composits: Compositional, Multivariate and Univariate Time Series Outlier Ensemble

An ensemble of time series outlier detection methods that can be used for compositional, multivariate and univariate data. It uses the four R packages 'forecast', 'tsoutliers', 'otsad' and 'anomalize' to detect time series outliers.

Version: 0.1.0
Depends: R (≥ 3.4.0)
Imports: otsad, tsoutliers, forecast, anomalize, dplyr, tibble, rlang, pracma, dobin, ICS, fastICA, gridExtra, grid, ggplot2, tidyr, kableExtra
Suggests: knitr, rmarkdown, tourr, stringr, broom, rgdal
Published: 2020-09-30
Author: Sevvandi Kandanaarachchi ORCID iD [aut, cre], Patricia Menendez ORCID iD [aut], Ursula Laa ORCID iD [aut], Ruben Loaiza-Maya ORCID iD [aut]
Maintainer: Sevvandi Kandanaarachchi <sevvandik at gmail.com>
License: GPL-3
URL: https://sevvandi.github.io/composits/
NeedsCompilation: no
Materials: README
CRAN checks: composits results

Downloads:

Reference manual: composits.pdf
Vignettes: composits
Package source: composits_0.1.0.tar.gz
Windows binaries: r-devel: composits_0.1.0.zip, r-release: composits_0.1.0.zip, r-oldrel: composits_0.1.0.zip
macOS binaries: r-release (arm64): composits_0.1.0.tgz, r-release (x86_64): composits_0.1.0.tgz, r-oldrel: composits_0.1.0.tgz

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