CRAN Package Check Results for Package fuseMLR

Last updated on 2026-06-08 02:51:10 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.0.4 13.21 103.88 117.09 OK
r-devel-linux-x86_64-debian-gcc 0.0.4 9.84 60.65 70.49 ERROR
r-devel-linux-x86_64-fedora-clang 0.0.4 23.00 166.19 189.19 OK
r-devel-linux-x86_64-fedora-gcc 0.0.4 22.00 169.81 191.81 OK
r-devel-windows-x86_64 0.0.4 12.00 122.00 134.00 OK
r-patched-linux-x86_64 0.0.4 15.22 99.08 114.30 OK
r-release-linux-x86_64 0.0.4 11.59 98.39 109.98 OK
r-release-macos-arm64 0.0.4 3.00 34.00 37.00 OK
r-release-macos-x86_64 0.0.4 10.00 129.00 139.00 OK
r-release-windows-x86_64 0.0.4 16.00 122.00 138.00 OK
r-oldrel-macos-arm64 0.0.4 3.00 52.00 55.00 OK
r-oldrel-macos-x86_64 0.0.4 9.00 119.00 128.00 OK
r-oldrel-windows-x86_64 0.0.4 18.00 146.00 164.00 OK

Check Details

Version: 0.0.4
Check: examples
Result: ERROR Running examples in ‘fuseMLR-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: cobra > ### Title: Cobra Meta Learner > ### Aliases: cobra > > ### ** Examples > > # Example usage > set.seed(123) > x_train <- data.frame(a = runif(10L), b = runif(10L)) > y_train <- sample(0L:1L, size = 10L, replace = TRUE) > > # Train the model with epsilon optimization > cobra_model <- cobra(x = x_train, y = y_train, tune = "epsilon", k_folds = 2) Tuning 'epsilon' via cross-validation with 2 folds. > > # Make predictions on new data > set.seed(156) > x_new <- data.frame(a = runif(5L), b = runif(5L)) > prediction <- predict(object = cobra_model, data = x_new) Error in loadNamespace(x) : there is no package called ‘caret’ Calls: predict ... loadNamespace -> withRestarts -> withOneRestart -> doWithOneRestart Execution halted Flavor: r-devel-linux-x86_64-debian-gcc

