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 2025-12-28 11:49:31 CET.

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

Package DQAgui

Current CRAN status: OK: 13

Package kdry

Current CRAN status: ERROR: 2, OK: 11

Version: 0.0.2
Check: examples
Result: ERROR Running examples in ‘kdry-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: mlh_reshape > ### Title: mlh_reshape > ### Aliases: mlh_reshape > > ### ** Examples > > set.seed(123) > class_0 <- rbeta(100, 2, 4) > class_1 <- (1 - class_0) * 0.4 > class_2 <- (1 - class_0) * 0.6 > dataset <- cbind("0" = class_0, "1" = class_1, "2" = class_2) > mlh_reshape(dataset) Error in xtfrm.data.frame(list(`0` = 0.219788839894465, `1` = 0.312084464042214, : cannot xtfrm data frames Calls: mlh_reshape ... [.data.table -> which.max -> xtfrm -> xtfrm.data.frame Execution halted Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc

Version: 0.0.2
Check: tests
Result: ERROR Running ‘testthat.R’ [6s/7s] 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 > > library(testthat) > library(kdry) > > test_check("kdry") Saving _problems/test-mlh-70.R [ FAIL 1 | WARN 0 | SKIP 6 | PASS 71 ] ══ Skipped tests (6) ═══════════════════════════════════════════════════════════ • On CRAN (6): 'test-lints.R:10:5', 'test-rep.R:3:1', 'test-rep.R:22:1', 'test-rep.R:42:1', 'test-rep.R:61:1', 'test-rep.R:75:1' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-mlh.R:70:5'): test mlh - mlh_outsample_row_indices ───────────── Error in `xtfrm.data.frame(structure(list(`0` = 0.219788839894465, `1` = 0.312084464042214, `2` = 0.468126696063321), row.names = c(NA, -1L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x55967c6c6fe0>, .data.table.locked = TRUE))`: cannot xtfrm data frames Backtrace: ▆ 1. ├─kdry::mlh_reshape(dataset) at test-mlh.R:70:5 2. │ ├─data.table::as.data.table(object)[, cn[which.max(.SD)], by = seq_len(nrow(object))] 3. │ └─data.table:::`[.data.table`(...) 4. └─base::which.max(.SD) 5. ├─base::xtfrm(`<data.table>`) 6. └─base::xtfrm.data.frame(`<data.table>`) [ FAIL 1 | WARN 0 | SKIP 6 | PASS 71 ] Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-debian-clang

Version: 0.0.2
Check: tests
Result: ERROR Running ‘testthat.R’ [4s/5s] 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 > > library(testthat) > library(kdry) > > test_check("kdry") Saving _problems/test-mlh-70.R [ FAIL 1 | WARN 0 | SKIP 6 | PASS 71 ] ══ Skipped tests (6) ═══════════════════════════════════════════════════════════ • On CRAN (6): 'test-lints.R:10:5', 'test-rep.R:3:1', 'test-rep.R:22:1', 'test-rep.R:42:1', 'test-rep.R:61:1', 'test-rep.R:75:1' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-mlh.R:70:5'): test mlh - mlh_outsample_row_indices ───────────── Error in `xtfrm.data.frame(structure(list(`0` = 0.219788839894465, `1` = 0.312084464042214, `2` = 0.468126696063321), row.names = c(NA, -1L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x560d8e5ae070>, .data.table.locked = TRUE))`: cannot xtfrm data frames Backtrace: ▆ 1. ├─kdry::mlh_reshape(dataset) at test-mlh.R:70:5 2. │ ├─data.table::as.data.table(object)[, cn[which.max(.SD)], by = seq_len(nrow(object))] 3. │ └─data.table:::`[.data.table`(...) 4. └─base::which.max(.SD) 5. ├─base::xtfrm(`<data.table>`) 6. └─base::xtfrm.data.frame(`<data.table>`) [ FAIL 1 | WARN 0 | SKIP 6 | PASS 71 ] Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-debian-gcc

