mlr3pipelines: Preprocessing Operators and Pipelines for 'mlr3'

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.

Version: 0.1.1
Depends: R (≥ 3.1.0)
Imports: backports, checkmate, data.table, digest, mlr3 (≥ 0.1.4), mlr3misc (≥ 0.1.4), paradox, R6, withr
Suggests: ggplot2, glmnet, igraph, knitr, lgr, lme4, mlbench, mlr3filters, mlr3learners, nloptr, rmarkdown, rpart, testthat, visNetwork, bestNormalize, fastICA, kernlab, smotefamily
Published: 2019-10-29
Author: Martin Binder [aut, cre], Florian Pfisterer ORCID iD [aut], Bernd Bischl ORCID iD [aut], Michel Lang ORCID iD [aut], Susanne Dandl [aut]
Maintainer: Martin Binder <mlr.developer at>
License: LGPL-3
NeedsCompilation: no
Materials: README NEWS
CRAN checks: mlr3pipelines results


Reference manual: mlr3pipelines.pdf
Vignettes: Comparing mlr3pipelines to other frameworks
Introduction to mlr3pipelines
Package source: mlr3pipelines_0.1.1.tar.gz
Windows binaries: r-devel:, r-devel-gcc8:, r-release:, r-oldrel:
OS X binaries: r-release: mlr3pipelines_0.1.1.tgz, r-oldrel: mlr3pipelines_0.1.1.tgz
Old sources: mlr3pipelines archive


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