ccid {ccid}R Documentation

ccid: a change-point detection method for estimating dynamic functional connectivity


The 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 and 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.


Andreas Anastasiou,, Piotr Fryzlewicz,, Ivor Cribben,


“Cross-covariance isolate detect: a new change-point method for estimating dynamic functional connectivity”, Anastasiou et al (2020), preprint.

See Also and detect.ic.


# See Examples for the function ``''.

[Package ccid version 1.0.0 Index]