Package: mlr3
Title: Machine Learning in R - Next Generation
Version: 0.20.1
Authors@R:
  c(
    person("Michel", "Lang", , "michellang@gmail.com", role = "aut",
           comment = c(ORCID = "0000-0001-9754-0393")),
    person("Bernd", "Bischl", , "bernd_bischl@gmx.net", role = "aut",
           comment = c(ORCID = "0000-0001-6002-6980")),
    person("Jakob", "Richter", , "jakob1richter@gmail.com", role = "aut",
           comment = c(ORCID = "0000-0003-4481-5554")),
    person("Patrick", "Schratz", , "patrick.schratz@gmail.com", role = "aut",
           comment = c(ORCID = "0000-0003-0748-6624")),
    person("Giuseppe", "Casalicchio", , "giuseppe.casalicchio@stat.uni-muenchen.de", role = "ctb",
           comment = c(ORCID = "0000-0001-5324-5966")),
    person("Stefan", "Coors", , "mail@stefancoors.de", role = "ctb",
           comment = c(ORCID = "0000-0002-7465-2146")),
    person("Quay", "Au", , "quayau@gmail.com", role = "ctb",
           comment = c(ORCID = "0000-0002-5252-8902")),
    person("Martin", "Binder", , "mlr.developer@mb706.com", role = "aut"),
    person("Florian", "Pfisterer", , "pfistererf@googlemail.com", role = "aut",
           comment = c(ORCID = "0000-0001-8867-762X")),
    person("Raphael", "Sonabend", , "raphaelsonabend@gmail.com", role = "aut",
           comment = c(ORCID = "0000-0001-9225-4654")),
    person("Lennart", "Schneider", , "lennart.sch@web.de", role = "ctb",
           comment = c(ORCID = "0000-0003-4152-5308")),
    person("Marc", "Becker", , "marcbecker@posteo.de", role = c("cre", "aut"),
           comment = c(ORCID = "0000-0002-8115-0400")),
    person("Sebastian", "Fischer", , "sebf.fischer@gmail.com", role = "aut",
           comment = c(ORCID = "0000-0002-9609-3197"))
  )
Description: Efficient, object-oriented programming on the
    building blocks of machine learning. Provides 'R6' objects for tasks,
    learners, resamplings, and measures. The package is geared towards
    scalability and larger datasets by supporting parallelization and
    out-of-memory data-backends like databases. While 'mlr3' focuses on
    the core computational operations, add-on packages provide additional
    functionality.
License: LGPL-3
URL: https://mlr3.mlr-org.com, https://github.com/mlr-org/mlr3
BugReports: https://github.com/mlr-org/mlr3/issues
Depends:
    R (>= 3.1.0)
Imports:
    R6 (>= 2.4.1),
    backports,
    checkmate (>= 2.0.0),
    data.table (>= 1.15.0),
    evaluate,
    future,
    future.apply (>= 1.5.0),
    lgr (>= 0.3.4),
    mlbench,
    mlr3measures (>= 0.6.0),
    mlr3misc (>= 0.15.0),
    parallelly,
    palmerpenguins,
    paradox (>= 0.10.0),
    RhpcBLASctl,
    uuid
Suggests:
    Matrix,
    callr,
    codetools,
    datasets,
    future.callr,
    mlr3data,
    progressr,
    remotes,
    rpart,
    testthat (>= 3.1.0)
Encoding: UTF-8
Config/testthat/edition: 3
Config/testthat/parallel: false
NeedsCompilation: no
Roxygen: list(markdown = TRUE, r6 = TRUE)
RoxygenNote: 7.3.2
Collate:
    'mlr_reflections.R'
    'BenchmarkResult.R'
    'DataBackend.R'
    'DataBackendCbind.R'
    'DataBackendDataTable.R'
    'DataBackendMatrix.R'
    'DataBackendRbind.R'
    'DataBackendRename.R'
    'HotstartStack.R'
    'Learner.R'
    'LearnerClassif.R'
    'mlr_learners.R'
    'LearnerClassifDebug.R'
    'LearnerClassifFeatureless.R'
    'LearnerClassifRpart.R'
    'LearnerRegr.R'
    'LearnerRegrDebug.R'
    'LearnerRegrFeatureless.R'
    'LearnerRegrRpart.R'
    'Measure.R'
    'mlr_measures.R'
    'MeasureAIC.R'
    'MeasureBIC.R'
    'MeasureClassif.R'
    'MeasureClassifCosts.R'
    'MeasureDebug.R'
    'MeasureElapsedTime.R'
    'MeasureInternalValidScore.R'
    'MeasureOOBError.R'
    'MeasureRegr.R'
    'MeasureSelectedFeatures.R'
    'MeasureSimilarity.R'
    'MeasureSimple.R'
    'Prediction.R'
    'PredictionClassif.R'
    'PredictionData.R'
    'PredictionDataClassif.R'
    'PredictionDataRegr.R'
    'PredictionRegr.R'
    'ResampleResult.R'
    'Resampling.R'
    'mlr_resamplings.R'
    'ResamplingBootstrap.R'
    'ResamplingCV.R'
    'ResamplingCustom.R'
    'ResamplingCustomCV.R'
    'ResamplingHoldout.R'
    'ResamplingInsample.R'
    'ResamplingLOO.R'
    'ResamplingRepeatedCV.R'
    'ResamplingSubsampling.R'
    'ResultData.R'
    'Task.R'
    'TaskSupervised.R'
    'TaskClassif.R'
    'mlr_tasks.R'
    'TaskClassif_breast_cancer.R'
    'TaskClassif_german_credit.R'
    'TaskClassif_iris.R'
    'TaskClassif_penguins.R'
    'TaskClassif_pima.R'
    'TaskClassif_sonar.R'
    'TaskClassif_spam.R'
    'TaskClassif_wine.R'
    'TaskClassif_zoo.R'
    'TaskGenerator.R'
    'mlr_task_generators.R'
    'TaskGenerator2DNormals.R'
    'TaskGeneratorCassini.R'
    'TaskGeneratorCircle.R'
    'TaskGeneratorFriedman1.R'
    'TaskGeneratorMoons.R'
    'TaskGeneratorSimplex.R'
    'TaskGeneratorSmiley.R'
    'TaskGeneratorSpirals.R'
    'TaskGeneratorXor.R'
    'TaskRegr.R'
    'TaskRegr_boston_housing.R'
    'TaskRegr_mtcars.R'
    'TaskUnsupervised.R'
    'as_benchmark_result.R'
    'as_data_backend.R'
    'as_learner.R'
    'as_measure.R'
    'as_prediction.R'
    'as_prediction_classif.R'
    'as_prediction_data.R'
    'as_prediction_regr.R'
    'as_resample_result.R'
    'as_resampling.R'
    'as_result_data.R'
    'as_task.R'
    'as_task_classif.R'
    'as_task_regr.R'
    'as_task_unsupervised.R'
    'assertions.R'
    'auto_convert.R'
    'benchmark.R'
    'benchmark_grid.R'
    'bibentries.R'
    'default_measures.R'
    'fix_factor_levels.R'
    'helper.R'
    'helper_data_table.R'
    'helper_exec.R'
    'helper_hashes.R'
    'helper_print.R'
    'install_pkgs.R'
    'marshal.R'
    'mlr_sugar.R'
    'mlr_test_helpers.R'
    'partition.R'
    'predict.R'
    'reexports.R'
    'resample.R'
    'set_threads.R'
    'set_validate.R'
    'task_converters.R'
    'worker.R'
    'zzz.R'
