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>.

Provides uniform testing procedures for existence and heterogeneity of threshold 
    effects in high-dimensional nonparametric panel regression models. The package accompanies
    the paper Chen, Keilbar, Su and Wang (2023) "Inference on many jumps in nonparametric panel
    regression models". arXiv preprint <doi:10.48550/arXiv.2312.01162>.

An implementation for high-dimensional time series analysis methods, including factor model for vector time series 
      proposed by Lam and Yao (2012) <doi:10.1214/12-AOS970> and Chang, Guo and Yao (2015)
      <doi:10.1016/j.jeconom.2015.03.024>, martingale difference test proposed by 
      Chang, Jiang and Shao (2023) <doi:10.1016/j.jeconom.2022.09.001>, principal 
      component analysis for vector time series proposed by Chang, Guo and Yao (2018) <doi:10.1214/17-AOS1613>,
      cointegration analysis proposed by Zhang, Robinson and Yao (2019)
      <doi:10.1080/01621459.2018.1458620>, unit root test proposed by Chang, Cheng and Yao (2022)
      <doi:10.1093/biomet/asab034>, white noise test proposed by Chang, Yao and Zhou (2017)
      <doi:10.1093/biomet/asw066>, CP-decomposition for matrix time 
      series proposed by Chang et al. (2023) <doi:10.1093/jrsssb/qkac011> and
      Chang et al. (2024) <doi:10.48550/arXiv.2410.05634>, and statistical inference for
      spectral density matrix proposed by Chang et al. (2022) 
      <doi:10.48550/arXiv.2212.13686>.