Articles • radiatR

Guide for AI agents: Conventions, failure behaviour and pitfalls an AI agent (or anyone scripting radiatR) needs before analysing directional data.
Circular Statistics and Distribution Overlays: Dispersion summaries, parametric fits, hypothesis tests with multiple-comparison correction, circular correlation, and rose, density and KDE distribution overlays for a headings data frame.
radiatR design contract: The design rules behind the radiatR API: which objects are rich classes, which stay plain tibbles, and the conventions the functions share.
Working with Loaders: Turning tracking exports into Tracks: experiment manifests, read_tracks() and read_tracks_dir(), and registering a custom loader dialect or format.
radiatR: The main workflow: from tracking files or a table of angles, through heading derivation and circular summaries, to a publication-ready radiate() plot.
Simulating Trajectories: Generating synthetic trajectories on the unit disk with simulate_tracks(), for pipeline testing, teaching, and power analysis.
Quantifying path shape: Per-trajectory spatial metrics – distance travelled, straightness, and displacement- versus turning-based tortuosity – on recorded x/y coordinates.