tidychangepoint: A Unified Framework for Analyzing Changepoint Detection in Univariate Time Series

Authors

Ben Baumer, Smith CollegeFollow
Biviana Marcela Suárez Sierra, Universidad EAFIT

Author ORCID Identifier

Ben Baumer: 0000-0002-3279-0516

Biviana Marcela Suárez Sierra: 0000-0003-2151-3537

Document Type

Article

Publication Date

2-6-2026

Publication Title

Statistical and Data Sciences: Faculty Publications

Abstract

We present tidychangepoint, a new R package for changepoint detection analysis. Most R packages for segmenting univariate time series focus on providing one or two algorithms for changepoint detection that work with a small set of models and penalized objective functions, and all of them return a custom, nonstandard object type. This makes comparing results across various algorithms, models, and penalized objective functions unnecessarily difficult. tidychangepoint solves this problem by wrapping functions from a variety of existing packages and storing the results in a common S3 class called tidycpt. The package then provides functionality for easily extracting comparable numeric or graphical information from a tidycpt object, all in a tidyverse-compliant framework. tidychangepoint is versatile: it supports both deterministic algorithms like PELT (from changepoint), and also flexible, randomized, genetic algorithms (via GA) that—via new functionality built into tidychangepoint—can be used with any compliant model-fitting function and any penalized objective function. By bringing all of these disparate tools together in a cohesive fashion, tidychangepoint facilitates comparative analysis of changepoint detection algorithms and models.

Comments

Published on github: https://beanumber.github.io/changepoint-paper/

Recommended Citation

Baumer, Ben and Suárez Sierra, Biviana Marcela, "tidychangepoint: A Unified Framework for Analyzing Changepoint Detection in Univariate Time Series" (2026). Statistical and Data Sciences: Faculty Publications, Smith College, Northampton, MA.
https://scholarworks.smith.edu/sds_facpubs/88

Digital Object Identifier (DOI)

https://doi.org/10.48550/arXiv.2407.14369

Rights

Licensed to Smith College and distributed CC-BY 4.0 under the Smith College Faculty Open Access Policy.

Version

Author's Submitted Manuscript