feat(hierarchical): add HERCOpt and NCOpt portfolio allocation methods

Extends the hierarchical portfolio module with two additional methods:

HERCOpt - Hierarchical Equal Risk Contribution (Raffinot 2018)
  Equal Risk Contribution (ERC) weights at every level of the hierarchy
  instead of the inverse-variance weights used by HRP. ERC accounts for
  within-cluster correlations, producing more balanced risk allocation
  when intra-cluster correlations are high. Implemented via cyclical
  coordinate descent (Roncalli 2013).

NCOpt - Nested Cluster Optimization (Lopez de Prado 2019)
  Two-level nested procedure: within-cluster optimization followed by
  meta-portfolio optimization across clusters. Supports three objectives
  at each level - min_variance, erc, and equal-weight - giving 9
  combinations. The meta-covariance is computed analytically from the
  full covariance matrix, so no return history is required.

Both classes:
  - Share the same API as HRPOpt (fit from returns or cov_matrix)
  - Export portfolio_performance() with expected return and Sharpe ratio
  - Are exported from pypfopt.__init__ and __all__
  - Accept all scipy linkage methods

Also adds two private helpers:
  - _erc_weights_ccd: ERC via multiplicative CCD (Roncalli 2013)
  - _min_var_weights: long-only min-variance with analytic + SLSQP fallback

53 tests across 9 test classes; all existing HRP tests still pass.

References
----------
Raffinot, T. (2018). Hierarchical clustering-based asset allocation.
Journal of Portfolio Management, 44(2), 89-99.

Lopez de Prado, M. (2019). A Robust Estimator of the Efficient Frontier.
SSRN Working Paper. https://ssrn.com/abstract=3469961

Roncalli, T. (2013). Introduction to Risk Parity and Budgeting.
CRC Press.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Author: hass-nation

pypfopt/__init__.py

@@ -11,7 +11,7 @@
     EfficientFrontier,
     EfficientSemivariance,
 )
-from .hierarchical_portfolio import HRPOpt
+from .hierarchical_portfolio import HRPOpt, HERCOpt, NCOpt
 from .risk_models import CovarianceShrinkage
 
 __version__ = "1.6.0"
@@ -28,5 +28,7 @@
     "EfficientCVaR",
     "EfficientCDaR",
     "HRPOpt",
+    "HERCOpt",
+    "NCOpt",
     "CovarianceShrinkage",
 ]