TY - JOUR
T1 - onlineBcp
T2 - An R package for online change point detection using a Bayesian approach
AU - Xu, Hongyan
AU - Yiğiter, Ayten
AU - Chen, Jie
N1 - Funding Information:
The authors would like to thank two anonymous reviewers and the editor for their suggestions and critiques which helped improve the manuscript.
Publisher Copyright:
© 2022 The Author(s)
PY - 2022/1
Y1 - 2022/1
N2 - 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.
AB - 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.
KW - Change point model
KW - Confidence interval
KW - Online change point detection
KW - Posterior probability
UR - http://www.scopus.com/inward/record.url?scp=85124466035&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85124466035&partnerID=8YFLogxK
U2 - 10.1016/j.softx.2022.100999
DO - 10.1016/j.softx.2022.100999
M3 - Article
AN - SCOPUS:85124466035
SN - 2352-7110
VL - 17
JO - SoftwareX
JF - SoftwareX
M1 - 100999
ER -