R Observatory: Metrics for fabisearch version 0.0.4.5

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

fabisearch

0.0.4.5

Change Point Detection in High-Dimensional Time Series Networks

0packages depend

2.8Kdownloads / year

–test coverage

11/13checks pass

Overview

About

Maintained by Martin OndrusFirst published 2021-02-247 releasesCRAN page ↗GitHub ↗

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.

Install

R (CRAN)pak (dev)Ubuntu (r2u)

install.packages("fabisearch", repos = "https://cloud.r-project.org/")

Installs the current CRAN release. Prebuilt binaries on Windows and macOS.

if (!requireNamespace("pak", quietly = TRUE))
  install.packages("pak", repos = "https://cloud.r-project.org/")
pak::pak("mondrus96/FaBiSearch")

Development version from mondrus96/FaBiSearch. Installs pak first if it is not already available.

Release

# enable cranapt (r2u) if it is not already set up, then install
grep -rqs r2u.stat.illinois.edu /etc/apt/sources.list /etc/apt/sources.list.d/ \
  || curl -fsSL https://raw.githubusercontent.com/eddelbuettel/r2u/master/inst/scripts/add_cranapt_noble.sh | sudo bash
sudo apt install -y r-cran-fabisearch

Fast Ubuntu binaries via r2u (no compilation). The cranapt setup runs once and is skipped when it is already enabled.

bspm (used by r2u) also needs make: sudo apt install make

Health

CRAN checks

2NOTE11OK

Failing flavors

NOTE r-devel-linux-x86_64-debian-clang
NOTE r-devel-linux-x86_64-debian-gcc

Slowest check: 2.5 min · r-oldrel-windows-x86_64

Code health

None

Tests · ratio 0.00

not tracked

Coverage

100%

Documentation · exports

7

Dependencies · direct

Check history

NOTE2026-03-10
12 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Documentation

READMENoVignettesNopkgdown siteNoNEWSYes · 33% structuredCode of conductNoContributing guideNo

Examples that run: 25%
Documented parameters: 100%
Return-value docs: 100%
References docs: 42%

Downloads

2.8K

CRAN downloads in the past year

Rank #14,136 · ~8/day · ~234/mo

Releases R versions

Daily download trend is not available in this view yet.

18630 days

72790 days

2.8K1 year

Compare downloads with other packages →

Also on189 r2u

Repository

GitHub ↗ mondrus96/fabisearch

2Stars

0Forks

0Open issues

0Open PRs

0Releases

58Commits

1Contributors

58 commits · Last activity 2024-08-20

Stars over time

2025-05-24 · 22026-07-07 · 2

Repository practices

Upstream repositoryBeta

1 development-tooling and community-health practice detected across 1 family in the upstream repository

Checks run against github.com/mondrus96/fabisearch on 2026-08-16.

Lint, format, editor (1)

RStudio project

How this is detected·Detection ruleset v1 (2026-07-18)

Dependencies

Declared dependencies

7 external dependencies (excludes base and recommended)

Depends (2)

R >= 3.10NMF

Imports (6)

rgl reshape2 foreach doParallel doRNGparallel

LinkingTo (0)

none

Suggests (1)

testthat

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Nothing depends on this yet.

Code & Tests

Datasets

People & History

People (2)

Maintainer (1)

Martin Ondrus

Author, Maintainer

Authors (2)

Martin Ondrus

Author, Maintainer

Ivor Cribben

Author

Package Timeline

7 releases. Pick two to compare their code metrics. R releases are shown for context.

R
R 4.6.0 released · 2026-04-24
R
R 4.5.0 released · 2025-04-11
R
R 4.4.0 released · 2024-04-24
R
R 4.3.0 released · 2023-04-21
0.0.4.5Latest
2023-01-12 · current release · diff ↗
unarchivedReturned to CRAN
2023-01-12
archivedRemoved from CRAN
2022-11-20
requires archived package 'pkgmaker'
R
R 4.2.0 released · 2022-04-22
0.0.4.4
2022-03-15 · diff ↗
0.0.4.3
2022-03-03 · diff ↗
0.0.4.1
2022-02-08 · diff ↗
0.0.3.9
2021-11-23 · diff ↗
unarchivedReturned to CRAN
2021-11-23
archivedRemoved from CRAN
2021-10-26
check problems were not corrected in time
0.0.3.6
2021-10-09 · diff ↗
R
R 4.1.0 released · 2021-05-18

Show 2 earlier events

0.0.2.4
2021-02-24
R
R 4.0.0 released · 2020-04-24

Package metadata

First published: 2021-02-24
Total releases: 7 / 5 yrs
License: MIT + file LICENSE OSI
Minimum R: ≥ 3.10
Bundled data: 3.4 MB / 7 files
Download size: 3.7 MB
Installed size: not tracked yet
With dependencies: not tracked yet

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("fabisearch")

Format

Ondrus, M., & Cribben, I. (2023). fabisearch: Change Point Detection in High-Dimensional Time Series Networks (Version 0.0.4.5) [Computer software]. https://doi.org/10.32614/CRAN.package.fabisearch

This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

Format

APA

Balamuta, J. J. (2026). R Observatory: Metrics for fabisearch version 0.0.4.5 [Data set]. HJJB, LLC. Data release v2026-08-22. https://doi.org/10.5281/zenodo.21843040

From data release v2026-08-22, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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