Package: mlr3
Title: Machine Learning in R - Next Generation
Version: 0.13.3
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: 7.1.2
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'
        '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: 2022-03-01 12:59:20 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: 2022-03-01 15:40:02 UTC
Built: R 4.0.5; ; 2022-04-18 00:18:46 UTC; windows
