breakfast-package: Breakfast: Methods for Fast Multiple Change-point Detection...

In breakfast: Methods for Fast Multiple Change-Point/Break-Point Detection and Estimation

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)

• Andreas Anastasiou

• Yining Chen

• Haeran Cho

• Piotr Fryzlewicz

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).

breakfast documentation built on Sept. 30, 2024, 9:43 a.m.