Package mlexperiments

Current CRAN status: ERROR: 1, OK: 12

Version: 0.0.8
Check: examples
Result: ERROR Running examples in 'mlexperiments-Ex.R' failed The error most likely occurred in: > ### Name: performance > ### Title: performance > ### Aliases: performance > > ### ** Examples > > dataset <- do.call( + cbind, + c(sapply(paste0("col", 1:6), function(x) { + rnorm(n = 500) + }, + USE.NAMES = TRUE, + simplify = FALSE + ), + list(target = sample(0:1, 500, TRUE)) + )) > > fold_list <- splitTools::create_folds( + y = dataset[, 7], + k = 3, + type = "stratified", + seed = 123 + ) > > glm_optimization <- mlexperiments::MLCrossValidation$new( + learner = LearnerGlm$new(), + fold_list = fold_list, + seed = 123 + ) > > glm_optimization$learner_args <- list(family = binomial(link = "logit")) > glm_optimization$predict_args <- list(type = "response") > glm_optimization$performance_metric_args <- list( + positive = "1", + negative = "0" + ) > glm_optimization$performance_metric <- list( + auc = metric("AUC"), sensitivity = metric("TPR"), + specificity = metric("TNR") + ) > glm_optimization$return_models <- TRUE > > # set data > glm_optimization$set_data( + x = data.matrix(dataset[, -7]), + y = dataset[, 7] + ) > > cv_results <- glm_optimization$execute() 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'. > > # predictions > preds <- mlexperiments::predictions( + object = glm_optimization, + newdata = data.matrix(dataset[, -7]), + na.rm = FALSE, + ncores = 2L, + type = "response" + ) Error in `[.data.table`(res, , `:=`(mean = mean(as.numeric(.SD), na.rm = na.rm), : attempt access index 3/3 in VECTOR_ELT Calls: <Anonymous> -> [ -> [.data.table Execution halted Flavor: r-devel-windows-x86_64

Version: 0.0.8
Check: tests
Result: ERROR Running 'testthat.R' [296s] 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'. Saving _problems/test-glm_predictions-79.R 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'. Saving _problems/test-glm_predictions-188.R CV fold: Fold1 CV fold: Fold2 CV fold: Fold3 Registering parallel backend using 2 cores. Running initial scoring function 11 times in 2 thread(s)... 22.5 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 0.64 seconds Noise could not be added to find unique parameter set. Stopping process and returning results so far. Registering parallel backend using 2 cores. Running initial scoring function 11 times in 2 thread(s)... 23.36 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 0.64 seconds Noise could not be added to find unique parameter set. Stopping process and returning results so far. Registering parallel backend using 2 cores. Running initial scoring function 4 times in 2 thread(s)... 8.95 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 0.52 seconds 3) Running FUN 2 times in 2 thread(s)... 3.55 seconds CV fold: Fold1 Registering parallel backend using 2 cores. Running initial scoring function 11 times in 2 thread(s)... 8.58 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 0.57 seconds Noise could not be added to find unique parameter set. Stopping process and returning results so far. CV fold: Fold2 Registering parallel backend using 2 cores. Running initial scoring function 11 times in 2 thread(s)... 8.45 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 0.56 seconds Noise could not be added to find unique parameter set. Stopping process and returning results so far. CV fold: Fold3 Registering parallel backend using 2 cores. Running initial scoring function 11 times in 2 thread(s)... 8.23 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 0.58 seconds Noise could not be added to find unique parameter set. Stopping process and returning results so far. 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. Registering parallel backend using 2 cores. Running initial scoring function 10 times in 2 thread(s)... 18.5 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 0.45 seconds 3) Running FUN 2 times in 2 thread(s)... 3.5 seconds 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. Registering parallel backend using 2 cores. Running initial scoring function 10 times in 2 thread(s)... 8.56 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 0.47 seconds 3) Running FUN 2 times in 2 thread(s)... 1.36 seconds CV fold: Fold2 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Registering parallel backend using 2 cores. Running initial scoring function 10 times in 2 thread(s)... 8.78 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 0.46 seconds 3) Running FUN 2 times in 2 thread(s)... 1.46 seconds CV fold: Fold3 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Registering parallel backend using 2 cores. Running initial scoring function 10 times in 2 thread(s)... 9.33 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 0.47 seconds 3) Running FUN 2 times in 2 thread(s)... 1.5 seconds 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. Registering parallel backend using 2 cores. Running initial scoring function 10 times in 2 thread(s)... 2.76 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 0.49 seconds 3) Running FUN 2 times in 2 thread(s)... 0.18 seconds 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. Registering parallel backend using 2 cores. Running initial scoring function 10 times in 2 thread(s)... 2.75 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 0.45 seconds 3) Running FUN 2 times in 2 thread(s)... 0.24 seconds CV fold: Fold2 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Registering parallel backend using 2 cores. Running initial scoring function 10 times in 2 thread(s)... 2.79 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 0.42 seconds 3) Running FUN 2 times in 2 thread(s)... 0.19 seconds CV fold: Fold3 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Registering parallel backend using 2 cores. Running initial scoring function 10 times in 2 thread(s)... 2.78 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 0.46 seconds 3) Running FUN 2 times in 2 thread(s)... 0.19 seconds 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 2 | WARN 0 | SKIP 1 | PASS 68 ] ══ Skipped tests (1) ═══════════════════════════════════════════════════════════ • On CRAN (1): 'test-lints.R:10:5' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-glm_predictions.R:73:5'): test predictions, binary - glm ─────── Error in ``[.data.table`(res, , `:=`(mean = mean(as.numeric(.SD), na.rm = na.rm), sd = stats::sd(as.numeric(.SD), na.rm = na.rm)), .SDcols = colnames(res), by = seq_len(nrow(res)))`: attempt access index 5/5 in VECTOR_ELT Backtrace: ▆ 1. └─mlexperiments::predictions(...) at test-glm_predictions.R:73:5 2. ├─...[] 3. └─data.table:::`[.data.table`(...) ── Error ('test-glm_predictions.R:182:5'): test predictions, regression - lm ─── Error in ``[.data.table`(res, , `:=`(mean = mean(as.numeric(.SD), na.rm = na.rm), sd = stats::sd(as.numeric(.SD), na.rm = na.rm)), .SDcols = colnames(res), by = seq_len(nrow(res)))`: attempt access index 5/5 in VECTOR_ELT Backtrace: ▆ 1. └─mlexperiments::predictions(...) at test-glm_predictions.R:182:5 2. ├─...[] 3. └─data.table:::`[.data.table`(...) [ FAIL 2 | WARN 0 | SKIP 1 | PASS 68 ] Error: ! Test failures. Execution halted Flavor: r-devel-windows-x86_64

