CRAN Package Check Results for Maintainer Lorenz A. Kapsner <lorenz.kapsner at gmail.com>

CRAN Package Check Results for Maintainer ‘Lorenz A. Kapsner <lorenz.kapsner at gmail.com>’

Last updated on 2026-01-20 23:50:44 CET.

Package	ERROR	OK
autonewsmd		13
BiasCorrector		13
DQAgui		13
DQAstats		13
kdry		13
mlexperiments	1	12
mllrnrs	2	11
mlsurvlrnrs	2	11
rBiasCorrection		13
sjtable2df		13

Package DQAgui

Current CRAN status: OK: 13

Package kdry

Current CRAN status: OK: 13

Package mlexperiments

Current CRAN status: ERROR: 1, OK: 12

Version: 0.0.8
Check: Rd cross-references
Result: NOTE Unknown package ‘ParBayesianOptimization’ in Rd xrefs Flavor: r-patched-linux-x86_64

Version: 0.0.8
Check: tests
Result: ERROR Running ‘testthat.R’ [120s/146s] 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") CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold4 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold5 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold1 CV fold: Fold2 CV fold: Fold3 CV fold: Fold4 CV fold: Fold5 Testing for identical folds in 2 and 1. CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold4 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold5 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold4 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold5 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold4 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold5 Parameter 'ncores' is ignored for learner 'LearnerGlm'. 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: Fold4 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold5 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold1 CV fold: Fold2 CV fold: Fold3 Saving _problems/test-knn-115.R Saving _problems/test-knn-182.R CV fold: Fold1 Saving _problems/test-knn-257.R CV fold: Fold1 CV fold: Fold2 CV fold: Fold3 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 fold: Fold3 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Saving _problems/test-rpart_classification-125.R Classification: using 'mean misclassification error' as optimization metric. Classification: using 'mean misclassification error' as optimization metric. 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. Saving _problems/test-rpart_classification-205.R CV fold: Fold1 Classification: using 'mean misclassification error' as optimization metric. Classification: using 'mean misclassification error' as optimization metric. Classification: using 'mean misclassification error' as optimization metric. CV fold: Fold2 Classification: using 'mean misclassification error' as optimization metric. Classification: using 'mean misclassification error' as optimization metric. Classification: using 'mean misclassification error' as optimization metric. CV fold: Fold3 Classification: using 'mean misclassification error' as optimization metric. Classification: using 'mean misclassification error' as optimization metric. 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. Saving _problems/test-rpart_regression-125.R 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: Fold1 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Saving _problems/test-rpart_regression-203.R 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 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 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 7 | WARN 0 | SKIP 1 | PASS 58 ] ══ Skipped tests (1) ═══════════════════════════════════════════════════════════ • On CRAN (1): 'test-lints.R:10:5' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-knn.R:115:5'): test bayesian tuner, initGrid - knn ───────────── Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─knn_optimization$execute(k = 3) at test-knn.R:115:5 2. └─private$select_optimizer(self, private) 3. └─BayesianOptimizer$new(...) 