vccp: Vine Copula Change Point Detection in Multivariate Time Series

Implements the Vine Copula Change Point (VCCP) methodology for the estimation of the number and location of multiple change points in the vine copula structure of multivariate time series. The method uses vine copulas, various state-of-the-art segmentation methods to identify multiple change points, and a likelihood ratio test or the stationary bootstrap for inference. The vine copulas allow for various forms of dependence between time series including tail, symmetric and asymmetric dependence. The functions have been extensively tested on simulated multivariate time series data and fMRI data. For details on the VCCP methodology, please see Xiong & Cribben (2021).

Version: 0.1.1
Imports: VineCopula, stats, graphics, mosum, mvtnorm
Suggests: knitr, rmarkdown
Published: 2021-05-29
Author: Xin Xiong [aut, cre], Ivor Cribben [aut]
Maintainer: Xin Xiong <xinxiong at hsph.harvard.edu>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: vccp results

Documentation:

Reference manual: vccp.pdf

Downloads:

Package source: vccp_0.1.1.tar.gz
Windows binaries: r-devel: vccp_0.1.1.zip, r-release: vccp_0.1.1.zip, r-oldrel: vccp_0.1.1.zip
macOS binaries: r-release (arm64): vccp_0.1.1.tgz, r-release (x86_64): vccp_0.1.1.tgz, r-oldrel: vccp_0.1.1.tgz
Old sources: vccp archive

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