Package mllrnrs

Current CRAN status: ERROR: 2, OK: 11

Version: 0.0.7
Check: tests
Result: ERROR Running ‘testthat.R’ [65s/265s] 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. Registering parallel backend using 2 cores. Running initial scoring function 10 times in 2 thread(s)... 7.55 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 12.845 seconds 3) Running FUN 2 times in 2 thread(s)... 0.554 seconds OMP: Warning #96: Cannot form a team with 3 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). CV fold: Fold2 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Registering parallel backend using 2 cores. Running initial scoring function 10 times in 2 thread(s)... 8.346 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 12.369 seconds 3) Running FUN 2 times in 2 thread(s)... 0.702 seconds CV fold: Fold3 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Registering parallel backend using 2 cores. Running initial scoring function 10 times in 2 thread(s)... 8.193 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 17.069 seconds 3) Running FUN 2 times in 2 thread(s)... 0.607 seconds CV fold: Fold1 Classification: using 'mean classification error' as optimization metric. Saving _problems/test-binary-287.R CV fold: Fold1 CV fold: Fold2 CV fold: Fold3 CV fold: Fold1 Saving _problems/test-multiclass-162.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 Saving _problems/test-multiclass-294.R CV fold: Fold1 Registering parallel backend using 2 cores. Running initial scoring function 5 times in 2 thread(s)... 6.892 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 1.903 seconds 3) Running FUN 2 times in 2 thread(s)... 0.922 seconds CV fold: Fold2 Registering parallel backend using 2 cores. Running initial scoring function 5 times in 2 thread(s)... 7.493 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 2.249 seconds 3) Running FUN 2 times in 2 thread(s)... 0.592 seconds CV fold: Fold3 Registering parallel backend using 2 cores. Running initial scoring function 5 times in 2 thread(s)... 6.746 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 2.353 seconds 3) Running FUN 2 times in 2 thread(s)... 0.942 seconds 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. Registering parallel backend using 2 cores. Running initial scoring function 10 times in 2 thread(s)... 9.821 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 21.466 seconds 3) Running FUN 2 times in 2 thread(s)... 1.08 seconds CV fold: Fold2 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Registering parallel backend using 2 cores. Running initial scoring function 10 times in 2 thread(s)... 9.533 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 3.618 seconds 3) Running FUN 2 times in 2 thread(s)... 0.994 seconds CV fold: Fold3 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Registering parallel backend using 2 cores. Running initial scoring function 10 times in 2 thread(s)... 8.939 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 31.815 seconds 3) Running FUN 2 times in 2 thread(s)... 0.942 seconds 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:287:5'): test nested cv, grid, binary - ranger ──────── Error in `xtfrm.data.frame(structure(list(`0` = 0.379858310721837, `1` = 0.620141689278164), row.names = c(NA, -1L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x55c38be73fe0>, .data.table.locked = TRUE))`: cannot xtfrm data frames Backtrace: ▆ 1. ├─ranger_optimizer$execute() at test-binary.R:287:5 2. │ └─mlexperiments:::.run_cv(self = self, private = private) 3. │ └─mlexperiments:::.fold_looper(self, private) 4. │ ├─base::do.call(private$cv_run_model, run_args) 5. │ └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<named list>`, fold_test = `<named list>`) 6. │ ├─base::do.call(.cv_run_nested_model, args) 7. │ └─mlexperiments (local) `<fn>`(...) 