Practical Tuning Series - Tune a Preprocessing Pipeline

Build a simple preprocessing pipeline and tune it.

Authors

Marc Becker

Theresa Ullmann

Michel Lang

Bernd Bischl

Jakob Richter

Martin Binder

Published

March 10, 2021

Scope

This is the second part of the practical tuning series. The other parts can be found here:

In this post, we build a simple preprocessing pipeline and tune it. For this, we are using the mlr3pipelines extension package. First, we start by imputing missing values in the Pima Indians Diabetes data set. After that, we encode a factor column to numerical dummy columns in the data set. Next, we combine both preprocessing steps to a Graph and create a GraphLearner. Finally, nested resampling is used to compare the performance of two imputation methods.

Prerequisites

We load the mlr3verse package which pulls in the most important packages for this example.

library(mlr3verse)

We initialize the random number generator with a fixed seed for reproducibility, and decrease the verbosity of the logger to keep the output clearly represented. The lgr package is used for logging in all mlr3 packages. The mlr3 logger prints the logging messages from the base package, whereas the bbotk logger is responsible for logging messages from the optimization packages (e.g. mlr3tuning ).

set.seed(7832)
lgr::get_logger("mlr3")$set_threshold("warn")
lgr::get_logger("bbotk")$set_threshold("warn")

In this example, we use the Pima Indians Diabetes data set which is used to predict whether or not a patient has diabetes. The patients are characterized by 8 numeric features of which some have missing values. We alter the data set by categorizing the feature pressure (blood pressure) into the categories "low", "mid", and "high".

# retrieve the task from mlr3
task = tsk("pima")

# create data frame with categorized pressure feature
data = task$data(cols = "pressure")
breaks = quantile(data$pressure, probs = c(0, 0.33, 0.66, 1), na.rm = TRUE)
data$pressure = cut(data$pressure, breaks, labels = c("low", "mid", "high"))

# overwrite the feature in the task
task$cbind(data)

# generate a quick textual overview
skimr::skim(task$data())
Data summary
Name task$data()
Number of rows 768
Number of columns 9
Key NULL
_______________________
Column type frequency:
factor 2
numeric 7
________________________
Group variables None

Variable type: factor

skim_variable n_missing complete_rate ordered n_unique top_counts
diabetes 0 1.00 FALSE 2 neg: 500, pos: 268
pressure 36 0.95 FALSE 3 low: 282, mid: 245, hig: 205

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
age 0 1.00 33.24 11.76 21.00 24.00 29.00 41.00 81.00 ▇▃▁▁▁
glucose 5 0.99 121.69 30.54 44.00 99.00 117.00 141.00 199.00 ▁▇▇▃▂
insulin 374 0.51 155.55 118.78 14.00 76.25 125.00 190.00 846.00 ▇▂▁▁▁
mass 11 0.99 32.46 6.92 18.20 27.50 32.30 36.60 67.10 ▅▇▃▁▁
pedigree 0 1.00 0.47 0.33 0.08 0.24 0.37 0.63 2.42 ▇▃▁▁▁
pregnant 0 1.00 3.85 3.37 0.00 1.00 3.00 6.00 17.00 ▇▃▂▁▁
triceps 227 0.70 29.15 10.48 7.00 22.00 29.00 36.00 99.00 ▆▇▁▁▁

We choose the xgboost algorithm from the xgboost package as learner.

learner = lrn("classif.xgboost", nrounds = 100, id = "xgboost", verbose = 0)

Missing Values

The task has missing data in five columns.

round(task$missings() / task$nrow, 2)
diabetes      age  glucose  insulin     mass pedigree pregnant pressure  triceps 
    0.00     0.00     0.01     0.49     0.01     0.00     0.00     0.05     0.30

The xgboost learner has an internal method for handling missing data but some learners cannot handle missing values. We will try to beat the internal method in terms of predictive performance. The mlr3pipelines package offers various methods to impute missing values.

mlr_pipeops$keys("^impute")
[1] "imputeconstant" "imputehist"     "imputelearner"  "imputemean"     "imputemedian"   "imputemode"    
[7] "imputeoor"      "imputesample"

We choose the PipeOpImputeOOR that adds the new factor level ".MISSING". to factorial features and imputes numerical features by constant values shifted below the minimum (default) or above the maximum.

