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STMotif: Discovery of Motifs in Spatial-Time Series

Allow to identify motifs in spatial-time series. A motif is a previously unknown subsequence of a (spatial) time series with relevant number of occurrences. For this purpose, the Combined Series Approach (CSA) is used.

Version: 2.0.2
Imports: stats, ggplot2, reshape2, scales, grDevices, RColorBrewer
Suggests: knitr, rmarkdown, testthat
Published: 2024-02-23
DOI: 10.32614/CRAN.package.STMotif
Author: Heraldo Borges [aut, cre] (CEFET/RJ), Amin Bazaz [aut] (Polytech'Montpellier), Esther Pacciti [aut] (INRIA/Polytech'Montpellier), Eduardo Ogasawara [aut] (CEFET/RJ)
Maintainer: Heraldo Borges <stmotif at eic.cefet-rj.br>
BugReports: https://github.com/heraldoborges/STMotif/issues
License: MIT + file LICENSE
URL: https://github.com/heraldoborges/STMotif/wiki
NeedsCompilation: no
Materials: NEWS
CRAN checks: STMotif results

Documentation:

Reference manual: STMotif.pdf
Vignettes: Spatial-Time Motif Discovery with STMotif

Downloads:

Package source: STMotif_2.0.2.tar.gz
Windows binaries: r-devel: STMotif_2.0.2.zip, r-release: STMotif_2.0.2.zip, r-oldrel: STMotif_2.0.2.zip
macOS binaries: r-release (arm64): STMotif_2.0.2.tgz, r-oldrel (arm64): STMotif_2.0.2.tgz, r-release (x86_64): STMotif_2.0.2.tgz, r-oldrel (x86_64): STMotif_2.0.2.tgz
Old sources: STMotif archive

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.