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 |
| In views: | MissingData |
| CRAN checks: | TVsMiss results |
| Reference manual: | TVsMiss.pdf |
| Package source: | TVsMiss_0.1.1.tar.gz |
| Windows binaries: | r-devel: TVsMiss_0.1.1.zip, r-release: TVsMiss_0.1.1.zip, r-oldrel: TVsMiss_0.1.1.zip |
| OS X binaries: | r-release: TVsMiss_0.1.1.tgz, r-oldrel: TVsMiss_0.1.1.tgz |
| Old sources: | TVsMiss archive |
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