onlineBcp: An R package for online change point detection using a Bayesian approach

Xu H., YİĞİTER A., Chen J.

SOFTWAREX, vol.17, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 17
  • Publication Date: 2022
  • Doi Number: 10.1016/j.softx.2022.100999
  • Journal Name: SOFTWAREX
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Emerging Sources Citation Index (ESCI), Scopus, INSPEC, Directory of Open Access Journals
  • Keywords: Change point model, Online change point detection, Posterior probability, Confidence interval, MULTIPLE, MOSUM
  • Hacettepe University Affiliated: Yes


Change point analysis has been useful for practical data analytics. In this paper, we provide a new R package, onlineBcp, based on an online Bayesian change point detection algorithm. This R package conveniently outputs the maximum posterior probabilities of multiple change points, loci of change points, basic statistics for segments separated by identified change points, confidence interval for each unknown segment mean and a plot displaying the segmented data. Practically, missing value pre-treatment of the data, before the change point detection algorithm is implemented, is built in this package. In addition, the Kolmogorov-Smirnov test for checking the normality assumption on each segment, post-change point detection, is included as an option in the package for the ease of data analytic and assumption checking flow. When additional data come in, the package provides a function to combine changes identified based on prior data and changes identified based on additional data and thus provides a fast detection of change points in the data stream when new batches of data are collected. (C) 2022 The Author(s). Published by Elsevier B.V.