mlr3fda: Extending 'mlr3' to Functional Data Analysis
Extends the 'mlr3' ecosystem to functional analysis by adding
support for irregular and regular functional data as defined in the
'tf' package. The package provides 'PipeOps' for preprocessing
functional columns and for extracting scalar features, thereby
allowing standard machine learning algorithms to be applied
afterwards. Available operations include simple functional features
such as the mean or maximum, smoothing, interpolation, flattening, and
functional 'PCA'.
| Version: |
0.3.0 |
| Depends: |
mlr3 (≥ 0.14.0), mlr3pipelines (≥ 0.5.2), R (≥ 4.1.0) |
| Imports: |
checkmate, data.table, lgr, mlr3misc (≥ 0.14.0), paradox, R6, tf (≥ 0.3.4) |
| Suggests: |
FDboost, lme4, mboost, rpart, testthat (≥ 3.2.0), tsfeatures, wavelets, withr |
| Published: |
2025-10-15 |
| DOI: |
10.32614/CRAN.package.mlr3fda |
| Author: |
Sebastian Fischer 
[aut, cre],
Maximilian Mücke
[aut],
Fabian Scheipl
[ctb],
Bernd Bischl
[ctb] |
| Maintainer: |
Sebastian Fischer <sebf.fischer at gmail.com> |
| BugReports: |
https://github.com/mlr-org/mlr3fda/issues |
| License: |
LGPL-3 |
| URL: |
https://mlr3fda.mlr-org.com, https://github.com/mlr-org/mlr3fda |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| In views: |
FunctionalData |
| CRAN checks: |
mlr3fda results |
Documentation:
Downloads:
Reverse dependencies:
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