Algorithms for lasso and fused-lasso
problems: implementation of the lars algorithm [1], for the
lasso and fusion penalization and EM-based algorithms for (logistic) lasso
and fused-lasso.

c(person("Quentin", "Grimonprez", role = c("aut","cre"), email =
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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.
Quentin Grimonprez [aut, cre],
Serge Iovleff [aut]

c(person("Quentin", "Grimonprez", role = c("aut","cre"), email = "quentingrim@yahoo.fr"),
person("Serge", "Iovleff", role = "aut"))

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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>.
ggplot2, testthat, EstimDiagnostics, MASS, corpcor, waldo