The goal of TVsMiss is to select variable in both low and high demesion through regularization likelihood method.
You can install TVsMiss from github with:
# install.packages("devtools")
devtools::install_github("yang0117/TVsMiss")rm(list = ls())
library(TVsMiss)
n <- 50
p <- 8
beta <- c(3,0,1.5,0,2,rep(0,p-5))
xm <- matrix(rnorm(n*p),ncol = p, nrow = n)
y <- xm %*% beta + rnorm(n)
colnames(xm) <- paste0("Var_",1:p)
fit02 <- tvsmiss(x=xm,y=y,method = "BIC")
fit02$selection_beta
#> Var_1 Var_2 Var_3 Var_4 Var_5 Var_6 Var_7
#> 0.5614923 0.0000000 0.1417931 0.0000000 0.2872000 0.0000000 0.0000000
#> Var_8
#> 0.0000000
plot(fit02,x.log=TRUE,label = TRUE)