A developing software suite for multiple change-point detection/estimation (data segmentation) in data sequences.
The current version implements the Gaussian mean-shift model, in which the data are assumed to be a piecewise-constant signal observed with i.i.d. Gaussian noise. Change-point detection in breakfast is carried out in two stages: (i) computation of a solution path, and (ii) model selection 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 and sol.tguh.
Check back future versions for more change-point models and further methods.
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.
browseVignettes(package = "breakfast") contains a detailed comparative simulation study of various methods
implemented in breakfast for the Gaussian mean-shift model.