Package: bfast
Version: 1.5.7
Date: 2014-08-27
Title: Breaks For Additive Season and Trend (BFAST)
Authors@R: c(person(given = "Jan", family = "Verbesselt", role = c("aut", "cre"), email = "Jan.Verbesselt@wur.nl"),
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"))
Author: Jan Verbesselt [aut, cre], Achim Zeileis [aut], Rob Hyndman [ctb]
Maintainer: Jan Verbesselt <Jan.Verbesselt@wur.nl>
Description: BFAST integrates the 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 added
based on a paper that has been submitted to Remote Sensing of Environment.
BFAST monitor provides functionality to detect disturbance in near real-time based on BFAST-type models.
BFAST approach is flexible approach that handles missing data without interpolation.
Furthermore now different models can be used to fit the time series data and detect structural changes (breaks).
Depends: R (>= 2.15.0)
Imports: graphics, stats, strucchange, zoo, forecast, sp, raster
Suggests:
License: GPL (>= 2)
URL: http://bfast.R-Forge.R-project.org/
LazyLoad: yes
LazyData: yes
Repository: CRAN
Repository/R-Forge/Project: bfast
Repository/R-Forge/Revision: 464
Repository/R-Forge/DateTimeStamp: 2014-08-27 18:49:54
Date/Publication: 2014-08-28 00:00:24
Packaged: 2014-08-27 20:15:07 UTC; rforge
NeedsCompilation: no

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 = "Masiliunas", role = c("aut", "cre"),
person(given = "Rob", family = "Hyndman", role = "ctb", email = "Rob.Hyndman@buseco.monash.edu.au"),
person(given = "Marius", family = "Appel", role = "ctb", email = "marius.appel@uni-muenster.de"),
person(given = "Martin", family = "Jung", role = "ctb", email = "m.jung@sussex.ac.uk"),
person(given = "Andrei", family = "Mirt", role = "ctb", email = "andrei.mirt@wur.nl",
person(given = c("Paulo", "Negri"), family = "Bernardino", role = "ctb",
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).
Imports: graphics, stats, zoo, forecast, Rcpp (>= 0.12.7), Rdpack (>=
Suggests: MASS, sfsmisc, stlplus, raster
Dainius Masiliunas [aut, cre] (<https://orcid.org/0000-0001-5654-1277>),