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2018). CCHS data used in this library is accessed and adapted in
Packaged: 2021-10-25 18:02:26 UTC; warsameyusuf

Title: Cross-Covariance Isolate Detect: a New Change-Point Method for
Imports: IDetect, hdbinseg, GeneNet, gdata
Authors@R: c(person("Andreas", "Anastasiou", email = "anastasiou.andreas@ucy.ac.cy", role = c("aut", "cre")),
person("Ivor", "Cribben", email = "cribben@ualberta.ca", role = "aut"),
Description: 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

Title: Convex Clustering Methods and Clustering Indexes
Description: 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

Packaged: Mon Apr 26 23:21:53 2004; dimi

Packaged: Wed Apr  6 16:02:15 2005; hornik

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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