is 0.1.
[1] 0.04270125
> df(C) # df of this distribution is 1.
[1] 1
> df(C) <- 2 # df of this distribution is now 2.
> 
> 
> 
> cleanEx(); ..nameEx <- "ChisqParameter-class"
> 
> ### * ChisqParameter-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ChisqParameter-class
> ### Title: Class "ChisqParameter"
> ### Aliases: ChisqParameter-class initialize,ChisqParameter-method
> 
> 
> ### ** Examples
> 
> W=new("ChisqParameter",df=1,ncp=1)
> ncp(W) # ncp of this distribution is 1.
[1] 1
> ncp(W)=2 # ncp of this distribution is now 2.
> 
> 
> 
> cleanEx(); ..nameEx <- "Contsimulation-class"
> 
> ### * Contsimulation-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Contsimulation-class
> ### Title: Class "Contsimulation"
> ### Aliases: Contsimulation-class Contsimulation
> ###   initialize,Contsimulation-method
> 
> 
> ### ** Examples
> 
> N <- Norm() # N is a standard normal distribution.
> C <- Cauchy() # C is a Cauchy distribution
> cs <- Contsimulation(filename = "csim",
+                      runs = 10,
+                      samplesize = 3,
+                      seed = setRNG(),
+                      distribution.id = N,
+                      distribution.c = C,
+                      rate = 0.1)
> simulate(cs)
> # Each of the 30 random numbers is ideal (N-distributed) with
> # probability 0.9 and contaminated (C-distributed) with
> # probability = 0.1
> summary(cs)
name of simulation:  csim 
number of runs:  10 
size of sample:  3 
rate of contamination:  0.1 
real Data:
           [,1]   [,2]     [,3]    [,4]   [,5]     [,6]
Min.    -0.6265 0.1836 -0.83560 -1.9890 0.3295 -0.82050
1st Qu.  0.4427 0.2867 -0.72840 -0.1970 0.4747 -0.43830
Median   1.5120 0.3898 -0.62120  1.5950 0.6198 -0.05613
Mean     1.8960 0.4519 -0.46080  0.4478 0.6914 -0.30720
3rd Qu.  3.1570 0.5860 -0.27330  1.6660 0.8724 -0.05053
Max.     4.8030 0.7821  0.07456  1.7380 1.1250 -0.04493
> Data(cs) # different data
            [,1]        [,2]        [,3]
 [1,] -0.6264538  1.51178117  4.80293716
 [2,]  0.1836433  0.38984324  0.78213630
 [3,] -0.8356286 -0.62124058  0.07456498
 [4,]  1.5952808  1.73761346 -1.98935170
 [5,]  0.3295078  1.12493092  0.61982575
 [6,] -0.8204684 -0.04493361 -0.05612874
 [7,]  0.4874291 -0.01619026 -0.15579551
 [8,]  0.7383247  0.94383621 -1.47075238
 [9,]  0.5757814 -0.84154958 -0.47815006
[10,] -0.3053884  0.59390132  0.41794156
> savedata(cs) # saves the object in the working directory of R...
> load("csim") # loads it again...
> Data(cs) # ...without the data - use simulate to return it!
NULL
> 
> 
> 
> cleanEx(); ..nameEx <- "Dataclass-class"
> 
> ### * Dataclass-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Dataclass-class
> ### Title: Class "Dataclass"
> ### Aliases: Dataclass-class Dataclass initialize,Dataclass-method
> 
> 
> ### ** Examples
> 
> D <- Dataclass(Data = matrix(c(1,2,3,4,5,6),2),
+                filename = "xyz.sav")
> # A new object of type "Dataclass" is created.
> savedata(D)
> # creates a file called "xyz.sav" where the information is saved and a
> # copy "xyz.sav.comment" without data
> Data(D) <- matrix(c(11,12,13,14,15,16),2) # changes the data of D
> cload("xyz.sav") # loads the object without data - it is called "D.comment"
> D.comment
filename of Dataclass:  xyz.sav 
number of runs:  2 
size of sample:  3 
> load("xyz.sav") # loads the original object "D"
> Data(D) # the original data: matrix(c(1,2,3,4,5,6),2)
     [,1] [,2] [,3]
[1,]    1    3    5
[2,]    2    4    6
> evaluate(object = D, estimator = mean) # returns the mean of each variable
An Evaluation Object
name of Dataobject:  D 
name of Datafile:  xyz.sav 
estimator:  mean 
Result:  num [1:2] 3 4
> 
> 
> 
> cleanEx(); ..nameEx <- "Dirac-class"
> 
> ### * Dirac-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Dirac-class
> ### Title: Class "Dirac"
> ### Aliases: Dirac-class Dirac initialize,Dirac-method
> ### Keywords: distribution
> 
> ### ** Examples
> 
> D <- Dirac(location = 0) # D is a Dirac distribution with location=0.
> r(D)(1)
[1] 0
> # r(D)(1) generates a pseudo-random-number according to a Dirac
> # distribution with location = 0,
> # which of course will take 0 as value almost surely.
> d(D)(0) # Density of this distribution is 1 for x = 0.
[1] 1
> p(D)(1) # Probability that x < 1 is 1.
