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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.
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| Version | Repository | File | Size |
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0.4.0 |
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0.4.0 |
rolling linux/noble R-4.5 | PAGE_0.4.0.tar.gz |
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0.4.0 |
rolling source/ R- | PAGE_0.4.0.tar.gz |
10.9 KiB |
0.4.0 |
latest linux/jammy R-4.5 | PAGE_0.4.0.tar.gz |
62.5 KiB |
0.4.0 |
latest linux/noble R-4.5 | PAGE_0.4.0.tar.gz |
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0.4.0 |
latest source/ R- | PAGE_0.4.0.tar.gz |
10.9 KiB |
0.4.0 |
2026-04-26 source/ R- | PAGE_0.4.0.tar.gz |
10.9 KiB |
0.4.0 |
2026-04-23 source/ R- | PAGE_0.4.0.tar.gz |
10.9 KiB |
0.4.0 |
2026-04-09 windows/windows R-4.5 | PAGE_0.4.0.zip |
68.0 KiB |