<?xml version="1.0" encoding="UTF-8"?>
<oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:title>Predictor-Assisted Graphical Models under Error-in-Variables</dc:title>
  <dc:title>R package PAGE version 0.4.0</dc:title>
  <dc:description>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.</dc:description>
  <dc:type>Software</dc:type>
  <dc:relation>Imports: glasso, lars, network, GGally, caret, randomForest, metrica,
MASS, stats, RSQLite</dc:relation>
  <dc:relation>Suggests: sna</dc:relation>
  <dc:creator>Wan-Yi Chang &lt;jessica306a@gmail.com&gt;</dc:creator>
  <dc:publisher>Comprehensive R Archive Network (CRAN)</dc:publisher>
  <dc:contributor>Wan-Yi Chang [aut, cre],
  Li-Pang Chen [aut]</dc:contributor>
  <dc:rights>GPL-3</dc:rights>
  <dc:date>2025-08-19</dc:date>
  <dc:format>application/tgz</dc:format>
  <dc:identifier>https://CRAN.R-project.org/package=PAGE</dc:identifier>
  <dc:identifier>doi:10.32614/CRAN.package.PAGE</dc:identifier>
</oai_dc:dc>