* using log directory ‘/home/hornik/tmp/CRAN_special_noSuggests/rlibkriging.Rcheck’
* using R Under development (unstable) (2025-07-21 r88439)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
    gcc-14 (Debian 14.2.0-19) 14.2.0
    GNU Fortran (Debian 14.2.0-19) 14.2.0
* running under: Debian GNU/Linux 13 (trixie)
* using session charset: UTF-8
* checking for file ‘rlibkriging/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘rlibkriging’ version ‘0.9-2’
* package encoding: UTF-8
* checking CRAN incoming feasibility ... [4s/6s] NOTE
Maintainer: ‘Yann Richet <yann.richet@irsn.fr>’

New submission

Package was archived on CRAN

Possibly misspelled words in DESCRIPTION:
  Ciyou (47:5)
  Nocedal (47:36)
  Sanderson (45:90)
  Zhu (47:11)

CRAN repository db overrides:
  X-CRAN-Comment: Archived on 2025-06-16 as issues were not corrected
    in time.
* checking package namespace information ... OK
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* checking whether package ‘rlibkriging’ can be installed ... [177s/142s] OK
* used C++ compiler: ‘g++-14 (Debian 14.2.0-19) 14.2.0’
* checking C++ specification ... OK
* checking package directory ... OK
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* checking use of S3 registration ... OK
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* checking examples ... [4s/4s] OK
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ... [75s/76s] ERROR
  Running ‘test-AllKrigingConcistency.R’ [2s/2s]
  Running ‘test-KrigingCopy.R’ [1s/1s]
  Running ‘test-KrigingFit.R’ [1s/1s]
  Running ‘test-KrigingLeaveOneOut.R’ [2s/2s]
  Running ‘test-KrigingLeaveOneOut_3d.R’ [1s/1s]
  Running ‘test-KrigingLogLik.R’ [2s/2s]
  Running ‘test-KrigingLogLikGradHess.R’ [5s/5s]
  Running ‘test-KrigingMethods.R’ [2s/2s]
  Running ‘test-KrigingPredict.R’ [7s/7s]
  Running ‘test-KrigingSimulate.R’ [1s/1s]
  Running ‘test-KrigingUpdate.R’ [1s/1s]
  Running ‘test-KrigingUpdateSimulate.R’ [2s/2s]
  Running ‘test-LinearAlgebra.R’ [1s/1s]
  Running ‘test-NoiseKrigingFit.R’ [1s/1s]
  Running ‘test-NoiseKrigingLogLik.R’ [2s/2s]
  Running ‘test-NoiseKrigingMethods.R’ [2s/2s]
  Running ‘test-NoiseKrigingPredict.R’ [5s/5s]
  Running ‘test-NoiseKrigingSimulate.R’ [3s/3s]
  Running ‘test-NoiseKrigingUpdate.R’ [1s/1s]
  Running ‘test-NoiseKrigingUpdateSimulate.R’ [2s/2s]
  Running ‘test-NuggetKrigingFit.R’ [1s/1s]
  Running ‘test-NuggetKrigingLogLik.R’ [3s/3s]
  Running ‘test-NuggetKrigingLogMargPost.R’ [4s/4s]
  Running ‘test-NuggetKrigingMethods.R’ [2s/2s]
  Running ‘test-NuggetKrigingPredict.R’ [5s/5s]
  Running ‘test-NuggetKrigingSimulate.R’ [1s/1s]
  Running ‘test-NuggetKrigingUpdate.R’ [1s/1s]
  Running ‘test-NuggetKrigingUpdateSimulate.R’ [2s/2s]
  Running ‘test-RobustGaSP-Nugget.R’ [0s/0s]
  Running ‘test-RobustGaSP.R’ [0s/0s]
  Running ‘test-RobustGaSPtrendlinear.R’ [0s/0s]
  Running ‘test-RobustGaSPvsKrigingLMP.R’ [0s/0s]
  Running ‘test-RobustGaSPvsNuggetKrigingLMP.R’ [0s/0s]
  Running ‘test-SaveLoad.R’ [1s/1s]
  Running ‘test-asDiceKriging.R’ [6s/6s]
  Running ‘test-estimnone.R’ [1s/1s]
  Running ‘test-normalize.R’ [2s/2s]
  Running ‘test-rlibkriging-demo.R’ [1s/1s]
  Running ‘test-unstableLL.R’ [1s/1s]
Running the tests in ‘tests/test-KrigingFit.R’ failed.
Complete output:
  > library(testthat)
  >  Sys.setenv('OMP_THREAD_LIMIT'=2)
  >  library(rlibkriging)
  