Version: 0.0.4
Check: tests
Result: ERROR Running ‘testthat.R’ [7s/10s] 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/testing-design.html#sec-tests-files-overview > # * https://testthat.r-lib.org/articles/special-files.html > > library(testthat) > library(fuseMLR) > > test_check("fuseMLR") Class : Data name : geneexpr ind. id. : IDS n : 49 p : 132 Class: HashTable id: test ----------------- [1] key class <0 rows> (or 0-length row.names) Learner : ranger TrainLayer : geneexpr Package : ranger Learn function : ranger Training of base model on layer geneexpr started. Training of base model on layer geneexpr done. Class : Model Learner info. ----------------------- Learner : ranger TrainLayer : geneexpr Package : ranger Learn function : ranger Train data info. ----------------------- TrainData : geneexpr Layer : geneexpr ind. id. : IDS target : disease n : 50 Missing : 0 p : 131 TrainLayer : geneexpr Status : Not trained Empty layer. TrainData : methylation Layer : methylation ind. id. : IDS target : disease n : 50 Missing : 0 p : 367 Layer geneexpr ---------------- TrainLayer : geneexpr Status : Not trained Empty layer. ---------------- Object(s) on layer geneexpr Empty layer Layer geneexpr ---------------- TrainLayer : geneexpr Status : Not trained Nb. of objects stored : 3 ---------------- Object(s) on layer geneexpr ---------------- Learner : ranger TrainLayer : geneexpr Package : ranger Learn function : ranger ---------------- ---------------- VarSel : varsel_geneexpr TrainLayer : geneexpr Package : Boruta Function : Boruta ---------------- ---------------- TrainData : geneexpr Layer : geneexpr Ind. id. : IDS Target : disease n : 50 Missing : 0 p : 131 ---------------- Training of base model on layer geneexpr started. Training of base model on layer geneexpr done. Layer geneexpr ---------------- TrainLayer : geneexpr Status : Trained Nb. of objects stored : 4 ---------------- Object(s) on layer geneexpr ---------------- Learner : ranger TrainLayer : geneexpr Package : ranger Learn function : ranger ---------------- ---------------- VarSel : varsel_geneexpr TrainLayer : geneexpr Package : Boruta Function : Boruta ---------------- ---------------- TrainData : geneexpr Layer : geneexpr Ind. id. : IDS Target : disease n : 50 Missing : 0 p : 131 ---------------- TrainMetaLayer : meta Status : Not trained Empty layer. MetaLayer ---------------- TrainMetaLayer : meta Status : Not trained Empty layer. ---------------- Object(s) on MetaLayer Empty layer Training : training Problem type : classification Status : Not trained Number of layers: 0 Layers trained : 0 Variable selection on layer geneexpr started. Variable selection on layer geneexpr done. Layer variable 1 geneexpr BRAF 2 geneexpr PEA15 3 geneexpr SHC1 VarSel : varsel_geneexpr TrainLayer : geneexpr Package : Boruta Function : Boruta Tuning 'epsilon' via cross-validation with 5 folds. Saving _problems/test-cobra-11.R Tuning 'alpha' and 'epsilon' via cross-validation with 5 folds. Saving _problems/test-cobra-20.R Using user-defined 'epsilon' = 0.1. Tuning 'epsilon' via cross-validation with 10 folds. Tuning 'epsilon' via cross-validation with 10 folds. Tuning 'epsilon' via cross-validation with 10 folds. Tuning 'epsilon' via cross-validation with 10 folds. Tuning 'epsilon' via cross-validation with 10 folds. Tuning 'epsilon' via cross-validation with 10 folds. Saving _problems/test-cobra-132.R Tuning 'alpha' and 'epsilon' via cross-validation with 10 folds. Saving _problems/test-cobra-141.R [ FAIL 4 | WARN 1 | SKIP 3 | PASS 169 ] ══ Skipped tests (3) ═══════════════════════════════════════════════════════════ • On CRAN (2): 'test-TrainMetaLayer.R:60:5', 'test-VarSel.R:45:5' • {caret} is not installed. (1): 'test-Training.R:183:5' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-cobra.R:4:3'): cobra works ─────────────────────────────────── Expected `{ ... }` not to throw any errors. Actually got a <packageNotFoundError> with message: there is no package called 'caret' ── Failure ('test-cobra.R:13:3'): cobra works ────────────────────────────────── Expected `{ ... }` not to throw any errors. Actually got a <packageNotFoundError> with message: there is no package called 'caret' ── Failure ('test-cobra.R:125:3'): cobra works ───────────────────────────────── Expected `{ ... }` not to throw any errors. Actually got a <packageNotFoundError> with message: there is no package called 'caret' ── Failure ('test-cobra.R:134:3'): cobra works ───────────────────────────────── Expected `{ ... }` not to throw any errors. Actually got a <packageNotFoundError> with message: there is no package called 'caret' [ FAIL 4 | WARN 1 | SKIP 3 | PASS 169 ] Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-debian-gcc

Version: 0.0.4
Check: re-building of vignette outputs
Result: ERROR Error(s) in re-building vignettes: ... --- re-building ‘fuseMLR.Rmd’ using rmarkdown Quitting from fuseMLR.Rmd:184-188 [lrner_train] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <error/rlang_error> Error in `loadNamespace()`: ! there is no package called 'caret' --- Backtrace: ▆ 1. ├─fuseMLR::fusemlr(training = training, use_var_sel = TRUE) 2. │ └─training$train(...) 3. │ └─self$createMetaTrainData(...) 4. │ ├─base::do.call(eval(parse(text = resampling_method)), resampling_arg) 5. │ └─base::eval(parse(text = resampling_method)) 6. │ └─base::eval(parse(text = resampling_method)) 7. └─base::loadNamespace(x) 8. └─base::withRestarts(stop(cond), retry_loadNamespace = function() NULL) 9. └─base (local) withOneRestart(expr, restarts[[1L]]) 10. └─base (local) doWithOneRestart(return(expr), restart) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Error: processing vignette 'fuseMLR.Rmd' failed with diagnostics: there is no package called 'caret' --- failed re-building ‘fuseMLR.Rmd’ SUMMARY: processing the following file failed: ‘fuseMLR.Rmd’ Error: Vignette re-building failed. Execution halted Flavor: r-devel-linux-x86_64-debian-gcc