[1] 1
> q(D)(.1) # q(D)(x) is always 0 (= location).
[1] 0
> location(D) # location of this distribution is 0.
[1] 0
> location(D) <- 2 # location of this distribution is now 2.
> 
> 
> 
> cleanEx(); ..nameEx <- "DiracParameter-class"
> 
> ### * DiracParameter-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: DiracParameter-class
> ### Title: Class "DiracParameter"
> ### Aliases: DiracParameter-class initialize,DiracParameter-method
> 
> 
> ### ** Examples
> 
> W=new("DiracParameter",location=1)
> location(W) # location of this distribution is 1.
[1] 1
> location(W)=2 # location of this distribution is now 2.
> 
> 
> 
> cleanEx(); ..nameEx <- "DiscreteDistribution-class"
> 
> ### * DiscreteDistribution-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: DiscreteDistribution-class
> ### Title: Class "DiscreteDistribution"
> ### Aliases: DiscreteDistribution-class
> ###   initialize,DiscreteDistribution-method
> ### Keywords: distribution
> 
> ### ** Examples
> 
> B = Binom(prob=0.1,size=10) # B is a Binomial distribution with prob=0.1 and size=10.
> P = Pois(lambda=1) # P is a Poisson distribution with lambda=1.
> D1 = B+1 # a new discrete distributions with exact slots d, p, q
> D2 = D1*3 # a new discrete distributions with exact slots d, p, q
> D3 = B+P # a new discrete distributions with approximated slots d, p, q
> D4 = D1+P # a new discrete distributions with approximated slots d, p, q
> support(D4) # the (approximated) support of this distribution is 1, 2, ..., 21
 [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21
> r(D4)(1) # one random number generated from this distribution, e.g. 4
[1] 2
> d(D4)(1) # The (approximated) density for x=1 is 0.1282716.
[1] 0.1282716
> p(D4)(1) # The (approximated) probability that x<=1 is 0.1282716.
[1] 0.1282716
> q(D4)(.5) # The (approximated) 50 percent quantile is 3.
[1] 3
> 
> 
> 
> cleanEx(); ..nameEx <- "EuclideanSpace-class"
> 
> ### * EuclideanSpace-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: EuclideanSpace-class
> ### Title: Class "EuclideanSpace"
> ### Aliases: EuclideanSpace-class initialize,EuclideanSpace-method
> 
> 
> ### ** Examples
> 
> E=new("EuclideanSpace",dimension=2)
> dimension(E) # The dimension of this space is 2.
[1] 2
> dimension(E)=3 # The dimension of this space is now 3.
> liesIn(E,c(0,0,0)) # TRUE
[1] TRUE
> liesIn(E,c(0,0)) # FALSE
[1] FALSE
> 
> 
> 
> cleanEx(); ..nameEx <- "Evaluation-class"
> 
> ### * Evaluation-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Evaluation-class
> ### Title: Class "Evaluation"
> ### Aliases: Evaluation-class initialize,Evaluation-method
> 
> 
> ### ** Examples
> 
> N <- Norm() # N is a standard normal distribution.
> C <- Cauchy() # C is a Cauchy distribution
> cs <- Contsimulation(filename = "csim",
+                      runs = 5,
+                      samplesize=5000,
+                      seed=setRNG(),
+                      distribution.id = N,
+                      distribution.c = C,
+                      rate = 0.1)
> simulate(cs)
> # Each of the 25000 random numbers is ideal (N-distributed) with
> # probability 0.9 and contaminated (C-distributed) with probability = 0.1
> summary(cs)
name of simulation:  csim 
number of runs:  5 
size of sample:  5000 
rate of contamination:  0.1 
real Data:
              [,1]       [,2]       [,3]       [,4]       [,5]
Min.    -1.033e+02 -1.725e+02 -339.10000 -4.633e+02 -6.713e+01
1st Qu. -7.080e-01 -6.981e-01   -0.69920 -6.746e-01 -6.808e-01
Median   3.138e-03 -2.468e-03   -0.01819  2.946e-03 -7.169e-03
Mean    -3.604e-02  3.034e-02   -0.09989 -1.779e-01  4.324e-01
3rd Qu.  7.001e-01  7.237e-01    0.65800  6.824e-01  6.791e-01
Max.     1.315e+02  8.266e+01  113.30000  3.011e+02  1.753e+03
> ev1 <- evaluate(cs, mean) # estimates the data with mean
> ev1 # bad results
An Evaluation Object
name of Dataobject:  cs 
name of Datafile:  csim 
estimator:  mean 
Result:  num [1:5] -0.0360  0.0303 -0.0999 -0.1779  0.4324
> ev2 <- evaluate(cs,median) # estimates the data with median
> ev2 # better results because median is robust
An Evaluation Object
name of Dataobject:  cs 
name of Datafile:  csim 
estimator:  median 
Result:  num [1:5]  0.00314 -0.00247 -0.01819  0.00295 -0.00717
> savedata(ev1)
> # saves the evaluation with result as "csim.mean" and without result as
> # "csim.mean.comment" in the working directory # of R - "csim" is the
> # filename of the Contsimulation object, mean the name of the estimator
> rm(ev1)
> cload("csim.mean")
> # loads the evaluation without result - the object is called ev1.comment
> ev1.comment
An Evaluation Object
name of Dataobject:  cs 
name of Datafile:  csim 
estimator:  mean 
Result:  NULL
> load("csim.mean") # loads the evaluation with result
> ev1
An Evaluation Object
name of Dataobject:  cs 
name of Datafile:  csim 
estimator:  mean 
Result:  num [1:5] -0.0360  0.0303 -0.0999 -0.1779  0.4324
> 
> 
> 
> cleanEx(); ..nameEx <- "Exp-class"
> 
> ### * Exp-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Exp-class
> ### Title: Class "Exp"
> ### Aliases: Exp-class Exp initialize,Exp-method
> ### Keywords: distribution
> 
> ### ** Examples
> 
> E <- Exp(rate = 1) # E is a exp distribution with rate = 1.
