Package: REN
Type: Package
Title: Regularization Ensemble for Robust Portfolio Optimization
Version: 0.1.0
Authors@R: c(
    person(given = "Hardik", family = "Dixit", role = "aut"),
    person(given = "Shijia", family = "Wang", role = "aut"),
    person(given = "Bonsoo", family = "Koo", role = c("aut", "cre"), email = "bonsoo.koo@monash.edu"),
    person(given = "Cash", family = "Looi", role = "aut"),
    person(given = "Hong", family = "Wang", role = "aut"))
Maintainer: Bonsoo Koo <bonsoo.koo@monash.edu>
Description: 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.
License: AGPL (>= 3)
Encoding: UTF-8
RoxygenNote: 7.3.2
Imports: lubridate, glmnet, quadprog, doParallel, Matrix, tictoc,
        corpcor, ggplot2, reshape2, foreach, stats, parallel
Suggests: knitr, rmarkdown, KernSmooth, cluster, testthat (>= 3.0.0)
VignetteBuilder: knitr
Config/testthat/edition: 3
Depends: R (>= 2.10)
LazyData: true
NeedsCompilation: no
Packaged: 2024-10-07 10:20:09 UTC; yez1
Author: Hardik Dixit [aut],
  Shijia Wang [aut],
  Bonsoo Koo [aut, cre],
  Cash Looi [aut],
  Hong Wang [aut]
Repository: CRAN
Date/Publication: 2024-10-10 16:30:05 UTC
Built: R 4.6.0; ; 2026-03-10 03:50:18 UTC; windows
