• using R Under development (unstable) (2025-02-15 r87723)
• using platform: x86_64-pc-linux-gnu
• R was compiled by     gcc (GCC) 14.2.1 20240912 (Red Hat 14.2.1-3)     GNU Fortran (GCC) 14.2.1 20240912 (Red Hat 14.2.1-3)
• running under: Fedora Linux 40 (Workstation Edition)
• using session charset: UTF-8
• using option ‘--no-stop-on-test-error’
• checking for file ‘rlibkriging/DESCRIPTION’ ... OK
• checking extension type ... Package
• this is package ‘rlibkriging’ version ‘0.9-1’
• package encoding: UTF-8
• checking package namespace information ... OK
• checking package dependencies ... OK
• checking if this is a source package ... OK
• checking if there is a namespace ... OK
• checking for executable files ... OK
• checking for hidden files and directories ... OK
• checking for portable file names ... OK
• checking for sufficient/correct file permissions ... OK
• checking whether package ‘rlibkriging’ can be installed ... [574s/254s] OK See the install log for details.
• used C++ compiler: ‘g++ (GCC) 14.2.1 20240912 (Red Hat 14.2.1-3)’
• checking C++ specification ... OK
• checking package directory ... OK
• checking DESCRIPTION meta-information ... OK
• checking top-level files ... OK
• checking for left-over files ... OK
• checking index information ... OK
• checking package subdirectories ... OK
• checking code files for non-ASCII characters ... OK
• checking R files for syntax errors ... OK
• checking whether the package can be loaded ... OK
• checking whether the package can be loaded with stated dependencies ... OK
• checking whether the package can be unloaded cleanly ... OK
• checking whether the namespace can be loaded with stated dependencies ... OK
• checking whether the namespace can be unloaded cleanly ... OK
• checking loading without being on the library search path ... OK
• checking use of S3 registration ... OK
• checking dependencies in R code ... OK
• checking S3 generic/method consistency ... OK
• checking replacement functions ... OK
• checking foreign function calls ... OK
• checking R code for possible problems ... [16s/20s] OK
• checking Rd files ... OK
• checking Rd metadata ... OK
• checking Rd line widths ... OK
• checking Rd cross-references ... OK
• checking for missing documentation entries ... OK
• checking for code/documentation mismatches ... OK
• checking Rd \usage sections ... OK
• checking Rd contents ... OK
• checking for unstated dependencies in examples ... OK
• checking line endings in shell scripts ... OK
• checking line endings in C/C++/Fortran sources/headers ... OK
• checking line endings in Makefiles ... OK
• checking compilation flags in Makevars ... OK
• checking for GNU extensions in Makefiles ... INFO GNU make is a SystemRequirements.
• checking for portable use of $(BLAS_LIBS) and $(LAPACK_LIBS) ... OK
• checking use of PKG_*FLAGS in Makefiles ... OK
• checking use of SHLIB_OPENMP_*FLAGS in Makefiles ... OK
• checking include directives in Makefiles ... OK
• checking pragmas in C/C++ headers and code ... OK
• checking compilation flags used ... OK
• checking compiled code ... OK
• checking examples ... OK
• checking for unstated dependencies in ‘tests’ ... OK