> r(E)(1) # one random number generated from this distribution, e.g. 0.4190765
[1] 0.7551818
> d(E)(1) # Density of this distribution is 0.3678794 for x = 1.
[1] 0.3678794
> p(E)(1) # Probability that x < 1 is 0.6321206.
[1] 0.6321206
> q(E)(.1) # Probability that x < 0.1053605 is 0.1.
[1] 0.1053605
> rate(E) # rate of this distribution is 1.
[1] 1
> rate(E) <- 2 # rate of this distribution is now 2.
> 
> 
> 
> cleanEx(); ..nameEx <- "ExpParameter-class"
> 
> ### * ExpParameter-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ExpParameter-class
> ### Title: Class "ExpParameter"
> ### Aliases: ExpParameter-class initialize,ExpParameter-method
> 
> 
> ### ** Examples
> 
> W=new("ExpParameter", rate=1)
> rate(W) # rate of this distribution is 1.
[1] 1
> rate(W)=2 # rate of this distribution is now 2.
> 
> 
> 
> cleanEx(); ..nameEx <- "FParameter-class"
> 
> ### * FParameter-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: FParameter-class
> ### Title: Class "FParameter"
> ### Aliases: FParameter-class initialize,FParameter-method
> 
> 
> ### ** Examples
> 
> W <- new("FParameter", df1 = 1, df2 = 1)
> df2(W) # df2 of this distribution is 1.
[1] 1
> df2(W) <- 2 # df2 of this distribution is now 2.
> 
> 
> 
> cleanEx(); ..nameEx <- "Fd-class"
> 
> ### * Fd-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Fd-class
> ### Title: Class "Fd"
> ### Aliases: Fd-class Fd initialize,Fd-method
> ### Keywords: distribution
> 
> ### ** Examples
> 
> F=Fd(df1=1,df2=1) # F is a F distribution with df=1 and df2=1.
> r(F)(1) # one random number generated from this distribution, e.g. 29.37863
[1] 0.1781142
> d(F)(1) # Density of this distribution is 0.1591549 for x=1 .
[1] 0.1591549
> p(F)(1) # Probability that x<1 is 0.5.
[1] 0.5
> q(F)(.1) # Probability that x<0.02508563 is 0.1.
[1] 0.02508563
> df1(F) # df1 of this distribution is 1.
[1] 1
> df1(F)=2 # df1 of this distribution is now 2.
> 
> 
> 
> cleanEx(); ..nameEx <- "GammaParameter-class"
> 
> ### * GammaParameter-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: GammaParameter-class
> ### Title: Class "GammaParameter"
> ### Aliases: GammaParameter-class initialize,GammaParameter-method
> 
> 
> ### ** Examples
> 
> W=new("GammaParameter",scale=1,shape=1)
> shape(W) # shape of this distribution is 1.
[1] 1
> shape(W)=2 # shape of this distribution is now 2.
> 
> 
> 
> cleanEx(); ..nameEx <- "Gammad-class"
> 
> ### * Gammad-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Gammad-class
> ### Title: Class "Gammad"
> ### Aliases: Gammad-class Gammad initialize,Gammad-method
> ### Keywords: distribution
> 
> ### ** Examples
> 
> G=Gammad(scale=1,shape=1) # G is a gamma distribution with scale=1 and shape=1.
> r(G)(1) # one random number generated from this distribution, e.g. 0.1304441
[1] 0.1551414
> d(G)(1) # Density of this distribution is 0.3678794 for x=1.
[1] 0.3678794
> p(G)(1) # Probability that x<1 is 0.6321206.
[1] 0.6321206
> q(G)(.1) # Probability that x<0.1053605 is 0.1.
[1] 0.1053605
> scale(G) # scale of this distribution is 1.
[1] 1
> scale(G)=2 # scale of this distribution is now 2.
> 
> 
> 
> cleanEx(); ..nameEx <- "Geom-class"
> 
> ### * Geom-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Geom-class
> ### Title: Class "Geom"
> ### Aliases: Geom-class Geom initialize,Geom-method
> ### Keywords: distribution
> 
> ### ** Examples
> 
> G <- Geom(prob = 0.5) # G is a geometric distribution with prob = 0.5.
> r(G)(1) # one random number generated from this distribution, e.g. 0
[1] 0
> d(G)(1) # Density of this distribution is 0.25 for x = 1.
[1] 0.25
> p(G)(1) # Probability that x<1 is 0.75.
