Package: fabisearch
Title: Change Point Detection in High-Dimensional Time Series Networks
Version: 0.0.2.4
Authors@R: c(person("Martin", "Ondrus", email = "mondrus@ualberta.ca", role = c("aut", "cre")), person("Ivor", "Cribben", email = "cribben@ualberta.ca", role = "aut"))
Description: 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).
URL: https://github.com/mondrus96/FaBiSearch
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Suggests: testthat (>= 3.0.0)
Config/testthat/edition: 3
Imports: rgl, reshape2
Depends: R (>= 3.10), NMF
Packaged: 2021-02-22 01:43:01 UTC; Martin
Author: Martin Ondrus [aut, cre],
  Ivor Cribben [aut]
Maintainer: Martin Ondrus <mondrus@ualberta.ca>
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2021-02-24 09:40:05 UTC
