v0.7.1

0.7.1

Changed

• fit_negbinomial performance. The Negative Binomial MLE now aggregates
  the data to distinct counts and scores each optimizer step with a single
  vectorized scipy.stats.nbinom.logpmf call weighted by the count
  multiplicities, instead of evaluating a scalar PMF once per observation per
  iteration. Fitted results are unchanged (the likelihood is identical up to
  floating point); only the runtime differs -- the two official
  fit_best_frequency model-selection tests drop from ~23.5s each to well
  under a second. Scoring in log space via logpmf also avoids the tail
  underflow of the previous log(pmf(...)).
• fit_negbinomial warning hygiene. The L-BFGS-B finite-difference
  gradient probes the edge of the feasible (r, p) box, where the negative
  log-likelihood is +inf; the resulting inf - inf inside SciPy's numerical
  derivative raised a benign numpy "invalid value encountered" RuntimeWarning
  during otherwise-successful fits. That specific floating-point warning is now
  suppressed for the duration of the optimization only.

Fixed

• examples/panjer_vs_simulation.py aggregate grid. The Panjer recursion
  step count is now sized to the discretized severity support
  (int(round(max_loss / h))) rather than a fixed 50,000. The old value
  computed the aggregate PMF out to ~250 aggregate means at large cost with no
  effect on the reported mean; the example now runs an order of magnitude
  faster while reproducing the same result.

Tests

• Added tests/estimation/test_negbinomial_parameterization.py, pinning that
  NegativeBinomial(r, p).pmf matches scipy.stats.nbinom(r, p) (and not the
  flipped (r, 1 - p) convention), that the fit's logpmf likelihood agrees
  with the model's own PMF, and that the fit stays frequency-aggregated,
  fast, and free of the finite-difference warning.