DESCRIPTION

Package: mlr3pipelines
Title: Preprocessing Operators and Pipelines for 'mlr3'
Version: 0.7.1
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 (>= 3.1.0)
Imports:
    backports,
    checkmate,
    data.table,
    digest,
    lgr,
    mlr3 (>= 0.20.0),
    mlr3misc (>= 0.9.0),
    paradox,
    R6,
    withr
Suggests:
    ggplot2,
    glmnet,
    igraph,
    knitr,
    lme4,
    mlbench,
    bbotk (>= 0.3.0),
    mlr3filters (>= 0.1.1),
    mlr3learners,
    mlr3measures,
    nloptr,
    quanteda,
    rmarkdown,
    rpart,
    stopwords,
    testthat,
    visNetwork,
    bestNormalize,
    fastICA,
    kernlab,
    smotefamily,
    evaluate,
    NMF,
    MASS,
    kknn,
    GenSA,
    methods,
    vtreat,
    future,
    htmlwidgets,
    ranger,
    themis
ByteCompile: true
Encoding: UTF-8
Config/testthat/edition: 3
Config/testthat/parallel: true
NeedsCompilation: no
Roxygen: list(markdown = TRUE, r6 = FALSE)
RoxygenNote: 7.3.2
VignetteBuilder: knitr
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'
    'PipeOpEncode.R'
    'PipeOpEncodeImpact.R'
    'PipeOpEncodeLmer.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'
    'reexports.R'
    'typecheck.R'
    'zzz.R'