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.

Version:	1.0-1
Imports:	Rcpp, proxy
LinkingTo:	Rcpp
Suggests:	rbenchmark, testthat (≥ 3.0.0)
Published:	2023-08-08
Author:	Victor Maus [aut, cre]
Maintainer:	Victor Maus <vwmaus1 at gmail.com>
BugReports:	https://github.com/vwmaus/twdtw/issues/
License:	GPL (≥ 3)
URL:	https://github.com/vwmaus/twdtw/
NeedsCompilation:	yes
Materials:	README NEWS
In views:	TimeSeries
CRAN checks:	twdtw results

Documentation:

Reference manual:

twdtw.pdf

Downloads:

Package source:	twdtw_1.0-1.tar.gz
Windows binaries:	r-devel: twdtw_1.0-1.zip, r-release: twdtw_1.0-1.zip, r-oldrel: twdtw_1.0-1.zip
macOS binaries:	r-release (arm64): twdtw_1.0-1.tgz, r-oldrel (arm64): twdtw_1.0-1.tgz, r-release (x86_64): twdtw_1.0-1.tgz, r-oldrel (x86_64): twdtw_1.0-1.tgz
Old sources:	twdtw archive

Linking:

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