Package: bfast
Version: 1.7.2
Title: Breaks for Additive Season and Trend
Authors@R: c(person(given = "Jan", family = "Verbesselt", role = c("aut"), email = "Jan.Verbesselt@wur.nl"),
             person(given = "Dainius", family = "Masili\u016Bnas", role = c("aut", "cre"),
             email = "pastas4@gmail.com", comment = c(ORCID = "0000-0001-5654-1277")),
             person(given = "Achim", family = "Zeileis", role = "aut", email = "Achim.Zeileis@R-project.org"),
             person(given = "Rob", family = "Hyndman", role = "ctb", email = "Rob.Hyndman@buseco.monash.edu.au"),
             person(given = "Marius", family = "Appel", role = "aut", email = "marius.appel@uni-muenster.de"),
             person(given = "Martin", family = "Jung", role = "ctb", email = "m.jung@sussex.ac.uk"),
             person(given = "Andrei", family = "M\u00EEr\u021B", role = "ctb", email = "andrei.mirt@wur.nl",
                    comment = c(ORCID = "0000-0003-3654-2090")),
             person(given = c("Paulo", "Negri"), family = "Bernardino", role = "ctb",
                    email = "paulo.negribernardino@wur.nl"),
             person(given = "Dongdong", family = "Kong", role = "ctb", email = "kongdd@mail2.sysu.edu.cn",
                    comment = c(ORCID = "0000-0003-1836-8172"))
             )
Description: Decomposition of time series into
    trend, seasonal, and remainder components with methods for detecting and
    characterizing abrupt changes within the trend and seasonal components. 'BFAST'
    can be used to analyze different types of satellite image time series and can
    be applied to other disciplines dealing with seasonal or non-seasonal time
    series, such as hydrology, climatology, and econometrics. The algorithm can be
    extended to label detected changes with information on the parameters of the
    fitted piecewise linear models. 'BFAST' monitoring functionality is described
    in Verbesselt et al. (2010) <doi:10.1016/j.rse.2009.08.014>. 'BFAST monitor'
    provides functionality to detect disturbance in near real-time based on 'BFAST'-
    type models, and is described in Verbesselt et al. (2012) <doi:10.1016/j.rse.2012.02.022>.
    'BFAST Lite' approach is a flexible approach that handles missing data
    without interpolation, and will be described in an upcoming paper.
    Furthermore, different models can now be used to fit the
    time series data and detect structural changes (breaks).
Depends: R (>= 3.0.0), strucchangeRcpp (>= 1.5-4)
Imports: graphics, stats, zoo, forecast, Rcpp (>= 0.12.7), Rdpack (>=
        0.7)
Suggests: MASS, sfsmisc, stlplus, terra
License: GPL (>= 2)
URL: https://bfast2.github.io/
BugReports: https://github.com/bfast2/bfast/issues
LazyLoad: yes
LazyData: yes
LinkingTo: Rcpp
RoxygenNote: 7.3.3
RdMacros: Rdpack
Encoding: UTF-8
NeedsCompilation: yes
Packaged: 2026-03-31 13:42:23 UTC; dainius
Author: Jan Verbesselt [aut],
  Dainius Masiliūnas [aut, cre] (ORCID:
    <https://orcid.org/0000-0001-5654-1277>),
  Achim Zeileis [aut],
  Rob Hyndman [ctb],
  Marius Appel [aut],
  Martin Jung [ctb],
  Andrei Mîrț [ctb] (ORCID: <https://orcid.org/0000-0003-3654-2090>),
  Paulo Negri Bernardino [ctb],
  Dongdong Kong [ctb] (ORCID: <https://orcid.org/0000-0003-1836-8172>)
Maintainer: Dainius Masiliūnas <pastas4@gmail.com>
Repository: CRAN
Date/Publication: 2026-04-01 05:10:46 UTC
Built: R 4.6.0; x86_64-apple-darwin20; 2026-04-25 18:37:54 UTC; unix
Archs: bfast.so.dSYM
