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 2025-12-25 19:50:52 CET.

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

Package DQAgui

Current CRAN status: OK: 13

Package kdry

Current CRAN status: ERROR: 1, OK: 12

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 Flavor: r-devel-linux-x86_64-debian-gcc

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: 0x563bdb8b9070>, .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: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### 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-linux-x86_64-debian-clang

Version: 0.0.8
Check: tests
Result: ERROR Running ‘testthat.R’ [182s/468s] 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)... 25.821 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 0.966 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)... 27.299 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 1.047 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)... 12.549 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 1.107 seconds 3) Running FUN 2 times in 2 thread(s)... 4.992 seconds CV fold: Fold1 Registering parallel backend using 2 cores. Running initial scoring function 11 times in 2 thread(s)... 15.024 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 1.182 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)... 15.514 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 1.76 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)... 14.044 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 1.209 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)... 25.531 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 1.07 seconds 3) Running FUN 2 times in 2 thread(s)... 3.963 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)... 12.95 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 1.055 seconds 3) Running FUN 2 times in 2 thread(s)... 2.257 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)... 13.459 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 1.165 seconds 3) Running FUN 2 times in 2 thread(s)... 2.238 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)... 14.172 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 0.91 seconds 3) Running FUN 2 times in 2 thread(s)... 2.592 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)... 6.354 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 1.301 seconds 3) Running FUN 2 times in 2 thread(s)... 0.677 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)... 5.334 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 0.997 seconds 3) Running FUN 2 times in 2 thread(s)... 0.352 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)... 4.992 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 1.189 seconds 3) Running FUN 2 times in 2 thread(s)... 0.472 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)... 6.47 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 1.051 seconds 3) Running FUN 2 times in 2 thread(s)... 0.559 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-linux-x86_64-debian-clang

Package mllrnrs

Current CRAN status: ERROR: 1, OK: 12

Version: 0.0.7
Check: tests
Result: ERROR Running ‘testthat.R’ [57s/189s] 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)... 6.164 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 8.461 seconds 3) Running FUN 2 times in 2 thread(s)... 1.041 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)... 6.32 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 6.19 seconds 3) Running FUN 2 times in 2 thread(s)... 0.57 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)... 6.159 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 9.689 seconds 3) Running FUN 2 times in 2 thread(s)... 0.711 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)... 4.135 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 0.853 seconds 3) Running FUN 2 times in 2 thread(s)... 0.436 seconds CV fold: Fold2 Registering parallel backend using 2 cores. Running initial scoring function 5 times in 2 thread(s)... 5.017 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)... 0.673 seconds CV fold: Fold3 Registering parallel backend using 2 cores. Running initial scoring function 5 times in 2 thread(s)... 5.455 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 1.269 seconds 3) Running FUN 2 times in 2 thread(s)... 0.69 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)... 7.35 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 9.088 seconds 3) Running FUN 2 times in 2 thread(s)... 0.765 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)... 6.388 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 2.637 seconds 3) Running FUN 2 times in 2 thread(s)... 0.943 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)... 6.687 seconds Starting Epoch 1 1) Fitting Gaussian Process... 2) Running local optimum search... 14.959 seconds 3) Running FUN 2 times in 2 thread(s)... 0.797 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: 0x55a12d6f8070>, .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$fun_optim_cv, kwargs) 19. │ └─mllrnrs (local) `<fn>`(...) 20. │ ├─base::do.call(ranger_predict, pred_args) 21. │ └─mllrnrs (local) `<fn>`(...) 22. │ └─kdry::mlh_reshape(preds) 23. │ ├─data.table::as.data.table(object)[, cn[which.max(.SD)], by = seq_len(nrow(object))] 24. │ └─data.table:::`[.data.table`(...) 25. └─base::which.max(.SD) 26. ├─base::xtfrm(`<dt[,2]>`) 27. └─base::xtfrm.data.frame(`<dt[,2]>`) ── Error ('test-multiclass.R:162:5'): test nested cv, grid, multiclass - lightgbm ── Error in `xtfrm.data.frame(structure(list(`0` = 0.20774260202068, `1` = 0.136781829323219, `2` = 0.655475568656101), row.names = c(NA, -1L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x55a12d6f8070>, .data.table.locked = TRUE))`: cannot xtfrm data frames Backtrace: ▆ 1. ├─lightgbm_optimizer$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: 0x55a12d6f8070>, .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