Riskfolio-lib

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- Implements a reformulation of OWA portfolio optimization to speed up calculations.

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- Implements 5 additional risk measures for mean risk model: Gini Mean Difference, Tail Gini, Range, CVaR range and Tail Gini range.
- Implements 4 additional risk measures for risk parity model: Gini Mean Difference, Tail Gini, CVaR range and Tail Gini range.
- Implements the OWA Portfolio Optimization model for custom vector of weights and a module to build OWA weights for some special cases.
- Implements a function to plot range risk measures.
- Adds the option to use Graphical Lasso, j-Logo, denoising and detoning covariance estimates.

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- Implement Nested Clustered Optimization (NCO) model with four objective functions.
- Implements the Relaxed Risk Parity model.
- Implements the Risk Budgeting approach for Risk Parity Portfolios with constraints.
- Adds the option to use custom covariance in Hierarchical Clustering Portfolios.

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- Redesigns of Riskfolio-Lib interface (Only import riskfolio for all functions).
- Implements Hierarchical Risk Parity (HRP) model with constraints on assets' weights.
- Implements a function that helps to build constraints for the HRP model.
- Implements the Direct Bubble Hierarchical Tree (DBHT) linkage method for HRP and HERC models.
- Implements a function that plots relationship among assets in a network using Minimum Spanning Tree (MST) and Planar Maximally Filtered Graph (PMFG).
- Adds two new codependence measures: mutual information and lower tail dependence index.

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- Implements Hierarchical Equal Risk Contribution with equally weights within clusters (HERC2).
- Implements a function that help us to discretize portfolio weights into number of shares given an investment amount.
- Implements the option to select the method to estimate covariance in HRP, HERC and HERC2.
- Adds the option to add constraints on the number of assets and the number of effective assets.
- Fixes an error in two_diff_gap_stat() when number of assets is too small.
- Fixes an error on forward_regression() and backward_regression() when there is no significant feature in regression modes using p-value criterion.
- Adds an example that shows how to build HERC2 portfolios.
- Adds an example that shows how to build constraints on the number of assets and number of effective assets.

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- Implements Hierarchical Risk Parity (HRP) and Hierarchical Equal Risk Parity (HERC).
- Implements the function plot_clusters() and plot_dendrogram() that help us to identify clusters based on a distance correlation metric.
- Implements the function assets_clusters() that help us to create asset classes based on hierarchical clusters.
- Adds an example that shows how to build Hierarchical Risk Parity portfolios.
- Adds an example that shows how to build Hierarchical Equal Risk Parity portfolios.