Package: TVsMiss
Type: Package
Title: Variable Selection for Missing Data
Version: 0.1.1
Date: 2018-04-05
Author: Jiwei Zhao, Yang Yang, and Ning Yang
Maintainer: Yang Yang <yyang39@buffalo.edu>
Description: Use a regularization likelihood method to achieve variable selection purpose. Likelihood can be worked with penalty lasso, smoothly clipped absolute deviations (SCAD), and minimax concave penalty (MCP). 
    Tuning parameter selection techniques include cross validation (CV), Bayesian information criterion (BIC) (low and high), stability of variable selection (sVS), stability of BIC (sBIC), and stability of estimation (sEST).
    More details see Jiwei Zhao, Yang Yang, and Yang Ning (2018) <arXiv:1703.06379> "Penalized pairwise pseudo likelihood for variable selection with nonignorable missing data." Statistica Sinica.
License: GPL (>= 2)
Imports: glmnet, Rcpp
NeedsCompilation: yes
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
LinkingTo: Rcpp
URL: https://github.com/yang0117/TVsMiss
BugReports: https://github.com/yang0117/TVsMiss/issues
Packaged: 2018-04-05 04:37:15 UTC; yangyang
Repository: CRAN
Date/Publication: 2018-04-05 05:16:08 UTC
