ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data
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Abstract (arXiv)
There are many different ways in which change point analysis can be performed, from purely parametric methods to those that are distribution free. The ecp package is designed to perform multiple change point analysis while making as few assumptions as possible. While many other change point methods are applicable only for univariate data, this R package is suitable for both univariate and multivariate observations. Estimation can be based upon either a hierarchical divisive or agglomerative algorithm. Divisive estimation sequentially identifies change points via a bisection algorithm. The agglomerative algorithm estimates change point locations by determining an optimal segmentation. Both approaches are able to detect any type of distributional change within the data. This provides an advantage over many existing change point algorithms which are only able to detect changes within the marginal distributions.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Sep 2013.
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Citing papers per year: 2021: 4; 2022: 3; 2023: 2; 2024: 2; 2025: 1; 2026: 1.
13 papers.
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- 2022Random Forests for Change Point Detectioncited by 14
- 2021Optimal network online change point localisationcited by 5
- 2021A computationally efficient framework for vector representation of persistence diagramscited by 3
- 2022Online Changepoint Detection on a Budgetcited by 3
- 2024Causal Discovery-Driven Change Point Detection in Time Seriescited by 2
- 2021IAD: Indirect Anomalous VMMs Detection in the Cloud-based Environmentcited by 0
- 2021PyChEst: a Python package for the consistent retrospective estimation of distributional changes in piece-wise stationary time seriescited by 0
- 2022Sedentary Behavior Estimation with Hip-worn Accelerometer Data: Segmentation, Classification and Thresholdingcited by 0
- 2023The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learningcited by 0
- 2023Change Point Detection with Conceptorscited by 0
- 2024Change Point Detection with Copula Entropy based Two-Sample Testcited by 0
- 2025Neural Network-Based Change Point Detection for Large-Scale Time-Evolving Datacited by 0
- 2026High-Dimensional Online Change Point Detection with Adaptive Thresholding and Interpretability21 Sep 2026
- 2025Neural Network-Based Change Point Detection for Large-Scale Time-Evolving Data12 Mar 2025
- 2024Causal Discovery-Driven Change Point Detection in Time Series10 Jul 2024
- 2024Change Point Detection with Copula Entropy based Two-Sample Test3 Feb 2024
- 2023Change Point Detection with Conceptors11 Aug 2023
- 2023The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learning19 Apr 2023
- 2022Sedentary Behavior Estimation with Hip-worn Accelerometer Data: Segmentation, Classification and Thresholding5 Jul 2022
- 2022Random Forests for Change Point Detection10 May 2022
- 2022Online Changepoint Detection on a Budget11 Jan 2022
- 2021PyChEst: a Python package for the consistent retrospective estimation of distributional changes in piece-wise stationary time series20 Dec 2021
- 2021IAD: Indirect Anomalous VMMs Detection in the Cloud-based Environment22 Nov 2021
- 2021A computationally efficient framework for vector representation of persistence diagrams16 Sep 2021
1 more
- 2021Optimal network online change point localisation14 Jan 2021
- 2022Random Forests for Change Point Detectioncited by 14
- 2021Optimal network online change point localisationcited by 5
- 2021A computationally efficient framework for vector representation of persistence diagramscited by 3
- 2022Online Changepoint Detection on a Budgetcited by 3
- 2024Causal Discovery-Driven Change Point Detection in Time Seriescited by 2
- 2021IAD: Indirect Anomalous VMMs Detection in the Cloud-based Environmentcited by 0
- 2021PyChEst: a Python package for the consistent retrospective estimation of distributional changes in piece-wise stationary time seriescited by 0
- 2022Sedentary Behavior Estimation with Hip-worn Accelerometer Data: Segmentation, Classification and Thresholdingcited by 0
- 2023The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learningcited by 0
- 2023Change Point Detection with Conceptorscited by 0
- 2024Change Point Detection with Copula Entropy based Two-Sample Testcited by 0
- 2025Neural Network-Based Change Point Detection for Large-Scale Time-Evolving Datacited by 0
12 most cited of 13 All 13 (JSON)
- 2026High-Dimensional Online Change Point Detection with Adaptive Thresholding and Interpretability21 Sep 2026
- 2025Neural Network-Based Change Point Detection for Large-Scale Time-Evolving Data12 Mar 2025
- 2024Causal Discovery-Driven Change Point Detection in Time Series10 Jul 2024
- 2024Change Point Detection with Copula Entropy based Two-Sample Test3 Feb 2024
- 2023Change Point Detection with Conceptors11 Aug 2023
- 2023The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learning19 Apr 2023
- 2022Sedentary Behavior Estimation with Hip-worn Accelerometer Data: Segmentation, Classification and Thresholding5 Jul 2022
- 2022Random Forests for Change Point Detection10 May 2022
- 2022Online Changepoint Detection on a Budget11 Jan 2022
- 2021PyChEst: a Python package for the consistent retrospective estimation of distributional changes in piece-wise stationary time series20 Dec 2021
- 2021IAD: Indirect Anomalous VMMs Detection in the Cloud-based Environment22 Nov 2021
- 2021A computationally efficient framework for vector representation of persistence diagrams16 Sep 2021
12 newest of 13 All 13 (JSON)