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DESCRIPTION
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DESCRIPTION
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Package: mlr3pipelines
Title: Preprocessing Operators and Pipelines for 'mlr3'
Version: 0.1.3.9000
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 = "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 = "Susanne",
family = "Dandl",
role = "aut",
email = "dandl.susanne@googlemail.com"),
person(given = "Lennart",
family = "Schneider",
role = "aut",
email = "lennart.sch@web.de",
comment = c(ORCID = "0000-0003-4152-5308")))
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 (>= 0.1.4),
mlr3misc (>= 0.1.4),
paradox,
R6,
withr
Suggests:
bibtex,
ggplot2,
glmnet,
igraph,
knitr,
lme4,
mlbench,
mlr3filters (>= 0.1.1),
mlr3learners,
mlr3measures,
nloptr,
quanteda,
rmarkdown,
rpart,
stopwords,
testthat,
visNetwork,
bestNormalize,
fastICA,
kernlab,
smotefamily,
evaluate,
kknn,
methods
VignetteBuilder:
knitr
RdMacros:
mlr3misc
ByteCompile: true
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Roxygen: list(markdown = TRUE, r6 = FALSE)
RoxygenNote: 7.1.1.9000
Collate:
'Graph.R'
'GraphLearner.R'
'mlr_pipeops.R'
'multiplicity.R'
'utils.R'
'PipeOp.R'
'PipeOpEnsemble.R'
'LearnerAvg.R'
'NO_OP.R'
'PipeOpTaskPreproc.R'
'PipeOpBoxCox.R'
'PipeOpBranch.R'
'PipeOpChunk.R'
'PipeOpClassBalancing.R'
'PipeOpClassWeights.R'
'PipeOpClassifAvg.R'
'PipeOpColApply.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'
'PipeOpMissingIndicators.R'
'PipeOpModelMatrix.R'
'PipeOpMultiplicity.R'
'PipeOpMutate.R'
'PipeOpNOP.R'
'PipeOpOVR.R'
'PipeOpPCA.R'
'PipeOpProxy.R'
'PipeOpQuantileBin.R'
'PipeOpRandomResponse.R'
'PipeOpRegrAvg.R'
'PipeOpRemoveConstants.R'
'PipeOpRenameColumns.R'
'PipeOpScale.R'
'PipeOpScaleMaxAbs.R'
'PipeOpScaleRange.R'
'PipeOpSelect.R'
'PipeOpSmote.R'
'PipeOpSpatialSign.R'
'PipeOpSubsample.R'
'PipeOpTextVectorizer.R'
'PipeOpThreshold.R'
'PipeOpTrafo.R'
'PipeOpUnbranch.R'
'PipeOpYeoJohnson.R'
'Selector.R'
'assert_graph.R'
'convert_task.R'
'greplicate.R'
'gunion.R'
'mlr_graphs.R'
'mlr_graphs_elements.R'
'operators.R'
'pipe.R'
'po.R'
'reexports.R'
'typecheck.R'
'zzz.R'