Package: anomalize
Type: Package
Title: Tidy Anomaly Detection
Version: 0.3.0.9000
Authors@R: c(
    person("Matt", "Dancho", email = "mdancho@business-science.io", role = c("aut", "cre")),
    person("Davis", "Vaughan", email = "dvaughan@business-science.io", role = c("aut"))
  )
Description: The 'anomalize' package enables a "tidy" workflow for
        detecting anomalies in data. The main functions are
        time_decompose(), anomalize(), and time_recompose(). When
        combined, it's quite simple to decompose time series, detect
        anomalies, and create bands separating the "normal" data from
        the anomalous data at scale (i.e. for multiple time series).
        Time series decomposition is used to remove trend and seasonal
        components via the time_decompose() function and methods
        include seasonal decomposition of time series by Loess ("stl")
        and seasonal decomposition by piecewise medians ("twitter").
        The anomalize() function implements two methods for anomaly
        detection of residuals including using an inner quartile range
        ("iqr") and generalized extreme studentized deviation ("gesd").
        These methods are based on those used in the 'forecast' package
        and the Twitter 'AnomalyDetection' package. Refer to the
        associated functions for specific references for these methods.
URL: https://business-science.github.io/anomalize/,
        https://github.com/business-science/anomalize
BugReports: https://github.com/business-science/anomalize/issues
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.0.0)
Imports: dplyr, glue, timetk, sweep, tibbletime (>= 0.1.5), purrr,
        rlang, tibble, tidyr (>= 1.0.0), ggplot2 (>= 3.4.0)
RoxygenNote: 7.2.3
Roxygen: list(markdown = TRUE)
Suggests: tidyquant, stringr, testthat (>= 3.0.0), knitr, rmarkdown
VignetteBuilder: knitr
Config/testthat/edition: 3
Config/pak/sysreqs: cmake make libicu-dev libuv1-dev libssl-dev
        libx11-dev
Repository: https://business-science.r-universe.dev
Date/Publication: 2023-12-28 15:14:42 UTC
RemoteUrl: https://github.com/business-science/anomalize
RemoteRef: HEAD
RemoteSha: f5d37063c83bb0b4b4256aed81dead489414b89c
NeedsCompilation: no
Packaged: 2026-06-15 08:13:09 UTC; root
Author: Matt Dancho [aut, cre],
  Davis Vaughan [aut]
Maintainer: Matt Dancho <mdancho@business-science.io>