4. └─mlexperiments (local) initialize(...) ── Error ('test-knn.R:182:5'): test bayesian tuner, initPoints - LearnerKnn ──── Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─knn_optimization$execute(k = 3) at test-knn.R:182:5 2. └─private$select_optimizer(self, private) 3. └─BayesianOptimizer$new(...) 4. └─mlexperiments (local) initialize(...) ── Error ('test-knn.R:257:5'): test nested cv, bayesian - knn ────────────────── Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─knn_optimization$execute() at test-knn.R:257:5 2. └─mlexperiments:::.run_cv(self = self, private = private) 3. └─mlexperiments:::.fold_looper(self, private) 4. ├─base::do.call(private$cv_run_model, run_args) 5. └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<list>`, fold_test = `<list>`) 6. ├─base::do.call(.cv_run_nested_model, args) 7. └─mlexperiments (local) `<fn>`(...) 8. └─hparam_tuner$execute(k = self$k_tuning) 9. └─private$select_optimizer(self, private) 10. └─BayesianOptimizer$new(...) 11. └─mlexperiments (local) initialize(...) ── Error ('test-rpart_classification.R:125:5'): test bayesian tuner, initGrid, classification - rpart ── Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─rpart_optimization$execute(k = 3) at test-rpart_classification.R:125:5 2. └─private$select_optimizer(self, private) 3. └─BayesianOptimizer$new(...) 4. └─mlexperiments (local) initialize(...) ── Error ('test-rpart_classification.R:205:5'): test nested cv, bayesian, classification - rpart ── Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─rpart_optimization$execute() at test-rpart_classification.R:205:5 2. └─mlexperiments:::.run_cv(self = self, private = private) 3. └─mlexperiments:::.fold_looper(self, private) 4. ├─base::do.call(private$cv_run_model, run_args) 5. └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<list>`, fold_test = `<list>`) 6. ├─base::do.call(.cv_run_nested_model, args) 7. └─mlexperiments (local) `<fn>`(...) 8. └─hparam_tuner$execute(k = self$k_tuning) 9. └─private$select_optimizer(self, private) 10. └─BayesianOptimizer$new(...) 11. └─mlexperiments (local) initialize(...) ── Error ('test-rpart_regression.R:125:5'): test bayesian tuner, initGrid, regression - rpart ── Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─rpart_optimization$execute(k = 3) at test-rpart_regression.R:125:5 2. └─private$select_optimizer(self, private) 3. └─BayesianOptimizer$new(...) 4. └─mlexperiments (local) initialize(...) ── Error ('test-rpart_regression.R:203:5'): test nested cv, bayesian, regression - rpart ── Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─rpart_optimization$execute() at test-rpart_regression.R:203:5 2. └─mlexperiments:::.run_cv(self = self, private = private) 3. └─mlexperiments:::.fold_looper(self, private) 4. ├─base::do.call(private$cv_run_model, run_args) 5. └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<list>`, fold_test = `<list>`) 6. ├─base::do.call(.cv_run_nested_model, args) 7. └─mlexperiments (local) `<fn>`(...) 8. └─hparam_tuner$execute(k = self$k_tuning) 9. └─private$select_optimizer(self, private) 10. └─BayesianOptimizer$new(...) 11. └─mlexperiments (local) initialize(...) [ FAIL 7 | WARN 0 | SKIP 1 | PASS 58 ] Error: ! Test failures. Execution halted Flavor: r-patched-linux-x86_64