[1] 0.75
> q(G)(.1) # x = 0 is the smallest value x such that p(G)(x) >= 0.1.
[1] 0
> prob(G) # prob of this distribution is 0.5.
[1] 0.5
> prob(G) <- 0.6 # prob of this distribution is now 0.6.
> 
> 
> 
> cleanEx(); ..nameEx <- "GeomParameter-class"
> 
> ### * GeomParameter-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: GeomParameter-class
> ### Title: Class "GeomParameter"
> ### Aliases: GeomParameter-class initialize,GeomParameter-method
> 
> 
> ### ** Examples
> 
> W=new("GeomParameter",prob=0.5)
> prob(W) # prob of this distribution is 0.5.
[1] 0.5
> prob(W)=0.4 # prob of this distribution is now 0.4.
> 
> 
> 
> cleanEx(); ..nameEx <- "Hyper-class"
> 
> ### * Hyper-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Hyper-class
> ### Title: Class "Hyper"
> ### Aliases: Hyper-class Hyper initialize,Hyper-method
> ### Keywords: distribution
> 
> ### ** Examples
> 
> H=Hyper(m=3,n=3,k=3) # H is a hypergeometric distribution with m=3,n=3,k=3.
> r(H)(1) # one random number generated from this distribution, e.g. 2
[1] 2
> d(H)(1) # Density of this distribution is  0.45 for x=1.
[1] 0.45
> p(H)(1) # Probability that x<1 is 0.5.
[1] 0.5
> q(H)(.1) # x=1 is the smallest value x such that p(H)(x)>=0.1.
[1] 1
> m(H) # m of this distribution is 3.
[1] 3
> m(H)=2 # m of this distribution is now 2.
> 
> 
> 
> cleanEx(); ..nameEx <- "HyperParameter-class"
> 
> ### * HyperParameter-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: HyperParameter-class
> ### Title: Class "HyperParameter"
> ### Aliases: HyperParameter-class initialize,HyperParameter-method
> 
> 
> ### ** Examples
> 
> W=new("HyperParameter",k=3, m=3, n=3)
> m(W) # m of this distribution is 3.
[1] 3
> m(W)=2 # m of this distribution is now 2.
> 
> 
> 
> cleanEx(); ..nameEx <- "Lnorm-class"
> 
> ### * Lnorm-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Lnorm-class
> ### Title: Class "Lnorm"
> ### Aliases: Lnorm-class Lnorm initialize,Lnorm-method
> ### Keywords: distribution
> 
> ### ** Examples
> 
> L=Lnorm(meanlog=1,sdlog=1) # L is a lnorm distribution with mean=1 and sd=1.
> r(L)(1) # one random number generated from this distribution, e.g. 3.608011
[1] 1.452878
> d(L)(1) # Density of this distribution is 0.2419707 for x=1.
[1] 0.2419707
> p(L)(1) # Probability that x<1 is 0.1586553.
[1] 0.1586553
> q(L)(.1) # Probability that x<0.754612 is 0.1.
[1] 0.754612
> meanlog(L) # meanlog of this distribution is 1.
[1] 1
> meanlog(L)=2 # meanlog of this distribution is now 2.
> 
> 
> 
> cleanEx(); ..nameEx <- "LnormParameter-class"
> 
> ### * LnormParameter-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: LnormParameter-class
> ### Title: Class "LnormParameter"
> ### Aliases: LnormParameter-class initialize,LnormParameter-method
> 
> 
> ### ** Examples
> 
> W=new("LnormParameter",sdlog=1,meanlog=0)
> meanlog(W) # meanlog of this distribution is 0.
[1] 0
> meanlog(W)=2 # meanlog of this distribution is now 2.
> 
> 
> 
> cleanEx(); ..nameEx <- "Logis-class"
> 
> ### * Logis-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Logis-class
> ### Title: Class "Logis"
> ### Aliases: Logis-class Logis initialize,Logis-method
> ### Keywords: distribution
> 
> ### ** Examples
> 
> L <- Logis(location = 1,scale = 1)
> # L is a logistic distribution with  location = 1 and scale = 1.
> r(L)(1) # one random number generated from this distribution, e.g. 5.87557
[1] -0.01753073
> d(L)(1) # Density of this distribution is 0.25 for x = 1.
[1] 0.25
> p(L)(1) # Probability that x < 1 is 0.5.
[1] 0.5
> q(L)(.1) # Probability that x < -1.197225 is 0.1.
[1] -1.197225
> location(L) # location of this distribution is 1.
[1] 1
> location(L) <- 2 # location of this distribution is now 2.
> 
> 
> 
> cleanEx(); ..nameEx <- "LogisParameter-class"
> 
> ### * LogisParameter-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: LogisParameter-class
> ### Title: Class "LogisParameter"
> ### Aliases: LogisParameter-class initialize,LogisParameter-method
> 
> 
> ### ** Examples
> 
> W=new("LogisParameter",location=0,scale=1)
> scale(W) # scale of this distribution is 1.
[1] 1
> scale(W)=2 # scale of this distribution is now 2.
