Package: dynR 0.1.2

Lucas França

dynR: Dynamic Connectivity Analysis for Neurophysiological Timeseries

An R port of the Python dynfc library for computing dynamic connectivity (dynFC) representations from multivariate neurophysiological timeseries, including BOLD fMRI, EEG, LFP, and related signals. Implements sliding-window Pearson correlation (Hansen et al., 2015), edge-centric cofluctuation analysis (Esfahlani et al., 2020; Faskowitz et al., 2020), instantaneous phase-locking via the Hilbert transform, dynamic phase-locking matrices (dPL), the LEiDA leading-eigenvector framework (Cabral et al., 2017; Lord et al., 2019), and the Kuramoto order parameter with metastability and Shannon entropy. Part of the Circadia Lab R ecosystem.

Authors:Lucas França [aut, cre], Mario Leocadio-Miguel [aut], Dafnis Batallé [aut]

dynR_0.1.2.tar.gz
dynR_0.1.2.zip(r-4.7)dynR_0.1.2.zip(r-4.6)dynR_0.1.2.zip(r-4.5)
dynR_0.1.2.tgz(r-4.6-any)dynR_0.1.2.tgz(r-4.5-any)
dynR_0.1.2.tar.gz(r-4.7-any)dynR_0.1.2.tar.gz(r-4.6-any)
dynR_0.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
dynR/json (API)

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

Bug tracker:https://github.com/circadia-bio/dynr/issues

Pkgdown/docs site:https://dynr.circadia-lab.uk

Datasets:
  • fc - Functional connectivity matrix
  • ts - BOLD fMRI timeseries

On CRAN:

Conda:

4.51 score 108 scripts 11 exports 23 dependencies

Last updated from:c08c64a0b2 (on v0.1.2). Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK140
source / vignettesOK204
linux-release-x86_64OK145
macos-release-arm64OK112
macos-oldrel-arm64OK93
windows-develOK91
windows-releaseOK88
windows-oldrelOK74
wasm-releaseOK133

Exports:bandpass_filtercofluctcorr_corrcorr_slidedo_eucliddyn_phase_lockdyn_transitionsget_leidahilbert_phaseskuramotoshannon_entropy

Dependencies:clicpp11dplyrgenericsgluegsignallifecyclemagrittrpillarpkgconfigpracmapurrrR6Rcpprlangstringistringrtibbletidyrtidyselectutf8vctrswithr

Phase-based dynamic FC: Hilbert transform, LEiDA, and Kuramoto
Overview | Step 1: Bandpass filtering | Step 2: Instantaneous phases via the Hilbert transform | Step 3: Dynamic phase-locking matrix (dPL) and LEiDA | The dPL matrix | The leading eigenvector (LEiDA) | Step 4: Brain state discovery with K-means | Choosing K | State sequence | Step 5: Kuramoto order parameter | The measure | Metastability | References
Rendered fromphase-based-fc.Rmdusingknitr::rmarkdown

Last update: 2026-06-28
Started: 2026-06-28

Correlation-based dynamic FC: sliding windows and edge cofluctuations
Overview | Sliding-window correlation | Window length: a critical choice | Validating against static FC | FC variability across windows | Edge-centric cofluctuations | Root-sum-square (RSS) cofluctuation | Correlation of correlations | Brain state analysis from sliding-window FC | References
Rendered fromsliding-window-fc.Rmdusingknitr::rmarkdown

Last update: 2026-06-28
Started: 2026-06-28

Getting started with dynR
What is dynamic functional connectivity? | When to use which approach | Package data | Pipeline overview | Quick example: phase-based pipeline | Quick example: correlation-based pipeline | Sanity check: single window equals static FC | Further reading | References
Rendered fromgetting-started.Rmdusingknitr::rmarkdown

Last update: 2026-06-28
Started: 2026-06-28