Package: mlr3pipelines
Title: Preprocessing Operators and Pipelines for 'mlr3'
Version: 0.9.0
Authors@R: 
    c(person(given = "Martin",
             family = "Binder",
             role = c("aut", "cre"),
             email = "mlr.developer@mb706.com"),
      person(given = "Florian",
             family = "Pfisterer",
             role = "aut",
             email = "pfistererf@googlemail.com",
             comment = c(ORCID = "0000-0001-8867-762X")),
      person(given = "Lennart",
             family = "Schneider",
             role = "aut",
             email = "lennart.sch@web.de",
             comment = c(ORCID = "0000-0003-4152-5308")),
      person(given = "Bernd",
             family = "Bischl",
             role = "aut",
             email = "bernd_bischl@gmx.net",
             comment = c(ORCID = "0000-0001-6002-6980")),
      person(given = "Michel",
             family = "Lang",
             role = "aut",
             email = "michellang@gmail.com",
             comment = c(ORCID = "0000-0001-9754-0393")),
      person(given = "Sebastian",
             family = "Fischer",
             role = "aut",
             email = "sebf.fischer@gmail.com",
             comment = c(ORCID = "0000-0002-9609-3197")),
      person(given = "Susanne",
             family = "Dandl",
             role = "aut",
             email = "dandl.susanne@googlemail.com"),
      person(given = "Keno",
             family = "Mersmann",
             role = "ctb",
             email = "keno.mersmann@gmail.com"),
      person(given = "Maximilian",
             family = "Mücke",
             role = "ctb",
             email = "muecke.maximilian@gmail.com",
             comment = c(ORCID = "0009-0000-9432-9795")),
      person(given = "Lona",
             family = "Koers",
             role = "ctb",
             email = "lona.koers@gmail.com"))
Description: Dataflow programming toolkit that enriches 'mlr3' with a diverse
  set of pipelining operators ('PipeOps') that can be composed into graphs.
  Operations exist for data preprocessing, model fitting, and ensemble
  learning. Graphs can themselves be treated as 'mlr3' 'Learners' and can
  therefore be resampled, benchmarked, and tuned.
License: LGPL-3
URL: https://mlr3pipelines.mlr-org.com,
        https://github.com/mlr-org/mlr3pipelines
BugReports: https://github.com/mlr-org/mlr3pipelines/issues
Depends: R (>= 3.3.0)
Imports: backports, checkmate, data.table, digest, lgr, mlr3 (>=
        0.20.0), mlr3misc (>= 0.17.0), paradox (>= 1.0.0), R6, withr
Suggests: ggplot2, glmnet, igraph, knitr, lme4, mlbench, bbotk (>=
        0.3.0), mlr3filters (>= 0.8.1), mlr3learners, mlr3measures,
        nloptr, quanteda, rmarkdown, rpart, stopwords, testthat,
        visNetwork, bestNormalize, fastICA, kernlab, smotefamily,
        evaluate, NMF, MASS, GenSA, methods, vtreat, future,
        htmlwidgets, ranger, themis
ByteCompile: true
Encoding: UTF-8
Config/testthat/edition: 3
Config/testthat/parallel: true
NeedsCompilation: no
RoxygenNote: 7.3.2
VignetteBuilder: knitr, rmarkdown