> 
> 
> 
> cleanEx(); ..nameEx <- "Naturals-class"
> 
> ### * Naturals-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Naturals-class
> ### Title: Class "Naturals"
> ### Aliases: Naturals-class initialize,Naturals-method
> ### Keywords: distribution
> 
> ### ** Examples
> 
> N=new("Naturals")
> liesIn(N,1) # TRUE
[1] TRUE
> liesIn(N,c(0,1)) # FALSE
[1] FALSE
> liesIn(N,0.1) # FALSE
[1] FALSE
> 
> 
> 
> cleanEx(); ..nameEx <- "Nbinom-class"
> 
> ### * Nbinom-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Nbinom-class
> ### Title: Class "Nbinom"
> ### Aliases: Nbinom-class Nbinom initialize,Nbinom-method
> ### Keywords: distribution
> 
> ### ** Examples
> 
> N=Nbinom(prob=0.5,size=1) # N is a binomial distribution with prob=0.5 and size=1.
> r(N)(1) # one random number generated from this distribution, e.g. 3
[1] 1
> d(N)(1) # Density of this distribution is  0.25 for x=1.
[1] 0.25
> p(N)(0.4) # Probability that x<0.4 is 0.5.
[1] 0.5
> q(N)(.1) # x=0 is the smallest value x such that p(B)(x)>=0.1.
[1] 0
> size(N) # size of this distribution is 1.
[1] 1
> size(N)=2 # size of this distribution is now 2.
> 
> 
> 
> cleanEx(); ..nameEx <- "NbinomParameter-class"
> 
> ### * NbinomParameter-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: NbinomParameter-class
> ### Title: Class "NbinomParameter"
> ### Aliases: NbinomParameter-class initialize,NbinomParameter-method
> 
> 
> ### ** Examples
> 
> W=new("NbinomParameter",prob=0.5,size=1)
> size(W) # size of this distribution is 1.
[1] 1
> size(W)=2 # size of this distribution is now 2.
> 
> 
> 
> cleanEx(); ..nameEx <- "Norm-class"
> 
> ### * Norm-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Norm-class
> ### Title: Class "Norm"
> ### Aliases: Norm-class Norm initialize,Norm-method
> ### Keywords: distribution
> 
> ### ** Examples
> 
> N=Norm(mean=1,sd=1) # N is a normal distribution with mean=1 and sd=1.
> r(N)(1) # one random number generated from this distribution, e.g. 2.257783
[1] 0.3735462
> d(N)(1) # Density of this distribution is  0.3989423 for x=1.
[1] 0.3989423
> p(N)(1) # Probability that x<1 is 0.5.
[1] 0.5
> q(N)(.1) # Probability that x<-0.2815516 is 0.1.
[1] -0.2815516
> mean(N) # mean of this distribution is 1.
[1] 1
> sd(N)=2 # sd of this distribution is now 2.
> M=Norm() # M is a normal distribution with mean=0 and sd=1.
> O=M+N # O is a normal distribution with mean=1 (=1+0) and sd=sqrt(5) (=sqrt(2^2+1^2)).
> 
> 
> 
> cleanEx(); ..nameEx <- "NormParameter-class"
> 
> ### * NormParameter-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: NormParameter-class
> ### Title: Class "NormParameter"
> ### Aliases: NormParameter-class initialize,NormParameter-method
> 
> 
> ### ** Examples
> 
> W <- new("NormParameter", mean = 0, sd = 1)
> sd(W) # sd of this distribution is 1.
[1] 1
> sd(W) <- 2 # sd of this distribution is now 2.
> 
> 
> 
> cleanEx(); ..nameEx <- "Pois-class"
> 
> ### * Pois-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Pois-class
> ### Title: Class "Pois"
> ### Aliases: Pois-class Pois initialize,Pois-method
> ### Keywords: distribution
> 
> ### ** Examples
> 
> P <- Pois(lambda = 1) # P is a Poisson distribution with lambda = 1.
> r(P)(1) # one random number generated from this distribution, e.g. 1
[1] 0
> d(P)(1) # Density of this distribution is 0.3678794 for x = 1.
[1] 0.3678794
> p(P)(0.4) # Probability that x < 0.4 is 0.3678794.
[1] 0.3678794
> q(P)(.1) # x = 0 is the smallest value x such that p(B)(x) >= 0.1.
[1] 0
> lambda(P) # lambda of this distribution is 1.
[1] 1
> lambda(P) <- 2 # lambda of this distribution is now 2.
> R <- Pois(lambda = 3) # R is a Poisson distribution with lambda = 2.
> S <- P + R # R is a Poisson distribution with lambda = 5(=2+3).
> 
> 
> 
> cleanEx(); ..nameEx <- "PoisParameter-class"
> 
> ### * PoisParameter-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: PoisParameter-class
> ### Title: Class "PoisParameter"
> ### Aliases: PoisParameter-class initialize,PoisParameter-method
> 
> 
> ### ** Examples
> 
> W <- new("PoisParameter",lambda = 1)
> lambda(W) # lambda of this distribution is 1.
[1] 1
> lambda(W) <- 2 # lambda of this distribution is now 2.
