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Predictor-Assisted Graphical Models under Error-in-Variables

v0.4.0 · Aug 19, 2025 · GPL-3

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.

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dependencies in R code

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  All declared Imports should be used.
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dependencies in R code

Namespace in Imports field not imported from: ‘RSQLite’
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NOTE 12 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 9, 2026
NOTE r-devel-linux-x86_64-fedora-clang

dependencies in R code

Namespace in Imports field not imported from: ‘RSQLite’
  All declared Imports should be used.
NOTE r-devel-linux-x86_64-fedora-gcc

dependencies in R code

Namespace in Imports field not imported from: ‘RSQLite’
  All declared Imports should be used.

Dependency Network

Dependencies Reverse dependencies glasso lars network GGally caret randomForest metrica MASS RSQLite PAGE

Version History

new 0.4.0 Mar 9, 2026