v3.0.3

Added

• Maximum-entropy smoothing for convex fits: fit_convex and RankingResult.fit_convex gain entropy (default 0.1), which adds entropy * Var(y) * sum(w * log w) to the simplex least-squares objective. The entropy gradient diverges at the simplex boundary, so the optimum is strictly interior: every weight stays strictly positive, and the fit is unique even when features duplicate each other (near-duplicates split the weight symmetrically instead of one arbitrarily taking it all). Scaling by Var(y) makes the strength invariant to target units. entropy=0 recovers plain least squares, where redundant features get exact zero weights.