• checking tests ... [314s/365s] ERROR   Running ‘test-AllKrigingConcistency.R’   Running ‘test-KrigingCopy.R’   Running ‘test-KrigingFit.R’   Running ‘test-KrigingLeaveOneOut.R’   Running ‘test-KrigingLeaveOneOut_3d.R’   Running ‘test-KrigingLogLik.R’   Running ‘test-KrigingLogLikGradHess.R’ [30s/30s]   Running ‘test-KrigingMethods.R’   Running ‘test-KrigingPredict.R’ [24s/28s]   Running ‘test-KrigingSimulate.R’   Running ‘test-KrigingUpdate.R’   Running ‘test-KrigingUpdateSimulate.R’   Running ‘test-LinearAlgebra.R’   Running ‘test-NoiseKrigingFit.R’ [11s/11s]   Running ‘test-NoiseKrigingLogLik.R’   Running ‘test-NoiseKrigingMethods.R’   Running ‘test-NoiseKrigingPredict.R’ [17s/19s]   Running ‘test-NoiseKrigingSimulate.R’ [10s/12s]   Running ‘test-NoiseKrigingUpdate.R’   Running ‘test-NoiseKrigingUpdateSimulate.R’   Running ‘test-NuggetKrigingFit.R’ [15s/16s]   Running ‘test-NuggetKrigingLogLik.R’ [10s/12s]   Running ‘test-NuggetKrigingLogMargPost.R’ [18s/21s]   Running ‘test-NuggetKrigingMethods.R’   Running ‘test-NuggetKrigingPredict.R’ [17s/19s]   Running ‘test-NuggetKrigingSimulate.R’   Running ‘test-NuggetKrigingUpdate.R’   Running ‘test-NuggetKrigingUpdateSimulate.R’   Running ‘test-RobustGaSP-Nugget.R’   Running ‘test-RobustGaSP.R’   Running ‘test-RobustGaSPtrendlinear.R’   Running ‘test-RobustGaSPvsKrigingLMP.R’   Running ‘test-RobustGaSPvsNuggetKrigingLMP.R’   Running ‘test-SaveLoad.R’   Running ‘test-asDiceKriging.R’ [21s/26s]   Running ‘test-estimnone.R’   Running ‘test-normalize.R’   Running ‘test-rlibkriging-demo.R’   Running ‘test-unstableLL.R’ Running the tests in ‘tests/test-RobustGaSP.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)   >   > library(RobustGaSP)   #########   ##   ## Robust Gaussian Stochastic Process, RobustGaSP Package   ## Copyright (C) 2016-2025 Mengyang Gu, Jesus Palomo and James O. Berger   #########      Attaching package: 'RobustGaSP'      The following object is masked from 'package:rlibkriging':          simulate      The following object is masked from 'package:stats':          simulate      >   > context("RobustGaSP / Fit: 1D")   >   > f = function(x) 1-1/2*(sin(12*x)/(1+x)+2*cos(7*x)*x^5+0.7)   > #plot(f)   > n <- 5   > set.seed(123)   > X <- as.matrix(runif(n))   > y = f(X)   > #points(X,y)   > k = RobustGaSP::rgasp(design=X,response=y)   The upper bounds of the range parameters are 184.9743   The initial values of range parameters are 3.699485   Start of the optimization 1 :   The number of iterations is 30    The value of the marginal posterior function is 2.497978    Optimized range parameters are 0.1921691    Optimized nugget parameter is 0    Convergence: TRUE   The initial values of range parameters are 0.05223118   Start of the optimization 2 :   The number of iterations is 30    The value of the marginal posterior function is 1.035387    Optimized range parameters are 0.05296527    Optimized nugget parameter is 0    Convergence: TRUE   > #library(rlibkriging)   > r <- Kriging(y, X,   + kernel="matern5_2",   + regmodel = "constant", normalize = FALSE,   + optim = "BFGS",   + objective = "LMP")   > # m = as.list(r)   >   > # Check lmp function   >   > lmp_rgasp = function(X, model=k) {if (!is.matrix(X)) X = matrix(X,ncol=1);   + # print(dim(X));   + apply(X,1,   + function(x) {   + #y=-logMargPostFun(r,matrix(unlist(x),ncol=2))$logMargPost   + y=RobustGaSP:::neg_log_marginal_post_approx_ref(param=(x),nugget=0, nugget.est=model@nugget.est,   + R0=model@R0,X=model@X, zero_mean=model@zero_mean,output=model@output,   + CL=model@CL,   + a=0.2,   + b=1/(length(model@output))^{1/dim(as.matrix(model@input))[2]}*(0.2+dim(as.matrix(model@input))[2]),   + kernel_type=rep(as.integer(3),ncol(X)),alpha=model@alpha   + )   + y})}   > lmp_rgasp(1)   [1] -1.901254   >   > plot(lmp_rgasp,xlim=c(0.01,6))   > abline(v=(log(k@beta_hat)))   >   > lmp_lk = function(X) {if (!is.matrix(X)) X = matrix(X,ncol=1);   + # print(dim(X));   + apply(X,1,   + function(x) {   + y=-logMargPostFun(r,matrix(unlist(exp(-(x))),ncol=1))$logMargPost   + y})}   > lmp_lk(1)   [1] -1.901254   >   > lines(seq(0.1,6,,5),lmp_lk(seq(0.1,6,,5)),col='red')   > abline(v=(log(1/as.list(r)$theta)),col='red')   >   > precision <- 1e-3   > test_that(desc=paste0("RobustGaSP / Fit: 1D / rgasp/lmp is the same that lk/lmp one"),   + expect_equal(lmp_rgasp(1),lmp_lk(1),tol = precision))   Test passed 🥳   > test_that(desc=paste0("RobustGaSP / Fit: 1D / fitted theta is the same that RobustGaSP one"),   + expect_equal(as.list(r)$theta[1],1/k@beta_hat,tol = precision))   Test passed 🥳   >   >   >   > dlmp_rgasp = function(X, model=k) {if (!is.matrix(X)) X = matrix(X,ncol=1);   + # print(dim(X));   + apply(X,1,   + function(x) {   +   + # print(RobustGaSP:::log_marginal_lik_deriv(param=(x),nugget=0,nugget_est=model@nugget.est,   + # R0=model@R0,X=model@X, zero_mean=model@zero_mean,   + # output=model@output,   + # kernel_type=rep(as.integer(3),ncol(X)),alpha=model@alpha))   + #   + # print(RobustGaSP:::log_approx_ref_prior_deriv(param=(x),nugget=0, nugget_est=model@nugget.est,   + # CL=model@CL,   + # a=0.2,   + # b=1/(length(model@output))^{1/dim(as.matrix(model@input))[2]}*(0.2+dim(as.matrix(model@input))[2])))   +   +   + #y=-logMargPostFun(r,matrix(unlist(x),ncol=2))$logMargPost   + y=RobustGaSP:::neg_log_marginal_post_approx_ref_deriv(param=(x),nugget=0, nugget.est=model@nugget.est,   + R0=model@R0,X=model@X, zero_mean=model@zero_mean,output=model@output,   + CL=model@CL,   + a=0.2,   + b=1/(length(model@output))^{1/dim(as.matrix(model@input))[2]}*(0.2+dim(as.matrix(model@input))[2]),   + kernel_type=rep(as.integer(3),ncol(X)),alpha=model@alpha   + )   + y})}   > dlmp_rgasp(1)   [1] -1.703845   >   > dlmp_lk = function(X) {if (!is.matrix(X)) X = matrix(X,ncol=1);   + apply(X,1,   + function(x) {   + y=-logMargPostFun(r,matrix(unlist(exp(-(x))),ncol=1),TRUE)$logMargPostGrad   + y})}   > -exp(-1)*dlmp_lk(1)   [1] -1.703845   >   > precision <- 1e-3   > test_that(desc=paste0("RobustGaSP / Fit: 1D / rgasp/lmp deriv is the same that lk/lmp deriv"),   + expect_equal(dlmp_rgasp(1),-exp(-1)*dlmp_lk(1),tol = precision))   Test passed 🎉   >   >   > # Check predict   >   > ntest <- 10   > Xtest <- seq(0,1,,ntest)   > Ytest_rgasp <- predict(k,matrix(Xtest,ncol=1))   > Ytest_libK <- predict(r,Xtest)   >   > plot(f)   > points(X,y)   > lines(Xtest,Ytest_rgasp$mean,col='blue')   > polygon(c(Xtest,rev(Xtest)),   + c(Ytest_rgasp$mean+2*Ytest_rgasp$sd,rev(Ytest_rgasp$mean-2*Ytest_rgasp$sd)),   + col=rgb(0,0,1,0.1), border=NA)   >   > lines(Xtest,Ytest_libK$mean,col='red')   > polygon(c(Xtest,rev(Xtest)),   + c(Ytest_libK$mean+2*Ytest_libK$stdev,rev(Ytest_libK$mean-2*Ytest_libK$stdev)),   + col=rgb(1,0,0,0.1), border=NA)   >   > precision <- 1e-3   > test_that(desc=paste0("pred mean is the same that RobustGaSP one"),   + expect_equal(predict(r,0.7)$mean[1],predict(k,matrix(0.7))$mean,tol = precision))   Test passed 😸   > test_that(desc=paste0("pred sd is the same that RobustGaSP one"),   + expect_equal(predict(r,0.7)$stdev[1],predict(k,matrix(0.7))$sd,tol = precision))   Test passed 🥇   >   >   > ## RobustGaSP examples   >   > #---------------------------------------   > # a 1 dimensional example   > #---------------------------------------   > context("RobustGaSP / 1 dimensional example")   >   >   > input=10*seq(0,1,1/14)   > output<-higdon.1.data(input)   > #the following code fit a GaSP with zero mean by setting zero.mean="Yes"   > model<- rgasp(design = input, response = output, zero.mean="No")   The upper bounds of the range parameters are 670.0756   The initial values of range parameters are 13.40151   Start of the optimization 1 :   The number of iterations is 30    The value of the marginal posterior function is -10.48964    Optimized range parameters are 13.2106    Optimized nugget parameter is 0    Convergence: TRUE   The initial values of range parameters are 0.08888889   Start of the optimization 2 :   The number of iterations is 30    The value of the marginal posterior function is -15.24592    Optimized range parameters are 0.1706386    Optimized nugget parameter is 0    Convergence: TRUE   > model      Call:   rgasp(design = input, response = output, zero.mean = "No")   Mean parameters: 2.174187e-10   Variance parameter: 4249.587   Range parameters: 13.2106   Noise parameter: 0   >   > testing_input = as.matrix(seq(0,10,1/100))   > model.predict<-predict(model,testing_input)   > names(model.predict)   [1] "mean" "lower95" "upper95" "sd"   >   > #########plot predictive distribution   > testing_output=higdon.1.data(testing_input)   > plot(testing_input,model.predict$mean,type='l',col='blue',   + xlab='input',ylab='output')   > polygon( c(testing_input,rev(testing_input)),c(model.predict$lower95,   + rev(model.predict$upper95)),col = "grey80", border = FALSE)   > lines(testing_input, testing_output)   > lines(testing_input,model.predict$mean,type='l',col='blue')   > lines(input, output,type='p')   >   > ## mean square erros   > mean((model.predict$mean-testing_output)^2)   [1] 4.63608e-05   >   > model_libK = Kriging(matrix(output,ncol=1), matrix(input,ncol=1),   + kernel="matern5_2",   + regmodel = "constant", normalize = FALSE,   + optim = "BFGS",   + objective = "LMP", parameters = NULL)   >   > lines(testing_input,predict(model_libK,testing_input)$mean,type='l',col='red')   > polygon(   + c(testing_input,rev(testing_input)),   + c(   + predict(model_libK,testing_input)$mean+2*predict(model_libK,testing_input)$stdev,   + rev(predict(model_libK,testing_input)$mean-2*predict(model_libK,testing_input)$stdev)),   + col = rgb(1,0,0,0.1), border = FALSE)   >   > precision <- 1e-3   > test_that(desc=paste0("RobustGaSP / 1 dimensional example / pred mean is the same that RobustGaSP one"),   + expect_equal(predict(model_libK,0.7)$mean[1],predict(model,matrix(0.7))$mean,tol = precision))   ── Failure: RobustGaSP / 1 dimensional example / pred mean is the same that RobustGaSP one ──   predict(model_libK, 0.7)$mean[1] not equal to predict(model, matrix(0.7))$mean.   1/1 mismatches   [1] 0.621 - 0.623 == -0.00162      Error:   ! Test failed   Backtrace:       ▆    1. ├─testthat::test_that(...)    2. │ └─withr (local) `<fn>`()    3. └─reporter$stop_if_needed()    4. └─rlang::abort("Test failed", call = NULL)   Execution halted
• checking PDF version of manual ... [12s/15s] OK
• checking HTML version of manual ... OK
• checking for non-standard things in the check directory ... OK
• checking for detritus in the temp directory ... OK
• DONE

Status: 1 ERROR