imputer = po("imputeoor")
print(imputer)

── PipeOp <imputeoor>: not trained ─────────────────────────────────────────────────────────────────────────────────────
Values: min=TRUE, offset=1, multiplier=1, create_empty_level=FALSE

── Input channels: 
   name  train predict
 <char> <char>  <char>
  input   Task    Task

── Output channels: 
   name  train predict
 <char> <char>  <char>
 output   Task    Task

As the output suggests, the in- and output of this pipe operator is a Task for both the training and the predict step. We can manually train the pipe operator to check its functionality:

task_imputed = imputer$train(list(task))[[1]]
task_imputed$missings()
diabetes      age pedigree pregnant  glucose  insulin     mass pressure  triceps 
       0        0        0        0        0        0        0        0        0

Let’s compare an observation with missing values to the observation with imputed observation.

rbind(
  task$data()[8,],
  task_imputed$data()[8,]
)
   diabetes   age glucose insulin  mass pedigree pregnant pressure triceps
     <fctr> <num>   <num>   <num> <num>    <num>    <num>   <fctr>   <num>
1:      neg    29     115      NA  35.3    0.134       10     <NA>      NA
2:      neg    29     115    -819  35.3    0.134       10 .MISSING     -86

Note that OOR imputation is in particular useful for tree-based models, but should not be used for linear models or distance-based models.

Factor Encoding

The xgboost learner cannot handle categorical features. Therefore, we must to convert factor columns to numerical dummy columns. For this, we argument the xgboost learner with automatic factor encoding.

The PipeOpEncode encodes factor columns with one of six methods. In this example, we use one-hot encoding which creates a new binary column for each factor level.

factor_encoding = po("encode", method = "one-hot")

We manually trigger the encoding on the task.

factor_encoding$train(list(task))
$output

── <TaskClassif> (768x11): Pima Indian Diabetes ────────────────────────────────────────────────────────────────────────
• Target: diabetes
• Target classes: pos (positive class, 35%), neg (65%)
• Properties: twoclass
• Features (10):
  • dbl (10): age, glucose, insulin, mass, pedigree, pregnant, pressure.high, pressure.low, pressure.mid, triceps

The factor column pressure has been converted to the three binary columns "pressure.low", "pressure.mid", and "pressure.high".

Constructing the Pipeline

We created two preprocessing steps which could be used to create a new task with encoded factor variables and imputed missing values. However, if we do this before resampling, information from the test can leak into our training step which typically leads to overoptimistic performance measures. To avoid this, we add the preprocessing steps to the Learner itself, creating a GraphLearner. For this, we create a Graph first.

graph = po("encode") %>>%
  po("imputeoor") %>>%
  learner
plot(graph, html = FALSE)

We use as_learner() to wrap the Graph into a GraphLearner with which allows us to use the graph like a normal learner.

graph_learner = as_learner(graph)

# short learner id for printing
graph_learner$id = "graph_learner"

The GraphLearner can be trained and used for making predictions. Instead of calling $train() or $predict() manually, we will directly use it for resampling. We choose a 3-fold cross-validation as the resampling strategy.

resampling = rsmp("cv", folds = 3)

rr = resample(task = task, learner = graph_learner, resampling = resampling)
rr$score()[, c("iteration", "task_id", "learner_id", "resampling_id", "classif.ce"), with = FALSE]
   iteration task_id    learner_id resampling_id classif.ce
       <int>  <char>        <char>        <char>      <num>
1:         1    pima graph_learner            cv  0.2851562
2:         2    pima graph_learner            cv  0.2539062
3:         3    pima graph_learner            cv  0.2773438

For each resampling iteration, the following steps are performed:

  1. The task is subsetted to the training indices.
  2. The factor encoder replaces factor features with dummy columns in the training task.
  3. The OOR imputer determines values to impute from the training task and then replaces all missing values with learned imputation values.
  4. The learner is applied on the modified training task and the model is stored inside the learner.

Next is the predict step:

  1. The task is subsetted to the test indices.
  2. The factor encoder replaces all factor features with dummy columns in the test task.
  3. The OOR imputer replaces all missing values of the test task with the imputation values learned on the training set.
  4. The learner’s predict method is applied on the modified test task.

By following this procedure, it is guaranteed that no information can leak from the training step to the predict step.

