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

References

• Auger, I. E., and C. E. Lawrence. 1989. Algorithms for the optimal identification of segment neighborhoods. Bulletin of Mathematical Biology 51 (1):39–54. doi: 10.1016/S0092-8240(89)80047-3.
   PubMed Web of Science ®Google Scholar
• Baghdadi, A., L. A. Cavuoto, A. Jones-Farmer, S. E. Rigdon, E. T. Esfahani, and F. M. Megahed. 2021. Monitoring worker fatigue using wearable devices: A case study to detect changes in gait parameters. Journal of Quality Technology 53 (1):47–71. doi: 10.1080/00224065.2019.1640097.
   Web of Science ®Google Scholar
• Bai, J., and P. Perron. 2003. Computation and analysis of multiple structural change models. Journal of Applied Econometrics 18 (1):1–22. doi: 10.1002/jae.659.
   Web of Science ®Google Scholar
• Capizzi, G., and G. Masarotto. 2013. Phase I distribution-free analysis of univariate data. Journal of Quality Technology 45 (3):273–84. doi: 10.1080/00224065.2013.11917938.
   Web of Science ®Google Scholar
• Capizzi, G., and G. Masarotto. 2017. Phase I distribution-free analysis of multivariate data. Technometrics 59 (4):484–95. doi: 10.1080/00401706.2016.1272494.
   Web of Science ®Google Scholar
• Capizzi, G., and G. Masarotto. 2018. Phase I distribution-free analysis with the R package dfphase1. In Frontiers in statistical quality control, ed. Knoth, S. and Schmid, W. 3–19, vol. 12. Springer, Cham, Switzerland. doi: 10.1007/978-3-319-75295-2_1
   Google Scholar
• Donoho, D. L., and J. M. Johnstone. 1994. Ideal spatial adaptation by wavelet shrinkage. Biometrika 81 (3):425–55. doi: 10.1093/biomet/81.3.425.
   Web of Science ®Google Scholar
• Erdman, C., and J. W. Emerson. 2007. bcp: An R package for performing a Bayesian analysis of change point problems. Journal of Statistical Software 23 (3):1–13. doi: 10.18637/jss.v023.i03.
   Web of Science ®Google Scholar
• Fearnhead, P., and P. Clifford. 2003. On-line inference for hidden Markov models via particle filters. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 65 (4):887–99. doi: 10.1111/1467-9868.00421.
   Google Scholar
• Fearnhead, P., and G. Rigaill. 2019. Changepoint detection in the presence of outliers. Journal of the American Statistical Association 114 (525):169–83. doi: 10.1080/01621459.2017.1385466.
   Web of Science ®Google Scholar
• Fong, Y., Y. Huang, P. B. Gilbert, and S. R. Permar. 2017. chngpt: Threshold regression model estimation and inference. BMC Bioinformatics 18 (1):454. doi: 10.1186/s12859-017-1863-x.
   PubMed Web of Science ®Google Scholar
• Fryzlewicz, P. 2014. Wild binary segmentation for multiple change-point detection. The Annals of Statistics 42 (6):2243–81. doi: 10.1214/14-AOS1245.
   Web of Science ®Google Scholar
• Harnish, P., B. Nelson, and G. Runger. 2009. Process partitions from time-ordered clusters. Journal of Quality Technology 41 (1):3–17. doi: 10.1080/00224065.2009.11917756.
   Web of Science ®Google Scholar
• Hawkins, D. M. 2001. Fitting multiple change-point models to data. Computational Statistics & Data Analysis 37 (3):323–41. doi: 10.1016/S0167-9473(00)00068-2.
   Web of Science ®Google Scholar
• Hawkins, D. M., P. Qiu, and C. W. Kang. 2003. The changepoint model for statistical process control. Journal of Quality Technology 35 (4):355–66. doi: 10.1080/00224065.2003.11980233.
