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 75.

Version: 1.2.0
Depends: R (≥ 3.6.0)
Imports: IDetect, hdbinseg, GeneNet, gdata
Suggests: testthat (≥ 2.0.0)
Published: 2022-02-01
DOI: 10.32614/CRAN.package.ccid
Author: Andreas Anastasiou [aut, cre], Ivor Cribben [aut], Piotr Fryzlewicz [aut]
Maintainer: Andreas Anastasiou <anastasiou.andreas at>
License: GPL-3
NeedsCompilation: no
Citation: ccid citation info
Materials: README
CRAN checks: ccid results


Reference manual: ccid.pdf


Package source: ccid_1.2.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): ccid_1.2.0.tgz, r-oldrel (arm64): ccid_1.2.0.tgz, r-release (x86_64): ccid_1.2.0.tgz, r-oldrel (x86_64): ccid_1.2.0.tgz
Old sources: ccid archive


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