yang0117/TVsMiss: Variable Selection for Missing Data
Version 0.1.1

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.

Getting started

Package details

AuthorJiwei Zhao, Yang Yang, and Ning Yang
MaintainerYang Yang <[email protected]>
LicenseGPL (>= 2)
URL https://github.com/yang0117/TVsMiss
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
yang0117/TVsMiss documentation built on April 10, 2018, 2:11 p.m.