> 
> 
> 
> cleanEx(); ..nameEx <- "Reals-class"
> 
> ### * Reals-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Reals-class
> ### Title: Class "Reals"
> ### Aliases: Reals-class initialize,Reals-method
> ### Keywords: distribution
> 
> ### ** Examples
> 
> R=new("Reals")
> liesIn(R,c(0,0)) # FALSE
[1] FALSE
> 
> 
> 
> cleanEx(); ..nameEx <- "RtoDPQ"
> 
> ### * RtoDPQ
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: RtoDPQ
> ### Title: RtoDPQ
> ### Aliases: RtoDPQ
> 
> 
> ### ** Examples
> 
> rn2 <- function(n){rnorm(n)^2}
> x <- RtoDPQ(r = rn2, e = 4, n = 512)
> # returns density, cumulative distribution and quantile function of
> # squared standard normal distribution
> x$dfun(4)
[1] 0.02973328
> RtoDPQ(r = rn2, e = 5, n = 1024) # for a better result
$dfun
function (v) 
.C("R_approx", as.double(x), as.double(y), as.integer(n), xout = as.double(v), 
    as.integer(length(v)), as.integer(method), as.double(yleft), 
    as.double(yright), as.double(f), NAOK = TRUE, PACKAGE = "base")$xout
<environment: 0x2e58ca0>

$pfun
function (x) 
pfun0(x)
<environment: 0x18c80fc>

$qfun
function (x) 
ifelse(x > 1, NA, ifelse(x < 0, NA, qfun(x)))
<environment: 0x18c80fc>

> 
> rp2 <- function(n){rpois(n, lambda = 1)^2}
> x <- RtoDPQ.d(r = rp2, e = 5)
> # returns density, cumulative distribution and quantile function of
> # squared Poisson distribution with parameter lambda=1
> 
> 
> 
> cleanEx(); ..nameEx <- "RtoDPQ.d"
> 
> ### * RtoDPQ.d
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: RtoDPQ.d
> ### Title: RtoDPQ.d
> ### Aliases: RtoDPQ.d
> 
> 
> ### ** Examples
> 
> rn2 <- function(n){rnorm(n)^2}
> x <- RtoDPQ(r = rn2, e = 4, n = 512)
> # returns density, cumulative distribution and quantile function of
> # squared standard  normal distribution
> 
> x$dfun(4)
[1] 0.02973328
> RtoDPQ(r = rn2, e = 5, n = 1024) # for a better result
$dfun
function (v) 
.C("R_approx", as.double(x), as.double(y), as.integer(n), xout = as.double(v), 
    as.integer(length(v)), as.integer(method), as.double(yleft), 
    as.double(yright), as.double(f), NAOK = TRUE, PACKAGE = "base")$xout
<environment: 0x2f5f32c>

$pfun
function (x) 
pfun0(x)
<environment: 0x2c3a2b0>

$qfun
function (x) 
ifelse(x > 1, NA, ifelse(x < 0, NA, qfun(x)))
<environment: 0x2c3a2b0>

> 
> rp2 <- function(n){rpois(n, lambda = 1)^2}
> x <- RtoDPQ.d(r = rp2, e = 5)
> # returns density, cumulative distribution and quantile function of
> # squared Poisson distribution with parameter lambda=1
> 
> 
> 
> cleanEx(); ..nameEx <- "Simulation-class"
> 
> ### * Simulation-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Simulation-class
> ### Title: Class "Simulation"
> ### Aliases: Simulation-class Simulation initialize,Simulation-method
> 
> 
> ### ** Examples
> 
> N=Norm() # N is a standard normal distribution.
> S=Simulation(filename="xyz",runs=10,samplesize=3,seed=setRNG(),distribution=N)
> Data(S) # no data yet
NULL
> simulate(S)
> Data(S) # now there are random numbers
            [,1]        [,2]        [,3]
 [1,] -0.6264538  1.51178117  0.91897737
 [2,]  0.1836433  0.38984324  0.78213630
 [3,] -0.8356286 -0.62124058  0.07456498
 [4,]  1.5952808 -2.21469989 -1.98935170
 [5,]  0.3295078  1.12493092  0.61982575
 [6,] -0.8204684 -0.04493361 -0.05612874
 [7,]  0.4874291 -0.01619026 -0.15579551
 [8,]  0.7383247  0.94383621 -1.47075238
 [9,]  0.5757814  0.82122120 -0.47815006
[10,] -0.3053884  0.59390132  0.41794156
> Data(S) # the same data as before because the seed has not changed
            [,1]        [,2]        [,3]
 [1,] -0.6264538  1.51178117  0.91897737
 [2,]  0.1836433  0.38984324  0.78213630
 [3,] -0.8356286 -0.62124058  0.07456498
 [4,]  1.5952808 -2.21469989 -1.98935170
 [5,]  0.3295078  1.12493092  0.61982575
 [6,] -0.8204684 -0.04493361 -0.05612874
 [7,]  0.4874291 -0.01619026 -0.15579551
 [8,]  0.7383247  0.94383621 -1.47075238
 [9,]  0.5757814  0.82122120 -0.47815006
[10,] -0.3053884  0.59390132  0.41794156
> seed(S)=setRNG()
> simulate(S)
> Data(S) # different data
             [,1]       [,2]       [,3]
 [1,]  1.35867955 -0.1645236  0.3981059
 [2,] -0.10278773 -0.2533617 -0.6120264
 [3,]  0.38767161  0.6969634  0.3411197
 [4,] -0.05380504  0.5566632 -1.1293631
 [5,] -1.37705956 -0.6887557  1.4330237
 [6,] -0.41499456 -0.7074952  1.9803999
 [7,] -0.39428995  0.3645820 -0.3672215
 [8,] -0.05931340  0.7685329 -1.0441346
 [9,]  1.10002537 -0.1123462  0.5697196
[10,]  0.76317575  0.8811077 -0.1350546
> savedata(S) # saves the object in the directory of R...
