Nothing
#' vccp: Detect multiple change points in the vine copula structure of multivariate time series by Vine Copula Change Point Model
#'
#' 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).
#'
#' @section vccp functions:
#' \link{mvn.sim.2.cps}, \link{getTestPlot} and \link{vccp.fun}
#' @examples
#' # See examples in the function vccp.fun.
#'
#' @section Author(s):
#' Xin Xiong, Ivor Cribben (\email{cribben@@ualberta.ca})
#' @section References:
#' "Beyond linear dynamic functional connectivity: a vine copula change point model", Xiong and Cribben (2021), bioRxiv 2021.04.25.441254.
#' @docType package
#' @name vccp
NULL
#> NULL
#' @importFrom VineCopula RVineStructureSelect
#' @importFrom VineCopula D2RVine
#' @importFrom VineCopula RVineCopSelect
#' @importFrom VineCopula RVineVuongTest
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.