v0.7.2

0.7.2

Changed

• fit_negbinomial now refuses non-overdispersed data. The negative
  binomial always satisfies variance/mean = 1/p > 1, so when the sample
  variance is not greater than the mean no finite (r, p) maximizes the
  likelihood -- the supremum is the Poisson limit (r -> inf, p -> 1). The
  fitter previously ran the optimizer anyway and returned an arbitrary,
  non-optimal finite result (with success=True) whose value was unstable
  across versions and platforms. It now raises ValueError pointing to
  fit_poisson, matching the long-standing contract of
  fit_negbinomial_moments. Because fit_best_frequency already drops
  candidates that fail to fit, model selection on underdispersed data now
  cleanly falls back to Poisson.

Removed

• Obsolete negbinomial fit warning suppression. The np.errstate guard
  added in 0.7.1 silenced a SciPy finite-difference RuntimeWarning that only
  arose when the optimizer chased the p -> 1 boundary on near-Poisson data.
  With such data now rejected up front, the optimizer only runs on
  well-conditioned interior problems and the warning no longer occurs, so the
  suppression has been removed rather than carried forward -- the guard is the
  root-cause fix.

Tests

• Extended tests/estimation/test_negbinomial_parameterization.py to pin the
  new refusal (underdispersed, equidispersed, and degenerate inputs raise),
  confirm overdispersed fits are warning-free without suppression, and verify
  fit_best_frequency falls back to Poisson on underdispersed data.