FLAG: Flexible and Accurate Gaussian Graphical Models

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) <doi:10.14711/thesis-991013223054603412>, Qian Y, Hu X, Yang C (2023) <doi:10.48550/arXiv.2306.17584>.

Version:	0.1
Imports:	stats, MASS
Published:	2025-04-12
Author:	Yueqi QIAN [aut, cre]
Maintainer:	Yueqi QIAN <yqianai at connect.ust.hk>
BugReports:	https://github.com/YangLabHKUST/FLAG/issues
License:	MIT + file LICENSE
URL:	https://github.com/YangLabHKUST/FLAG
NeedsCompilation:	no
CRAN checks:	FLAG results

Documentation:

Reference manual:

FLAG.pdf

Downloads:

Package source:	FLAG_0.1.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-devel (arm64): FLAG_0.1.tgz, r-release (arm64): FLAG_0.1.tgz, r-oldrel (arm64): FLAG_0.1.tgz, r-devel (x86_64): FLAG_0.1.tgz, r-release (x86_64): FLAG_0.1.tgz, r-oldrel (x86_64): FLAG_0.1.tgz

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