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: |
0.4.1 |
| Depends: |
R (≥ 3.1.0) |
| Imports: |
backports, checkmate, data.table, digest, lgr, mlr3 (≥
0.6.0), 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 |
| Published: |
2022-05-15 |
| Author: |
Martin Binder [aut, cre],
Florian Pfisterer 
[aut],
Lennart Schneider
[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 |
| Citation: |
mlr3pipelines citation info |
| Materials: |
README NEWS |
| CRAN checks: |
mlr3pipelines results |