L-UQ 1.1.0

[1.1.0] — 2026-07-11

Adds uncertainty-aware identification and a population-truth benchmark,
in response to peer-review-style feedback on the manuscript.

Added

• identify_dist_bootstrap (Python) / Identify_dist_bootstrap.m
  (MATLAB): bootstrap-based, uncertainty-aware distribution
  identification. Resamples the data with replacement, re-identifies on
  each resample, and returns per-family selection frequencies, 95%
  percentile confidence intervals for (t3, t4), and a clear/ambiguous
  status flag. Addresses the fact that at small n the single "closest"
  family is often not statistically distinguishable from the runner-up.
• Bootstrap identification unit tests in all three suites (Python +5,
  MATLAB +3, Octave section G).

Changed

• The replication benchmark (replication/run_all.py) now scores every
  fit against the KNOWN parent distribution (population truth) rather
  than the small sample's own histogram: integrated absolute CDF error,
  extreme-quantile error, and the risk-relevant tail-probability error
  P(X > x_c) at the true 99th percentile, with Jensen-Shannon retained
  only as a secondary diagnostic. This removes histogram-binning
  sensitivity and the circularity of scoring a fit against the noisy
  sample it was estimated from.
• The benchmark additionally records identification accuracy (true
  family ranked first / in top three, split by 2-parameter point vs
  3-parameter curve families, and fallback rate), written to
  replication/output/identification_accuracy.csv.