Tuning the Pipeline

Let’s have a look at the parameter set of the GraphLearner. It consists of the xgboost hyperparameters, and additionally, the parameter of the PipeOp encode and imputeoor. All hyperparameters are prefixed with the id of the respective PipeOp or learner.

as.data.table(graph_learner$param_set)[, c("id", "class", "lower", "upper", "nlevels"), with = FALSE]
                                     id    class lower upper nlevels
                                 <char>   <char> <num> <num>   <num>
 1:                       encode.method ParamFct    NA    NA       5
 2:               encode.affect_columns ParamUty    NA    NA     Inf
 3:                       imputeoor.min ParamLgl    NA    NA       2
 4:                    imputeoor.offset ParamDbl     0   Inf     Inf
 5:                imputeoor.multiplier ParamDbl     0   Inf     Inf
 6:            imputeoor.affect_columns ParamUty    NA    NA     Inf
 7:        imputeoor.create_empty_level ParamLgl    NA    NA       2
 8:                       xgboost.alpha ParamDbl     0   Inf     Inf
 9:               xgboost.approxcontrib ParamLgl    NA    NA       2
10:                  xgboost.base_score ParamDbl  -Inf   Inf     Inf
11:                     xgboost.booster ParamFct    NA    NA       3
12:                   xgboost.callbacks ParamUty    NA    NA     Inf
13:           xgboost.colsample_bylevel ParamDbl     0     1     Inf
14:            xgboost.colsample_bynode ParamDbl     0     1     Inf
15:            xgboost.colsample_bytree ParamDbl     0     1     Inf
16:                      xgboost.device ParamUty    NA    NA     Inf
17: xgboost.disable_default_eval_metric ParamLgl    NA    NA       2
18:       xgboost.early_stopping_rounds ParamInt     1   Inf     Inf
19:                         xgboost.eta ParamDbl     0     1     Inf
20:                       xgboost.evals ParamUty    NA    NA     Inf
21:                 xgboost.eval_metric ParamUty    NA    NA     Inf
22:               xgboost.custom_metric ParamUty    NA    NA     Inf
23:          xgboost.extmem_single_page ParamLgl    NA    NA       2
24:            xgboost.feature_selector ParamFct    NA    NA       5
25:                       xgboost.gamma ParamDbl     0   Inf     Inf
26:                 xgboost.grow_policy ParamFct    NA    NA       2
27:     xgboost.interaction_constraints ParamUty    NA    NA     Inf
28:              xgboost.iterationrange ParamUty    NA    NA     Inf
29:                      xgboost.lambda ParamDbl     0   Inf     Inf
30:                     xgboost.max_bin ParamInt     2   Inf     Inf
31:        xgboost.max_cached_hist_node ParamInt  -Inf   Inf     Inf
32:           xgboost.max_cat_to_onehot ParamInt  -Inf   Inf     Inf
33:           xgboost.max_cat_threshold ParamDbl  -Inf   Inf     Inf
34:              xgboost.max_delta_step ParamDbl     0   Inf     Inf
35:                   xgboost.max_depth ParamInt     0   Inf     Inf
36:                  xgboost.max_leaves ParamInt     0   Inf     Inf
37:                    xgboost.maximize ParamLgl    NA    NA       2
38:            xgboost.min_child_weight ParamDbl     0   Inf     Inf
39:                     xgboost.missing ParamDbl  -Inf   Inf     Inf
40:        xgboost.monotone_constraints ParamUty    NA    NA     Inf
41:                     xgboost.nrounds ParamInt     1   Inf     Inf
42:              xgboost.normalize_type ParamFct    NA    NA       2
43:                     xgboost.nthread ParamInt     1   Inf     Inf
44:           xgboost.num_parallel_tree ParamInt     1   Inf     Inf
45:                   xgboost.objective ParamUty    NA    NA     Inf
46:                    xgboost.one_drop ParamLgl    NA    NA       2
47:               xgboost.print_every_n ParamInt     1   Inf     Inf
48:                   xgboost.rate_drop ParamDbl     0     1     Inf
49:                xgboost.refresh_leaf ParamLgl    NA    NA       2
50:                        xgboost.seed ParamInt  -Inf   Inf     Inf
51:          xgboost.seed_per_iteration ParamLgl    NA    NA       2
52:             xgboost.sampling_method ParamFct    NA    NA       2
53:                 xgboost.sample_type ParamFct    NA    NA       2
54:                   xgboost.save_name ParamUty    NA    NA     Inf
55:                 xgboost.save_period ParamInt     0   Inf     Inf
56:            xgboost.scale_pos_weight ParamDbl  -Inf   Inf     Inf
57:                   xgboost.skip_drop ParamDbl     0     1     Inf
58:                   xgboost.subsample ParamDbl     0     1     Inf
59:                       xgboost.top_k ParamInt     0   Inf     Inf
60:                    xgboost.training ParamLgl    NA    NA       2
61:                 xgboost.tree_method ParamFct    NA    NA       5
62:      xgboost.tweedie_variance_power ParamDbl     1     2     Inf
63:                     xgboost.updater ParamUty    NA    NA     Inf
64:                     xgboost.use_rmm ParamLgl    NA    NA       2
65:           xgboost.validate_features ParamLgl    NA    NA       2
66:                     xgboost.verbose ParamInt     0     2       3
67:                   xgboost.verbosity ParamInt     0     2       3
68:                   xgboost.xgb_model ParamUty    NA    NA     Inf
                                     id    class lower upper nlevels
                                 <char>   <char> <num> <num>   <num>

