TVsMiss (0.1.1)

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Variable Selection for Missing Data.

https://github.com/yang0117/TVsMiss
http://cran.r-project.org/web/packages/TVsMiss

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) "Penalized pairwise pseudo likelihood for variable selection with nonignorable missing data." Statistica Sinica.

Maintainer: Yang Yang
Author(s): Jiwei Zhao, Yang Yang, and Ning Yang

License: GPL (>= 2)

Uses: glmnet, Rcpp

Released about 1 year ago.


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