8. │ └─hparam_tuner$execute(k = self$k_tuning) 9. │ └─mlexperiments:::.run_tuning(self = self, private = private, optimizer = optimizer) 10. │ └─mlexperiments:::.run_optimizer(...) 11. │ └─optimizer$execute(x = private$x, y = private$y, method_helper = private$method_helper) 12. │ ├─base::do.call(...) 13. │ └─mlexperiments (local) `<fn>`(...) 14. │ └─base::lapply(...) 15. │ └─mlexperiments (local) FUN(X[[i]], ...) 16. │ ├─base::do.call(FUN, fun_parameters) 17. │ └─mlexperiments (local) `<fn>`(...) 18. │ ├─base::do.call(private$fun_optim_cv, kwargs) 19. │ └─mllrnrs (local) `<fn>`(...) 20. │ ├─base::do.call(ranger_predict, pred_args) 21. │ └─mllrnrs (local) `<fn>`(...) 22. │ └─kdry::mlh_reshape(preds) 23. │ ├─data.table::as.data.table(object)[, cn[which.max(.SD)], by = seq_len(nrow(object))] 24. │ └─data.table:::`[.data.table`(...) 25. └─base::which.max(.SD) 26. ├─base::xtfrm(`<dt[,2]>`) 27. └─base::xtfrm.data.frame(`<dt[,2]>`) ── Error ('test-multiclass.R:162:5'): test nested cv, grid, multiclass - lightgbm ── Error in `xtfrm.data.frame(structure(list(`0` = 0.20774260202068, `1` = 0.136781829323219, `2` = 0.655475568656101), row.names = c(NA, -1L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x55c38be73fe0>, .data.table.locked = TRUE))`: cannot xtfrm data frames Backtrace: ▆ 1. ├─lightgbm_optimizer$execute() at test-multiclass.R:162:5 2. │ └─mlexperiments:::.run_cv(self = self, private = private) 3. │ └─mlexperiments:::.fold_looper(self, private) 4. │ ├─base::do.call(private$cv_run_model, run_args) 5. │ └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<named list>`, fold_test = `<named list>`) 6. │ ├─base::do.call(.cv_run_nested_model, args) 7. │ └─mlexperiments (local) `<fn>`(...) 8. │ └─mlexperiments:::.cv_fit_model(...) 9. │ ├─base::do.call(self$learner$predict, pred_args) 10. │ └─mlexperiments (local) `<fn>`(...) 11. │ ├─base::do.call(private$fun_predict, kwargs) 12. │ └─mllrnrs (local) `<fn>`(...) 13. │ └─kdry::mlh_reshape(preds) 14. │ ├─data.table::as.data.table(object)[, cn[which.max(.SD)], by = seq_len(nrow(object))] 15. │ └─data.table:::`[.data.table`(...) 16. └─base::which.max(.SD) 17. ├─base::xtfrm(`<dt[,3]>`) 18. └─base::xtfrm.data.frame(`<dt[,3]>`) ── Error ('test-multiclass.R:294:5'): test nested cv, grid, multi:softprob - xgboost, with weights ── Error in `xtfrm.data.frame(structure(list(`0` = 0.250160574913025, `1` = 0.124035485088825, `2` = 0.62580394744873), row.names = c(NA, -1L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x55c38be73fe0>, .data.table.locked = TRUE))`: cannot xtfrm data frames Backtrace: ▆ 1. ├─xgboost_optimizer$execute() at test-multiclass.R:294:5 2. │ └─mlexperiments:::.run_cv(self = self, private = private) 3. │ └─mlexperiments:::.fold_looper(self, private) 4. │ ├─base::do.call(private$cv_run_model, run_args) 5. │ └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<named list>`, fold_test = `<named list>`) 6. │ ├─base::do.call(.cv_run_nested_model, args) 7. │ └─mlexperiments (local) `<fn>`(...) 8. │ └─mlexperiments:::.cv_fit_model(...) 9. │ ├─base::do.call(self$learner$predict, pred_args) 10. │ └─mlexperiments (local) `<fn>`(...) 11. │ ├─base::do.call(private$fun_predict, kwargs) 12. │ └─mllrnrs (local) `<fn>`(...) 13. │ └─kdry::mlh_reshape(preds) 14. │ ├─data.table::as.data.table(object)[, cn[which.max(.SD)], by = seq_len(nrow(object))] 15. │ └─data.table:::`[.data.table`(...) 16. └─base::which.max(.SD) 17. ├─base::xtfrm(`<dt[,3]>`) 18. └─base::xtfrm.data.frame(`<dt[,3]>`) [ FAIL 3 | WARN 0 | SKIP 3 | PASS 25 ] Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-debian-clang

