CRAN Package Check Results for Package fuseMLR

Last updated on 2026-05-16 16:50:10 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.0.2 12.44 105.36 117.80 OK
r-devel-linux-x86_64-debian-gcc 0.0.2 8.20 78.83 87.03 OK
r-devel-linux-x86_64-fedora-clang 0.0.2 24.00 115.11 139.11 ERROR
r-devel-linux-x86_64-fedora-gcc 0.0.2 23.00 172.33 195.33 OK
r-devel-windows-x86_64 0.0.2 13.00 118.00 131.00 OK
r-patched-linux-x86_64 0.0.2 15.40 99.71 115.11 OK
r-release-linux-x86_64 0.0.2 10.69 99.38 110.07 OK
r-release-macos-arm64 0.0.2 3.00 33.00 36.00 OK
r-release-macos-x86_64 0.0.2 9.00 102.00 111.00 OK
r-release-windows-x86_64 0.0.2 15.00 120.00 135.00 OK
r-oldrel-macos-arm64 0.0.2 OK
r-oldrel-macos-x86_64 0.0.2 9.00 111.00 120.00 OK
r-oldrel-windows-x86_64 0.0.2 15.00 140.00 155.00 OK

Additional issues

M1mac

Check Details

Version: 0.0.2
Check: tests
Result: ERROR Running ‘testthat.R’ [21s/27s] 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. Saving _problems/test-Training-148.R VarSel : varsel_geneexpr TrainLayer : geneexpr Package : Boruta Function : Boruta Tuning 'epsilon' via cross-validation with 5 folds. Optimal epsilon: 0.071. Tuning with 5 folds. Tuning 'alpha' and 'epsilon' via cross-validation with 5 folds. Optimal alpha: 1 (1 Learner(s)). Optimal epsilon: 0.313. Tuning with 5 folds. 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. Optimal epsilon: 0.669. Tuning with 10 folds. Tuning 'alpha' and 'epsilon' via cross-validation with 10 folds. Optimal alpha: 1 (1 Learner(s)). Optimal epsilon: 0.217. Tuning with 10 folds. [ FAIL 1 | WARN 3 | SKIP 2 | PASS 170 ] ══ Skipped tests (2) ═══════════════════════════════════════════════════════════ • On CRAN (2): 'test-TrainMetaLayer.R:60:5', 'test-VarSel.R:43:5' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-Training.R:148:5'): Training: all tests ──────────────────────── Error in `fru::fru(x, y, trees = trees, importance = TRUE, threads = threads, ...)`: unused arguments (num.trees = 50, mtry = 3) Backtrace: ▆ 1. ├─testthat::expect_warning(...) at test-Training.R:141:3 2. │ └─testthat:::expect_condition_matching_(...) 3. │ └─testthat:::quasi_capture(...) 4. │ ├─testthat (local) .capture(...) 5. │ │ └─base::withCallingHandlers(...) 6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo)) 7. └─training$varSelection() at test-Training.R:148:5 8. └─layer$varSelection(ind_subset = ind_subset, verbose = self$getVerbose()) 9. └─varsel$varSelection(ind_subset = ind_subset) 10. ├─base::do.call(eval(parse(text = varsel)), varsel_param) 11. ├─Boruta (local) `<fn>`(num.trees = 50L, mtry = 3L, x = `<df[,131]>`, y = `<fct>`) 12. └─Boruta:::Boruta.default(...) 13. └─Boruta (local) addShadowsAndGetImp(decReg, runs) 14. └─Boruta (local) getImp(cbind(x[, decReg != "Rejected"], xSha), y, ...) 15. └─fru::importance(...) [ FAIL 1 | WARN 3 | SKIP 2 | PASS 170 ] Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-fedora-clang

Version: 0.0.2
Check: re-building of vignette outputs
Result: ERROR Error(s) in re-building vignettes: --- re-building ‘fuseMLR.Rmd’ using rmarkdown Quitting from fuseMLR.Rmd:162-167 [varsel] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <error/rlang_error> Error in `fru::fru()`: ! unused arguments (num.trees = 1000, mtry = 3, probability = TRUE) --- Backtrace: ▆ 1. └─fuseMLR::varSelection(training = training) 2. └─training$varSelection(ind_subset = ind_subset, verbose = training$getVerbose()) 3. └─layer$varSelection(ind_subset = ind_subset, verbose = self$getVerbose()) 4. └─varsel$varSelection(ind_subset = ind_subset) 5. ├─base::do.call(eval(parse(text = varsel)), varsel_param) 6. ├─Boruta (local) `<fn>`(...) 7. └─Boruta:::Boruta.default(...) 8. └─Boruta (local) addShadowsAndGetImp(decReg, runs) 9. └─Boruta (local) getImp(cbind(x[, decReg != "Rejected"], xSha), y, ...) 10. └─fru::importance(...) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Error: processing vignette 'fuseMLR.Rmd' failed with diagnostics: unused arguments (num.trees = 1000, mtry = 3, probability = TRUE) --- 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-fedora-clang