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: 0.1.2
Depends: mlr3 (≥ 0.14.0), R (≥ 3.1.0)
Imports: checkmate, data.table, lgr, mlr3misc (≥ 0.14.0), mlr3pipelines (≥ 0.5.2), paradox, R6, tf (≥ 0.3.4)
Suggests: rpart, testthat (≥ 3.0.0), zoo
Published: 2024-05-30
DOI: 10.32614/CRAN.package.mlr3fda
Author: Sebastian Fischer ORCID iD [aut, cre], Maximilian Muecke ORCID iD [aut], Fabian Scheipl ORCID iD [ctb], Bernd Bischl ORCID iD [ctb]
Maintainer: Sebastian Fischer <sebf.fischer at>
License: LGPL-3
NeedsCompilation: no
Materials: README NEWS
In views: FunctionalData
CRAN checks: mlr3fda results


Reference manual: mlr3fda.pdf


Package source: mlr3fda_0.1.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): mlr3fda_0.1.2.tgz, r-oldrel (arm64): mlr3fda_0.1.2.tgz, r-release (x86_64): mlr3fda_0.1.2.tgz, r-oldrel (x86_64): mlr3fda_0.1.2.tgz
Old sources: mlr3fda archive


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