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vccp  

Vine Copula Change Point Detection in Multivariate Time Series
View on CRAN: Click here


Download and install vccp package within the R console
Install from CRAN:
install.packages("vccp")

Install from Github:
library("remotes")
install_github("cran/vccp")

Install by package version:
library("remotes")
install_version("vccp", "0.1.1")



Attach the package and use:
library("vccp")
Maintained by
Xin Xiong
[Scholar Profile | Author Map]
First Published: 2021-03-03
Latest Update: 2021-05-29
Description:
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).
How to cite:
Xin Xiong (2021). vccp: Vine Copula Change Point Detection in Multivariate Time Series. R package version 0.1.1, https://cran.r-project.org/web/packages/vccp. Accessed 20 Feb. 2025.
Previous versions and publish date:
0.1.0 (2021-03-03 10:20)
Other packages that cited vccp R package
View vccp citation profile
Other R packages that vccp depends, imports, suggests or enhances
Complete documentation for vccp
Functions, R codes and Examples using the vccp R package
Some associated functions: getTestPlot . mvn.sim.2.cps . vccp.fun . vccp . 
Some associated R codes: BootsTest.R . MOSUM_VC.R . MOSUM_VC_Boot.R . MOSUM_VC_Vuong.R . NBS_FindPoints.R . NBS_VC.R . OBS_VC.R . OBS_VC_Boot.R . OBS_VC_Vuong.R . Sim_Data.R . VCCP.R . VCCP_package.R . VuongTest.R . WBS_VC.R . getTestPlot.R .  Full vccp package functions and examples
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