doi:10.1080/01621459.2013.849605>, Zhang W. et al (2017) <doi:10.1109/ICDMW.2017.44>, Arlot S. et al (2019). Two methods make use of dynamic programming and pruning, with no distributional assumptions other than the existence of certain absolute moments in one method. Hierarchical and exact search methods are included. All methods return the set of estimated change- points as well as other summary information.">

ecp: Non-Parametric Multiple Change-Point Analysis of Multivariate Data (original) (raw)

Implements various procedures for finding multiple change-points from Matteson D. et al (2013) <doi:10.1080/01621459.2013.849605>, Zhang W. et al (2017) <doi:10.1109/ICDMW.2017.44>, Arlot S. et al (2019). Two methods make use of dynamic programming and pruning, with no distributional assumptions other than the existence of certain absolute moments in one method. Hierarchical and exact search methods are included. All methods return the set of estimated change- points as well as other summary information.

Version: 3.1.6
Depends: R (≥ 3.00), Rcpp
LinkingTo: Rcpp
Suggests: mvtnorm, MASS, combinat, R.rsp
Published: 2024-08-26
DOI: 10.32614/CRAN.package.ecp
Author: Nicholas A. James [aut], Wenyu Zhang [aut, cre], David S. Matteson [aut]
Maintainer: Wenyu Zhang
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Citation: ecp citation info
Materials:
In views: TimeSeries
CRAN checks: ecp results

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