The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
Interpretation of time series data is affected by model choices. Different models can give different or even contradicting estimates of patterns, trends, and mechanisms for the same data–a limitation alleviated by the Bayesian estimator of abrupt change,seasonality, and trend (BEAST) of this package. BEAST seeks to improve time series decomposition by forgoing the "single-best-model" concept and embracing all competing models into the inference via a Bayesian model averaging scheme. It is a flexible tool to uncover abrupt changes (i.e., change-points, breakpoints, structural breaks, or join-points), cyclic variations (e.g., seasonality), and nonlinear trends in time-series observations. BEAST not just tells when changes occur but also quantifies how likely the detected changes are true. It detects not just piecewise linear trends but also arbitrary nonlinear trends. BEAST is applicable to real-valued time series data of all kinds, be it for remote sensing, economics, climate sciences, ecology, and hydrology. Example applications include its use to identify regime shifts in ecological data, map forest disturbance and land degradation from satellite imagery, detect market trends in economic data, pinpoint anomaly and extreme events in climate data, and unravel system dynamics in biological data. Details on BEAST are reported in Zhao et al. (2019) <doi:10.1016/j.rse.2019.04.034>.
| Version: | 1.0.1 |
| Depends: | R (≥ 2.10.0), methods, utils |
| Imports: | grid |
| Published: | 2024-08-30 |
| DOI: | 10.32614/CRAN.package.Rbeast |
| 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 at osu.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/zhaokg/Rbeast |
| NeedsCompilation: | yes |
| Citation: | Rbeast citation info |
| Materials: | README NEWS |
| In views: | Bayesian, Environmetrics, TimeSeries |
| CRAN checks: | Rbeast results |
| Reference manual: | Rbeast.pdf |
| Package source: | Rbeast_1.0.1.tar.gz |
| Windows binaries: | r-devel: Rbeast_1.0.1.zip, r-release: Rbeast_1.0.1.zip, r-oldrel: Rbeast_1.0.1.zip |
| macOS binaries: | r-release (arm64): Rbeast_1.0.1.tgz, r-oldrel (arm64): Rbeast_1.0.1.tgz, r-release (x86_64): Rbeast_1.0.1.tgz, r-oldrel (x86_64): Rbeast_1.0.1.tgz |
| Old sources: | Rbeast archive |
Please use the canonical form https://CRAN.R-project.org/package=Rbeast to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.