Collate: 'CnfAtom.R' 'CnfClause.R' 'CnfFormula.R'
        'CnfFormula_simplify.R' 'CnfSymbol.R' 'CnfUniverse.R' 'Graph.R'
        'GraphLearner.R' 'mlr_pipeops.R' 'multiplicity.R' 'utils.R'
        'PipeOp.R' 'PipeOpEnsemble.R' 'LearnerAvg.R' 'NO_OP.R'
        'PipeOpTaskPreproc.R' 'PipeOpADAS.R' 'PipeOpBLSmote.R'
        'PipeOpBoxCox.R' 'PipeOpBranch.R' 'PipeOpChunk.R'
        'PipeOpClassBalancing.R' 'PipeOpClassWeights.R'
        'PipeOpClassifAvg.R' 'PipeOpColApply.R' 'PipeOpColRoles.R'
        'PipeOpCollapseFactors.R' 'PipeOpCopy.R' 'PipeOpDateFeatures.R'
        'PipeOpDecode.R' 'PipeOpEncode.R' 'PipeOpEncodeImpact.R'
        'PipeOpEncodeLmer.R' 'PipeOpEncodePL.R' 'PipeOpFeatureUnion.R'
        'PipeOpFilter.R' 'PipeOpFixFactors.R' 'PipeOpHistBin.R'
        'PipeOpICA.R' 'PipeOpImpute.R' 'PipeOpImputeConstant.R'
        'PipeOpImputeHist.R' 'PipeOpImputeLearner.R'
        'PipeOpImputeMean.R' 'PipeOpImputeMedian.R'
        'PipeOpImputeMode.R' 'PipeOpImputeOOR.R' 'PipeOpImputeSample.R'
        'PipeOpKernelPCA.R' 'PipeOpLearner.R' 'PipeOpLearnerCV.R'
        'PipeOpLearnerPICVPlus.R' 'PipeOpLearnerQuantiles.R'
        'PipeOpMissingIndicators.R' 'PipeOpModelMatrix.R'
        'PipeOpMultiplicity.R' 'PipeOpMutate.R' 'PipeOpNMF.R'
        'PipeOpNOP.R' 'PipeOpNearmiss.R' 'PipeOpOVR.R' 'PipeOpPCA.R'
        'PipeOpProxy.R' 'PipeOpQuantileBin.R'
        'PipeOpRandomProjection.R' 'PipeOpRandomResponse.R'
        'PipeOpRegrAvg.R' 'PipeOpRemoveConstants.R'
        'PipeOpRenameColumns.R' 'PipeOpRowApply.R' 'PipeOpScale.R'
        'PipeOpScaleMaxAbs.R' 'PipeOpScaleRange.R' 'PipeOpSelect.R'
        'PipeOpSmote.R' 'PipeOpSmoteNC.R' 'PipeOpSpatialSign.R'
        'PipeOpSubsample.R' 'PipeOpTextVectorizer.R'
        'PipeOpThreshold.R' 'PipeOpTomek.R' 'PipeOpTrafo.R'
        'PipeOpTuneThreshold.R' 'PipeOpUnbranch.R' 'PipeOpVtreat.R'
        'PipeOpYeoJohnson.R' 'Selector.R' 'TaskRegr_boston_housing.R'
        'assert_graph.R' 'bibentries.R' 'greplicate.R' 'gunion.R'
        'mlr_graphs.R' 'operators.R' 'pipeline_bagging.R'
        'pipeline_branch.R' 'pipeline_convert_types.R'
        'pipeline_greplicate.R' 'pipeline_ovr.R' 'pipeline_robustify.R'
        'pipeline_stacking.R' 'pipeline_targettrafo.R' 'po.R' 'ppl.R'
        'preproc.R' 'reexports.R' 'typecheck.R' 'zzz.R'
Packaged: 2025-07-31 14:15:06 UTC; user
Author: Martin Binder [aut, cre],
  Florian Pfisterer [aut] (ORCID:
    <https://orcid.org/0000-0001-8867-762X>),
  Lennart Schneider [aut] (ORCID:
    <https://orcid.org/0000-0003-4152-5308>),
  Bernd Bischl [aut] (ORCID: <https://orcid.org/0000-0001-6002-6980>),
  Michel Lang [aut] (ORCID: <https://orcid.org/0000-0001-9754-0393>),
  Sebastian Fischer [aut] (ORCID:
    <https://orcid.org/0000-0002-9609-3197>),
  Susanne Dandl [aut],
  Keno Mersmann [ctb],
  Maximilian Mücke [ctb] (ORCID: <https://orcid.org/0009-0000-9432-9795>),
  Lona Koers [ctb]
Maintainer: Martin Binder <mlr.developer@mb706.com>
Repository: CRAN
Date/Publication: 2025-07-31 23:20:11 UTC
Built: R 4.4.3; ; 2025-11-01 04:06:44 UTC; windows
