Package: twdtw 1.0-1

Victor Maus

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_1.0-1.tar.gz
twdtw_1.0-1.tar.gz(r-4.5-noble)twdtw_1.0-1.tar.gz(r-4.4-noble)
twdtw_1.0-1.tgz(r-4.4-emscripten)
twdtw.pdf |twdtw.html
twdtw/json (API)
NEWS

# Install 'twdtw' in R:
install.packages('twdtw', repos = c('https://cran.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

fortrancpp

2.48 score 2 packages 2 scripts 319 downloads 4 exports 2 dependencies

Last updated 1 years agofrom:c6e659f785. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 06 2024
R-4.5-linux-x86_64OKDec 06 2024

Exports:date_to_numeric_cyclemax_cycle_lengthplot_cost_matrixtwdtw

Dependencies:proxyRcpp