• using R version 4.6.1 (2026-06-24)
• using platform: x86_64-pc-linux-gnu
• R was compiled by     gcc-16 (Debian 16.1.0-2) 16.1.0     GNU Fortran (Debian 16.1.0-2) 16.1.0
• running under: Debian GNU/Linux forky/sid
• using session charset: UTF-8 * current time: 2026-07-25 15:54:00 UTC
• checking for file ‘mlexperiments/DESCRIPTION’ ... OK
• this is package ‘mlexperiments’ version ‘1.0.0’
• 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 ‘mlexperiments’ can be installed ... OK See the install log for details.
• checking package directory ... OK
• checking for future file timestamps ... OK
• checking ‘build’ 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 ... [0s/1s] OK
• checking whether the package can be loaded with stated dependencies ... [0s/1s] OK
• checking whether the package can be unloaded cleanly ... [0s/0s] OK
• checking whether the namespace can be loaded with stated dependencies ... [0s/0s] OK
• checking whether the namespace can be unloaded cleanly ... [0s/1s] OK
• checking loading without being on the library search path ... [0s/1s] OK
• checking whether startup messages can be suppressed ... [0s/1s] 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 ... [7s/9s] OK
• checking Rd files ... [1s/1s] 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 installed files from ‘inst/doc’ ... OK
• checking files in ‘vignettes’ ... OK
• checking examples ... [4s/6s] OK
• checking for unstated dependencies in ‘tests’ ... OK
• checking tests ... [7m/11m] ERROR   Running ‘testthat.R’ [7m/11m] Running the tests in ‘tests/testthat.R’ failed. Complete output:   > # This file is part of the standard setup for testthat.   > # It is recommended that you do not modify it.   > #   > # Where should you do additional test configuration?   > # Learn more about the roles of various files in:   > # * https://r-pkgs.org/tests.html   > # * https://testthat.r-lib.org/reference/test_package.html#special-files   >   > Sys.setenv("OMP_THREAD_LIMIT" = 2)   > Sys.setenv("Ncpu" = 2)   >   > library(testthat)   > library(mlexperiments)   >   > test_check("mlexperiments")   Saving _problems/test-fold_equality-5.R   Saving _problems/test-glm-5.R   Saving _problems/test-glm_predictions-5.R      CV fold: Fold1      CV fold: Fold2      CV progress [==================================>-----------------] 2/3 ( 67%)      CV fold: Fold3      CV progress [====================================================] 3/3 (100%)                                                                                       Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...   ... reducing initialization grid to 4 rows.   elapsed = 5.793 Round = 1 k = 16.0000 Value = -0.1487759   elapsed = 6.292 Round = 2 k = 64.0000 Value = -0.123666   elapsed = 5.931 Round = 3 k = 10.0000 Value = -0.1638418   elapsed = 5.683 Round = 4 k = 34.0000 Value = -0.1321406   elapsed = 5.323 Round = 5 k = 80.0000 Value = -0.1217828   elapsed = 5.266 Round = 6 k = 50.0000 Value = -0.1246077       Best Parameters Found:   Round = 5 k = 80.0000 Value = -0.1217828      Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...   ... reducing initialization grid to 4 rows.   elapsed = 5.421 Round = 1 k = 16.0000 Value = -0.1487759   elapsed = 5.742 Round = 2 k = 64.0000 Value = -0.123666   elapsed = 5.592 Round = 3 k = 10.0000 Value = -0.1638418   elapsed = 3.705 Round = 4 k = 34.0000 Value = -0.1321406   elapsed = 3.868 Round = 5 k = 80.0000 Value = -0.1217828   elapsed = 5.346 Round = 6 k = 50.0000 Value = -0.1246077       Best Parameters Found:   Round = 5 k = 80.0000 Value = -0.1217828   elapsed = 5.893 Round = 1 k = 24.0000 Value = -0.1440678   elapsed = 5.501 Round = 2 k = 63.0000 Value = -0.1233522   elapsed = 4.741 Round = 3 k = 34.0000 Value = -0.1321406   elapsed = 5.295 Round = 4 k = 71.0000 Value = -0.1220967   elapsed = 4.852 Round = 5 k = 2.0000 Value = -0.2743252   elapsed = 5.574 Round = 6 k = 80.0000 Value = -0.1217828       Best Parameters Found:   Round = 6 k = 80.0000 Value = -0.1217828      Parameter settings [=============================>---------------] 2/3 ( 67%)   Parameter settings [=============================================] 3/3 (100%)                                                                                    CV fold: Fold1      Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...   ... reducing initialization grid to 4 rows.   elapsed = 3.205 Round = 1 k = 16.0000 Value = -0.1520798   elapsed = 2.827 Round = 2 k = 64.0000 Value = -0.1313681   elapsed = 3.969 Round = 3 k = 10.0000 Value = -0.1859821   elapsed = 2.867 Round = 4 k = 34.0000 Value = -0.1398453   elapsed = 2.54 Round = 5 k = 65.0000 Value = -0.132307   elapsed = 3.138 Round = 6 k = 52.0000 Value = -0.1290153       Best Parameters Found:   Round = 6 k = 52.0000 Value = -0.1290153      CV fold: Fold2      CV progress [==================================>-----------------] 2/3 ( 67%)      Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...   ... reducing initialization grid to 4 rows.   elapsed = 2.843 Round = 1 k = 16.0000 Value = -0.1577268   elapsed = 2.127 Round = 2 k = 64.0000 Value = -0.1153539   elapsed = 3.708 Round = 3 k = 10.0000 Value = -0.1732636   elapsed = 3.071 Round = 4 k = 34.0000 Value = -0.1360669   elapsed = 2.938 Round = 5 k = 80.0000 Value = -0.1082897   elapsed = 2.115 Round = 6 k = 51.0000 Value = -0.1243006       Best Parameters Found:   Round = 5 k = 80.0000 Value = -0.1082897      CV fold: Fold3      CV progress [====================================================] 3/3 (100%)                                                                                       Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...   ... reducing initialization grid to 4 rows.   elapsed = 1.838 Round = 1 k = 16.0000 Value = -0.1577195   elapsed = 2.045 Round = 2 k = 64.0000 Value = -0.1299503   elapsed = 2.407 Round = 3 k = 10.0000 Value = -0.1798522   elapsed = 2.192 Round = 4 k = 34.0000 Value = -0.1384196   elapsed = 3.083 Round = 5 k = 77.0000 Value = -0.1304132   elapsed = 4.625 Round = 6 k = 50.0000 Value = -0.1341896       Best Parameters Found:   Round = 2 k = 64.0000 Value = -0.1299503      CV fold: Fold1      Parameter settings [=======>------------------------------------] 2/11 ( 18%)   Parameter settings [===========>--------------------------------] 3/11 ( 27%)   Parameter settings [===============>----------------------------] 4/11 ( 36%)   Parameter settings [===================>------------------------] 5/11 ( 45%)   Parameter settings [=======================>--------------------] 6/11 ( 55%)   Parameter settings [===========================>----------------] 7/11 ( 64%)   Parameter settings [===============================>------------] 8/11 ( 73%)   Parameter settings [===================================>--------] 9/11 ( 82%)   Parameter settings [======================================>----] 10/11 ( 91%)   Parameter settings [===========================================] 11/11 (100%)                                                                                    CV fold: Fold2      CV progress [==================================>-----------------] 2/3 ( 67%)      Parameter settings [=======>------------------------------------] 2/11 ( 18%)   Parameter settings [===========>--------------------------------] 3/11 ( 27%)   Parameter settings [===============>----------------------------] 4/11 ( 36%)   Parameter settings [===================>------------------------] 5/11 ( 45%)   Parameter settings [=======================>--------------------] 6/11 ( 55%)   Parameter settings [===========================>----------------] 7/11 ( 64%)   Parameter settings [===============================>------------] 8/11 ( 73%)   Parameter settings [===================================>--------] 9/11 ( 82%)   Parameter settings [======================================>----] 10/11 ( 91%)   Parameter settings [===========================================] 11/11 (100%)                                                                                    CV fold: Fold3      CV progress [====================================================] 3/3 (100%)                                                                                             Parameter settings [=======>------------------------------------] 2/11 ( 18%)   Parameter settings [===========>--------------------------------] 3/11 ( 27%)   Parameter settings [===============>----------------------------] 4/11 ( 36%)   Parameter settings [===================>------------------------] 5/11 ( 45%)   Parameter settings [=======================>--------------------] 6/11 ( 55%)   Parameter settings [===========================>----------------] 7/11 ( 64%)   Parameter settings [===============================>------------] 8/11 ( 73%)   Parameter settings [===================================>--------] 9/11 ( 82%)   Parameter settings [======================================>----] 10/11 ( 91%)   Parameter settings [===========================================] 11/11 (100%)                                                                                    CV fold: Fold1   Parameter 'ncores' is ignored for learner 'LearnerLm'.      CV fold: Fold2   Parameter 'ncores' is ignored for learner 'LearnerLm'.      CV fold: Fold3   Parameter 'ncores' is ignored for learner 'LearnerLm'.      CV fold: Fold1   Parameter 'ncores' is ignored for learner 'LearnerLm'.      CV fold: Fold2   Parameter 'ncores' is ignored for learner 'LearnerLm'.      CV fold: Fold3   Parameter 'ncores' is ignored for learner 'LearnerLm'.      CV fold: Fold1   Parameter 'ncores' is ignored for learner 'LearnerLm'.      CV fold: Fold2   Parameter 'ncores' is ignored for learner 'LearnerLm'.      CV fold: Fold3   Parameter 'ncores' is ignored for learner 'LearnerLm'.      CV fold: Fold1   Parameter 'ncores' is ignored for learner 'LearnerLm'.      CV fold: Fold2   Parameter 'ncores' is ignored for learner 'LearnerLm'.      CV fold: Fold3   Parameter 'ncores' is ignored for learner 'LearnerLm'.      CV fold: Fold1      CV fold: Fold2      CV progress [==================================>-----------------] 2/3 ( 67%)      CV fold: Fold3      CV progress [====================================================] 3/3 (100%)                                                                                       Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...   ... reducing initialization grid to 10 rows.       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 3.036 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09196485       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 4.208 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.09196485       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 3.405 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.09196485       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 4.141 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.09196485       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 3.363 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.09196485       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 3.743 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.09196485       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 5.793 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.09196485       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 6.255 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.09196485       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 6.038 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.09196485       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 4.139 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2715003       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 3.235 Round = 11 minsplit = 100.0000 cp = 0.03567893 maxdepth = 4.0000 Value = -0.106403       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 4.807 Round = 12 minsplit = 100.0000 cp = 0.03567893 maxdepth = 4.0000 Value = -0.106403       Best Parameters Found:   Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09196485       Classification: using 'mean misclassification error' as optimization metric.      