Regularization Ensemble for Robust Portfolio Optimization R package REN version 0.1.0 Portfolio optimization is achieved through a combination of regularization techniques and ensemble methods that are designed to generate stable out-of-sample return predictions, particularly in the presence of strong correlations among assets. The package includes functions for data preparation, parallel processing, and portfolio analysis using methods such as Mean-Variance, James-Stein, LASSO, Ridge Regression, and Equal Weighting. It also provides visualization tools and performance metrics, such as the Sharpe ratio, volatility, and maximum drawdown, to assess the results. Software Depends: R (>= 2.10) Imports: lubridate, glmnet, quadprog, doParallel, Matrix, tictoc, corpcor, ggplot2, reshape2, foreach, stats, parallel Suggests: knitr, rmarkdown, KernSmooth, cluster, testthat (>= 3.0.0) Bonsoo Koo <bonsoo.koo@monash.edu> Comprehensive R Archive Network (CRAN) Hardik Dixit [aut], Shijia Wang [aut], Bonsoo Koo [aut, cre], Cash Looi [aut], Hong Wang [aut] AGPL (>= 3) 2024-10-10 application/tgz https://CRAN.R-project.org/package=REN doi:10.32614/CRAN.package.REN