Title: High-Dimensional Orthogonal Maximin Distance Designs
Authors@R: c(person("Xu He", role = c("aut", "cre"), email = "hexu@amss.ac.cn"), person("Fasheng Sun", role = c("aut")))
Description: Contains functions to construct high-dimensional orthogonal maximin distance designs in two, four, eight, and sixteen levels from rotating the Kronecker product of sub-Hadamard matrices.

Authors@R: c(person("Quentin", "Grimonprez", role = c("aut","cre"), email =
"quentin.grimonprez@inria.fr"), person("Serge", "Iovleff", role = "ctb"))
Copyright: inria 2013-2014 for HDPenReg. Serge Iovleff is the copyright
Description: This package contains algorithms for lasso and fused-lasso
problems. It contains an implementation of the lars algorithm [1], for the
lasso and fusion penalization and EM-based algorithms for (logistic) lasso
Packaged: 2014-06-24 13:22:46 UTC; grimonprez

Packaged: 2014-07-16 12:04:43 UTC; grimonprez

Packaged: 2014-07-28 13:05:18 UTC; grimonprez

Packaged: 2014-09-08 07:49:55 UTC; grimonprez

Packaged: 2014-11-03 16:15:07 UTC; grimonprez

Description: Algorithms for lasso and fused-lasso
problems: implementation of the lars algorithm [1], for the

"quentin.grimonprez@inria.fr"), person("Serge", "Iovleff", role = "aut"))
Description: Algorithms for lasso and fused-lasso problems: implementation of
the lars algorithm for lasso and fusion penalization and EM-based
algorithms for (logistic) lasso  and fused-lasso penalization.
Packaged: 2016-01-26 10:15:09 UTC; grimonprez

Packaged: 2018-01-19 16:31:12 UTC; grimonprez

Packaged: 2019-04-10 08:03:01 UTC; grimonprez

Authors@R: c(person("Quentin", "Grimonprez", role = c("aut","cre"), email = "quentingrim@yahoo.fr"),
person("Serge", "Iovleff", role = "aut"))
Packaged: 2020-10-03 16:23:37 UTC; grimonprez

Title: High-Dimensional Shrinkage Optimal Portfolios
Dmitry Otryakhin [aut, cre] (<https://orcid.org/0000-0002-4700-7221>),
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)
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
Suggests: ggplot2, testthat, EstimDiagnostics, MASS, corpcor, waldo