Version: 0.0.7
Check: tests
Result: ERROR Running ‘testthat.R’ [59s/209s] 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. Registering parallel backend using 2 cores. Running initial scoring function 10 times in 2 thread(s)... 7.152 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 8.805 seconds 3) Running FUN 2 times in 2 thread(s)... 0.817 seconds CV fold: Fold2 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Registering parallel backend using 2 cores. Running initial scoring function 10 times in 2 thread(s)... 6.318 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 7.414 seconds 3) Running FUN 2 times in 2 thread(s)... 0.495 seconds CV fold: Fold3 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Registering parallel backend using 2 cores. Running initial scoring function 10 times in 2 thread(s)... 7.459 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 9.935 seconds 3) Running FUN 2 times in 2 thread(s)... 0.609 seconds CV fold: Fold1 Classification: using 'mean classification error' as optimization metric. Saving _problems/test-binary-287.R CV fold: Fold1 CV fold: Fold2 CV fold: Fold3 CV fold: Fold1 Saving _problems/test-multiclass-162.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 Saving _problems/test-multiclass-294.R CV fold: Fold1 Registering parallel backend using 2 cores. Running initial scoring function 5 times in 2 thread(s)... 6.463 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 1.223 seconds 3) Running FUN 2 times in 2 thread(s)... 0.921 seconds CV fold: Fold2 Registering parallel backend using 2 cores. Running initial scoring function 5 times in 2 thread(s)... 7.079 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 1.552 seconds 3) Running FUN 2 times in 2 thread(s)... 1.171 seconds CV fold: Fold3 Registering parallel backend using 2 cores. Running initial scoring function 5 times in 2 thread(s)... 6.36 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 2.029 seconds 3) Running FUN 2 times in 2 thread(s)... 1.043 seconds 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. Registering parallel backend using 2 cores. Running initial scoring function 10 times in 2 thread(s)... 9.391 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 8.585 seconds 3) Running FUN 2 times in 2 thread(s)... 0.722 seconds CV fold: Fold2 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Registering parallel backend using 2 cores. Running initial scoring function 10 times in 2 thread(s)... 7.384 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 3.063 seconds 3) Running FUN 2 times in 2 thread(s)... 1.138 seconds CV fold: Fold3 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Registering parallel backend using 2 cores. Running initial scoring function 10 times in 2 thread(s)... 6.34 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 14.623 seconds 3) Running FUN 2 times in 2 thread(s)... 0.97 seconds 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:287:5'): test nested cv, grid, binary - ranger ──────── Error in `xtfrm.data.frame(structure(list(`0` = 0.379858310721837, `1` = 0.620141689278164), row.names = c(NA, -1L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x55fcc8b59070>, .data.table.locked = TRUE))`: cannot xtfrm data frames Backtrace: ▆ 1. ├─ranger_optimizer$execute() at test-binary.R:287:5 2. │ └─mlexperiments:::.run_cv(self = self, private = private) 3. │ └─mlexperiments:::.fold_looper(self, private) 4. │ ├─base::do.call(private$cv_run_model, run_args) 5. │ └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<named list>`, fold_test = `<named list>`) 6. │ ├─base::do.call(.cv_run_nested_model, args) 7. │ └─mlexperiments (local) `<fn>`(...) 