Package mllrnrs

Current CRAN status: ERROR: 2, OK: 11

Version: 0.0.7
Check: tests
Result: ERROR Running ‘testthat.R’ [61s/68s] 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 > # https://github.com/Rdatatable/data.table/issues/5658 > Sys.setenv("OMP_THREAD_LIMIT" = 2) > Sys.setenv("Ncpu" = 2) > > library(testthat) > library(mllrnrs) > > test_check("mllrnrs") CV fold: Fold1 CV fold: Fold1 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Saving _problems/test-binary-225.R CV fold: Fold1 Classification: using 'mean classification error' as optimization metric. Classification: using 'mean classification error' as optimization metric. Classification: using 'mean classification error' as optimization metric. CV fold: Fold2 Classification: using 'mean classification error' as optimization metric. Classification: using 'mean classification error' as optimization metric. Classification: using 'mean classification error' as optimization metric. CV fold: Fold3 Classification: using 'mean classification error' as optimization metric. Classification: using 'mean classification error' as optimization metric. Classification: using 'mean classification error' as optimization metric. CV fold: Fold1 CV fold: Fold2 CV fold: Fold3 CV fold: Fold1 CV fold: Fold2 CV fold: Fold3 CV fold: Fold1 Classification: using 'mean classification error' as optimization metric. Classification: using 'mean classification error' as optimization metric. Classification: using 'mean classification error' as optimization metric. CV fold: Fold2 Classification: using 'mean classification error' as optimization metric. Classification: using 'mean classification error' as optimization metric. Classification: using 'mean classification error' as optimization metric. CV fold: Fold3 Classification: using 'mean classification error' as optimization metric. Classification: using 'mean classification error' as optimization metric. Classification: using 'mean classification error' as optimization metric. CV fold: Fold1 CV fold: Fold2 CV fold: Fold3 CV fold: Fold1 Saving _problems/test-regression-107.R CV fold: Fold1 CV fold: Fold2 CV fold: Fold3 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 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 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: Fold1 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Saving _problems/test-regression-309.R CV fold: Fold1 CV fold: Fold2 CV fold: Fold3 [ FAIL 3 | WARN 0 | SKIP 3 | PASS 25 ] ══ Skipped tests (3) ═══════════════════════════════════════════════════════════ • On CRAN (3): 'test-binary.R:57:5', 'test-lints.R:10:5', 'test-multiclass.R:57:5' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-binary.R:225:5'): test nested cv, bayesian, binary - lightgbm ── Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─lightgbm_optimizer$execute() at test-binary.R:225:5 2. └─mlexperiments:::.run_cv(self = self, private = private) 3. └─mlexperiments:::.fold_looper(self, private) 4. ├─base::do.call(private$cv_run_model, run_args) 5. └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<list>`, fold_test = `<list>`) 6. ├─base::do.call(.cv_run_nested_model, args) 7. └─mlexperiments (local) `<fn>`(...) 8. └─hparam_tuner$execute(k = self$k_tuning) 9. └─private$select_optimizer(self, private) 10. └─BayesianOptimizer$new(...) 11. └─mlexperiments (local) initialize(...) ── Error ('test-regression.R:107:5'): test nested cv, bayesian, regression - glmnet ── Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─glmnet_optimizer$execute() at test-regression.R:107:5 2. └─mlexperiments:::.run_cv(self = self, private = private) 3. └─mlexperiments:::.fold_looper(self, private) 4. ├─base::do.call(private$cv_run_model, run_args) 5. └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<list>`, fold_test = `<list>`) 6. ├─base::do.call(.cv_run_nested_model, args) 7. └─mlexperiments (local) `<fn>`(...) 8. └─hparam_tuner$execute(k = self$k_tuning) 9. └─private$select_optimizer(self, private) 10. └─BayesianOptimizer$new(...) 11. └─mlexperiments (local) initialize(...) ── Error ('test-regression.R:309:5'): test nested cv, bayesian, reg:squarederror - xgboost ── Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─xgboost_optimizer$execute() at test-regression.R:309:5 2. └─mlexperiments:::.run_cv(self = self, private = private) 3. └─mlexperiments:::.fold_looper(self, private) 4. ├─base::do.call(private$cv_run_model, run_args) 5. └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<list>`, fold_test = `<list>`) 6. ├─base::do.call(.cv_run_nested_model, args) 7. └─mlexperiments (local) `<fn>`(...) 8. └─hparam_tuner$execute(k = self$k_tuning) 9. └─private$select_optimizer(self, private) 10. └─BayesianOptimizer$new(...) 11. └─mlexperiments (local) initialize(...) [ FAIL 3 | WARN 0 | SKIP 3 | PASS 25 ] Error: ! Test failures. Execution halted Flavor: r-patched-linux-x86_64

