Package: HDShOP
Title: High-Dimensional Shrinkage Optimal Portfolios
Version: 0.1.7
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 (>= 3.0.0), EstimDiagnostics, MASS,
        corpcor, waldo
RdMacros: Rdpack
RoxygenNote: 7.3.3
Config/testthat/edition: 3
Repository: https://otryakhin-dmitry.r-universe.dev
Date/Publication: 2025-11-18 13:29:01 UTC
RemoteUrl:
        https://github.com/otryakhin-dmitry/high-dimensional-shrinkage-optimal-portfolios
RemoteRef: HEAD
RemoteSha: 41bacbde585bacb431e1a8ec0fdc3c1f74a68412
NeedsCompilation: no
Packaged: 2026-06-17 08:06:01 UTC; root