mlr3fda0.3.0 package

Extending 'mlr3' to Functional Data Analysis

mlr_pipeops_fda.bsignal

B-spline Feature Extraction

mlr_pipeops_fda.cor

Cross-Correlation of Functional Data

mlr_pipeops_fda.extract

Extracts Simple Features from Functional Columns

mlr_pipeops_fda.flatten

Flattens Functional Columns

mlr_pipeops_fda.fpca

Functional Principal Component Analysis

mlr_pipeops_fda.interpol

Interpolate Functional Columns

mlr_pipeops_fda.random_effect

Extracts Random Effects from Functional Columns

mlr_pipeops_fda.scalerange

Linearly Transform the Domain of Functional Data

mlr_pipeops_fda.smooth

Smoothing Functional Columns

mlr_pipeops_fda.tsfeats

Time Series Feature Extraction

mlr_pipeops_fda.wavelets

Discrete Wavelet transform features

mlr_pipeops_fda.zoom

Zoom In/Out on Functional Columns

mlr_tasks_dti

Diffusion Tensor Imaging (DTI) Regression Task

mlr_tasks_fuel

Fuel Regression Task

mlr_tasks_phoneme

Phoneme Classification Task

mlr3fda-package

mlr3fda: Extending 'mlr3' to Functional Data Analysis

Download source packageRead PDF manual

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'.

  • Maintainer: Sebastian Fischer
  • License: LGPL-3
  • Last published: 2025-10-15

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