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


News

TVsMiss 0.1.1

  • Change '&' to '&&' in C code

TVsMiss 0.1.0

  • first version

Reference manual

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install.packages("TVsMiss")

0.1.1 by Yang Yang, a year ago


https://github.com/yang0117/TVsMiss


Report a bug at https://github.com/yang0117/TVsMiss/issues


Browse source code at https://github.com/cran/TVsMiss


Authors: Jiwei Zhao , Yang Yang , and Ning Yang


Documentation:   PDF Manual  


Task views: Missing Data


GPL (>= 2) license


Imports glmnet, Rcpp

Linking to Rcpp


See at CRAN