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r-tvsmiss

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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.

Installation

To install this package, run one of the following:

Conda
$conda install r_test::r-tvsmiss

Usage Tracking

0.1.1
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Downloads (Last 6 months): 0

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Summary

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.

Last Updated

Dec 12, 2019 at 18:05

License

GPL-2

Total Downloads

6

Version Downloads

6

Supported Platforms

linux-64
macOS-64
win-64