Two functions for financial portfolio optimization by linear programming are provided. One function implements Benders decomposition algorithm and can be used for very large data sets. The other, applicable for moderate sample sizes, finds optimal portfolio which has the smallest distance to a given benchmark portfolio.

Design, backtest, and analyze portfolio strategies using simple,
    English-like function chains. Includes technical indicators, flexible stock
    selection, portfolio construction methods (equal weighting, signal weighting,
    inverse volatility, hierarchical risk parity), and a compact backtesting
    engine for portfolio returns, drawdowns, and summary metrics.

Provides simple methods to extract data portions from various objects. 
    The relative portion size and the way the portion is selected can be chosen.

The functions are designed to find the efficient mean-variance frontier or 
 portfolio weights for static portfolio (called  Markowitz portfolio) analysis in resource 
 economics or nature conservation. Using the nonlinear programming solver ('Rsolnp'), 
 this package deals with the quadratic minimization of the variance-covariances without 
 shorting (i.e., non-negative portfolio weights) studied in Ando and Mallory (2012) 
 <doi:10.1073/pnas.1114653109>. See the examples, testing versions, and more details from: 
 <https://github.com/ysd2004/portn>.

Following Sommer (2022) <https://mediatum.ub.tum.de/1658240>
    portfolio level risk estimates (e.g. Value at Risk, Expected
    Shortfall) are estimated by modeling each asset univariately by an
    ARMA-GARCH model and then their cross dependence via a Vine Copula
    model in a rolling window fashion. One can even condition on
    variables/time series at certain quantile levels to stress test the
    risk measure estimates.

Win ratio approach to partially ordered data, such as multivariate ordinal 
  responses under product (consensus) or prioritized order. Two-sample tests and multiplicative
  regression models are implemented (Mao, 2024, under revision).