Parameter settings [=============================>---------------] 2/3 ( 67%)    Classification: using 'mean misclassification error' as optimization metric.      Parameter settings [=============================================] 3/3 (100%)                                                                                     Classification: using 'mean misclassification error' as optimization metric.      CV fold: Fold1      Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...   ... reducing initialization grid to 10 rows.       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 4.026 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09558117       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 3.269 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.09558117       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 3.374 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.09558117       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 3.827 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.09558117       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 2.765 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.09558117       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 2.636 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.09558117       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 2.403 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.09558117       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 3.082 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.09558117       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 3.704 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.09558117       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 1.655 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2853118       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 2.041 Round = 11 minsplit = 83.0000 cp = 0.03306856 maxdepth = 6.0000 Value = -0.09746574       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 2.138 Round = 12 minsplit = 78.0000 cp = 0.02853145 maxdepth = 30.0000 Value = -0.09558117       Best Parameters Found:   Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09558117      CV fold: Fold2      CV progress [==================================>-----------------] 2/3 ( 67%)      Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...   ... reducing initialization grid to 10 rows.       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 3.04 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.08333478       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 2.939 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.08333478       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 2.40 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.08333478       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 2.217 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.08333478       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 2.363 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.08333478       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 2.224 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.08333478       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 2.004 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.08333478       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 2.498 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.08333478       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 2.538 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.08333478       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 1.121 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2806083       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 2.885 Round = 11 minsplit = 83.0000 cp = 0.03306856 maxdepth = 6.0000 Value = -0.08333478       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 2.762 Round = 12 minsplit = 82.0000 cp = 0.07214655 maxdepth = 30.0000 Value = -0.08333478       Best Parameters Found:   Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.08333478      CV fold: Fold3      CV progress [====================================================] 3/3 (100%)                                                                                       Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...   ... reducing initialization grid to 10 rows.       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 3.007 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.1130091       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 2.532 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.1130091       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 3.14 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.1148897       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 2.932 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.1130091       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 2.729 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.1130091       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 3.091 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.1148897       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 3.237 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.1130091       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 2.762 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.1130091       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 2.618 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.1130091       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 1.861 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2796667       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 3.197 Round = 11 minsplit = 10.0000 cp = 0.07788214 maxdepth = 15.0000 Value = -0.1130091       Classification: using 'mean misclassification error' as optimization metric.   elapsed = 3.212 Round = 12 minsplit = 78.0000 cp = 0.0395761 maxdepth = 30.0000 Value = -0.1130091       Best Parameters Found:   Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.1130091      CV fold: Fold1       Classification: using 'mean misclassification error' as optimization metric.      