BayesChange: an R package for Bayesian Change Point Analysis

2025/11/06 by Luca Danese, Danese, Luca, Riccardo Corradin +3 · 1 voice
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Computation (stat.CO) #FOS: Computer and information sciences #Statistical Methods and Inference #Tensor decomposition and applications #stat.CO

paper · pdf · doi:10.48550/arxiv.2511.04785

openalex publication_date 2025/11/06 · arxiv published 2025/11/06 · arxiv updated 2025/11/06 · openalex created_date 2025/11/11 · openalex updated_date 2026/07/28

Abstract

We introduce BayesChange, a computationally efficient R package, built on C++, for Bayesian change point detection and clustering of observations sharing common change points. While many R packages exist for change point analysis, BayesChange offers methods not currently available elsewhere. The core functions are implemented in C++ to ensures computational efficiency, while an R user interface simplifies the package usage. The BayesChange package includes two R wrappers that integrate the C++ backend functions, along with S3 methods for summarizing the results. We present the theory beyond each method, the algorithms for posterior simulation and we illustrate the package's usage through synthetic examples.

Citations

• Model-based clustering of time-dependent observations with common structural changes
• Search Algorithms and Loss Functions for Bayesian Clustering
• ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data
• CONTINUOUS INSPECTION SCHEMES

Discussions

• Luca Danese, Riccardo Corradin, Andrea Ongaro: BayesChange: an R package for Bayesian Change Point Analysis https://arxiv.org/abs/2511.04785 https://arxiv.org/pdf/2511.04785 https://arxiv.org/html/251 [bsky, 0 points, 0 comments]

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