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0.4.0Predictor-Assisted Graphical Models under Error-in-Variables
Overview
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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- NOTE r-devel-linux-x86_64-fedora-clang
- NOTE r-devel-linux-x86_64-fedora-gcc
- NOTE2026-06-0911 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-06-0810 OK · 2 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- NOTE2026-05-0211 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-04-259 OK · 2 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- NOTE2026-03-1012 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
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Package metadata
- First published
- 2025-07-21
- Total releases
- 4 / 1 yrs
- License
- GPL-3 OSI
- Download size
- 11 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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