Running 'AKMCS.R' [3s/3s]
  Running 'MetaIS.R' [3s/4s]
  Running 'MonteCarlo.R' [3s/4s]
  Running 'MovingParticles.R' [9s/11s]
  Running 'SubsetSimulation.R' [4s/5s]
Running the tests in 'tests/SubsetSimulation.R' failed.
Complete output:
  > ## -----------------------------------------------------------------------------
  > ## Calcul de probabilite associee a des evenemenrs rares
  > ##  * Limit State Function (LSF) : 
  > ##    !!! DEFINITION DANS ESPACE NORME CENTRE : LSF DOIT COMPORTER DES TRANSFO
  > ##        ISO-PROBABILISTES SI NECESSAIRES
  > ##  * Methode   : Subset
  > ##  * Reference : WAARTS  
  > ## -----------------------------------------------------------------------------
  > ##    Copyright (C) 2015
  > ##    Gilles DEFAUX
  > ##    CEA / DAM / DIF
  > ##    gilles.defaux@cea.fr
  > ## -----------------------------------------------------------------------------
  > 
  > ## On efface toutes les donnees
  > rm(list=ls(all=TRUE))
  > 
  > ## Source Subset Simulation files
  > library(mistral)
  > 
  > 
  > ## -----------------------------------
  > ## PARAMETRES ET OPTIONS
  > ## -----------------------------------
  > 
  > set.seed(123456)
  > 
  > p0     = 0.1     # Subset cutoff probability
  > NbSim  = 5000    # Monte-Carlo population size for subsets estimation
  > 
  > 
  > 
  > ## -----------------------------------
  > ## DEFINITION DU PROBLEME
  > ## -----------------------------------
  > 
  > Dim = 2
  > 
  > ## Modele Stochastique
  > distX1 <- list(type='Norm', MEAN=0.0, STD=1.0, P1=NULL, P2=NULL, NAME='X1')
  > distX2 <- list(type='Norm', MEAN=0.0, STD=1.0, P1=NULL, P2=NULL, NAME='X2')
  > 
  > input.margin <- list(distX1,distX2)
  > input.Rho    <- diag(Dim)
  > L0           <- chol(input.Rho)
  > 
  > lsf = function(U) {   
  +     # U <- as.matrix(U)
  +     X <- UtoX(U, input.margin, L0)
  +     G <- 5.0 - 0.2*(X[1,]-X[2,])^2.0 - (X[1,]+X[2,])/sqrt(2.0)
  +     return(G)
  + }
  > 
  > ## TEST
  > U0 <- c(1.0,1.0)
  > lsf(U0)
  [1] 3.585786
  > 
  > 
  > ## -----------------------------------
  > ## CALCUL
  > ## -----------------------------------
  > 
  > SS = SubsetSimulation( dimension            = Dim,
  +                        lsf                  = lsf,
  +                        p_0                  = p0,
  +                        N                    = NbSim,
  +                        q                    = 0.0,
  +                        lower.tail           = TRUE,
  +                        plot                 = TRUE,
  +                        output_dir           = NULL,
  +                        verbose              = 2)
  ==========================================================================================
                                Beginning of Subset Simulation algorithm 
  ==========================================================================================
  
    * Generate the first N = 5000 samples by crude MC 
    * Evaluate the limit state function 
    * Find the quantile q_0 verifying P[lsf(U) < q_0] = p_0 
     - q_0 = 3.125452 
    * Evaluate actual probability 
     - P = 0.1 
  Error: Unknown colour name: 1
  Execution halted