{ "_id": "6ac361a2d743750d376db2ef", "Package": "dynR", "Title": "Dynamic Connectivity Analysis for Neurophysiological Timeseries", "Version": "0.1.5", "Authors@R": "c(\nperson(\"Lucas\", \"França\",\nemail = \"lucas.franca@northumbria.ac.uk\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0003-0853-1319\")),\nperson(\"Mario\", \"Leocadio-Miguel\",\nemail = \"mario.miguel@northumbria.ac.uk\",\nrole = \"aut\",\ncomment = c(ORCID = \"0000-0002-7248-3529\")),\nperson(\"Dafnis\", \"Batallé\",\nemail = \"dafnis.batalle@kcl.ac.uk\",\nrole = \"aut\",\ncomment = c(ORCID = \"0000-0003-2097-979X\")))", "Description": "An R port of the Python dynfc library for computing\ndynamic connectivity (dynFC) representations from multivariate\nneurophysiological timeseries, including BOLD fMRI, EEG, LFP,\nand related signals. Implements sliding-window Pearson\ncorrelation (Hansen et al., 2015), edge-centric cofluctuation\nanalysis (Esfahlani et al., 2020; Faskowitz et al., 2020),\ninstantaneous phase-locking via the Hilbert transform, dynamic\nphase-locking matrices (dPL), the LEiDA leading-eigenvector\nframework (Cabral et al., 2017; Lord et al., 2019), and the\nKuramoto order parameter with metastability and Shannon\nentropy. 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