CRAN Package Check Results for Maintainer ‘Martin Binder <mlr.developer at mb706.com>’

Last updated on 2025-05-15 13:51:38 CEST.

Package	ERROR	NOTE	OK
miesmuschel			13
mlr		8	5
mlr3pipelines	2		11
paradox			13
ParamHelpers			13

Package miesmuschel

Current CRAN status: OK: 13

Package mlr

Current CRAN status: NOTE: 8, OK: 5

Version: 2.19.2
Check: Rd cross-references
Result: NOTE Found the following Rd file(s) with Rd \link{} targets missing package anchors: TuneMultiCritControl.Rd: OptPath batchmark.Rd: data.table getClassWeightParam.Rd: LearnerParam getLearnerParamSet.Rd: ParamSet getParamSet.Rd: ParamSet makeBaseWrapper.Rd: ParamSet makeExtractFDAFeatMethod.Rd: ParamSet makeModelMultiplexerParamSet.Rd: ParamSet makeTuneControlMBO.Rd: OptPath Please provide package anchors for all Rd \link{} targets not in the package itself and the base packages. Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-windows-x86_64, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-windows-x86_64

Version: 2.19.2
Check: package dependencies
Result: NOTE Package suggested but not available for checking: ‘Rmpi’ Flavors: r-oldrel-macos-arm64, r-oldrel-macos-x86_64

Version: 2.19.2
Check: installed package size
Result: NOTE installed size is 5.6Mb sub-directories of 1Mb or more: R 2.0Mb data 2.3Mb Flavors: r-oldrel-macos-arm64, r-oldrel-macos-x86_64

Package mlr3pipelines

Current CRAN status: ERROR: 2, OK: 11

Additional issues

noSuggests

Version: 0.7.2
Check: examples
Result: ERROR Running examples in ‘mlr3pipelines-Ex.R’ failed The error most likely occurred in: > ### Name: mlr_pipeops_imputelearner > ### Title: Impute Features by Fitting a Learner > ### Aliases: mlr_pipeops_imputelearner PipeOpImputeLearner > > ### ** Examples > > ## Don't show: > if (requireNamespace("rpart")) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf + ## End(Don't show) + library("mlr3") + + task = tsk("pima") + task$missings() + + po = po("imputelearner", lrn("regr.rpart")) + new_task = po$train(list(task = task))[[1]] + new_task$missings() + + # '$state' of the "regr.rpart" Learner, trained to predict the 'mass' column: + po$state$model$mass + + library("mlr3learners") + # to use the "regr.kknn" Learner, prefix it with its own imputation method! + # The "imputehist" PipeOp is used to train "regr.kknn"; predictions of this + # trained Learner are then used to impute the missing values in the Task. + po = po("imputelearner", + po("imputehist") %>>% lrn("regr.kknn") + ) + + new_task = po$train(list(task = task))[[1]] + new_task$missings() + ## Don't show: + }) # examplesIf > library("mlr3") > task = tsk("pima") > task$missings() diabetes age glucose insulin mass pedigree pregnant pressure 0 0 5 374 11 0 0 35 triceps 227 > po = po("imputelearner", lrn("regr.rpart")) > new_task = po$train(list(task = task))[[1]] > new_task$missings() diabetes age pedigree pregnant glucose insulin mass pressure 0 0 0 0 0 0 0 0 triceps 0 > po$state$model$mass $model n= 757 node), split, n, deviance, yval * denotes terminal node 1) root 757 36254.3300 32.45746 2) triceps< 25.5 219 5537.6560 27.93196 4) triceps< 20.5 144 3140.7800 26.68333 * 5) triceps>=20.5 75 1741.3150 30.32933 10) pressure< 83 64 1081.6090 29.37813 * 11) pressure>=83 11 264.8855 35.86364 * 3) triceps>=25.5 538 24405.7800 34.29963 6) triceps< 35.5 380 14414.2500 32.50474 12) pressure< 74.5 223 6772.1180 31.49013 24) glucose< 73.5 8 44.1000 24.20000 * 25) glucose>=73.5 215 6287.0300 31.76140 50) pregnant>=0.5 190 4822.6790 31.28947 * 51) pregnant< 0.5 25 1100.4420 35.34800 * 13) pressure>=74.5 157 7086.5100 33.94586 26) insulin< 187 122 4736.5000 33.05656 * 27) insulin>=187 35 1917.2070 37.04571 * 7) triceps>=35.5 158 5822.9770 38.61646 14) pregnant>=1.5 92 2351.3170 37.02174 * 15) pregnant< 1.5 66 2911.5580 40.83939 * $param_vals $param_vals$xval [1] 0 $log Empty data.table (0 rows and 3 cols): stage,class,msg $train_time [1] 0.025 $task_hash [1] "a666d2778d446faf" $feature_names [1] "age" "glucose" "insulin" "pedigree" "pregnant" "pressure" "triceps" $validate NULL $mlr3_version [1] ‘0.23.0’ $data_prototype Empty data.table (0 rows and 8 cols): .impute_col,age,glucose,insulin,pedigree,pregnant... $task_prototype Empty data.table (0 rows and 8 cols): .impute_col,age,glucose,insulin,pedigree,pregnant... $train_task <TaskRegr:imputing> (768 x 8) * Target: .impute_col * Properties: - * Features (7): - dbl (7): age, glucose, insulin, pedigree, pregnant, pressure, triceps attr(,"class") [1] "learner_state" "list" > library("mlr3learners") > po = po("imputelearner", po("imputehist") %>>% lrn("regr.kknn")) Warning: Package 'kknn' required but not installed for Learner 'regr.kknn' Warning: Package 'kknn' required but not installed for Learner 'imputehist.regr.kknn' > new_task = po$train(list(task = task))[[1]] Error: The following packages could not be loaded: kknn This happened PipeOp imputelearner's $train() Execution halted Flavor: r-devel-linux-x86_64-fedora-clang