> load("xyz") # loads it again...
> Data(S) # ...without the data - use simulate to return it!
NULL
> 
> 
> 
> cleanEx(); ..nameEx <- "TParameter-class"
> 
> ### * TParameter-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: TParameter-class
> ### Title: Class "TParameter"
> ### Aliases: TParameter-class initialize,TParameter-method
> 
> 
> ### ** Examples
> 
> W=new("TParameter",df=1)
> df(W) # df of this distribution is 1.
[1] 1
> df(W)=2 # df of this distribution is now 2.
> 
> 
> 
> cleanEx(); ..nameEx <- "Td-class"
> 
> ### * Td-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Td-class
> ### Title: Class "Td"
> ### Aliases: Td-class Td initialize,Td-method
> ### Keywords: distribution
> 
> ### ** Examples
> 
> T <- Td(df = 1) # T is a t distribution with df = 1.
> r(T)(1) # one random number generated from this distribution, e.g. -0.09697573
[1] -0.5947235
> d(T)(1) # Density of this distribution is 0.1591549 for x = 1.
[1] 0.1591549
> p(T)(1) # Probability that x < 1 is 0.75.
[1] 0.75
> q(T)(.1) # Probability that x < -3.077684 is 0.1.
[1] -3.077684
> df(T) # df of this distribution is 1.
[1] 1
> df(T) <- 2 # df of this distribution is now 2.
> 
> 
> 
> cleanEx(); ..nameEx <- "UniNormParameter-class"
> 
> ### * UniNormParameter-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: UniNormParameter-class
> ### Title: Class "UniNormParameter"
> ### Aliases: UniNormParameter-class initialize,UniNormParameter-method
> 
> 
> ### ** Examples
> 
> W <- new("UniNormParameter", mean = 0, sd = 1)
> sd(W) # sd of this distribution is 1
[1] 1
> sd(W) <- 2 # sd of this distribution is now 2
> 
> 
> 
> cleanEx(); ..nameEx <- "Unif-class"
> 
> ### * Unif-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Unif-class
> ### Title: Class "Unif"
> ### Aliases: Unif-class Unif initialize,Unif-method
> ### Keywords: distribution
> 
> ### ** Examples
> 
> U=Unif(Min=0,Max=2) # U is a uniform distribution with Min=0 and Max=2.
> r(U)(1) # one random number generated from this distribution, e.g. 1.984357
[1] 0.5310173
> d(U)(1) # Density of this distribution is 0.5 for x=1.
[1] 0.5
> p(U)(1) # Probability that x<1 is 0.5.
[1] 0.5
> q(U)(.1) # Probability that x<0.2 is 0.1.
[1] 0.2
> Min(U) # Min of this distribution is 0.
[1] 0
> Min(U)=1 # Min of this distribution is now 1.
> 
> 
> 
> cleanEx(); ..nameEx <- "UnifParameter-class"
> 
> ### * UnifParameter-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: UnifParameter-class
> ### Title: Class "UnifParameter"
> ### Aliases: UnifParameter-class initialize,UnifParameter-method
> 
> 
> ### ** Examples
> 
> W=new("UnifParameter",Min=0,Max=1)
> Max(W) # Max of this distribution is 1.
[1] 1
> Max(W)=2 # Max of this distribution is now 2.
> 
> 
> 
> cleanEx(); ..nameEx <- "Weibull-class"
> 
> ### * Weibull-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Weibull-class
> ### Title: Class "Weibull"
> ### Aliases: Weibull-class Weibull initialize,Weibull-method
> ### Keywords: distribution
> 
> ### ** Examples
> 
> W=Weibull(shape=1,scale=1) # W is a Weibull distribution with shape=1 and scale=1.
> r(W)(1) # one random number generated from this distribution, e.g. 0.5204105
[1] 1.326108
> d(W)(1) # Density of this distribution is 0.3678794 for x=1.
[1] 0.3678794
> p(W)(1) # Probability that x<1 is 0.6321206.
[1] 0.6321206
> q(W)(.1) # Probability that x<0.1053605 is 0.1.
[1] 0.1053605
> shape(W) # shape of this distribution is 1.
[1] 1
> shape(W)=2 # shape of this distribution is now 2.
> 
> 
> 
> cleanEx(); ..nameEx <- "WeibullParameter-class"
> 
> ### * WeibullParameter-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: WeibullParameter-class
> ### Title: Class "WeibullParameter"
> ### Aliases: WeibullParameter-class initialize,WeibullParameter-method
> 
> 
> ### ** Examples
> 
> W=new("WeibullParameter",shape=1,scale=1)
> shape(W) # shape of this distribution is 1.
