# ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data

arXiv: https://arxiv.org/abs/1309.3295 · HTML: https://mlsanity.com/paper/1309.3295/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2013.

## Built on by (most cited first, 13 of 13)

- [Random Forests for Change Point Detection](https://mlsanity.com/paper/2205.04997/) (2022)
- [Optimal network online change point localisation](https://mlsanity.com/paper/2101.05477/) (2021)
- [A computationally efficient framework for vector representation of persistence diagrams](https://mlsanity.com/paper/2109.08239/) (2021)
- [Online Changepoint Detection on a Budget](https://mlsanity.com/paper/2201.03710/) (2022)
- [Causal Discovery-Driven Change Point Detection in Time Series](https://mlsanity.com/paper/2407.07290/) (2024)
- [IAD: Indirect Anomalous VMMs Detection in the Cloud-based Environment](https://mlsanity.com/paper/2111.11052/) (2021)
- [PyChEst: a Python package for the consistent retrospective estimation of distributional changes in piece-wise stationary time series](https://mlsanity.com/paper/2112.10565/) (2021)
- [Sedentary Behavior Estimation with Hip-worn Accelerometer Data: Segmentation, Classification and Thresholding](https://mlsanity.com/paper/2207.01809/) (2022)
- [The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learning](https://mlsanity.com/paper/2304.09914/) (2023)
- [Change Point Detection with Conceptors](https://mlsanity.com/paper/2308.06213/) (2023)
- [Change Point Detection with Copula Entropy based Two-Sample Test](https://mlsanity.com/paper/2403.07892/) (2024)
- [Neural Network-Based Change Point Detection for Large-Scale Time-Evolving Data](https://mlsanity.com/paper/2503.09541/) (2025)
- [High-Dimensional Online Change Point Detection with Adaptive Thresholding and Interpretability](https://mlsanity.com/paper/2609.24278/) (2026)

## 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.