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I am Daniel Palomar, a Professor at the Hong Kong University of Science and Technology (HKUST). I work on optimization problems related to financial systems and data analytics in general. My webpage is https://www.danielppalomar.com

Here you'll find repositories that host practical implementations of the research published by the Convex Group. Video presentations are available on YouTube: https://www.youtube.com/danielpalomar.

• spectralGraphTopology: Structured graph learning via Laplacian spectral constraints (NeurIPS 2019) [CRAN]
• sparseGraph: Nonconvex Sparse Graph Learning under Laplacian-structured Graphical Model (NeurIPS 2020)
• fingraph: Graphical Models in Heavy-Tailed Markets (NeurIPS 2021) [CRAN]
• bipartite: Learning Bipartite Graphs: Heavy Tails and Multiple Components (NeurIPS 2022) [CRAN]

• riskParityPortfolio: Design of Risk Parity Portfolios [CRAN] - Listed in Task View on Empirical Finance
• riskparity.py: SCRIP: Successive Convex Optimization Methods for Risk Parity Portfolio Design (IEEE TSP 2015)
• highOrderPortfolios: Design of High-Order Portfolios via Mean, Variance, Skewness, and Kurtosis [CRAN]
• portfolioBacktest: Automated Backtesting of Portfolios over Multiple Datasets [CRAN]
• sparseIndexTracking: Design of Portfolio of Stocks to Track an Index [CRAN]

• intradayModel: Modeling and Forecasting Financial Intraday Signals [CRAN]
• fitHeavyTail: Mean and Covariance Matrix Estimation under Heavy Tails [CRAN]
• imputeFin: Imputation of Financial Time Series with Missing Values [CRAN] - Listed in Task View on Missing Data
• sparseEigen: Computation of Sparse Eigenvectors of a Matrix [CRAN]

• TRexSelector: Performs fast variable selection in high-dimensional settings while controlling the false discovery rate (FDR) at a user-defined target level [CRAN]
• tlars: Computes the solution path of the Terminating-LARS (T-LARS) algorithm [CRAN]