We will tune the encode method.

graph_learner$param_set$values$encode.method = to_tune(c("one-hot", "treatment"))

We define a tuning instance and use grid search since we want to try all encode methods.

instance = tune(
  tuner = tnr("grid_search"),
  task = task,
  learner = graph_learner,
  resampling = rsmp("cv", folds = 3),
  measure = msr("classif.ce")
)

The archive shows us the performance of the model with different encoding methods.

print(instance$archive)

── <ArchiveBatchTuning> with 2 evaluations ─────────────────────────────────────────────────────────────────────────────
   encode.method classif.ce warnings errors batch_nr
          <char>      <num>    <int>  <int>    <int>
1:       one-hot       0.26        0      0        1
2:     treatment       0.26        0      0        2
   encode.method classif.ce x_domain_encode.method warnings errors batch_nr
          <char>      <num>                 <char>    <int>  <int>    <int>
1:       one-hot       0.26                one-hot        0      0        1
2:     treatment       0.26              treatment        0      0        2

Nested Resampling

We create one GraphLearner with imputeoor and test it against a GraphLearner that uses the internal imputation method of xgboost. Applying nested resampling ensures a fair comparison of the predictive performances.

graph_1 = po("encode") %>>%
  learner
graph_learner_1 = GraphLearner$new(graph_1)

graph_learner_1$param_set$values$encode.method = to_tune(c("one-hot", "treatment"))

at_1 = auto_tuner(
  learner = graph_learner_1,
  resampling = resampling,
  measure = msr("classif.ce"),
  terminator = trm("none"),
  tuner = tnr("grid_search"),
  store_models = TRUE
)
graph_2 = po("encode") %>>%
  po("imputeoor") %>>%
  learner
graph_learner_2 = GraphLearner$new(graph_2)

graph_learner_2$param_set$values$encode.method = to_tune(c("one-hot", "treatment"))

at_2 = auto_tuner(
  learner = graph_learner_2,
  resampling = resampling,
  measure = msr("classif.ce"),
  terminator = trm("none"),
  tuner = tnr("grid_search"),
  store_models = TRUE
)

We run the benchmark.

resampling_outer = rsmp("cv", folds = 3)
design = benchmark_grid(task, list(at_1, at_2), resampling_outer)

bmr = benchmark(design, store_models = TRUE)

We compare the aggregated performances on the outer test sets which give us an unbiased performance estimate of the GraphLearners with the different encoding methods.

bmr$aggregate()
      nr task_id                     learner_id resampling_id iters classif.ce
   <int>  <char>                         <char>        <char> <int>      <num>
1:     1    pima           encode.xgboost.tuned            cv     3  0.2916667
2:     2    pima encode.imputeoor.xgboost.tuned            cv     3  0.2864583
Hidden columns: resample_result
autoplot(bmr)

Note that in practice, it is required to tune preprocessing hyperparameters jointly with the hyperparameters of the learner. Otherwise, comparing preprocessing steps is not feasible and can lead to wrong conclusions.