Version: 0.0.7
Check: tests
Result: ERROR Running ‘testthat.R’ [90s/100s] 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 > # https://github.com/Rdatatable/data.table/issues/5658 > Sys.setenv("OMP_THREAD_LIMIT" = 2) > Sys.setenv("Ncpu" = 2) > > library(testthat) > library(mllrnrs) > > test_check("mllrnrs") CV fold: Fold1 CV fold: Fold1 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Saving _problems/test-binary-225.R CV fold: Fold1 Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================>---------------] 2/3 ( 67%) Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================================] 3/3 (100%) Classification: using 'mean classification error' as optimization metric. CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================>---------------] 2/3 ( 67%) Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================================] 3/3 (100%) Classification: using 'mean classification error' as optimization metric. CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================>---------------] 2/3 ( 67%) Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================================] 3/3 (100%) Classification: using 'mean classification error' as optimization metric. CV fold: Fold1 Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold1 Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold1 Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================>---------------] 2/3 ( 67%) Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================================] 3/3 (100%) Classification: using 'mean classification error' as optimization metric. CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================>---------------] 2/3 ( 67%) Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================================] 3/3 (100%) Classification: using 'mean classification error' as optimization metric. CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================>---------------] 2/3 ( 67%) Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================================] 3/3 (100%) Classification: using 'mean classification error' as optimization metric. CV fold: Fold1 Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold1 Saving _problems/test-regression-107.R CV fold: Fold1 Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold1 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: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) 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: Fold3 CV progress [====================================================] 3/3 (100%) 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. Saving _problems/test-regression-309.R CV fold: Fold1 Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) [ FAIL 3 | WARN 0 | SKIP 3 | PASS 25 ] ══ Skipped tests (3) ═══════════════════════════════════════════════════════════ • On CRAN (3): 'test-binary.R:57:5', 'test-lints.R:10:5', 'test-multiclass.R:57:5' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-binary.R:225:5'): test nested cv, bayesian, binary - lightgbm ── Error: Package "rBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─lightgbm_optimizer$execute() at test-binary.R:225:5 2. └─mlexperiments:::.run_cv(self = self, private = private) 3. └─mlexperiments:::.fold_looper(self, private) 4. ├─base::do.call(private$cv_run_model, run_args) 5. └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<list>`, fold_test = `<list>`) 6. ├─base::do.call(.cv_run_nested_model, args) 7. └─mlexperiments (local) `<fn>`(...) 8. └─hparam_tuner$execute(k = self$k_tuning) 9. └─private$select_optimizer(self, private) 10. └─BayesianOptimizer$new(...) 11. └─mlexperiments (local) initialize(...) ── Error ('test-regression.R:107:5'): test nested cv, bayesian, regression - glmnet ── Error: Package "rBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─glmnet_optimizer$execute() at test-regression.R:107:5 2. └─mlexperiments:::.run_cv(self = self, private = private) 3. └─mlexperiments:::.fold_looper(self, private) 4. ├─base::do.call(private$cv_run_model, run_args) 5. └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<list>`, fold_test = `<list>`) 6. ├─base::do.call(.cv_run_nested_model, args) 7. └─mlexperiments (local) `<fn>`(...) 8. └─hparam_tuner$execute(k = self$k_tuning) 9. └─private$select_optimizer(self, private) 10. └─BayesianOptimizer$new(...) 11. └─mlexperiments (local) initialize(...) ── Error ('test-regression.R:309:5'): test nested cv, bayesian, reg:squarederror - xgboost ── Error: Package "rBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─xgboost_optimizer$execute() at test-regression.R:309:5 2. └─mlexperiments:::.run_cv(self = self, private = private) 3. └─mlexperiments:::.fold_looper(self, private) 4. ├─base::do.call(private$cv_run_model, run_args) 5. └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<list>`, fold_test = `<list>`) 6. ├─base::do.call(.cv_run_nested_model, args) 7. └─mlexperiments (local) `<fn>`(...) 8. └─hparam_tuner$execute(k = self$k_tuning) 9. └─private$select_optimizer(self, private) 10. └─BayesianOptimizer$new(...) 11. └─mlexperiments (local) initialize(...) [ FAIL 3 | WARN 0 | SKIP 3 | PASS 25 ] Error: ! Test failures. Execution halted Flavor: r-release-linux-x86_64

