cpop: Detecting changes in piecewise-linear signals

Grose, Daniel and Fearnhead, Paul (2023) cpop: Detecting changes in piecewise-linear signals. Journal of Statistical Software. ISSN 1548-7660 (In Press)

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Changepoint detection is an important problem with applications across many application domains. There are many different types of changes that one may wish to detect, and a wide-range of algorithms and software for detecting them. However there are relatively few approaches for detecting changes-in-slope in the mean of a signal plus noise model. We describe the R package, cpop, available on the Comprehensive R Archive Network (CRAN). This package implements CPOP, a dynamic programming algorithm, to find the optimal set of changes that minimises an L_0 penalised cost, with the cost being a weighted residual sum of squares. The package has extended the CPOP algorithm so it can analyse data that is unevenly spaced, allow for heterogeneous noise variance, and allows for a grid of potential change locations to be different from the locations of the data points. There is also an implementation that uses the CROPS algorithm to detect all segmentations that are optimal as you vary the L_0 penalty for adding a change across a continuous range of values.

Item Type:
Journal Article
Journal or Publication Title:
Journal of Statistical Software
Uncontrolled Keywords:
?? changepointschange-in-slopedynamic programmingpiecewise linear modelsstructural breaksstatistics and probabilitysoftwarestatistics, probability and uncertainty ??
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Deposited On:
24 Aug 2022 10:15
In Press
Last Modified:
15 Jul 2024 22:56