Cross-Covariance Isolate Detect: a New Change-Point Method for
Estimating Dynamic Functional Connectivity
c(person("Andreas", "Anastasiou", email = "anastasiou.andreas@ucy.ac.cy", role = c("aut", "cre")),
person("Ivor", "Cribben", email = "cribben@ualberta.ca", role = "aut"),
person("Piotr", "Fryzlewicz", email = "p.fryzlewicz@lse.ac.uk", role = "aut"))
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) <doi:10.1101/2020.12.20.423696>.
Andreas Anastasiou [aut, cre],
Ivor Cribben [aut],
Piotr Fryzlewicz [aut]

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.

Convex Clustering Methods and Clustering Indexes
Convex Clustering methods, including Kmeans algorithm,
On-line Update algorithm (Hard Competitive Learning) and
Neural Gas algorithm (Soft Competitive Learning) and
calculation of several indexes for finding the number of
clusters in a data set.