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 (2020) .


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1.0.0 by Andreas Anastasiou, a year ago

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Authors: Andreas Anastasiou [aut, cre] , Ivor Cribben [aut] , Piotr Fryzlewicz [aut]

Documentation:   PDF Manual  

GPL-3 license

Imports IDetect, hdbinseg, GeneNet, gdata

Suggests testthat

See at CRAN