ccid: Cross-Covariance Isolate Detect: a New Change-Point Method for
Estimating Dynamic Functional Connectivity
Provides efficient implementation of 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
main routines in the package have been extensively tested on fMRI data.
For details on the CCID methodology, please see Anastasiou et
al (2022), Cross-covariance isolate detect: A new change-point method for
estimating dynamic functional connectivity. Medical Image Analysis, Volume
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