[aut],
Bernd Bischl
[aut],
Michel Lang [aut],
Susanne Dandl [aut]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
[aut],
Bernd Bischl
[aut],
Michel Lang [aut],
Susanne Dandl [aut] |
| Maintainer: | Martin Binder <mlr.developer at mb706.com> |
| BugReports: | https://github.com/mlr-org/mlr3pipelines/issues |
| License: | LGPL-3 |
| URL: | https://mlr3pipelines.mlr-org.com, https://github.com/mlr-org/mlr3pipelines |
| 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: mlr3pipelines_0.1.1.zip, r-devel-gcc8: mlr3pipelines_0.1.1.zip, r-release: mlr3pipelines_0.1.1.zip, r-oldrel: mlr3pipelines_0.1.1.zip |
| 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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