Package: mlr3automl
Title: Automated Machine Learning for 'mlr3'
Version: 0.1.0
Authors@R: c(
    person("Marc", "Becker", , "marcbecker@posteo.de", role = c("cre", "aut", "cph"),
           comment = c(ORCID = "0000-0002-8115-0400")),
    person("Damir", "Pulatov", , "damirpolat@protonmail.com", role = "aut"),
    person("Baisu", "Zhou", , "baisu.zhou@outlook.com", role = "aut"),
    person("Lona", "Koers", , "lona.koers@gmail.com", role = "aut")
  )
Description: Flexible automated machine learning (AutoML) system for the
    'mlr3' ecosystem. Automatically selects a suitable machine learning
    algorithm and tunes its hyperparameters for a given task. Constructs
    preprocessing pipelines with multiple parallel branches using
    'mlr3pipelines' and jointly optimizes them together with the learners
    using 'mlr3tuning'. The optimization is driven by asynchronous
    decentralized Bayesian optimization by Egele et al. (2023)
    <doi:10.1109/e-Science58273.2023.10254839>.
License: LGPL-3
URL: https://mlr3automl.mlr-org.com,
        https://github.com/mlr-org/mlr3automl
BugReports: https://github.com/mlr-org/mlr3automl/issues
Depends: mlr3 (>= 1.6.0), mlr3tuning (>= 1.6.0), R (>= 3.3.0), rush (>=
        1.2.1)
Imports: bbotk (>= 1.7.1), checkmate, data.table, lhs, mlr3learners (>=
        0.14.0), mlr3mbo (>= 1.2.0), mlr3misc (>= 0.15.1),
        mlr3pipelines, paradox (>= 1.0.1), R6, utils
Suggests: callr, e1071, fastai, glmnet, kknn, lgr, lightgbm, MASS,
        mirai, mlr3extralearners, mlr3torch (>= 0.3.3), mlr3viz,
        ranger, redux, reticulate, rpart, testthat (>= 3.0.0), torch,
        xgboost (>= 3.2.1.1)
Additional_repositories: https://mlr-org.r-universe.dev
Config/roxygen2/version: 8.0.0.9000
Config/testthat/edition: 3
Config/testthat/parallel: false
Encoding: UTF-8
NeedsCompilation: no
Collate: 'mlr_auto.R' 'Auto.R' 'AutoCatboost.R' 'AutoExtraTrees.R'
        'AutoFTTransformer.R' 'AutoFastai.R' 'AutoGlmnet.R'
        'AutoKKNN.R' 'AutoLda.R' 'AutoLightGBM.R' 'AutoMLP.R'
        'AutoRanger.R' 'AutoResNet.R' 'AutoSVM.R' 'AutoTabPFN.R'
        'AutoXgboost.R' 'aaa.R' 'LearnerAuto.R' 'LearnerClassifAuto.R'
        'LearnerClassifAutoCatboost.R'
        'LearnerClassifAutoFTTransformer.R'
        'LearnerClassifAutoFastai.R' 'LearnerClassifAutoGlmnet.R'
        'LearnerClassifAutoKKNN.R' 'LearnerClassifAutoLightGBM.R'
        'LearnerClassifAutoMLP.R' 'LearnerClassifAutoRanger.R'
        'LearnerClassifAutoResNet.R' 'LearnerClassifAutoSVM.R'
        'LearnerClassifAutoTabPFN.R' 'LearnerClassifAutoXgboost.R'
        'LearnerRegrAuto.R' 'LearnerRegrAutoCatboost.R'
        'LearnerRegrAutoFTTransformer.R' 'LearnerRegrAutoGlmnet.R'
        'LearnerRegrAutoKKNN.R' 'LearnerRegrAutoLightGBM.R'
        'LearnerRegrAutoMLP.R' 'LearnerRegrAutoRanger.R'
        'LearnerRegrAutoResNet.R' 'LearnerRegrAutoSVM.R'
        'LearnerRegrAutoTabPFN.R' 'LearnerRegrAutoXgboost.R' 'helper.R'
        'install_python_learners.R' 'isolated_model.R'
        'mlr_callbacks.R' 'sugar.R' 'train_auto.R' 'zzz.R'
Packaged: 2026-08-20 09:05:18 UTC; marc
Author: Marc Becker [cre, aut, cph] (ORCID:
    <https://orcid.org/0000-0002-8115-0400>),
  Damir Pulatov [aut],
  Baisu Zhou [aut],
  Lona Koers [aut]
Maintainer: Marc Becker <marcbecker@posteo.de>
Repository: CRAN
Date/Publication: 2026-09-03 12:20:03 UTC
Built: R 4.5.3; ; 2026-09-06 23:53:12 UTC; windows
