CRAN Package Check Results for Package rlibkriging

Last updated on 2026-07-12 13:51:03 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.0-0 251.11 234.19 485.30 OK
r-devel-linux-x86_64-debian-gcc 1.0-0 173.54 154.79 328.33 OK
r-devel-linux-x86_64-fedora-clang 1.1-0 304.00 5655.26 5959.26 ERROR
r-devel-linux-x86_64-fedora-gcc 1.1-0 583.00 5684.22 6267.22 ERROR
r-devel-windows-x86_64 1.1-0 406.00 281.00 687.00 OK
r-patched-linux-x86_64 1.0-0 238.46 217.59 456.05 OK
r-release-linux-x86_64 1.0-0 242.24 217.65 459.89 OK
r-release-macos-arm64 1.1-0 66.00 24.00 90.00 OK
r-release-macos-x86_64 1.1-0 191.00 256.00 447.00 OK
r-release-windows-x86_64 1.0-0 442.00 281.00 723.00 OK
r-oldrel-macos-arm64 1.1-0 56.00 30.00 86.00 OK
r-oldrel-macos-x86_64 1.1-0 191.00 208.00 399.00 OK
r-oldrel-windows-x86_64 1.0-0 562.00 357.00 919.00 OK

Check Details

Version: 1.1-0
Check: tests
Result: ERROR Running ‘test-AllKrigingConcistency.R’ Running ‘test-KrigingConstructorConsistency.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’ Running ‘test-KrigingMethods.R’ Running ‘test-KrigingNoiseFit.R’ Running ‘test-KrigingNoiseLogLik.R’ Running ‘test-KrigingNoiseMethods.R’ Running ‘test-KrigingNoisePredict.R’ Running ‘test-KrigingNoiseSimulate.R’ Running ‘test-KrigingNoiseUpdate.R’ Running ‘test-KrigingNoiseUpdateSimulate.R’ Running ‘test-KrigingNuggetFit.R’ Running ‘test-KrigingNuggetLogLik.R’ Running ‘test-KrigingNuggetLogMargPost.R’ Running ‘test-KrigingNuggetMethods.R’ Running ‘test-KrigingNuggetPredict.R’ Running ‘test-KrigingNuggetSimulate.R’ Running ‘test-KrigingNuggetUpdate.R’ Running ‘test-KrigingNuggetUpdateSimulate.R’ Running ‘test-KrigingPredict.R’ [17s/10s] Running ‘test-KrigingSimulate.R’ Running ‘test-KrigingUpdate.R’ Running ‘test-KrigingUpdateSimulate.R’ Running ‘test-LinearAlgebra.R’ Running ‘test-MLPKriging.R’ Running ‘test-NestedKriging.R’ [90m/45m] 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-WarpKriging.R’ Running ‘test-asDiceKriging.R’ Running ‘test-estimnone.R’ Running ‘test-new-features.R’ Running ‘test-normalize.R’ Running ‘test-rlibkriging-demo.R’ Running ‘test-unstableLL.R’ Running the tests in ‘tests/test-NestedKriging.R’ failed. Complete output: > library(testthat) > Sys.setenv('OMP_THREAD_LIMIT'=2) > > library(rlibkriging) Attaching package: 'rlibkriging' The following object is masked from 'package:stats': kernel The following objects are masked from 'package:base': beta, load, save > > #library(testthat) > #library(rlibkriging) > > f <- function(X) apply(X, 1, function(x) sin(3 * x[1]) + cos(5 * x[2]) + x[1] * x[2]) > > set.seed(123) > X <- matrix(runif(2 * 200), ncol = 2) > y <- f(X) > Xt <- matrix(runif(2 * 100), ncol = 2) > > test_that("NestedKriging fits and predicts with all aggregations", { + for (agg in c("PoE", "gPoE", "BCM", "rBCM", "NK")) { + k <- NestedKriging(y, X, kernel = "matern5_2", nb_groups = 4, aggregation = agg) + p <- predict(k, Xt) + expect_length(p$mean, nrow(Xt)) + expect_length(p$stdev, nrow(Xt)) + expect_true(all(is.finite(p$mean))) + expect_true(all(p$stdev >= 0)) + rmse <- sqrt(mean((p$mean - f(Xt))^2)) + expect_lt(rmse, 0.5 * sd(y)) + } + }) OMP: Warning #96: Cannot form a team with 24 threads, using 2 instead. OMP: Hint Consider unsetting KMP_DEVICE_THREAD_LIMIT (KMP_ALL_THREADS), KMP_TEAMS_THREAD_LIMIT, and OMP_THREAD_LIMIT (if any are set). Test passed with 25 successes 🌈. > > test_that("NK aggregation interpolates the design", { + k <- NestedKriging(y, X, kernel = "matern5_2", nb_groups = 4, aggregation = "NK") + p <- predict(k, X) + expect_lt(max(abs(p$mean - y)), 1e-3) + expect_lt(max(p$stdev), 1e-2) + }) Test passed with 2 successes 🎉. > > test_that("accessors are consistent", { + k <- NestedKriging(y, X, kernel = "gauss", nb_groups = 4) + expect_equal(k$kernel(), "gauss") + expect_equal(k$aggregation(), "NK") + expect_equal(k$nb_groups(), 4) + expect_length(k$theta(), 2) + expect_gt(k$sigma2(), 0) + expect_equal(sort(unlist(k$groups())), 1:nrow(X)) # 1-based partition + }) Test passed with 6 successes 😀. > > test_that("close to full Kriging on moderate n", { + kf <- Kriging(y, X, kernel = "matern5_2") + pf <- predict(kf, Xt, return_stdev = FALSE) + k <- NestedKriging(y, X, kernel = "matern5_2", nb_groups = 4, aggregation = "NK") + p <- predict(k, Xt, return_stdev = FALSE) + expect_lt(mean(abs(p$mean - pf$mean)), 0.05 * sd(y)) + }) Flavor: r-devel-linux-x86_64-fedora-clang

