DESCRIPTION

Package: otsad
Type: Package
Title: Online Time Series Anomaly Detectors
Version: 1.0.0
Authors@R: c(
    person("Alaiñe", "Iturria", email = "aiturria@ikerlan.es", role = c("aut","cre")),
    person("Jacinto", "Carrasco", email = "jacintocc@decsai.ugr.es", role = c("aut")),
    person("Francisco", "Herrera", email = "herrera@decsai.ugr.es", role = c("aut")),
    person("Santiago", "Charramendieta", email = "scharramendieta@ikerlan.es", role = c("aut")),
    person("Karmele", "Intxausti", email = "kintxausti@ikerlan.es", role = c("aut")))
Description: Implements a set of online fault detectors for time-series, called: PEWMA see M. Carter
             et al. (2012) <doi:10.1109/SSP.2012.6319708>, SD-EWMA and TSSD-EWMA see H. Raza et al. 
             (2015) <doi:10.1016/j.patcog.2014.07.028>, KNN-CAD see E. Burnaev et al. (2016)
             <arXiv:1608.04585>, KNN-LDCD see V. Ishimtsev et al. (2017) <arXiv:1706.03412>, 
             CAD-OSE see M. Smirnov (2018) <https://github.com/smirmik/CAD> and EORELM-AD see
             Iturria (2021) <https://github.com/alaineiturria/otsad>. The first three 
             algorithms belong to prediction-based techniques and the last three belong to 
             window-based techniques. In addition, the SD-EWMA and PEWMA algorithms are algorithms 
             designed to work in stationary environments, while the other four 
             are algorithms designed to work in non-stationary environments.
Depends:
    R (>= 4.1.0)
SystemRequirements: 
  Python (>= 3.0.1); bencode-python3 (1.0.2)
License: AGPL (>= 3)
URL: https://github.com/alaineiturria/otsad
BugReports: https://github.com/alaineiturria/otsad/issues
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Suggests: 
    testthat,
    stream,
    knitr,
    rmarkdown
Imports: 
    stats,
    ggplot2,
    plotly,
    sigmoid,
    reticulate,
    pracma,
    rlist,
    R6
VignetteBuilder: knitr