Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
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Authors: Nicholas A. James, David S. Matteson
Title: 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. Hierarchical estimation can be based upon either a 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.

Page views:: 5712. Submitted: 2013-06-08. Published: 2015-01-21.
Paper: ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data     Download PDF (Downloads: 4569)
ecp_1.6.2.tar.gz: R source package Download (Downloads: 344; 1MB) Replication materials Download (Downloads: 621; 4MB)

DOI: 10.18637/jss.v062.i07

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.