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: | 0.4.0 |
| Imports: | glasso,lars,network,GGally,caret,randomForest,metrica,MASS, stats,RSQLite |
| Suggests: | sna |
| Published: | 2025-08-19 |
| 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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