Package: Rbeast
Type: Package
Version: 1.0.2
Date: 2025-12-09
Title: Bayesian Change-Point Detection and Time Series Decomposition
Authors@R: c(person(given = "Tongxi",   family = "Hu",       role = "aut",           email = "hu.1555@osu.edu"),
             person(given = "Yang",     family = "Li",       role = "aut",           email = "li.10015@osu.edu"),             
			 person(given = "Xuesong",  family = "Zhang",    role = "aut",           email= "xuesong.zhang@usda.gov" ),
			 person(given = "Kaiguang", family = "Zhao",     role = c("aut", "cre"), email = "zhao.1423@osu.edu"),
             person(given = "Jack",     family = "Dongarra", role = "ctb"),
             person(given = "Cleve",    family = "Moler",    role = "ctb")
             )
Author: Tongxi Hu [aut],
  Yang Li [aut],
  Xuesong Zhang [aut],
  Kaiguang Zhao [aut, cre],
  Jack Dongarra [ctb],
  Cleve Moler [ctb]
Maintainer: Kaiguang Zhao <zhao.1423@osu.edu>
Depends: R (>= 2.10.0),methods, utils
Description: BEAST is a Bayesian estimator of abrupt change, seasonality, and trend for decomposing univariate time series and 1D sequential data. Interpretation of time series depends on model choice; different models can yield contrasting or contradicting estimates of patterns, trends, and mechanisms. BEAST alleviates this by abandoning the single-best-model paradigm and instead using Bayesian model averaging over many competing decompositions. It detects and characterizes abrupt changes (changepoints, breakpoints, structural breaks, joinpoints), cyclic or seasonal variation, and nonlinear trends. BEAST not only detects when changes occur but also quantifies how likely the changes are true. It estimates not just piecewise linear trends but also arbitrary nonlinear trends. BEAST is generically applicable to any real-valued time series, such as those from remote sensing, economics, climate science, ecology, hydrology, and other environmental and biological systems. Example applications include identifying regime shifts in ecological data, mapping forest disturbance and land degradation from satellite image time series, detecting market trends in economic indicators, pinpointing anomalies and extreme events in climate records, and analyzing system dynamics in biological time series. Details are given in Zhao et al. (2019) <doi:10.1016/j.rse.2019.04.034>.
LazyData: true
Imports: grid
License: GPL (>= 2)
URL: https://github.com/zhaokg/Rbeast
NeedsCompilation: yes
Packaged: 2025-12-17 19:43:05 UTC; zhaokg
Repository: CRAN
Date/Publication: 2025-12-19 09:00:02 UTC
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