DESCRIPTION

Package: mlr3
Title: Machine Learning in R - Next Generation
Version: 0.8.0-9000
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 (>= 1.12.8),
    digest,
    future.apply (>= 1.5.0),
    lgr (>= 0.3.4),
    mlbench,
    mlr3measures (>= 0.3.0),
    mlr3misc (>= 0.5.0),
    paradox (>= 0.4.0),
    uuid
Suggests:
    Matrix,
    callr,
    datasets,
    distr6,
    evaluate,
    future,
    future.callr,
    mlr3data,
    progressr,
    rpart,
    testthat
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Roxygen: list(markdown = TRUE, r6 = TRUE)
RoxygenNote: 7.1.1
Collate:
    'mlr_reflections.R'
    'BenchmarkResult.R'
    'DataBackend.R'
    'DataBackendCbind.R'
    'DataBackendDataTable.R'
    'DataBackendMatrix.R'
    'DataBackendRbind.R'
    'DataBackendRename.R'
    'Learner.R'
    'LearnerClassif.R'
    'mlr_learners.R'
    'LearnerClassifDebug.R'
    'LearnerClassifFeatureless.R'
    'LearnerClassifRpart.R'
    'LearnerRegr.R'
    'LearnerRegrFeatureless.R'
    'LearnerRegrRpart.R'
    'Measure.R'
    'MeasureClassif.R'
    'mlr_measures.R'
    'MeasureClassifCosts.R'
    'MeasureDebug.R'
    'MeasureElapsedTime.R'
    'MeasureOOBError.R'
    'MeasureRegr.R'
    'MeasureSelectedFeatures.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'
    '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_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'
    'Task_cbind.R'
    'Task_rbind.R'
    'as_data_backend.R'
    'assertions.R'
    'auto_convert.R'
    'benchmark.R'
    'benchmark_grid.R'
    'bibentries.R'
    'default_measures.R'
    'fix_factor_levels.R'
    'helper.R'
    'mlr_coercions.R'
    'mlr_sugar.R'
    'predict.R'
    'reexports.R'
    'resample.R'
    'task_converters.R'
    'worker.R'
    'zzz.R'