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]), PSIS (Pan et al. (2016) [doi:10.1080/01621459.2014.998760]), CAS (Xie et al. (2020) [doi:101080/01621459.2019.1573734]), CI-SIS (Cheng and Wang (2022) [doi:10.1016/j.cmpb.2022.107269]), and CSIS (Cheng et al. (2023) [doi:10.1007/s00180-023-01399-5]).


{r install} install.packages("MFSIS")


Here are many extensive examples that can let you quickly learn how to use this package. The author’s original intention in writing this package is ease of use. Here is a simple example to illustrate its use. For example, I want to use SIRS method to screen high-dimensional data. I want to get the most active 30 features. {r SIRS} library(MFSIS) n=100; p=200; pho=0.5; data=gendata1(n,p,pho) data=cbind(data[[1]],data[[2]]) colnames(data)[1:ncol(data)]=c(paste0("X",1:(ncol(data)-1)),"Y") data=as.matrix(data) X=data[,1:(ncol(data)-1)]; Y=data[,ncol(data)]; A=SIRS(X,Y,30);A