Change-Point Detection by Sample-Splitting Methods

Implements multiple change searching algorithms for a variety of frequently considered parametric change-point models. In particular, it integrates a criterion proposed by Zou, Wang and Li (2020) to select the number of change-points in a data-driven fashion. Moreover, it also provides interfaces for user-customized change-point models with one's own cost function and parameter estimation routine. It is easy to get started with the cpss.* set of functions by accessing their documentation pages (e.g., ?cpss).

cpss

Change-Point Detection by Sample-Splitting Methods.

Installation

You can install the released version of cpss from CRAN with:

install.packages("cpss")

Or the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("ghwang-nk/cpss")

Getting started

It is easy to get started with the cpss.* set of functions by accessing their documentation pages (e.g., ?cpss).

library(cpss)
?cpss.mean
?cpss.var
?cpss.meanvar
?cpss.glm
?cpss.lm
?cpss.em
?cpss.custom

References

1. Zou, C., Wang, G., and Li, R. (2020) Consistent selection of the number of change-points via sample-splitting. The Annals of Statistics, 48, 413–439.

2. Wang, G., and Zou, C. (2022+) cpss: An R package for change-point detection by sample-splitting methods. To appear in Journal of Quality Technology.