Package: twdtw
Title: Time-Weighted Dynamic Time Warping
Version: 1.0-1
Authors@R: 
    c(
      person(
        given = "Victor", 
        family = "Maus", 
        role = c("aut", "cre"),
        email = "vwmaus1@gmail.com",
        comment = c(ORCID = "0000-0002-7385-4723")
      )
    )
Description: 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.
License: GPL (>= 3)
URL: https://github.com/vwmaus/twdtw/
BugReports: https://github.com/vwmaus/twdtw/issues/
Encoding: UTF-8
RoxygenNote: 7.2.3
Imports: Rcpp, proxy
Suggests: rbenchmark, testthat (>= 3.0.0)
LinkingTo: Rcpp
Collate: 'RcppExports.R' 'convert_date_to_numeric.R' 'init.R'
        'plot_cost_matrix.R' 'twdtw.R' 'zzz.R'
Config/testthat/edition: 3
NeedsCompilation: yes
Packaged: 2023-08-07 23:39:33 UTC; maus
Author: Victor Maus [aut, cre] (<https://orcid.org/0000-0002-7385-4723>)
Maintainer: Victor Maus <vwmaus1@gmail.com>
Repository: CRAN
Date/Publication: 2023-08-08 07:20:02 UTC
Built: R 4.6.0; aarch64-apple-darwin23; 2026-04-25 21:19:43 UTC; unix
Archs: twdtw.so.dSYM