Applying nested resampling can be shortened by using the auto_tuner()-shortcut.

graph_1 = po("encode") %>>% learner
graph_learner_1 = as_learner(graph_1)
graph_learner_1$param_set$values$encode.method = to_tune(c("one-hot", "treatment"))

at_1 = auto_tuner(
  method = "grid_search",
  learner = graph_learner_1,
  resampling = resampling,
  measure = msr("classif.ce"),
  store_models = TRUE)

graph_2 = po("encode") %>>% po("imputeoor") %>>% learner
graph_learner_2 = as_learner(graph_2)
graph_learner_2$param_set$values$encode.method = to_tune(c("one-hot", "treatment"))

at_2 = auto_tuner(
  method = "grid_search",
  learner = graph_learner_2,
  resampling = resampling,
  measure = msr("classif.ce"),
  store_models = TRUE)

design = benchmark_grid(task, list(at_1, at_2), rsmp("cv", folds = 3))

bmr = benchmark(design, store_models = TRUE)

Final Model

We train the chosen GraphLearner with the AutoTuner to get a final model with optimized hyperparameters.

at_2$train(task)

The trained model can now be used to make predictions on new data at_2$predict(). The pipeline ensures that the preprocessing is always a part of the train and predict step.

Resources

The mlr3book includes chapters on pipelines and hyperparameter tuning. The mlr3cheatsheets contain frequently used commands and workflows of mlr3.

