{ "_id": "6a47ee21e06c18f0a6524c96", "Package": "twdtw", "Title": "Time-Weighted Dynamic Time Warping", "Version": "1.0-2", "Authors@R": "c(\nperson(\ngiven = \"Victor\",\nfamily = \"Maus\",\nrole = c(\"aut\", \"cre\"),\nemail = \"vwmaus1@gmail.com\",\ncomment = c(ORCID = \"0000-0002-7385-4723\")\n)\n)", "Description": "Implements Time-Weighted Dynamic Time Warping (TWDTW), a\nmeasure for quantifying time series similarity. The TWDTW\nalgorithm, described in Maus et al. (2016)\n and Maus et al. (2019)\n, is applicable to multi-dimensional\ntime series of various resolutions. It is particularly suitable\nfor comparing time series with seasonality for environmental\nand ecological data analysis, covering domains such as remote\nsensing imagery, climate data, hydrology, and animal movement.\nThe 'twdtw' package offers a user-friendly 'R' interface,\nefficient 'Fortran' routines for TWDTW calculations, flexible\ntime weighting definitions, as well as utilities for time\nseries preprocessing and visualization.", "License": "GPL (>= 3)", "URL": "https://github.com/vwmaus/twdtw/", "BugReports": "https://github.com/vwmaus/twdtw/issues/", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.2.3", "Collate": "'RcppExports.R' 'convert_date_to_numeric.R' 'init.R'\n'plot_cost_matrix.R' 'twdtw.R' 'zzz.R'", "Config/testthat/edition": "3", "Repository": "https://vwmaus.r-universe.dev", "Date/Publication": "2023-12-26 13:10:02 UTC", "RemoteUrl": "https://github.com/vwmaus/twdtw", "RemoteRef": "HEAD", "RemoteSha": 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