mlr3: Machine Learning in R - Next Generation

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.

Version: 0.1.4
Depends: R (≥ 3.1.0)
Imports: backports, checkmate (≥ 1.9.3), data.table, digest, lgr (≥ 0.3.0), Metrics, mlbench, mlr3misc (≥ 0.1.5), paradox, uuid, R6
Suggests: bibtex, callr, datasets, evaluate, future (≥ 1.9.0), future.apply (≥ 1.1.0), future.callr, Matrix, rpart, testthat, titanic
Published: 2019-10-28
Author: Michel Lang ORCID iD [cre, aut], Bernd Bischl ORCID iD [aut], Jakob Richter ORCID iD [aut], Patrick Schratz ORCID iD [aut], Giuseppe Casalicchio ORCID iD [ctb], Stefan Coors ORCID iD [ctb], Quay Au ORCID iD [ctb], Martin Binder [aut]
Maintainer: Michel Lang <michellang at>
License: LGPL-3
NeedsCompilation: no
Materials: README NEWS
In views: MachineLearning
CRAN checks: mlr3 results


Reference manual: mlr3.pdf
Package source: mlr3_0.1.4.tar.gz
Windows binaries: r-devel:, r-devel-gcc8:, r-release:, r-oldrel:
OS X binaries: r-release: mlr3_0.1.4.tgz, r-oldrel: mlr3_0.1.4.tgz
Old sources: mlr3 archive

Reverse dependencies:

Reverse imports: mlr3db, mlr3filters, mlr3learners, mlr3pipelines, mlr3tuning
Reverse suggests: DALEXtra


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