Session Information

sessioninfo::session_info(info = "packages")
═ Session info ═══════════════════════════════════════════════════════════════════════════════════════════════════════
─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────
 package           * version    date (UTC) lib source
 backports           1.5.0      2024-05-23 [1] RSPM
 base64enc           0.1-6      2026-02-02 [1] RSPM
 bbotk               1.8.1      2025-11-26 [1] RSPM
 checkmate           2.3.4      2026-02-03 [1] RSPM
 class               7.3-23     2025-01-01 [2] CRAN (R 4.5.2)
 cli                 3.6.5      2025-04-23 [1] RSPM
 cluster             2.1.8.1    2025-03-12 [2] CRAN (R 4.5.2)
 codetools           0.2-20     2024-03-31 [2] CRAN (R 4.5.2)
 crayon              1.5.3      2024-06-20 [1] RSPM
 data.table        * 1.18.2.1   2026-01-27 [1] RSPM
 DEoptimR            1.1-4      2025-07-27 [1] RSPM
 digest              0.6.39     2025-11-19 [1] RSPM
 diptest             0.77-2     2025-08-20 [1] RSPM
 dplyr               1.2.0      2026-02-03 [1] RSPM
 evaluate            1.0.5      2025-08-27 [1] RSPM
 farver              2.1.2      2024-05-13 [1] RSPM
 fastmap             1.2.0      2024-05-15 [1] RSPM
 flexmix             2.3-20     2025-02-28 [1] RSPM
 fpc                 2.2-14     2026-01-14 [1] RSPM
 future            * 1.69.0     2026-01-16 [1] RSPM
 future.apply        1.20.2     2026-02-20 [1] RSPM
 generics            0.1.4      2025-05-09 [1] RSPM
 ggplot2             4.0.2      2026-02-03 [1] RSPM
 globals             0.19.0     2026-02-02 [1] RSPM
 glue                1.8.0      2024-09-30 [1] RSPM
 gtable              0.3.6      2024-10-25 [1] RSPM
 htmltools           0.5.9      2025-12-04 [1] RSPM
 htmlwidgets         1.6.4      2023-12-06 [1] RSPM
 igraph              2.2.2      2026-02-12 [1] RSPM
 jsonlite            2.0.0      2025-03-27 [1] RSPM
 kernlab             0.9-33     2024-08-13 [1] RSPM
 knitr               1.51       2025-12-20 [1] RSPM
 labeling            0.4.3      2023-08-29 [1] RSPM
 lattice             0.22-7     2025-04-02 [2] CRAN (R 4.5.2)
 lgr                 0.5.2      2026-01-30 [1] RSPM
 lifecycle           1.0.5      2026-01-08 [1] RSPM
 listenv             0.10.0     2025-11-02 [1] RSPM
 magrittr            2.0.4      2025-09-12 [1] RSPM
 MASS                7.3-65     2025-02-28 [2] CRAN (R 4.5.2)
 Matrix              1.7-4      2025-08-28 [2] CRAN (R 4.5.2)
 mclust              6.1.2      2025-10-31 [1] RSPM
 mlr3              * 1.4.0      2026-02-19 [1] RSPM
 mlr3cluster         0.2.0      2026-02-04 [1] RSPM
 mlr3cmprsk          0.0.1      2026-02-27 [1] Github (mlr-org/mlr3cmprsk@5a04c29)
 mlr3data            0.9.0      2024-11-08 [1] RSPM
 mlr3extralearners   1.4.0      2026-01-26 [1] https://m~
 mlr3filters         0.9.0      2025-09-12 [1] RSPM
 mlr3fselect       * 1.5.0      2025-11-27 [1] RSPM
 mlr3hyperband       1.0.0      2025-07-10 [1] RSPM
 mlr3inferr          0.2.1      2025-11-26 [1] RSPM
 mlr3learners        0.14.0     2025-12-13 [1] RSPM
 mlr3mbo             0.3.3      2025-10-10 [1] RSPM
 mlr3measures        1.2.0      2025-11-25 [1] RSPM
 mlr3misc            0.21.0     2026-02-26 [1] RSPM
 mlr3pipelines       0.10.0     2025-11-07 [1] RSPM
 mlr3tuning          1.5.1      2025-12-14 [1] RSPM
 mlr3tuningspaces    0.6.0      2025-05-16 [1] RSPM
 mlr3verse         * 0.3.1      2025-01-14 [1] RSPM
 mlr3viz             0.11.0     2026-02-22 [1] RSPM
 mlr3website       * 0.0.0.9000 2026-02-27 [1] Github (mlr-org/mlr3website@f6e32a7)
 modeltools          0.2-24     2025-05-02 [1] RSPM
 nnet                7.3-20     2025-01-01 [2] CRAN (R 4.5.2)
 otel                0.2.0      2025-08-29 [1] RSPM
 palmerpenguins      0.1.1      2022-08-15 [1] RSPM
 paradox             1.0.1      2024-07-09 [1] RSPM
 parallelly          1.46.1     2026-01-08 [1] RSPM
 pillar              1.11.1     2025-09-17 [1] RSPM
 pkgconfig           2.0.3      2019-09-22 [1] RSPM
 prabclus            2.3-5      2026-01-14 [1] RSPM
 purrr               1.2.1      2026-01-09 [1] RSPM
 R6                  2.6.1      2025-02-15 [1] RSPM
 RColorBrewer        1.1-3      2022-04-03 [1] RSPM
 Rcpp                1.1.1      2026-01-10 [1] RSPM
 repr                1.1.7      2024-03-22 [1] RSPM
 rlang               1.1.7      2026-01-09 [1] RSPM
 rmarkdown           2.30       2025-09-28 [1] RSPM
 robustbase          0.99-7     2026-02-05 [1] RSPM
 S7                  0.2.1      2025-11-14 [1] RSPM
 scales              1.4.0      2025-04-24 [1] RSPM
 sessioninfo         1.2.3      2025-02-05 [1] RSPM
 skimr               2.2.2      2026-01-10 [1] RSPM
 spacefillr          0.4.0      2025-02-24 [1] RSPM
 stringi             1.8.7      2025-03-27 [1] RSPM
 stringr             1.6.0      2025-11-04 [1] RSPM
 survdistr           0.0.1      2026-02-27 [1] Github (mlr-org/survdistr@d7babd1)
 survival            3.8-3      2024-12-17 [2] CRAN (R 4.5.2)
 tibble              3.3.1      2026-01-11 [1] RSPM
 tidyr               1.3.2      2025-12-19 [1] RSPM
 tidyselect          1.2.1      2024-03-11 [1] RSPM
 uuid                1.2-2      2026-01-23 [1] RSPM
 vctrs               0.7.1      2026-01-23 [1] RSPM
 viridisLite         0.4.3      2026-02-04 [1] RSPM
 withr               3.0.2      2024-10-28 [1] RSPM
 xfun                0.56       2026-01-18 [1] RSPM
 xgboost             3.2.0.1    2026-02-10 [1] RSPM
 yaml                2.3.12     2025-12-10 [1] RSPM

 [1] /usr/local/lib/R/site-library
 [2] /usr/local/lib/R/library
 * ── Packages attached to the search path.

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