Parameter settings [=============================>---------------] 2/3 ( 67%)    Classification: using 'mean misclassification error' as optimization metric.      Parameter settings [=============================================] 3/3 (100%)                                                                                     Classification: using 'mean misclassification error' as optimization metric.      CV fold: Fold2      CV progress [==================================>-----------------] 2/3 ( 67%)       Classification: using 'mean misclassification error' as optimization metric.      Parameter settings [=============================>---------------] 2/3 ( 67%)    Classification: using 'mean misclassification error' as optimization metric.      Parameter settings [=============================================] 3/3 (100%)                                                                                     Classification: using 'mean misclassification error' as optimization metric.      CV fold: Fold3      CV progress [====================================================] 3/3 (100%)                                                                                        Classification: using 'mean misclassification error' as optimization metric.      Parameter settings [=============================>---------------] 2/3 ( 67%)    Classification: using 'mean misclassification error' as optimization metric.      Parameter settings [=============================================] 3/3 (100%)                                                                                     Classification: using 'mean misclassification error' as optimization metric.      CV fold: Fold1      CV fold: Fold2      CV fold: Fold3      Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...   ... reducing initialization grid to 10 rows.       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.156 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.45392       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.076 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -23.45392       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.134 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -23.45392       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.147 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -23.45392       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.134 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -23.45392       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.123 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -23.45392       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.16 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -23.45392       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.141 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -23.45392       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.098 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -23.45392       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.076 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -27.95512       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.153 Round = 11 minsplit = 34.0000 cp = 0.0376811 maxdepth = 27.0000 Value = -23.45392       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.145 Round = 12 minsplit = 25.0000 cp = 0.05205146 maxdepth = 7.0000 Value = -23.45392       Best Parameters Found:   Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.45392       Regression: using 'mean squared error' as optimization metric.      Parameter settings [=============================>---------------] 2/3 ( 67%)    Regression: using 'mean squared error' as optimization metric.      Parameter settings [=============================================] 3/3 (100%)                                                                                     Regression: using 'mean squared error' as optimization metric.      CV fold: Fold1      Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...   ... reducing initialization grid to 10 rows.       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.129 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.46583       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.143 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -27.46583       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.068 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -27.46583       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.07 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -27.46583       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.157 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -27.46583       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.095 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -27.46583       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.088 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -27.46583       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.15 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -27.46583       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.07 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -27.46583       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.086 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -32.38194       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.067 Round = 11 minsplit = 69.0000 cp = 0.05877216 maxdepth = 20.0000 Value = -27.46583       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.091 Round = 12 minsplit = 83.0000 cp = 0.08284277 maxdepth = 30.0000 Value = -27.46583       Best Parameters Found:   Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.46583      CV fold: Fold2      CV progress [==================================>-----------------] 2/3 ( 67%)      Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...   ... reducing initialization grid to 10 rows.       