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  "Title": "Cross-Covariance Isolate Detect: a New Change-Point Method for\nEstimating Dynamic Functional Connectivity",
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  "Authors@R": "c(person(\"Andreas\", \"Anastasiou\", email = \"anastasiou.andreas@ucy.ac.cy\", role = c(\"aut\", \"cre\")),\nperson(\"Ivor\", \"Cribben\", email = \"cribben@ualberta.ca\", role = \"aut\"),\nperson(\"Piotr\", \"Fryzlewicz\", email = \"p.fryzlewicz@lse.ac.uk\", role = \"aut\"))",
  "Description": "Provides efficient implementation of the Cross-Covariance\nIsolate Detect (CCID) methodology for the estimation of the\nnumber and location of multiple change-points in the\nsecond-order (cross-covariance or network) structure of\nmultivariate, possibly high-dimensional time series. The method\nis motivated by the detection of change points in functional\nconnectivity networks for functional magnetic resonance imaging\n(fMRI), electroencephalography (EEG), magentoencephalography\n(MEG) and electrocorticography (ECoG) data. The main routines\nin the package have been extensively tested on fMRI data. For\ndetails on the CCID methodology, please see Anastasiou et al\n(2020).",
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  "URL": "https://github.com/Anastasiou-Andreas/ccid",
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  "Repository": "https://anastasiou-andreas.r-universe.dev",
  "Date/Publication": "2020-12-22 06:51:36 UTC",
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