Package: mlr3

Title: Machine Learning in R - Next Generation

Version: 1.8.0.9000

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")),

person("Lona", "Koers", , "lona.koers@gmail.com", role = "ctb"),

person("John", "Zobolas", , "bblodfon@gmail.com", role = "ctb",

comment = c(ORCID = "0000-0002-3609-8674")),

person("Maximilian", "Mücke", , "muecke.maximilian@gmail.com", role = "ctb",

comment = c(ORCID = "0009-0000-9432-9795")),

person("Keno", "Mersmann", , "keno.mersmann@gmail.com", role = "ctb")

)

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 (>= 3.5.0)

Imports:

R6 (>= 2.4.1),

backports (>= 1.5.0),

checkmate (>= 2.0.0),

cli,

data.table (>= 1.15.0),

evaluate (>= 1.0.4),

future,

future.apply (>= 1.5.0),

lgr (>= 0.3.4),

mirai (>= 2.4.1),

methods,

mlbench (>= 2.1-11),

mlr3measures (>= 1.2.0),

mlr3misc (>= 0.21.0),

parallelly,

palmerpenguins,

paradox (>= 1.0.1),

uuid

Suggests:

callr,

codetools,

datasets,

future.callr,

mlr3data,

progressr,

remotes,

RhpcBLASctl,

rpart,

testthat (>= 3.3.0)

Encoding: UTF-8

Config/testthat/edition: 3

Config/testthat/parallel: false

NeedsCompilation: no

Roxygen: list(markdown = TRUE, r6 = TRUE)

Collate:

'mlr_reflections.R'

'BenchmarkResult.R'

'CallbackResample.R'

'ContextResample.R'

'warn_deprecated.R'

'DataBackend.R'

'DataBackendCbind.R'

'DataBackendDataTable.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'

'MeasureValidScore.R'

'MeasureBestValidScore.R'

'MeasureClassif.R'

'MeasureClassifCosts.R'

'MeasureDebug.R'

'MeasureElapsedTime.R'

'MeasureInternalValidScore.R'

'MeasureOOBError.R'

'MeasureRegr.R'

'MeasureRegrPinball.R'

'MeasureRegrRQR.R'

'MeasureRegrRSQ.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_diabetes.R'

'TaskClassif_german_credit.R'

'TaskClassif_iris.R'

'TaskClassif_penguins.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'

'TaskGeneratorPeak.R'

'TaskGeneratorSimplex.R'

'TaskGeneratorSmiley.R'

'TaskGeneratorSpirals.R'

'TaskGeneratorXor.R'

'TaskRegr.R'

'TaskRegr_california_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_fallback.R'

'default_measures.R'

'fix_factor_levels.R'

'helper.R'

'helper_data_table.R'

'helper_exec.R'

'helper_hashes.R'

'install_pkgs.R'

'marshal.R'

'mlr_callbacks.R'

'mlr_sugar.R'

'mlr_test_helpers.R'

'partition.R'

'predict.R'

'reexports.R'

'resample.R'

'score_roc_measures.R'

'set_threads.R'

'set_validate.R'

'task_converters.R'

'worker.R'

'zzz.R'

Config/roxygen2/version: 8.1.0