breakfast-package: Breakfast: Methods for Fast Multiple Change-point Detection...
| breakfast-package | R Documentation |
Breakfast: Methods for Fast Multiple Change-point Detection and Estimation
Description
A developing software suite for multiple change-point detection/estimation (data segmentation) in data sequences.
Details
The current version implements methods for detecting changes in the data sequence modelled as (i) a piecewise-constant function plus i.i.d. Gaussian noise, (ii) a piecewise-constant function plus autoregressive time series, (iii) a piecewise-linear and continuous function plus i.i.d. Gaussian noise, and (iv) a piecewise-linear and discontinuous function plus i.i.d. Gaussian noise. This is carried out via a two-stage procedure combining solution path generation and model selection methodologies. Change-point detection in breakfast is carried out in two stages, first the computation of a solution path, followed by a model selection step along the path. A variety of solution path and model selection methods are included, which can be accessed individually, or through breakfast. Currently supported solution path methods are: sol.idetect, sol.idetect_seq, sol.wbs, sol.wbs2, sol.not, sol.tguh and sol.wcm.
Currently supported model selection methods are: model.ic, model.lp, model.sdll, model.thresh and model.gsa.
Check back future versions for more change-point models and the corresponding methods.
Author(s)
We would like to thank Shakeel Gavioli-Akilagun, Anica Kostic, Shuhan Yang and Christine Yuen for their comments and suggestions that helped improve this package.
See Also
browseVignettes(package = "breakfast") contains a detailed comparative simulation study of various methods
implemented in breakfast for the models (i), (iii) and (iv).
