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

Documentation:

Reference manual:

PAGE.html , PAGE.pdf

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Package source:	PAGE_0.1.0.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): PAGE_0.1.0.tgz, r-oldrel (x86_64): PAGE_0.1.0.tgz

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