vccp: vccp: Detect multiple change points in the vine copula...

In vccp: Vine Copula Change Point Detection in Multivariate Time Series

Description

The vccp package 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).

vccp functions

mvn.sim.2.cps, getTestPlot and vccp.fun

Author(s)

Xin Xiong, Ivor Cribben ([e-mail])

References

"Beyond linear dynamic functional connectivity: a vine copula change point model", Xiong and Cribben (2021), bioRxiv 2021.04.25.441254.

Examples

# See examples in the function vccp.fun.

vccp documentation built on May 29, 2021, 5:06 p.m.