R/ccid-package.R

In ccid: Cross-Covariance Isolate Detect: a New Change-Point Method for Estimating Dynamic Functional Connectivity

#' ccid: a change-point detection method for estimating dynamic functional connectivity
#'
#' The \code{ccid} package implements the Cross-Covariance Isolate Detect
#' (CCID) methodology for the estimation of the number and location of
#' multiple change-points in the second-order (cross-covariance or network)
#' structure of multivariate, possibly high-dimensional time series. The
#' method is motivated by the detection of change points in functional
#' connectivity networks for functional magnetic resonance imaging (fMRI),
#' electroencephalography (EEG), magentoencephalography (MEG) and
#' electrocorticography (ECoG) data. The stopping rules used for the
#' change-point detection rely either on thresholding or on the optimization
#' of a model selection criterion. The main routines of the package are
#' \code{\link{detect.th}} and \code{\link{detect.ic}}. The functions have been
#' extensively tested on fMRI data, therefore, their parameters have been
#' tuned to work well on this data and the functions might not work well
#' in other structures, such as time series that are negatively serially
#' correlated.
#'
#' @author Andreas Anastasiou, \email{anastasiou.andreas@ucy.ac.cy}, Piotr Fryzlewicz, \email{p.fryzlewicz@lse.ac.uk}, Ivor Cribben, \email{cribben@ualberta.ca}
#' @references ``Cross-covariance isolate detect: a new change-point method for estimating
#'   dynamic functional connectivity'', Anastasiou et al (2020), preprint.
#' @seealso
#' \code{\link{detect.th}} and \code{\link{detect.ic}}.
#' @examples
#' # See Examples for the function ``detect.th''.
#' @docType package
#' @name ccid
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