Package: HDShOP
Title: High-Dimensional Shrinkage Optimal Portfolios
Version: 0.1.0
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: Applications of the shrinkage-type methods for estimation and inference of high-
    dimensional mean-variance portfolios. The techniques developed in Bodnar et al. (2018) 
    <doi:10.1016/j.ejor.2017.09.028>, Bodnar et al. (2019) <doi:10.1109/TSP.2019.2929964>,
    Bodnar et al. (2020) <doi:10.1109/TSP.2020.3037369> 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 with shrinkage means and covariance matrices 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
LazyData: yes
Encoding: UTF-8
Depends: R (>= 3.5.0)
Imports: Rdpack,
Suggests: ggplot2, testthat, EstimDiagnostics, MASS, corpcor, waldo
RdMacros: Rdpack
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2021-07-29 20:58:49 UTC; d
Repository: CRAN
Date/Publication: 2021-08-02 08:50:05 UTC
