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FLAG (version 0.1)

Flexible and Accurate Gaussian Graphical Models

Description

In order to achieve accurate estimation without sparsity assumption on the precision matrix, element-wise inference on the precision matrix, and joint estimation of multiple Gaussian graphical models, a novel method is proposed and efficient algorithm is implemented. FLAG() is the main function given a data matrix, and FlagOneEdge() will be used when one pair of random variables are interested where their indices should be given. Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications, see Qian Y (2023) , Qian Y, Hu X, Yang C (2023) .

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install.packages('FLAG')

Monthly Downloads

114

Version

0.1

License

MIT + file LICENSE

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Maintainer

Yueqi QIAN

Last Published

April 12th, 2025

Functions in FLAG (0.1)

FLAG

FLAG is the main function to fulfill the whole process.
InferWald

Infer by the Wald test.
FlagOneEdge

Use FLAG to infer one edge. Given n*p data matrix, when we only interest in the conditional dependence between i-th and j-th variables.
GetSeRho

Get the standard error of rho.
FlagOnePairEta0

FLAG for one pair of random variables fixing eta as zero, using likelihood-ratio test.
FlagOnePair

FLAG for one pair of random variables, using random effects model. This is a repeated function for FLAG.