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

Hongyan Xu, Ayten Yiğiter, Jie Chen

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number100999
JournalSoftwareX
Volume17
DOIs
StatePublished - Jan 2022
Externally publishedYes

Keywords

  • Change point model
  • Confidence interval
  • Online change point detection
  • Posterior probability

ASJC Scopus subject areas

  • Software
  • Computer Science Applications

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