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

TL;DR

The ecp R package offers a flexible, distribution-free method for detecting multiple change points in both univariate and multivariate data using hierarchical algorithms, improving over existing methods limited to univariate or parametric approaches.

Contribution

It introduces a versatile R package capable of nonparametric multiple change point detection for multivariate data, with both divisive and agglomerative algorithms.

Findings

Effective detection of various distributional changes

Applicable to multivariate data

No strong distributional assumptions required

Abstract

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.