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mlr3learners: Recommended Learners for 'mlr3'

Recommended Learners for 'mlr3'. Extends 'mlr3' with interfaces to essential machine learning packages on CRAN. This includes, but is not limited to: (penalized) linear and logistic regression, linear and quadratic discriminant analysis, k-nearest neighbors, naive Bayes, support vector machines, and gradient boosting.

Version: 0.9.0
Depends: mlr3 (≥ 0.21.1), R (≥ 3.1.0)
Imports: checkmate, data.table, mlr3misc (≥ 0.9.4), paradox (≥ 1.0.0), R6
Suggests: DiceKriging, e1071, glmnet, kknn, knitr, lgr, MASS, nnet, pracma, ranger, rgenoud, rmarkdown, testthat (≥ 3.0.0), xgboost (≥ 1.6.0)
Published: 2024-11-23
DOI: 10.32614/CRAN.package.mlr3learners
Author: Michel Lang ORCID iD [aut], Quay Au ORCID iD [aut], Stefan Coors ORCID iD [aut], Patrick Schratz ORCID iD [aut], Marc Becker ORCID iD [cre, aut]
Maintainer: Marc Becker <marcbecker at posteo.de>
BugReports: https://github.com/mlr-org/mlr3learners/issues
License: LGPL-3
URL: https://mlr3learners.mlr-org.com, https://github.com/mlr-org/mlr3learners
NeedsCompilation: no
Materials: README NEWS
CRAN checks: mlr3learners results

Documentation:

Reference manual: mlr3learners.pdf

Downloads:

Package source: mlr3learners_0.9.0.tar.gz
Windows binaries: r-devel: mlr3learners_0.9.0.zip, r-release: mlr3learners_0.9.0.zip, r-oldrel: mlr3learners_0.9.0.zip
macOS binaries: r-release (arm64): mlr3learners_0.9.0.tgz, r-oldrel (arm64): mlr3learners_0.9.0.tgz, r-release (x86_64): mlr3learners_0.9.0.tgz, r-oldrel (x86_64): mlr3learners_0.9.0.tgz
Old sources: mlr3learners archive

Reverse dependencies:

Reverse depends: GenericML, mlr3superlearner
Reverse imports: DoubleML, highMLR, mlr3fairness, mlr3shiny, mlr3verse, sense, SIAMCAT, spFSR
Reverse suggests: counterfactuals, cpi, explainer, MantaID, mcboost, miesmuschel, mlr3benchmark, mlr3filters, mlr3fselect, mlr3hyperband, mlr3mbo, mlr3pipelines, mlr3spatial, mlr3summary, mlr3tuning, mlr3tuningspaces, mlr3viz, mlrintermbo, paradox, vetiver, vivid

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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