Package: fabisearch 0.0.4.5

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 the 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 requires minimal assumptions. Lastly, we provide interactive, 3-dimensional, brain-specific network visualization capability in a flexible, stand-alone function. This function can be conveniently used with any node coordinate atlas, and nodes can be color coded according to community membership, if applicable. The output is an elegantly displayed network laid over a cortical surface, which can be rotated in the 3-dimensional space. 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) <arxiv:2103.06347>. For a more detailed explanation and applied examples of the fabisearch package, please see Ondrus and Cribben (2022), preprint.

Authors:Martin Ondrus [aut, cre], Ivor Cribben [aut]

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fabisearch.pdf |fabisearch.html
fabisearch/json (API)
NEWS

# Install 'fabisearch' in R:
install.packages('fabisearch', repos = c('https://mondrus96.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/mondrus96/fabisearch/issues

Datasets:
  • AALatlas - Automated Anatomical Labeling (AAL) atlas coordinates
  • AALfmri - 90 ROI data from the NYU test-retest resting state fMRI data set
  • adjmatrix - Adjacency matrix for the NYU test-restest resting-state fMRI data set
  • gordatlas - Gordon atlas coordinates
  • gordfmri - 333 ROI data from the NYU test-retest resting state fMRI data set
  • logSP500 - Daily adjusted logarithmic returns for the Standard and Poor's 500
  • sim2 - A simulated data set

On CRAN:

3.00 score 1 stars 2 scripts 200 downloads 4 exports 70 dependencies

Last updated 3 months agofrom:75dd38c483. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 03 2024
R-4.5-winOKNov 03 2024
R-4.5-linuxOKNov 03 2024
R-4.4-winOKNov 03 2024
R-4.4-macOKNov 03 2024
R-4.3-winOKNov 03 2024
R-4.3-macOKNov 03 2024

Exports:detect.cpsest.netnet.3dplotopt.rank

Dependencies:base64encBiobaseBiocGenericsBiocManagerbslibcachemcliclustercodetoolscolorspacedigestdoParalleldoRNGevaluatefansifarverfastmapfontawesomeforeachfsgenericsggplot2gluegridBasegtablehighrhtmltoolshtmlwidgetsisobanditeratorsjquerylibjsonliteknitrlabelinglatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellnlmeNMFpillarpkgconfigplyrR6rappdirsRColorBrewerRcppregistryreshape2rglrlangrmarkdownrngtoolssassscalesstringistringrtibbletinytexutf8vctrsviridisLitewithrxfunyaml