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/>.

Allows to detect spatial clusters of abnormal values on multivariate or functional data. Martin KULLDORFF and Lan HUANG and Kevin KONTY (2009) <doi:10.1186/1476-072X-8-58>, Inkyung JUNG and Ho Jin CHO (2015) <doi:10.1186/s12942-015-0024-6>, Lionel CUCALA and Michael GENIN and Caroline LANIER and Florent OCCELLI (2017) <doi:10.1016/j.spasta.2017.06.001>, Lionel CUCALA and Michael GENIN and Florent OCCELLI and Julien SOULA (2019) <doi:10.1016/j.spasta.2018.10.002>, Camille FREVENT and Mohamed-Salem AHMED and Matthieu MARBAC and Michael GENIN (2021) <doi:10.1016/j.spasta.2021.100550>, Zaineb SMIDA and Lionel CUCALA and Ali GANNOUN and Ghislain Durif (2022) <doi:10.1016/j.csda.2021.107378>, Camille FREVENT and Mohamed-Salem AHMED and Sophie DABO-NIANG and Michael GENIN (2023) <doi:10.1093/jrsssc/qlad017>.

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.

Procedures for high-dimensional time series analysis including factor analysis 
      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 (2022) <doi:10.1016/j.jeconom.2022.09.001> in press,principal 
      component analysis proposed by Chang, Guo and Yao (2018) <doi:10.1214/17-AOS1613>,
      identifying cointegration proposed by Zhang, Robinson and Yao (2019)
      <doi:10.1080/01621459.2018.1458620>, unit root test proposed by Chang, Cheng and Yao (2021)
      <doi:10.1093/biomet/asab034>, white noise test proposed by Chang, Yao and Zhou (2017)
      <doi:10.1093/biomet/asw066>, CP-decomposition for high-dimensional matrix time 
      series proposed by Chang, He, Yang and Yao(2023) <doi:10.1093/jrsssb/qkac011> and
      Chang, Du, Huang and Yao (2024+), and Statistical inference for high-dimensional 
      spectral density matrix porposed by Chang, Jiang, McElroy and Shao (2023) 
      <doi:10.48550/arXiv.2212.13686>.