Package: mlr3
Title: Machine Learning in R - Next Generation
Version: 0.13.4
Authors@R: 
    c(person(given = "Michel",
             family = "Lang",
             role = c("cre", "aut"),
             email = "michellang@gmail.com",
             comment = c(ORCID = "0000-0001-9754-0393")),
      person(given = "Bernd",
             family = "Bischl",
             role = "aut",
             email = "bernd_bischl@gmx.net",
             comment = c(ORCID = "0000-0001-6002-6980")),
      person(given = "Jakob",
             family = "Richter",
             role = "aut",
             email = "jakob1richter@gmail.com",
             comment = c(ORCID = "0000-0003-4481-5554")),
      person(given = "Patrick",
             family = "Schratz",
             role = "aut",
             email = "patrick.schratz@gmail.com",
             comment = c(ORCID = "0000-0003-0748-6624")),
      person(given = "Giuseppe",
             family = "Casalicchio",
             role = "ctb",
             email = "giuseppe.casalicchio@stat.uni-muenchen.de",
             comment = c(ORCID = "0000-0001-5324-5966")),
      person(given = "Stefan",
             family = "Coors",
             role = "ctb",
             email = "mail@stefancoors.de",
             comment = c(ORCID = "0000-0002-7465-2146")),
      person(given = "Quay",
             family = "Au",
             role = "ctb",
             email = "quayau@gmail.com",
             comment = c(ORCID = "0000-0002-5252-8902")),
      person(given = "Martin",
             family = "Binder",
             role = "aut",
             email = "mlr.developer@mb706.com"),
      person(given = "Marc",
             family = "Becker",
             role = "ctb",
             email = "marcbecker@posteo.de",
             comment = c(ORCID = "0000-0002-8115-0400")))
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 (>=
        1.14.2), evaluate, future, future.apply (>= 1.5.0), lgr (>=
        0.3.4), mlbench, mlr3measures (>= 0.4.1), mlr3misc (>= 0.10.0),
        parallelly, palmerpenguins, paradox (>= 0.8.0), uuid
Suggests: Matrix, callr, codetools, datasets, distr6, future.callr,
        mlr3data, progressr, remotes, rpart, testthat (>= 3.1.0)
Encoding: UTF-8
Config/testthat/edition: 3
Config/testthat/parallel: false
NeedsCompilation: no
RoxygenNote: 7.2.1
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'
        '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' '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'
        'mlr_sugar.R' 'partition.R' 'predict.R' 'reexports.R'
        'resample.R' 'set_threads.R' 'task_converters.R' 'worker.R'
        'zzz.R'
Packaged: 2022-07-21 12:21:51 UTC; michel
Author: Michel Lang [cre, aut] (<https://orcid.org/0000-0001-9754-0393>),
  Bernd Bischl [aut] (<https://orcid.org/0000-0001-6002-6980>),
  Jakob Richter [aut] (<https://orcid.org/0000-0003-4481-5554>),
  Patrick Schratz [aut] (<https://orcid.org/0000-0003-0748-6624>),
  Giuseppe Casalicchio [ctb] (<https://orcid.org/0000-0001-5324-5966>),
  Stefan Coors [ctb] (<https://orcid.org/0000-0002-7465-2146>),
  Quay Au [ctb] (<https://orcid.org/0000-0002-5252-8902>),
  Martin Binder [aut],
  Marc Becker [ctb] (<https://orcid.org/0000-0002-8115-0400>)
Maintainer: Michel Lang <michellang@gmail.com>
Repository: CRAN
Date/Publication: 2022-07-21 13:30:02 UTC
