cpss: an R package for change-point detection by sample-splitting methods

Abstract

Change-point detection is a popular statistical method for Phase I analysis in statistical process control. The cpss package has been developed to provide users with multiple choices of change-point searching algorithms for a variety of frequently considered parametric change-point models, including the univariate and multivariate mean and/or (co)variance change models, changes in linear models and generalized linear models, and change models in exponential families. In particular, it integrates the recently proposed COPSS criterion to determine the number of change-points in a data-driven fashion that avoids selecting or specifying additional tuning parameters in existing approaches. Hence it is more convenient to use in practical applications. In addition, the cpss package brings great possibilities to handle user-customized change-point models.

Keywords:

• change-point detection
• cross-validation
• data-driven
• sample-splitting
• statistical process control

Supplementary material

The supplementary material contains the source code of the cpss package and all reproducible examples in Section 4.

Acknowledgments

The authors would like to acknowledge the editor, the associate editor, and two referees for their constructive comments and suggestions, which improved the quality of the article greatly. We would like to thank the students Xiaoyang Chen and Yan Qu for helpful discussions when we are preparing the first draft of this article.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Wang was supported by NNSF of China Grant 11901314. Zou was supported by NNSF of China Grants (11925106, 11690015, 11931001 and 11971247), NSF of Tianjin Grant (18JCJQJC46000 and 18ZXZNGX00140), and the 111Project B20016.

Notes on contributors

Guanghui Wang

Guanghui Wang is an Associate Professor in Academy of Statistics and Interdisciplinary Sciences at East China Normal University. His research interests lie in change-point detection and high-dimensional inference. His email address is [email protected].

Changliang Zou

Changliang Zou is a Professor in School of Statistics and Data Science at Nankai University. His research interests include change-point and outlier detection, on-line learning for streaming data, massive data analysis, high-dimensional inference and statistical process control. His email address is [email protected].