Directional Fragility Diagnostics for EEG Connectivity

Documentation for package ‘metricT’ version 0.2.0

metric_t	Metric-T: diagnostic for directional fragility
metric_t_trend	Metric-T for a continuous covariate, with stratified exact permutation
mt_bands	Default frequency bands (Hz), as in Kimura (2026)
mt_clean_epochs	Detrend epochs and drop those with large excursions
mt_connectivity	wPLI and MSC from the same cross-spectrum (one subject)
mt_dc	Direction consistency (DC)
mt_epoch	Cut a continuous recording into fixed-length epochs
mt_features_from_eeg_csv	Features from one CSV file of preprocessed EEG per subject
mt_features_from_epochs	Subject-by-feature matrices from per-subject epoch arrays
mt_labelings	Metric-T under alternative labelling rules
mt_labelset	Metric-T over the complete set of admissible labellings
mt_label_space	Enumerate the complete two-group labelling space
mt_locate	Locate a labelling in the labelling space
mt_motion_bands	Frequency bands for paced repetitive movement (Hz)
mt_null_plot	Permutation null distribution of T for one condition
mt_read_features_csv	Write and read features as a plain CSV table
mt_read_npy	Read a NumPy .npy file (base R, no Python needed)
mt_read_npz	Read the feature archive written by the Python reference pipeline
mt_simulate	Simulate EEG with tunable coupling-mode composition
mt_simulate_motion	Simulate pose-estimated repetitive movement
mt_write_features_csv	Write and read features as a plain CSV table
mt_wy_maxt	Westfall-Young max-T step-down adjustment
plot.metricT	Plot direction consistency for both metrics
run_metricT_app	Launch the Metric-T graphical interface