Package index
Unified outlier cleaning
One-call entry point — orchestrates the four primitives with the class-aware consensus rule (default; user-configurable via consensus =), plus topological block expansion. Two post-run diagnostics: mt_diagnose_clean_track() for cleaning health, mt_diagnose_flags() for per-detector flag composition + consensus-mode comparison.
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mt_clean_track() - Unified outlier detection for a move2 track
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mt_diagnose_clean_track() - Post-run diagnostic suite for mt_clean_track results
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mt_diagnose_flags() - Per-detector flag audit for a cleaned move2 object
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v_phys_estimate() - Allometric estimate of physiological maximum speed
Outlier-detection primitives
The four primitives composed by mt_clean_track(). Run individually for fine-grained control.
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mt_flag_outliers_bridge() - Flag outliers via Brownian bridge residuals
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mt_flag_outliers_detour() - Flag outliers using path-vs-displacement detour ratio
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mt_flag_outliers() - Flag or remove outliers in movement data based on joint movement probabilities
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mt_flag_speed_cap() - Flag fixes by a speed cap (data-driven or physiological)
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mt_suggest_speed_cap() - Suggest a data-driven speed cap from the observed step-speed distribution
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mt_peel_speed() - Iterative speed peel at a fixed physiological cap
Consensus rule (combine per-detector flags)
Externalised voting/consensus across detector outputs. Used internally by mt_clean_track() and exported so users running their own cascades (or running the four primitives standalone) can apply the same rules to their own per-detector flag columns.
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mt_flag_consensus() - Combine per-detector flag columns into a single outlier decision
State-aware bridge variants (standalone)
Per-axis bridge motion variance with an envelope flag rule across three Z channels. Exported as standalone primitives, not currently wired into mt_clean_track()’s cascade.
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mt_flag_outliers_dbgb() - Flag outliers via state-aware bridge residuals (dBGB envelope)
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mt_flag_outliers_dbbmm() - Flag outliers via dBBMM-Z (isotropic submodel of dBGB)
Alternative outlier strategies
Voting / scanning alternatives on the probability surface. See mt_clean_track()’s @seealso for when each is the right reach.
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mt_sequential_outliers() - Sequential outlier detection for movement data
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mt_combined_outliers() - Combined outlier detection using multiple methods
Multi-scale persistence annotation
Detector-agnostic confidence helper that annotates flagged fixes with a persistence score across temporal scales.
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mt_persistence_score() - Multi-scale persistence score for outlier flags
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mt_filter_gps_quality() - Filter fixes by GPS-geometry quality
Utilisation distributions and motion variance
Dynamic Brownian-bridge variance estimation, UD computation on a common grid, accessors and segment masking for the variance pipeline.
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mt_dbbmm_variance() - Dynamic Brownian Motion Variance Estimation
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mt_dbbmm_ud() - Dynamic Brownian Bridge Movement Model — Utilisation Distribution
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mt_dbgb_variance() - Dynamic Bivariate Gaussian Bridge Variance Estimation
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mt_dbgb_ud() - Dynamic Bivariate Gaussian Bridge — Utilisation Distribution
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mt_motion_variance() - Extract Motion Variance
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mt_suggest_dbbmm_window() - Suggest dBBMM / dBGB sliding-window parameters from track sampling
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ud_volume() - Convert a utilisation distribution to cumulative-volume space
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ud_outer_probability() - Outer probability at a set of locations
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mt_mask_segments() - Exclude segments from a dBBMM or dBGB utilisation distribution
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emd() - Earth-mover's distance between utilisation distributions
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mt_corridor() - Identify corridor segments in a move2 trajectory
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mt_thin_distance() - Thin a track by cumulative along-track distance
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mt_thin_time() - Thin a track to a target time interval (tolerance-constrained)
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print(<mt_dbbmm_variance>) - Print method for mt_dbbmm_variance
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print(<mt_dbgb_variance>) - Print method for mt_dbgb_variance