Package index • dynR

Data

Example datasets bundled with dynR.

ts: BOLD fMRI timeseries (200 parcels, 600 timepoints)
fc: Functional connectivity matrix (200 parcels)

Pipelines

High-level wrappers running the full phase-based or correlation-based pipeline in one call.

leida_pipeline(): LEiDA pipeline
sw_pipeline(): Sliding-window pipeline

Signal preprocessing

Filter and prepare BOLD timeseries prior to dynFC analysis.

bandpass_filter(): Butterworth bandpass filter

Phase-based methods

Instantaneous phase extraction, phase-locking, and Kuramoto order parameter.

hilbert_phases(): Hilbert transform phase extraction
dyn_phase_lock(): Dynamic phase-locking matrix (dPL)
get_leida(): Leading eigenvector decomposition (LEiDA)
kuramoto(): Kuramoto order parameter and metastability

Correlation-based methods

Sliding-window FC and edge-centric cofluctuation.

corr_slide(): Sliding window correlation
cofluct(): Edge-centric cofluctuation analysis
corr_corr(): Correlation of correlations matrix

State dynamics

Quantify the temporal structure of brain-state sequences.

dyn_transitions(): State transition probabilities (Markov analysis)

Visualisation

Plot FC matrices, synchrony time series, and brain state sequences.

plot_fc(): Plot a functional connectivity matrix
plot_synchrony(): Plot Kuramoto synchrony time series
plot_state_sequence(): Plot a brain state sequence
plot(<dynR_leida>): Plot method for dynR_leida objects
plot(<dynR_sw>): Plot method for dynR_sw objects

Multi-subject analysis

Batch processing across participants and stacking for cross-subject clustering.

batch_leida(): Batch LEiDA pipeline across subjects
batch_sw(): Batch sliding-window pipeline across subjects
stack_leida(): Stack LEiDA eigenvectors across subjects
stack_synchrony(): Stack synchrony vectors across subjects

Utilities

Entropy and distance helpers.

shannon_entropy(): Shannon entropy
do_euclid(): Euclidean distance between consecutive points