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.068 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.77709       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.066 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -23.77709       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.067 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -23.77709       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.068 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -23.77709       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.065 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -23.77709       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.075 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -23.77709       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.062 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -23.77709       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.09 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -23.77709       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.071 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -23.77709       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.105 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -28.80713       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.072 Round = 11 minsplit = 69.0000 cp = 0.05877216 maxdepth = 20.0000 Value = -23.77709       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.093 Round = 12 minsplit = 12.0000 cp = 0.05804747 maxdepth = 30.0000 Value = -23.77709       Best Parameters Found:   Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.77709      CV fold: Fold3      CV progress [====================================================] 3/3 (100%)                                                                                       Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...   ... reducing initialization grid to 10 rows.       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.12 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.74913       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.14 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -27.74913       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.144 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -27.74913       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.131 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -27.74913       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.073 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -27.74913       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.073 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -27.74913       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.072 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -27.74913       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.094 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -27.74913       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.072 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -27.74913       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.066 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -35.47854       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.075 Round = 11 minsplit = 10.0000 cp = 0.06931338 maxdepth = 20.0000 Value = -27.74913       Regression: using 'mean squared error' as optimization metric.   elapsed = 0.069 Round = 12 minsplit = 72.0000 cp = 0.05062197 maxdepth = 30.0000 Value = -27.74913       Best Parameters Found:   Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.74913      CV fold: Fold1       Regression: using 'mean squared error' as optimization metric.       Regression: using 'mean squared error' as optimization metric.       Regression: using 'mean squared error' as optimization metric.      CV fold: Fold2      CV progress [==================================>-----------------] 2/3 ( 67%)       Regression: using 'mean squared error' as optimization metric.       Regression: using 'mean squared error' as optimization metric.       Regression: using 'mean squared error' as optimization metric.      CV fold: Fold3      CV progress [====================================================] 3/3 (100%)                                                                                        Regression: using 'mean squared error' as optimization metric.       Regression: using 'mean squared error' as optimization metric.       Regression: using 'mean squared error' as optimization metric.   [ FAIL 3 | WARN 3 | SKIP 1 | PASS 58 ]      ══ Skipped tests (1) ═══════════════════════════════════════════════════════════   • On CRAN (1): 'test-lints.R:10:5'      ══ Failed tests ════════════════════════════════════════════════════════════════   ── Error ('test-fold_equality.R:3:1'): (code run outside of `test_that()`) ─────   Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found   Backtrace:       ▆    1. ├─stats::na.omit(data.table::as.data.table(PimaIndiansDiabetes2)) at test-fold_equality.R:3:1    2. └─data.table::as.data.table(PimaIndiansDiabetes2)   ── Error ('test-glm.R:3:1'): (code run outside of `test_that()`) ───────────────   Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found   Backtrace:       ▆    1. ├─stats::na.omit(data.table::as.data.table(PimaIndiansDiabetes2)) at test-glm.R:3:1    2. └─data.table::as.data.table(PimaIndiansDiabetes2)   ── Error ('test-glm_predictions.R:3:1'): (code run outside of `test_that()`) ───   Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found   Backtrace:       ▆    1. ├─stats::na.omit(data.table::as.data.table(PimaIndiansDiabetes2)) at test-glm_predictions.R:3:1    2. └─data.table::as.data.table(PimaIndiansDiabetes2)      [ FAIL 3 | WARN 3 | SKIP 1 | PASS 58 ]   Error:   ! Test failures.   Execution halted
• checking for unstated dependencies in vignettes ... OK
• checking package vignettes ... OK
• checking re-building of vignette outputs ... [40s/41s] OK
• checking PDF version of manual ... [7s/9s] OK
• checking HTML version of manual ... [3s/4s] OK
• checking for non-standard things in the check directory ... OK
• DONE

Status: 1 ERROR