  Attaching package: 'rlibkriging'
  
  The following objects are masked from 'package:base':
  
      load, save
  
  > 
  > context("Fit: 1D")
  > 
  > f = function(x) 1-1/2*(sin(12*x)/(1+x)+2*cos(7*x)*x^5+0.7)
  > n <- 5
  > set.seed(123)
  > X <- as.matrix(runif(n))
  > y = f(X)
  > k = NULL
  > r = NULL
  > k = DiceKriging::km(design=X,response=y,covtype = "gauss",control = list(trace=F))
  > r <- Kriging(y, X, "gauss")
  > 
  > ll = Vectorize(function(x) logLikelihoodFun(r,x)$logLikelihood)
  > plot(ll,xlim=c(0.000001,10))
  >   for (x in seq(0.000001,10,,11)){
  +     envx = new.env()
  +     ll2x = logLikelihoodFun(r,x)$logLikelihood
  +     gll2x = logLikelihoodFun(r,x,return_grad = T)$logLikelihoodGrad
  +     arrows(x,ll2x,x+.1,ll2x+.1*gll2x,col='red')
  +   }
  > 
  > theta_ref = optimize(ll,interval=c(0.001,2),maximum=T)$maximum
  > abline(v=theta_ref,col='black')
  > abline(v=as.list(r)$theta,col='red')
  > abline(v=k@covariance@range.val,col='blue')
  > 
  > test_that(desc="Fit: 1D / fit of theta by DiceKriging is right",
  +           expect_equal(theta_ref, k@covariance@range.val, tol= 1e-3))
  Test passed 😸
  > 
  > test_that(desc="Fit: 1D / fit of theta by libKriging is right",
  +           expect_equal(array(theta_ref), array(as.list(r)$theta), tol= 0.01))
  Test passed 🥳
  > 
  > #############################################################
  > 
  > context("Fit: 2D (Branin)")
  > 
  > f = function(X) apply(X,1,DiceKriging::branin)
  > n <- 15
  > set.seed(1234)
  > X <- cbind(runif(n),runif(n))
  > y = f(X)
  > k = NULL
  > r = NULL
  > k = DiceKriging::km(design=X,response=y,covtype = "gauss",control = list(trace=F),parinit = c(.2,.5))
  > r <- Kriging(y, X, "gauss", parameters=list(theta=matrix(c(.2,.5),ncol=2)))
  > 
  > ll = function(X) {if (!is.matrix(X)) X = matrix(X,ncol=2);
  +                   # print(dim(X));
  +                   apply(X,1,
  +                     function(x) {
  +                       # print(dim(x))
  +                       #print(matrix(unlist(x),ncol=2));
  +                       y=-logLikelihoodFun(r,matrix(unlist(x),ncol=2))$logLikelihood
  +                       #print(y);
  +                       y})}
  > #DiceView::contourview(ll,xlim=c(0.01,2),ylim=c(0.01,2))
  > x=seq(0.01,2,,5)
  > contour(x,x,matrix(ll(as.matrix(expand.grid(x,x))),nrow=length(x)),nlevels = 30)
  > 
  > theta_ref = optim(par=matrix(c(.2,.5),ncol=2),ll,lower=c(0.01,0.01),upper=c(2,2),method="L-BFGS-B")$par
  > points(theta_ref,col='black')
  > points(as.list(r)$theta[1],as.list(r)$theta[2],col='red')
  > points(k@covariance@range.val[1],k@covariance@range.val[2],col='blue')
  > 
  > test_that(desc="Fit: 2D (Branin) / fit of theta 2D is _quite_ the same that DiceKriging one",
  +           expect_equal(ll(array(as.list(r)$theta)), ll(k@covariance@range.val), tol=1e-1))
  Test passed 😸
  > 
  > 
  > 
  > #############################################################
  > 
  > context("Fit: 2D (Branin) multistart")
  > 
  > f = function(X) apply(X,1,DiceKriging::branin)
  > n <- 15
  > set.seed(1234)
  > X <- cbind(runif(n),runif(n))
  > y = f(X)
  > k = NULL
  > r = NULL
  > 
  > parinit = matrix(runif(10*ncol(X)),ncol=ncol(X))
  > k <- tryCatch( # needed to catch warning due to %dopar% usage when using multistart
  +     withCallingHandlers(
  +       {
  +         error_text <- "No error."
  +         DiceKriging::km(design=X,response=y,covtype = "gauss",multistart = 10, parinit=parinit,control = list(trace=F))
  +       },
  +       warning = function(e) {
  +         error_text <<- trimws(paste0("WARNING: ", e))
  +         invokeRestart("muffleWarning")
  +       }
  +     ),
  +     error = function(e) {
  +       return(list(value = NA, error_text = trimws(paste0("ERROR: ", e))))
  +     },
  +     finally = {
  +     }
  +   )
  > r <- Kriging(y, X, "gauss", parameters=list(theta=parinit))
  > l = as.list(r)
  > 
  > # save(list=ls(),file="fit-2d-multistart.Rdata")
  > 
  > ll = function(X) {if (!is.matrix(X)) X = matrix(X,ncol=2);
  + # print(dim(X));
  + apply(X,1,
  +       function(x) {
  +         # print(dim(x))
  +         #print(matrix(unlist(x),ncol=2));
  +         y=-logLikelihoodFun(r,matrix(unlist(x),ncol=2))$logLikelihood
  +         #print(y);
  +         y})}
  > #DiceView::contourview(ll,xlim=c(0.01,2),ylim=c(0.01,2))
  > x=seq(0.01,2,,5)
  > contour(x,x,matrix(ll(as.matrix(expand.grid(x,x))),nrow=length(x)),nlevels = 30)
  > 
  > theta_ref = optim(par=matrix(c(.2,.5),ncol=2),ll,lower=c(0.01,0.01),upper=c(2,2),method="L-BFGS-B")$par
  > points(theta_ref,col='black')
  > points(as.list(r)$theta[1],as.list(r)$theta[2],col='red')
  > points(k@covariance@range.val[1],k@covariance@range.val[2],col='blue')
  Error in k@covariance : 
    no applicable method for `@` applied to an object of class "list"
  Calls: points
  Execution halted
Running the tests in ‘tests/test-NoiseKrigingFit.R’ failed.
Complete output:
  > library(testthat)
  >  Sys.setenv('OMP_THREAD_LIMIT'=2)
  >  library(rlibkriging)
  