Package mlsurvlrnrs

Current CRAN status: ERROR: 2, OK: 11

Version: 0.0.7
Check: tests
Result: ERROR Running ‘testthat.R’ [10s/14s] 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(mlsurvlrnrs) > > test_check("mlsurvlrnrs") CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerSurvCoxPHCox'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerSurvCoxPHCox'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerSurvCoxPHCox'. CV fold: Fold1 Saving _problems/test-surv_glmnet_cox-99.R CV fold: Fold1 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Saving _problems/test-surv_ranger_cox-110.R CV fold: Fold1 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Saving _problems/test-surv_rpart_cox-108.R CV fold: Fold1 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Saving _problems/test-surv_xgboost_aft-121.R CV fold: Fold1 CV fold: Fold2 CV fold: Fold3 CV fold: Fold1 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Saving _problems/test-surv_xgboost_cox-118.R CV fold: Fold1 CV fold: Fold2 CV fold: Fold3 [ FAIL 5 | WARN 0 | SKIP 1 | PASS 9 ] ══ Skipped tests (1) ═══════════════════════════════════════════════════════════ • On CRAN (1): 'test-lints.R:10:5' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-surv_glmnet_cox.R:99:5'): test nested cv, grid - surv_glmnet_cox ── Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─surv_glmnet_cox_optimizer$execute() at test-surv_glmnet_cox.R:99:5 2. └─mlexperiments:::.run_cv(self = self, private = private) 3. └─mlexperiments:::.fold_looper(self, private) 4. ├─base::do.call(private$cv_run_model, run_args) 5. └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<list>`, fold_test = `<list>`) 6. ├─base::do.call(.cv_run_nested_model, args) 7. └─mlexperiments (local) `<fn>`(...) 8. └─hparam_tuner$execute(k = self$k_tuning) 9. └─private$select_optimizer(self, private) 10. └─BayesianOptimizer$new(...) 11. └─mlexperiments (local) initialize(...) ── Error ('test-surv_ranger_cox.R:110:5'): test nested cv, bayesian - surv_ranger_cox ── Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─surv_ranger_cox_optimizer$execute() at test-surv_ranger_cox.R:110:5 2. └─mlexperiments:::.run_cv(self = self, private = private) 3. └─mlexperiments:::.fold_looper(self, private) 4. ├─base::do.call(private$cv_run_model, run_args) 5. └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<list>`, fold_test = `<list>`) 6. ├─base::do.call(.cv_run_nested_model, args) 7. └─mlexperiments (local) `<fn>`(...) 8. └─hparam_tuner$execute(k = self$k_tuning) 9. └─private$select_optimizer(self, private) 10. └─BayesianOptimizer$new(...) 11. └─mlexperiments (local) initialize(...) ── Error ('test-surv_rpart_cox.R:108:5'): test nested cv, bayesian - surv_rpart_cox ── Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─surv_rpart_cox_optimizer$execute() at test-surv_rpart_cox.R:108:5 2. └─mlexperiments:::.run_cv(self = self, private = private) 3. └─mlexperiments:::.fold_looper(self, private) 4. ├─base::do.call(private$cv_run_model, run_args) 5. └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<list>`, fold_test = `<list>`) 6. ├─base::do.call(.cv_run_nested_model, args) 7. └─mlexperiments (local) `<fn>`(...) 8. └─hparam_tuner$execute(k = self$k_tuning) 9. └─private$select_optimizer(self, private) 10. └─BayesianOptimizer$new(...) 11. └─mlexperiments (local) initialize(...) ── Error ('test-surv_xgboost_aft.R:121:3'): test nested cv, bayesian - surv_xgboost_aft ── Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─surv_xgboost_aft_optimizer$execute() at test-surv_xgboost_aft.R:121:3 2. └─mlexperiments:::.run_cv(self = self, private = private) 3. └─mlexperiments:::.fold_looper(self, private) 4. ├─base::do.call(private$cv_run_model, run_args) 5. └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<list>`, fold_test = `<list>`) 6. ├─base::do.call(.cv_run_nested_model, args) 7. └─mlexperiments (local) `<fn>`(...) 8. └─hparam_tuner$execute(k = self$k_tuning) 9. └─private$select_optimizer(self, private) 10. └─BayesianOptimizer$new(...) 11. └─mlexperiments (local) initialize(...) ── Error ('test-surv_xgboost_cox.R:118:3'): test nested cv, bayesian - surv_xgboost_cox ── Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─surv_xgboost_cox_optimizer$execute() at test-surv_xgboost_cox.R:118:3 2. └─mlexperiments:::.run_cv(self = self, private = private) 3. └─mlexperiments:::.fold_looper(self, private) 4. ├─base::do.call(private$cv_run_model, run_args) 5. └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<list>`, fold_test = `<list>`) 6. ├─base::do.call(.cv_run_nested_model, args) 7. └─mlexperiments (local) `<fn>`(...) 8. └─hparam_tuner$execute(k = self$k_tuning) 9. └─private$select_optimizer(self, private) 10. └─BayesianOptimizer$new(...) 11. └─mlexperiments (local) initialize(...) [ FAIL 5 | WARN 0 | SKIP 1 | PASS 9 ] Error: ! Test failures. Execution halted Flavor: r-patched-linux-x86_64