Version: 0.7.2
Check: examples
Result: ERROR Running examples in ‘mlr3pipelines-Ex.R’ failed The error most likely occurred in: > ### Name: mlr_pipeops_imputelearner > ### Title: Impute Features by Fitting a Learner > ### Aliases: mlr_pipeops_imputelearner PipeOpImputeLearner > > ### ** Examples > > ## Don't show: > if (requireNamespace("rpart")) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf + ## End(Don't show) + library("mlr3") + + task = tsk("pima") + task$missings() + + po = po("imputelearner", lrn("regr.rpart")) + new_task = po$train(list(task = task))[[1]] + new_task$missings() + + # '$state' of the "regr.rpart" Learner, trained to predict the 'mass' column: + po$state$model$mass + + library("mlr3learners") + # to use the "regr.kknn" Learner, prefix it with its own imputation method! + # The "imputehist" PipeOp is used to train "regr.kknn"; predictions of this + # trained Learner are then used to impute the missing values in the Task. + po = po("imputelearner", + po("imputehist") %>>% lrn("regr.kknn") + ) + + new_task = po$train(list(task = task))[[1]] + new_task$missings() + ## Don't show: + }) # examplesIf > library("mlr3") > task = tsk("pima") > task$missings() diabetes age glucose insulin mass pedigree pregnant pressure 0 0 5 374 11 0 0 35 triceps 227 > po = po("imputelearner", lrn("regr.rpart")) > new_task = po$train(list(task = task))[[1]] > new_task$missings() diabetes age pedigree pregnant glucose insulin mass pressure 0 0 0 0 0 0 0 0 triceps 0 > po$state$model$mass $model n= 757 node), split, n, deviance, yval * denotes terminal node 1) root 757 36254.3300 32.45746 2) triceps< 25.5 219 5537.6560 27.93196 4) triceps< 20.5 144 3140.7800 26.68333 * 5) triceps>=20.5 75 1741.3150 30.32933 10) pressure< 83 64 1081.6090 29.37813 * 11) pressure>=83 11 264.8855 35.86364 * 3) triceps>=25.5 538 24405.7800 34.29963 6) triceps< 35.5 380 14414.2500 32.50474 12) pressure< 74.5 223 6772.1180 31.49013 24) glucose< 73.5 8 44.1000 24.20000 * 25) glucose>=73.5 215 6287.0300 31.76140 50) pregnant>=0.5 190 4822.6790 31.28947 * 51) pregnant< 0.5 25 1100.4420 35.34800 * 13) pressure>=74.5 157 7086.5100 33.94586 26) insulin< 187 122 4736.5000 33.05656 * 27) insulin>=187 35 1917.2070 37.04571 * 7) triceps>=35.5 158 5822.9770 38.61646 14) pregnant>=1.5 92 2351.3170 37.02174 * 15) pregnant< 1.5 66 2911.5580 40.83939 * $param_vals $param_vals$xval [1] 0 $log Empty data.table (0 rows and 3 cols): stage,class,msg $train_time [1] 0.015 $task_hash [1] "a666d2778d446faf" $feature_names [1] "age" "glucose" "insulin" "pedigree" "pregnant" "pressure" "triceps" $validate NULL $mlr3_version [1] ‘0.23.0’ $data_prototype Empty data.table (0 rows and 8 cols): .impute_col,age,glucose,insulin,pedigree,pregnant... $task_prototype Empty data.table (0 rows and 8 cols): .impute_col,age,glucose,insulin,pedigree,pregnant... $train_task <TaskRegr:imputing> (768 x 8) * Target: .impute_col * Properties: - * Features (7): - dbl (7): age, glucose, insulin, pedigree, pregnant, pressure, triceps attr(,"class") [1] "learner_state" "list" > library("mlr3learners") > po = po("imputelearner", po("imputehist") %>>% lrn("regr.kknn")) Warning: Package 'kknn' required but not installed for Learner 'regr.kknn' Warning: Package 'kknn' required but not installed for Learner 'imputehist.regr.kknn' > new_task = po$train(list(task = task))[[1]] Error: The following packages could not be loaded: kknn This happened PipeOp imputelearner's $train() Execution halted Flavor: r-devel-linux-x86_64-fedora-gcc

Package paradox

Current CRAN status: OK: 13

Package ParamHelpers

Current CRAN status: OK: 13