[1] 1
> shape(W)=2 # shape of this distribution is now 2.
> 
> 
> 
> cleanEx(); ..nameEx <- "cload"
> 
> ### * cload
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: cload
> ### Title: cload
> ### Aliases: cload
> 
> 
> ### ** Examples
> 
> # see Dataclass and Evaluation for examples
> ## The function is currently defined as
> function(filename){
+   eval.parent(parse(text=paste("load(\"",filename,".comment\")", sep = "")))
+   }
function (filename) 
{
    eval.parent(parse(text = paste("load(\"", filename, ".comment\")", 
        sep = "")))
}
> 
> 
> 
> cleanEx(); ..nameEx <- "distroptions"
> 
> ### * distroptions
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: distroptions
> ### Title: function to change the global variables of the package `distr'
> ### Aliases: distroptions DefaultNrGridPoints DistrResolution TruncQuantile
> ###   DefaultNrFFTGridPointsExponent RtoDPQ.e
> 
> 
> ### ** Examples
> 
> distroptions("RtoDPQ.e") # returns the value of RtoDPQ.e, by default = 5
[1] 5
> distroptions("RtoDPQ.e",6) # now RtoDPQ.e = 6
> 
> ## The function is currently defined as
> function (arg = "missing", value = -1) 
+ {
+     globals <- list(DefaultNrFFTGridPointsExponent = DefaultNrFFTGridPointsExponent, 
+         DefaultNrGridPoints = DefaultNrGridPoints, DistrResolution = DistrResolution, 
+         RtoDPQ.e = RtoDPQ.e, TruncQuantile = TruncQuantile)
+     if (arg == "missing") {
+         print(globals)
+         return(invisible())
+     }
+     if (!any(arg == names(globals))) 
+         stop(paste("No such variable:", arg))
+     if (value == -1) 
+         switch(arg, DefaultNrGridPoints = DefaultNrGridPoints, 
+             DistrResolution = DistrResolution, TruncQuantile = TruncQuantile, 
+             DefaultNrFFTGridPointsExponent = DefaultNrFFTGridPointsExponent, 
+             RtoDPQ.e = RtoDPQ.e)
+     else eval.parent(parse(text = paste("assignInNamespace(\"", 
+         arg, "\",", value, ", \"distr\")", sep = "")))
+   }
function (arg = "missing", value = -1) 
{
    globals <- list(DefaultNrFFTGridPointsExponent = DefaultNrFFTGridPointsExponent, 
        DefaultNrGridPoints = DefaultNrGridPoints, DistrResolution = DistrResolution, 
        RtoDPQ.e = RtoDPQ.e, TruncQuantile = TruncQuantile)
    if (arg == "missing") {
        print(globals)
        return(invisible())
    }
    if (!any(arg == names(globals))) 
        stop(paste("No such variable:", arg))
    if (value == -1) 
        switch(arg, DefaultNrGridPoints = DefaultNrGridPoints, 
            DistrResolution = DistrResolution, TruncQuantile = TruncQuantile, 
            DefaultNrFFTGridPointsExponent = DefaultNrFFTGridPointsExponent, 
            RtoDPQ.e = RtoDPQ.e)
    else eval.parent(parse(text = paste("assignInNamespace(\"", 
        arg, "\",", value, ", \"distr\")", sep = "")))
}
> 
> 
> 
> cleanEx(); ..nameEx <- "simplifyr-methods"
> 
> ### * simplifyr-methods
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: simplifyr-methods
> ### Title: Methods for Function simplifyr in Package `distr'
> ### Aliases: simplifyr-methods simplifyr
> ###   simplifyr,UnivariateDistribution-method
> 
> 
> ### ** Examples
> 
> F <- ( Norm() + Binom() + Pois() + Exp() ) * 2 - 10
> system.time(r(F)(10^6))
[1] 2.03 0.17 2.20 0.00 0.00
> simplifyr(F, size = 10^6)
> system.time(r(F)(10^6))
[1] 0.35 0.03 0.38 0.00 0.00
> 
> 
> cleanEx(); ..nameEx <- "standardMethods"
> 
> ### * standardMethods
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: standardMethods
> ### Title: standardMethods
> ### Aliases: standardMethods
> 
> 
> ### ** Examples
> 
> setClass("testclass", representation(a = "numeric", b = "character"))
[1] "testclass"
> standardMethods("testclass")
if(!isGeneric("a")) setGeneric("a", function(object) standardGeneric("a"))
if(!isGeneric("b")) setGeneric("b", function(object) standardGeneric("b"))
setMethod("a", "testclass", function(object) object@a)
setMethod("b", "testclass", function(object) object@b)
if(!isGeneric("a<-")) setGeneric("a<-", function(object, value) standardGeneric("a<-"))
if(!isGeneric("b<-")) setGeneric("b<-", function(object, value) standardGeneric("b<-"))
setReplaceMethod("a", "testclass", function(object, value){ object@a = value; object})
setReplaceMethod("b", "testclass", function(object, value){ object@b = value; object})
> #directory = "C:/Dokumente und Einstellungen/X/Eigene Dateien/Studium/R/SWP/"
> #standardMethods("testclass", writetofile = TRUE, directory = directory)
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  24.44 9.4 34.05 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')