Package: twdtw 1.0-2

twdtw: Time-Weighted Dynamic Time Warping

Implements Time-Weighted Dynamic Time Warping (TWDTW), a measure for quantifying time series similarity. The TWDTW algorithm, described in Maus et al. (2016) <doi:10.1109/JSTARS.2016.2517118> and Maus et al. (2019) <doi:10.18637/jss.v088.i05>, is applicable to multi-dimensional time series of various resolutions. It is particularly suitable for comparing time series with seasonality for environmental and ecological data analysis, covering domains such as remote sensing imagery, climate data, hydrology, and animal movement. The 'twdtw' package offers a user-friendly 'R' interface, efficient 'Fortran' routines for TWDTW calculations, flexible time weighting definitions, as well as utilities for time series preprocessing and visualization.

Authors:Victor Maus [aut, cre]

twdtw_1.0-2.tar.gz
twdtw_1.0-2.zip(r-4.5)twdtw_1.0-2.zip(r-4.4)twdtw_1.0-2.zip(r-4.3)
twdtw_1.0-2.tgz(r-4.4-x86_64)twdtw_1.0-2.tgz(r-4.4-arm64)twdtw_1.0-2.tgz(r-4.3-x86_64)twdtw_1.0-2.tgz(r-4.3-arm64)
twdtw_1.0-2.tar.gz(r-4.5-noble)twdtw_1.0-2.tar.gz(r-4.4-noble)
twdtw.pdf |twdtw.html
twdtw/json (API)
NEWS

# Install 'twdtw' in R:
install.packages('twdtw', repos = c('https://vwmaus.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/vwmaus/twdtw/issues

Uses libs:
  • fortran– Runtime library for GNU Fortran applications
  • c++– GNU Standard C++ Library v3

On CRAN:

4.18 score 5 stars 2 packages 2 scripts 265 downloads 5 exports 2 dependencies

Last updated 11 months agofrom:d740a38151. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 25 2024
R-4.5-win-x86_64OKOct 25 2024
R-4.5-linux-x86_64OKOct 25 2024
R-4.4-win-x86_64OKOct 25 2024
R-4.4-mac-x86_64OKOct 25 2024
R-4.4-mac-aarch64OKOct 25 2024
R-4.3-win-x86_64OKOct 25 2024
R-4.3-mac-x86_64OKOct 25 2024
R-4.3-mac-aarch64OKOct 25 2024

Exports:date_to_numeric_cyclemax_cycle_lengthplot_cost_matrixto_date_timetwdtw

Dependencies:proxyRcpp