Last updated on 2025-05-18 19:50:58 CEST.
| Package | ERROR | NOTE | OK |
|---|---|---|---|
| miesmuschel | 13 | ||
| mlr | 8 | 5 | |
| mlr3pipelines | 5 | 8 | |
| paradox | 13 | ||
| ParamHelpers | 13 |
Current CRAN status: OK: 13
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
Current CRAN status: ERROR: 5, OK: 8
Version: 0.7.2
Check: examples
Result: ERROR
Running examples in ‘mlr3pipelines-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### 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] 0.01
$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 $train()
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 0.7.2
Check: examples
Result: ERROR
Running examples in ‘mlr3pipelines-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: mlr_pipeops_imputelearner
> ### Title: Impute Features by Fitting a Learner
> ### Aliases: mlr_pipeops_imputelearner PipeOpImputeLearner
>
> ### ** Examples
>
> ## Don't show:
> if (requireNamespace("rpart")) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
+ ## End(Don't show)
+ library("mlr3")
+
+ task = tsk("pima")
+ task$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] 0.007
$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...
$train_task
<TaskRegr:imputing> (768 x 8)
* Target: .impute_col
* Properties: -
* Features (7):
- dbl (7): age, glucose, insulin, pedigree, pregnant, pressure,
triceps
attr(,"class")
[1] "learner_state" "list"
> library("mlr3learners")
> po = po("imputelearner", po("imputehist") %>>% lrn("regr.kknn"))
Error in dictionary_retrieve_item(self, key, .dicts_suggest) :
Element with key 'regr.kknn' not found in DictionaryLearner! Did you mean 'regr.nnet'?
Calls: <Anonymous> ... dictionary_get -> dictionary_retrieve_item -> stopf
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 0.7.2
Check: examples
Result: ERROR
Running examples in ‘mlr3pipelines-Ex.R’ failed
The error most likely occurred in:
> ### Name: mlr_pipeops_imputelearner
> ### Title: Impute Features by Fitting a Learner
> ### Aliases: mlr_pipeops_imputelearner PipeOpImputeLearner
>
> ### ** Examples
>
> ## Don't show:
> if (requireNamespace("rpart")) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
+ ## End(Don't show)
+ library("mlr3")
+
+ task = tsk("pima")
+ task$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] 0.016
$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...
$train_task
<TaskRegr:imputing> (768 x 8)
* Target: .impute_col
* Properties: -
* Features (7):
- dbl (7): age, glucose, insulin, pedigree, pregnant, pressure,
triceps
attr(,"class")
[1] "learner_state" "list"
> library("mlr3learners")
> po = po("imputelearner", po("imputehist") %>>% lrn("regr.kknn"))
Error in dictionary_retrieve_item(self, key, .dicts_suggest) :
Element with key 'regr.kknn' not found in DictionaryLearner! Did you mean 'regr.nnet'?
Calls: <Anonymous> ... dictionary_get -> dictionary_retrieve_item -> stopf
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 0.7.2
Check: examples
Result: ERROR
Running examples in ‘mlr3pipelines-Ex.R’ failed
The error most likely occurred in:
> ### Name: mlr_pipeops_imputelearner
> ### Title: Impute Features by Fitting a Learner
> ### Aliases: mlr_pipeops_imputelearner PipeOpImputeLearner
>
> ### ** Examples
>
> ## Don't show:
> if (requireNamespace("rpart")) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
+ ## End(Don't show)
+ library("mlr3")
+
+ task = tsk("pima")
+ task$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 $train()
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc
Version: 0.7.2
Check: examples
Result: ERROR
Running examples in ‘mlr3pipelines-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: mlr_pipeops_imputelearner
> ### Title: Impute Features by Fitting a Learner
> ### Aliases: mlr_pipeops_imputelearner PipeOpImputeLearner
>
> ### ** Examples
>
> ## Don't show:
> if (requireNamespace("rpart")) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
+ ## End(Don't show)
+ library("mlr3")
+
+ task = tsk("pima")
+ task$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] 0.007
$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-release-linux-x86_64
Current CRAN status: OK: 13
Current CRAN status: OK: 13