fabisearch: Change Point Detection in High-Dimensional Time Series Networks

Implementation of the Factorized Binary Search (FaBiSearch) methodology for the estimation of the number and location of multiple change points in the network (or clustering) structure of multivariate high-dimensional time series. The method is motivated by the detection of change points in functional connectivity networks for functional magnetic resonance imaging (fMRI) data. FaBiSearch uses non-negative matrix factorization (NMF), an unsupervised dimension reduction technique, and a new binary search algorithm to identify multiple change points. It also requires minimal assumptions. The main routines of the package are detect.cps(), for multiple change point detection, est.net(), for estimating a network between stationary multivariate time series, net.3dplot(), for plotting the estimated functional connectivity networks, and opt.rank(), for finding the optimal rank in NMF for a given data set. The functions have been extensively tested on simulated multivariate high-dimensional time series data and fMRI data. For details on the FaBiSearch methodology, please see Ondrus et al. (2021).

Version: 0.0.2.4
Depends: R (≥ 3.10), NMF
Imports: rgl, reshape2
Suggests: testthat (≥ 3.0.0)
Published: 2021-02-24
Author: Martin Ondrus [aut, cre], Ivor Cribben [aut]
Maintainer: Martin Ondrus <mondrus at ualberta.ca>
License: MIT + file LICENSE
URL: https://github.com/mondrus96/FaBiSearch
NeedsCompilation: no
CRAN checks: fabisearch results

Downloads:

Reference manual: fabisearch.pdf
Package source: fabisearch_0.0.2.4.tar.gz
Windows binaries: r-devel: fabisearch_0.0.2.4.zip, r-release: fabisearch_0.0.2.4.zip, r-oldrel: not available
macOS binaries: r-release: fabisearch_0.0.2.4.tgz, r-oldrel: not available

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