TeachingDemos, plotrix, fmsb, rgdal, swfscMisc, rgeos,

Imports: Rcpp, parallel, pbapply, purrr, matrixStats, SpatialNP, sp,
sf, DT, TeachingDemos, plotrix, fmsb, swfscMisc, rgeos,

Title: High Dimensional Stimulation Immune Mapping ('HDStIM')
Description: 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.
Imports: tibble, ggplot2, uwot, dplyr, tidyr, broom, tidyselect,
Author: Rohit Farmer [aut, cre] (<https://orcid.org/0000-0003-4197-3047>),

Title: High Dimensional Time Series Analysis Tools
Authors@R: c(person("Chen", "Lin", role = c("aut", "cre"),
person("Guanghui", "Cheng", role = "aut", email = "chenggh845@nenu.edu.cn"),
person("Jinyuan", "Chang", role = "aut", email = "changjinyuan.swufe.edu.cn"),
person("Qiwei ", "Yao", role = "aut", email = "q.yao@lse.ac.uk"))
Description: 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 (2021) preprint, principal component analysis proposed by Chang, Guo and Yao (2018) <doi:10.1214/17-AOS1613>, unit root test proposed by Chang, Cheng and Yao (2021) <arXiv:2006.07551> and white noise test proposed by Chang, Yao and Zhou (2017) <doi:10.1093/biomet/asw066>.

Author: R.Aidan L.Berge C.Bouveyron S.Girard
Description: New disciminant analysis and data clustering methods for
high dimensional data, based on the asumption that
high-dimensional data live in different subspaces with low
dimensionality proposing a new parametrization of the Gaussian
mixture model which combines the ideas of dimension reduction