Package: HDShOP
Title: High-Dimensional Shrinkage Optimal Portfolios
Version: 0.1.5
Authors@R: 
    c(person(given = "Taras", 
             family = "Bodnar", 
             role = "aut",
             comment = c(ORCID = "0000-0001-7855-8221")),
      person(given = "Solomiia", 
             family = "Dmytriv", 
             role = "aut",
             comment = c(ORCID = "0000-0003-1855-3044")),
      person(given = "Yarema", 
             family = "Okhrin", 
             role = "aut",
             comment = c(ORCID = "0000-0003-4704-5233")),
      person(given = "Dmitry", 
             family = "Otryakhin", 
             role = c("aut", "cre"),             
             email = "d.otryakhin.acad@protonmail.ch", 
             comment = c(ORCID = "0000-0002-4700-7221")),             
      person(given = "Nestor", 
             family = "Parolya", 
             role = "aut",
             comment = c(ORCID = "0000-0003-2147-2288")))
Maintainer: Dmitry Otryakhin <d.otryakhin.acad@protonmail.ch>
Author: Taras Bodnar [aut] (<https://orcid.org/0000-0001-7855-8221>),
  Solomiia Dmytriv [aut] (<https://orcid.org/0000-0003-1855-3044>),
  Yarema Okhrin [aut] (<https://orcid.org/0000-0003-4704-5233>),
  Dmitry Otryakhin [aut, cre] (<https://orcid.org/0000-0002-4700-7221>),
  Nestor Parolya [aut] (<https://orcid.org/0000-0003-2147-2288>)
Description: Constructs shrinkage estimators of high-dimensional mean-variance portfolios and performs 
    high-dimensional tests on optimality of a given portfolio. The techniques developed in 
    Bodnar et al. (2018 <doi:10.1016/j.ejor.2017.09.028>, 2019 <doi:10.1109/TSP.2019.2929964>, 
    2020 <doi:10.1109/TSP.2020.3037369>, 2021 <doi:10.1080/07350015.2021.2004897>) 
    are central to the package. They provide simple and feasible estimators and tests for optimal 
    portfolio weights, which are applicable for 'large p and large n' situations where p is the 
    portfolio dimension (number of stocks) and n is the sample size. The package also includes tools
    for constructing portfolios based on shrinkage estimators of the mean vector and covariance matrix
    as well as a new Bayesian estimator for the Markowitz efficient frontier recently developed by 
    Bauder et al. (2021) <doi:10.1080/14697688.2020.1748214>.
License: GPL-3
URL:
        https://github.com/Otryakhin-Dmitry/global-minimum-variance-portfolio
BugReports: https://github.com/Otryakhin-Dmitry/global-minimum-variance-portfolio/issues
LazyData: yes
Encoding: UTF-8
Depends: R (>= 3.5.0)
Imports: Rdpack, lattice
Suggests: ggplot2, testthat, EstimDiagnostics, MASS, corpcor, waldo
RdMacros: Rdpack
RoxygenNote: 7.3.1
NeedsCompilation: no
Packaged: 2024-03-24 22:18:04 UTC; otryakhin-d
Repository: CRAN
Date/Publication: 2024-03-25 05:50:02 UTC
Built: R 4.5.1; ; 2025-10-06 01:38:26 UTC; windows
