TVsMiss: Variable Selection for Missing Data

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.

Version: 0.1.1
Imports: glmnet, Rcpp
LinkingTo: Rcpp
Published: 2018-04-05
Author: Jiwei Zhao, Yang Yang, and Ning Yang
Maintainer: Yang Yang <yyang39 at buffalo.edu>
BugReports: https://github.com/yang0117/TVsMiss/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/yang0117/TVsMiss
NeedsCompilation: yes
Materials: NEWS
CRAN checks: TVsMiss results

Downloads:

Reference manual: TVsMiss.pdf
Package source: TVsMiss_0.1.1.tar.gz
Windows binaries: r-prerel: TVsMiss_0.1.1.zip, r-release: TVsMiss_0.1.1.zip, r-oldrel: TVsMiss_0.1.1.zip
OS X binaries: r-prerel: TVsMiss_0.1.1.tgz, r-release: TVsMiss_0.1.1.tgz
Old sources: TVsMiss archive

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