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Temporal and Spatial Process Science

Recurrence analysis preserves repeated temporal structure that is lost in total dwell and transition counts.

recurrence <- gaze_recurrence(samples, representation = "coordinates")
recurrence_features(recurrence)
plot_recurrence_matrix(recurrence)
plot_diagonal_recurrence_profile(recurrence)
windowed <- windowed_recurrence(recurrence, window = 120, step = 30)
plot_windowed_recurrence(windowed)
cross <- cross_recurrence(samples$pupil_bc, samples$eda, channels = "pupil_eda")
plot_crossmodal_recurrence(cross)

The experimental point-process layer models where fixations occur and can add a recent-fixation history term.

point_process <- fit_fixation_point_process(
  fixations,
  interaction = "self_exciting",
  x_col = "x_norm",
  y_col = "y_norm",
  time_col = "onset"
)
plot_fixation_intensity(point_process)
plot_spatial_residuals(point_process)
plot_observed_expected_fixations(point_process)
diagnose_gaze_point_process(point_process)

These models are experimental until parameter recovery, predictive checks, and external validation are complete.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.