   Web of Science ®Google Scholar
• Haynes, K., I. A. Eckley, and P. Fearnhead. 2017. Computationally efficient changepoint detection for a range of penalties. Journal of Computational and Graphical Statistics 26 (1):134–43. doi: 10.1080/10618600.2015.1116445.
   Web of Science ®Google Scholar
• Haynes, K., and R. Killick. 2021. changepoint.np: Methods for nonparametric changepoint detection, r package version 1.0.3.
   Google Scholar
• James, N. A., and D. S. Matteson. 2014. ecp: An R package for nonparametric multiple change point analysis of multivariate data. Journal of Statistical Software 62 (7):1–25. doi: 10.18637/jss.v062.i07.
   Web of Science ®Google Scholar
• Jones-Farmer, L. A., W. H. Woodall, S. H. Steiner, and C. W. Champ. 2014. An overview of phase I analysis for process improvement and monitoring. Journal of Quality Technology 46 (3):265–80. doi: 10.1080/00224065.2014.11917969.
   Web of Science ®Google Scholar
• Killick, R., and I. A. Eckley. 2014. changepoint: An R package for changepoint analysis. Journal of Statistical Software 58 (3):1–19. doi: 10.18637/jss.v058.i03.
   Web of Science ®Google Scholar
• Killick, R., P. Fearnhead, and I. A. Eckley. 2012. Optimal detection of changepoints with a linear computational cost. Journal of the American Statistical Association 107 (500):1590–8. doi: 10.1080/01621459.2012.737745.
   Web of Science ®Google Scholar
• Muggeo, V. M. 2008. Segmented: An R package to fit regression models with broken-line relationships. R News 8:20–5.
   Google Scholar
• O Ruanaidh, J. J., and W. J. Fitzgerald. 1996. Numerical Bayesian methods applied to signal processing. New York: Springer-Verlag.
   Google Scholar
• Ross, G. J. 2015. Parametric and nonparametric sequential change detection in R: The cpm package. Journal of Statistical Software 66:1–20.
   Web of Science ®Google Scholar
• Sullivan, J. H. 2002. Detection of multiple change points from clustering individual observations. Journal of Quality Technology 34 (4):371–83. doi: 10.1080/00224065.2002.11980170.
   Web of Science ®Google Scholar
• Turner, C. D., Jr, J. H. Sullivan, R. G. Batson, and W. H. Woodall. 2001. A program for retrospective change-point analysis of individual observations. Journal of Quality Technology 33 (2):242–57. doi: 10.1080/00224065.2001.11980071.
   Web of Science ®Google Scholar
• Vostrikova, L. J. 1981. Discovery of “discord” in multidimensional random processes. Doklady Akademii Nauk SSSR. 259:270–4.
   Google Scholar
• Woodall, W. H., and D. C. Montgomery. 1999. Research issues and ideas in statistical process control. Journal of Quality Technology 31 (4):376–86. doi: 10.1080/00224065.1999.11979944.
   Web of Science ®Google Scholar
• Xiao, X., P. Chen, Z. Ye, and K.-L. Tsui. 2021. On computing multiple change points for the gamma distribution. Journal of Quality Technology 53 (3):267–88. doi: 10.1080/00224065.2020.1717398.
   Web of Science ®Google Scholar
• Zeileis, A., F. Leisch, K. Hornik, and C. Kleiber. 2002. strucchange: An R package for testing for structural change in linear regression models. Journal of Statistical Software 7 (2):1–38. doi: 10.18637/jss.v007.i02.
   Google Scholar
• Zhang, Z., and N. Doganaksoy. 2021. Change point detection and issue localization based on fleet-wide fault data. Journal of Quality Technology :1–13. doi: 10.1080/00224065.2021.1937409.
   Web of Science ®Google Scholar
• Zou, C., G. Wang, and R. Li. 2020. Consistent selection of the number of change-points via sample-splitting. Annals of Statistics 48 (1):413–39. doi: 10.1214/19-AOS1814.
   Web of Science ®Google Scholar