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

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.

References in corpus (3)

• Natural Scales in Geographical Patterns
• DISCO analysis: A nonparametric extension of analysis of variance
• The group fused Lasso for multiple change-point detection

Cited by in corpus (5)

• A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data
• New efficient algorithms for multiple change-point detection with kernels
• A computationally efficient framework for vector representation of persistence diagrams
• Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction
• Online network change point detection with missing values and temporal dependence