Title: Multivariate Functional Principal Component Analysis for Data
Observed on Different Dimensional Domains
Authors@R: person("Clara", "Happ", email = "clara.happ@stat.uni-muenchen.de",
Description: Calculate a multivariate functional principal component analysis
for data observed on different dimensional domains. The estimation algorithm
relies on univariate basis expansions for each element of the multivariate
functional data. Multivariate and univariate functional data objects are
represented by S4 classes for this type of data implemented in the package
Imports: abind, foreach, irlba, Matrix, methods, mgcv, plyr, stats

functional data  (Happ & Greven, 2018) <doi:10.1080/01621459.2016.1273115>. Multivariate and univariate functional data objects are
'funData'. For more details on the general concepts of both packages and a case study, see Happ (2018) <arXiv:1707.02129>.
Author: Clara Happ [aut, cre] (<https://orcid.org/0000-0003-4737-3835>)

Suggests: covr, fda, testthat (>= 2.0.0)

Authors@R: person("Clara", "Happ-Kurz", email = "chk_R@gmx.de",
Multivariate and univariate functional data objects are
'funData'. For more details on the general concepts of both packages and a case
Imports: abind, foreach, irlba, Matrix(>= 1.5-0), methods, mgcv (>=
Author: Clara Happ-Kurz [aut, cre] (<https://orcid.org/0000-0003-4737-3835>)

Authors@R: person("Clara", "Happ-Kurz", email = "clara.happ@stat.uni-muenchen.de",

Imports: abind, foreach, irlba, Matrix, methods, mgcv (>= 1.8-33),

Title: Creation and Evaluation of Multilevel Fixed and Sequential
evaluating acceptance sampling plans for attributes when there
are k (>=2) levels of product quality. Plans can be multilevel

Title: Moder-Free Sure Independent Screening Procedures
person("Xuewei", "Cheng", role = c("aut","cre"), email = "xwcheng@csu.edu.cn"),
person("Hong", "Wang", role = c("aut"), email = "wh@csu.edu.cn"),
person("Liping", "Zhu", role = c("aut"), email = "zhu.liping@ruc.edu.cn"),
person("Wei", "Zhong", role = c("aut"), email = "wzhong@xmu.edu.cn"),
person("Hanpu", "Zhou", role = c("aut"), email = "zhouhanpu@csu.edu.cn")
Description: An implementation of popular screening methods that are commonly employed in ultra-high and high dimensional data. Through this publicly available package, we provide a unified framework to carry out model-free screening procedures including SIS (Fan and Lv (2008) <doi:10.1111/j.1467-9868.2008.00674.x>), SIRS(Zhu et al. (2011)<doi:10.1198/jasa.2011.tm10563>), DC-SIS (Li et al. (2012) <doi:10.1080/01621459.2012.695654>), MDC-SIS(Shao and Zhang (2014) <doi:10.1080/01621459.2014.887012>), Bcor-SIS (Pan et al. (2019) <doi:10.1080/01621459.2018.1462709>), PC-Screen (Liu et al. (2020) <doi:10.1080/01621459.2020.1783274>), WLS (Zhong et al.(2021) <doi:10.1080/01621459.2021.1918554>), Kfilter (Mai and Zou (2015) <doi:10.1214/14-AOS1303>), MVSIS (Cui et al. (2015) <doi:10.1080/01621459.2014.920256>) and CSIS.
Imports: survival, MASS, Ball, reticulate, stats, pkgdown, crayon, cli,
Suggests: knitr, randomForestSRC, SIS, glmnet, ncvreg, parallel,