theft: Tools for Handling Extraction of Features from Time Series

Consolidates and calculates different sets of time-series features from multiple 'R' and 'Python' packages including 'Rcatch22' Henderson, T. (2021) <doi:10.5281/zenodo.5546815>, 'feasts' O'Hara-Wild, M., Hyndman, R., and Wang, E. (2021) <>, 'tsfeatures' Hyndman, R., Kang, Y., Montero-Manso, P., Talagala, T., Wang, E., Yang, Y., and O'Hara-Wild, M. (2020) <>, 'tsfresh' Christ, M., Braun, N., Neuffer, J., and Kempa-Liehr A.W. (2018) <doi:10.1016/j.neucom.2018.03.067>, 'TSFEL' Barandas, M., et al. (2020) <doi:10.1016/j.softx.2020.100456>, and 'Kats' Facebook Infrastructure Data Science (2021) <>. Provides a standardised workflow from feature calculation to feature processing, machine learning classification procedures, and the production of statistical graphics.

Depends: R (≥ 3.5.0)
Imports: rlang, stats, dplyr, ggplot2, tidyr, reshape2, scales, tibble, purrr, broom, tsibble, fabletools, tsfeatures, feasts, Rcatch22, reticulate, Rtsne, R.matlab, e1071, janitor
Suggests: lifecycle, cachem, bslib, knitr, markdown, rmarkdown, pkgdown, testthat
Published: 2023-10-06
Author: Trent Henderson [cre, aut], Annie Bryant [ctb] (Balanced classification accuracy)
Maintainer: Trent Henderson <then6675 at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README
In views: TimeSeries
CRAN checks: theft results


Reference manual: theft.pdf
Vignettes: Introduction to theft


Package source: theft_0.5.4.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): theft_0.5.4.1.tgz, r-oldrel (arm64): theft_0.5.4.1.tgz, r-release (x86_64): theft_0.5.4.1.tgz
Old sources: theft archive


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