PAGE: Predictor-Assisted Graphical Models under Error-in-Variables
We consider the network structure detection for variables Y with auxiliary variables X accommodated, which are possibly subject to measurement error. The following three functions are designed to address various structures by different methods : one is NP_Graph() that is used for handling the nonlinear relationship between the responses and the covariates, another is Joint_Gaussian() that is used for correction in linear regression models via the Gaussian maximum likelihood, and the other Cond_Gaussian() is for linear regression models via conditional likelihood function.
| Version: |
0.1.0 |
| Imports: |
glasso, lars, network, GGally, caret, randomForest, metrica, MASS, stats |
| Suggests: |
sna |
| Published: |
2025-07-21 |
| DOI: |
10.32614/CRAN.package.PAGE |
| Author: |
Wan-Yi Chang [aut, cre],
Li-Pang Chen [aut] |
| Maintainer: |
Wan-Yi Chang <jessica306a at gmail.com> |
| License: |
GPL-3 |
| NeedsCompilation: |
yes |
| CRAN checks: |
PAGE results |
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