Version: 0.0.7
Check: tests
Result: ERROR Running ‘testthat.R’ [12s/14s] 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(mlsurvlrnrs) > > test_check("mlsurvlrnrs") CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerSurvCoxPHCox'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerSurvCoxPHCox'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerSurvCoxPHCox'. CV fold: Fold1 Saving _problems/test-surv_glmnet_cox-99.R CV fold: Fold1 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Saving _problems/test-surv_ranger_cox-110.R CV fold: Fold1 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Saving _problems/test-surv_rpart_cox-108.R CV fold: Fold1 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Saving _problems/test-surv_xgboost_aft-121.R CV fold: Fold1 Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold3 CV progress [====================================================] 3/3 (100%) 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 10 rows. Saving _problems/test-surv_xgboost_cox-118.R CV fold: Fold1 Parameter settings [=============================================] 3/3 (100%) CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Parameter settings [=============================================] 3/3 (100%) [ FAIL 5 | WARN 0 | SKIP 1 | PASS 9 ] ══ Skipped tests (1) ═══════════════════════════════════════════════════════════ • On CRAN (1): 'test-lints.R:10:5' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-surv_glmnet_cox.R:99:5'): test nested cv, grid - surv_glmnet_cox ── Error: Package "rBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─surv_glmnet_cox_optimizer$execute() at test-surv_glmnet_cox.R:99:5 2. └─mlexperiments:::.run_cv(self = self, private = private) 3. └─mlexperiments:::.fold_looper(self, private) 4. ├─base::do.call(private$cv_run_model, run_args) 5. └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<list>`, fold_test = `<list>`) 6. ├─base::do.call(.cv_run_nested_model, args) 7. └─mlexperiments (local) `<fn>`(...) 8. └─hparam_tuner$execute(k = self$k_tuning) 9. └─private$select_optimizer(self, private) 10. └─BayesianOptimizer$new(...) 11. └─mlexperiments (local) initialize(...) ── Error ('test-surv_ranger_cox.R:110:5'): test nested cv, bayesian - surv_ranger_cox ── Error: Package "rBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─surv_ranger_cox_optimizer$execute() at test-surv_ranger_cox.R:110:5 2. └─mlexperiments:::.run_cv(self = self, private = private) 3. └─mlexperiments:::.fold_looper(self, private) 4. ├─base::do.call(private$cv_run_model, run_args) 5. └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<list>`, fold_test = `<list>`) 6. ├─base::do.call(.cv_run_nested_model, args) 7. └─mlexperiments (local) `<fn>`(...) 8. └─hparam_tuner$execute(k = self$k_tuning) 9. └─private$select_optimizer(self, private) 10. └─BayesianOptimizer$new(...) 11. └─mlexperiments (local) initialize(...) ── Error ('test-surv_rpart_cox.R:108:5'): test nested cv, bayesian - surv_rpart_cox ── Error: Package "rBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─surv_rpart_cox_optimizer$execute() at test-surv_rpart_cox.R:108:5 2. └─mlexperiments:::.run_cv(self = self, private = private) 3. └─mlexperiments:::.fold_looper(self, private) 4. ├─base::do.call(private$cv_run_model, run_args) 5. └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<list>`, fold_test = `<list>`) 6. ├─base::do.call(.cv_run_nested_model, args) 7. └─mlexperiments (local) `<fn>`(...) 8. └─hparam_tuner$execute(k = self$k_tuning) 9. └─private$select_optimizer(self, private) 10. └─BayesianOptimizer$new(...) 11. └─mlexperiments (local) initialize(...) ── Error ('test-surv_xgboost_aft.R:121:3'): test nested cv, bayesian - surv_xgboost_aft ── Error: Package "rBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─surv_xgboost_aft_optimizer$execute() at test-surv_xgboost_aft.R:121:3 2. └─mlexperiments:::.run_cv(self = self, private = private) 3. └─mlexperiments:::.fold_looper(self, private) 4. ├─base::do.call(private$cv_run_model, run_args) 5. └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<list>`, fold_test = `<list>`) 6. ├─base::do.call(.cv_run_nested_model, args) 7. └─mlexperiments (local) `<fn>`(...) 8. └─hparam_tuner$execute(k = self$k_tuning) 9. └─private$select_optimizer(self, private) 10. └─BayesianOptimizer$new(...) 11. └─mlexperiments (local) initialize(...) ── Error ('test-surv_xgboost_cox.R:118:3'): test nested cv, bayesian - surv_xgboost_cox ── Error: Package "rBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─surv_xgboost_cox_optimizer$execute() at test-surv_xgboost_cox.R:118:3 2. └─mlexperiments:::.run_cv(self = self, private = private) 3. └─mlexperiments:::.fold_looper(self, private) 4. ├─base::do.call(private$cv_run_model, run_args) 5. └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<list>`, fold_test = `<list>`) 6. ├─base::do.call(.cv_run_nested_model, args) 7. └─mlexperiments (local) `<fn>`(...) 8. └─hparam_tuner$execute(k = self$k_tuning) 9. └─private$select_optimizer(self, private) 10. └─BayesianOptimizer$new(...) 11. └─mlexperiments (local) initialize(...) [ FAIL 5 | WARN 0 | SKIP 1 | PASS 9 ] Error: ! Test failures. Execution halted Flavor: r-release-linux-x86_64

Package rBiasCorrection

Current CRAN status: OK: 13

Package sjtable2df

Current CRAN status: OK: 13

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