  Attaching package: 'rlibkriging'
  
  The following objects are masked from 'package:base':
  
      load, save
  
  > 
  > ##library(rlibkriging, lib.loc="bindings/R/Rlibs")
  > ##library(testthat)
  > 
  > context("Fit: 1D")
  > 
  > f = function(x) 1-1/2*(sin(12*x)/(1+x)+2*cos(7*x)*x^5+0.7)
  > n <- 5
  > set.seed(123)
  > X <- as.matrix(runif(n))
  > y = f(X) + 0.1*rnorm(nrow(X))
  > k = NULL
  > r = NULL
  > k = DiceKriging::km(design=X,response=y,noise.var=rep(0.1^2,nrow(X)),covtype = "gauss",control = list(trace=F),nugget.estim=F,optim.method='BFGS',multistart = 20)
  Error in f(model, envir = envir.logLik) : Package "foreach" not found
  Calls: <Anonymous> -> f
  Execution halted
Running the tests in ‘tests/test-NuggetKrigingFit.R’ failed.
Complete output:
  > library(testthat)
  >  Sys.setenv('OMP_THREAD_LIMIT'=2)
  >  library(rlibkriging)
  
  Attaching package: 'rlibkriging'
  
  The following objects are masked from 'package:base':
  
      load, save
  
  > 
  > ##library(rlibkriging, lib.loc="bindings/R/Rlibs")
  > ##library(testthat)
  > 
  > context("Fit: 1D")
  > 
  > f = function(x) 1-1/2*(sin(12*x)/(1+x)+2*cos(7*x)*x^5+0.7)
  > n <- 5
  > set.seed(123)
  > X <- as.matrix(runif(n))
  > y = f(X)
  > k = NULL
  > r = NULL
  > k = DiceKriging::km(design=X,response=y,covtype = "gauss",control = list(trace=F),nugget.estim=T,optim.method='BFGS',multistart = 20)
  Error in f(model, envir = envir.logLik) : Package "foreach" not found
  Calls: <Anonymous> -> f
  Execution halted
* checking PDF version of manual ... [4s/4s] OK
* checking HTML version of manual ... [2s/2s] OK
* checking for non-standard things in the check directory ... OK
* checking for detritus in the temp directory ... OK
* DONE
Status: 1 ERROR, 1 NOTE