8. │ └─hparam_tuner$execute(k = self$k_tuning) 9. │ └─mlexperiments:::.run_tuning(self = self, private = private, optimizer = optimizer) 10. │ └─mlexperiments:::.run_optimizer(...) 11. │ └─optimizer$execute(x = private$x, y = private$y, method_helper = private$method_helper) 12. │ ├─base::do.call(...) 13. │ └─mlexperiments (local) `<fn>`(...) 14. │ └─base::lapply(...) 15. │ └─mlexperiments (local) FUN(X[[i]], ...) 16. │ ├─base::do.call(FUN, fun_parameters) 17. │ └─mlexperiments (local) `<fn>`(...) 18. │ ├─base::do.call(private$fun_optim_cv, kwargs) 19. │ └─mllrnrs (local) `<fn>`(...) 20. │ ├─base::do.call(ranger_predict, pred_args) 21. │ └─mllrnrs (local) `<fn>`(...) 22. │ └─kdry::mlh_reshape(preds) 23. │ ├─data.table::as.data.table(object)[, cn[which.max(.SD)], by = seq_len(nrow(object))] 24. │ └─data.table:::`[.data.table`(...) 25. └─base::which.max(.SD) 26. ├─base::xtfrm(`<dt[,2]>`) 27. └─base::xtfrm.data.frame(`<dt[,2]>`) ── Error ('test-multiclass.R:162:5'): test nested cv, grid, multiclass - lightgbm ── Error in `xtfrm.data.frame(structure(list(`0` = 0.20774260202068, `1` = 0.136781829323219, `2` = 0.655475568656101), row.names = c(NA, -1L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x55fcc8b59070>, .data.table.locked = TRUE))`: cannot xtfrm data frames Backtrace: ▆ 1. ├─lightgbm_optimizer$execute() at test-multiclass.R:162:5 2. │ └─mlexperiments:::.run_cv(self = self, private = private) 3. │ └─mlexperiments:::.fold_looper(self, private) 4. │ ├─base::do.call(private$cv_run_model, run_args) 5. │ └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<named list>`, fold_test = `<named list>`) 6. │ ├─base::do.call(.cv_run_nested_model, args) 7. │ └─mlexperiments (local) `<fn>`(...) 8. │ └─mlexperiments:::.cv_fit_model(...) 9. │ ├─base::do.call(self$learner$predict, pred_args) 10. │ └─mlexperiments (local) `<fn>`(...) 11. │ ├─base::do.call(private$fun_predict, kwargs) 12. │ └─mllrnrs (local) `<fn>`(...) 13. │ └─kdry::mlh_reshape(preds) 14. │ ├─data.table::as.data.table(object)[, cn[which.max(.SD)], by = seq_len(nrow(object))] 15. │ └─data.table:::`[.data.table`(...) 16. └─base::which.max(.SD) 17. ├─base::xtfrm(`<dt[,3]>`) 18. └─base::xtfrm.data.frame(`<dt[,3]>`) ── Error ('test-multiclass.R:294:5'): test nested cv, grid, multi:softprob - xgboost, with weights ── Error in `xtfrm.data.frame(structure(list(`0` = 0.250160574913025, `1` = 0.124035485088825, `2` = 0.62580394744873), row.names = c(NA, -1L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x55fcc8b59070>, .data.table.locked = TRUE))`: cannot xtfrm data frames Backtrace: ▆ 1. ├─xgboost_optimizer$execute() at test-multiclass.R:294:5 2. │ └─mlexperiments:::.run_cv(self = self, private = private) 3. │ └─mlexperiments:::.fold_looper(self, private) 4. │ ├─base::do.call(private$cv_run_model, run_args) 5. │ └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<named list>`, fold_test = `<named list>`) 6. │ ├─base::do.call(.cv_run_nested_model, args) 7. │ └─mlexperiments (local) `<fn>`(...) 8. │ └─mlexperiments:::.cv_fit_model(...) 9. │ ├─base::do.call(self$learner$predict, pred_args) 10. │ └─mlexperiments (local) `<fn>`(...) 11. │ ├─base::do.call(private$fun_predict, kwargs) 12. │ └─mllrnrs (local) `<fn>`(...) 13. │ └─kdry::mlh_reshape(preds) 14. │ ├─data.table::as.data.table(object)[, cn[which.max(.SD)], by = seq_len(nrow(object))] 15. │ └─data.table:::`[.data.table`(...) 16. └─base::which.max(.SD) 17. ├─base::xtfrm(`<dt[,3]>`) 18. └─base::xtfrm.data.frame(`<dt[,3]>`) [ FAIL 3 | WARN 0 | SKIP 3 | PASS 25 ] Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-debian-gcc

Package mlsurvlrnrs

Current CRAN status: OK: 13

Package rBiasCorrection

Current CRAN status: OK: 13

Package sjtable2df

Current CRAN status: OK: 13