Version: 1.1-0
Check: tests
Result: ERROR Running ‘test-AllKrigingConcistency.R’ Running ‘test-KrigingConstructorConsistency.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’ Running ‘test-KrigingMethods.R’ Running ‘test-KrigingNoiseFit.R’ Running ‘test-KrigingNoiseLogLik.R’ Running ‘test-KrigingNoiseMethods.R’ Running ‘test-KrigingNoisePredict.R’ [11s/12s] Running ‘test-KrigingNoiseSimulate.R’ Running ‘test-KrigingNoiseUpdate.R’ Running ‘test-KrigingNoiseUpdateSimulate.R’ Running ‘test-KrigingNuggetFit.R’ Running ‘test-KrigingNuggetLogLik.R’ Running ‘test-KrigingNuggetLogMargPost.R’ [12s/12s] Running ‘test-KrigingNuggetMethods.R’ Running ‘test-KrigingNuggetPredict.R’ Running ‘test-KrigingNuggetSimulate.R’ Running ‘test-KrigingNuggetUpdate.R’ Running ‘test-KrigingNuggetUpdateSimulate.R’ Running ‘test-KrigingPredict.R’ [13s/13s] Running ‘test-KrigingSimulate.R’ Running ‘test-KrigingUpdate.R’ Running ‘test-KrigingUpdateSimulate.R’ Running ‘test-LinearAlgebra.R’ Running ‘test-MLPKriging.R’ Running ‘test-NestedKriging.R’ [90m/46m] 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-WarpKriging.R’ Running ‘test-asDiceKriging.R’ Running ‘test-estimnone.R’ Running ‘test-new-features.R’ Running ‘test-normalize.R’ Running ‘test-rlibkriging-demo.R’ Running ‘test-unstableLL.R’ Running the tests in ‘tests/test-NestedKriging.R’ failed. Complete output: > library(testthat) > Sys.setenv('OMP_THREAD_LIMIT'=2) > > library(rlibkriging) Attaching package: 'rlibkriging' The following object is masked from 'package:stats': kernel The following objects are masked from 'package:base': beta, load, save > > #library(testthat) > #library(rlibkriging) > > f <- function(X) apply(X, 1, function(x) sin(3 * x[1]) + cos(5 * x[2]) + x[1] * x[2]) > > set.seed(123) > X <- matrix(runif(2 * 200), ncol = 2) > y <- f(X) > Xt <- matrix(runif(2 * 100), ncol = 2) > > test_that("NestedKriging fits and predicts with all aggregations", { + for (agg in c("PoE", "gPoE", "BCM", "rBCM", "NK")) { + k <- NestedKriging(y, X, kernel = "matern5_2", nb_groups = 4, aggregation = agg) + p <- predict(k, Xt) + expect_length(p$mean, nrow(Xt)) + expect_length(p$stdev, nrow(Xt)) + expect_true(all(is.finite(p$mean))) + expect_true(all(p$stdev >= 0)) + rmse <- sqrt(mean((p$mean - f(Xt))^2)) + expect_lt(rmse, 0.5 * sd(y)) + } + }) Test passed with 25 successes 🌈. > > test_that("NK aggregation interpolates the design", { + k <- NestedKriging(y, X, kernel = "matern5_2", nb_groups = 4, aggregation = "NK") + p <- predict(k, X) + expect_lt(max(abs(p$mean - y)), 1e-3) + expect_lt(max(p$stdev), 1e-2) + }) Test passed with 2 successes 🎉. > > test_that("accessors are consistent", { + k <- NestedKriging(y, X, kernel = "gauss", nb_groups = 4) + expect_equal(k$kernel(), "gauss") + expect_equal(k$aggregation(), "NK") + expect_equal(k$nb_groups(), 4) + expect_length(k$theta(), 2) + expect_gt(k$sigma2(), 0) + expect_equal(sort(unlist(k$groups())), 1:nrow(X)) # 1-based partition + }) Test passed with 6 successes 😀. > > test_that("close to full Kriging on moderate n", { + kf <- Kriging(y, X, kernel = "matern5_2") + pf <- predict(kf, Xt, return_stdev = FALSE) + k <- NestedKriging(y, X, kernel = "matern5_2", nb_groups = 4, aggregation = "NK") + p <- predict(k, Xt, return_stdev = FALSE) + expect_lt(mean(abs(p$mean - pf$mean)), 0.05 * sd(y)) + }) Flavor: r-devel-linux-x86_64-fedora-gcc