Functions for calculating the hazard discrimination summary and its
    standard errors, as described in Liang and Heagerty (2016) <doi:10.1111/biom.12628>.

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>.

Contains ten datasets used in the chapters and exercises of Paul, Alice (2023) "Health Data Science in R" <https://alicepaul.github.io/health-data-science-using-r/>.

A method for identifying responses to experimental stimulation in mass or flow cytometry that uses high dimensional analysis of measured parameters and can be performed with an end-to-end unsupervised approach. In the context of in vitro stimulation assays where high-parameter cytometry was used to monitor intracellular response markers, using cell populations annotated either through automated clustering or manual gating for a combined set of stimulated and unstimulated samples, 'HDStIM' labels cells as responding or non-responding. The package also provides auxiliary functions to rank intracellular markers based on their contribution to identifying responses and generating diagnostic plots.

Implements an efficient algorithm for fitting the entire regularization path of support vector machine models with elastic-net penalties using a generalized coordinate descent scheme. The framework also supports SCAD and MCP penalties. It is designed for high-dimensional datasets and emphasizes numerical accuracy and computational efficiency. This package implements the algorithms proposed in Tang, Q., Zhang, Y., & Wang, B. (2022) <https://openreview.net/pdf?id=RvwMTDYTOb>.