ELCIC: The Empirical Likelihood-Based Consistent Information Criterion
We developed a consistent and robust information criterion to conduct model selection for semiparametric models. It is free of distribution specification and powerful to locate the true model given large sample size. This package provides several usage of ELCIC with applications in generalized linear model (GLM), generalized estimating equation (GEE) for longitudinal data, and weighted GEE (WGEE) for missing longitudinal data under the mechanism of missing at random and drop-out. Chixaing Chen, Ming Wang, Rongling Wu, Runze Li (2020) <doi:10.5705/ss.202020.0254>.
| Version: |
0.2.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
MASS, mvtnorm, PoisNor, bindata, geepack, wgeesel |
| Suggests: |
knitr, rmarkdown, markdown, testthat (≥ 3.0.0) |
| Published: |
2022-02-14 |
| Author: |
Chixiang Chen [cre],
Biyi Shen [aut],
Ming Wang [aut] |
| Maintainer: |
Chixiang Chen <chencxxy at hotmail.com> |
| BugReports: |
https://github.com/chencxxy28/ELCIC/issues |
| License: |
Artistic-2.0 |
| URL: |
https://github.com/chencxxy28/ELCIC |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| CRAN checks: |
ELCIC results |
Documentation:
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