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FLAG: The flexible and accurate Gaussian graphical model in R

The R package FLAG implements the methods based on the paper Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications. FLAG aims to estimate precision matrix entries accurately and efficiently, and further quantify the uncertainty of each entry, which allows for better leveraging of the common structure across different groups through meta-analysis.

Installation

For a quick start, you can install the development version of FLAG from GitHub with:

# install.packages("devtools")devtools::install_github("YangLabHKUST/FLAG")

Example

  • This is a basic example which shows you how to solve a common problem:
library(FLAG)
rm(list= ls())
set.seed(1234)
N=20P=10pi=0.2Pre=matrix(sample(c(0.2, 0.4), P*P, replace=T) * rbinom(P*P, 1, pi), nrow=P, ncol=P )
Pre[lower.tri(Pre)] = t(Pre)[lower.tri(Pre)]
diag(Pre) =1vals<- eigen(Pre)$valuesSigma= solve(Pre)
Z=MASS::mvrnorm(N, rep(0, P), Sigma)
Z.c= scale(Z, center=T, scale=F)
results= FLAG(Z.c)
results$precision.est

Real data analysis

Gene co-expression in the Human Brain

library(FLAG)

For the detailed experiments, please refer to NumericalResults/RealData/GeneExpression/gene_expression_data_analysis.ipynb

Term association in the university webpages

library(FLAG)

For the detailed experiments, please refer to NumericalResults/RealData/TermAssociation/meta_analysis.ipynb

Price comovement of S&P 100 stocks

library(FLAG)

For the detailed experiments, please refer to NumericalResults/RealData/StockComovement/network_measure.ipynb

Citing our work

If you find the FLAG package or any of the source code in this repository useful for your work, please cite:

Qian, Y., Hu, X., & Yang, C. (2023). Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications. arXiv e-prints, arXiv-2306. https://doi.org/10.48550/arXiv.2306.17584

Contact

Please feel free to contact Yueqi Qian, Prof. Xianghong Hu, or Prof. Can Yang if any inquiries.

About

Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications

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GitHub - YangLabHKUST/FLAG: Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications · GitHub
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FLAG: The flexible and accurate Gaussian graphical model in R
The R package FLAG implements the methods based on the paper Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications.
FLAG aims to estimate precision matrix entries accurately and efficiently, and further quantify the uncertainty of each entry, which allows for better leveraging of the common structure across different groups through meta-analysis.
Installation
For a quick start, you can install the development version of FLAG
from GitHub with:
# install.packages("devtools")devtools::install_github("YangLabHKUST/FLAG")
Example
  • This is a basic example which shows you how to solve a common problem:
library(FLAG)
rm(list= ls())
set.seed(1234)
N=20P=10pi=0.2Pre=matrix(sample(c(0.2, 0.4), P*P, replace=T) * rbinom(P*P, 1, pi), nrow=P, ncol=P )
Pre[lower.tri(Pre)] = t(Pre)[lower.tri(Pre)]
diag(Pre) =1vals<- eigen(Pre)$valuesSigma= solve(Pre)
Z=MASS::mvrnorm(N, rep(0, P), Sigma)
Z.c= scale(Z, center=T, scale=F)
results= FLAG(Z.c)
results$precision.est
Real data analysis
Gene co-expression in the Human Brain
library(FLAG)

For the detailed experiments, please refer to NumericalResults/RealData/GeneExpression/gene_expression_data_analysis.ipynb
Term association in the university webpages
library(FLAG)

For the detailed experiments, please refer to NumericalResults/RealData/TermAssociation/meta_analysis.ipynb
Price comovement of S&P 100 stocks
library(FLAG)

For the detailed experiments, please refer to NumericalResults/RealData/StockComovement/network_measure.ipynb
Citing our work
If you find the FLAG package or any of the source code in this
repository useful for your work, please cite:
Qian, Y., Hu, X., & Yang, C. (2023).
Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications.
arXiv e-prints, arXiv-2306.
https://doi.org/10.48550/arXiv.2306.17584
Contact
Please feel free to contact Yueqi
Qian, Prof. Xianghong Hu, or Prof. Can
Yang if any inquiries.
About
Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications
Topics
empirical-bayesgaussian-graphical-modelsjoint-estimationlow-rank-updateprecision-matrixrrandom-effects-modelstatistical-inference-for-data-science
Resources
Readme
License
Activity
Custom properties
Stars
5 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
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Contributors
Languages

, 'i');
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GitHub - YangLabHKUST/FLAG: Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications · GitHub
Skip to content
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FLAG: The flexible and accurate Gaussian graphical model in R
The R package FLAG implements the methods based on the paper Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications.
FLAG aims to estimate precision matrix entries accurately and efficiently, and further quantify the uncertainty of each entry, which allows for better leveraging of the common structure across different groups through meta-analysis.
Installation
For a quick start, you can install the development version of FLAG
from GitHub with:
# install.packages("devtools")devtools::install_github("YangLabHKUST/FLAG")
Example
  • This is a basic example which shows you how to solve a common problem:
library(FLAG)
rm(list= ls())
set.seed(1234)
N=20P=10pi=0.2Pre=matrix(sample(c(0.2, 0.4), P*P, replace=T) * rbinom(P*P, 1, pi), nrow=P, ncol=P )
Pre[lower.tri(Pre)] = t(Pre)[lower.tri(Pre)]
diag(Pre) =1vals<- eigen(Pre)$valuesSigma= solve(Pre)
Z=MASS::mvrnorm(N, rep(0, P), Sigma)
Z.c= scale(Z, center=T, scale=F)
results= FLAG(Z.c)
results$precision.est
Real data analysis
Gene co-expression in the Human Brain
library(FLAG)

For the detailed experiments, please refer to NumericalResults/RealData/GeneExpression/gene_expression_data_analysis.ipynb
Term association in the university webpages
library(FLAG)

For the detailed experiments, please refer to NumericalResults/RealData/TermAssociation/meta_analysis.ipynb
Price comovement of S&P 100 stocks
library(FLAG)

For the detailed experiments, please refer to NumericalResults/RealData/StockComovement/network_measure.ipynb
Citing our work
If you find the FLAG package or any of the source code in this
repository useful for your work, please cite:
Qian, Y., Hu, X., & Yang, C. (2023).
Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications.
arXiv e-prints, arXiv-2306.
https://doi.org/10.48550/arXiv.2306.17584
Contact
Please feel free to contact Yueqi
Qian, Prof. Xianghong Hu, or Prof. Can
Yang if any inquiries.
About
Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications
Topics
empirical-bayesgaussian-graphical-modelsjoint-estimationlow-rank-updateprecision-matrixrrandom-effects-modelstatistical-inference-for-data-science
Resources
Readme
License
Activity
Custom properties
Stars
5 stars
Watchers
1 watching
Forks
0 forks
Report repository
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GitHub - YangLabHKUST/FLAG: Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications · GitHub
Skip to content
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FLAG: The flexible and accurate Gaussian graphical model in R
The R package FLAG implements the methods based on the paper Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications.
FLAG aims to estimate precision matrix entries accurately and efficiently, and further quantify the uncertainty of each entry, which allows for better leveraging of the common structure across different groups through meta-analysis.
Installation
For a quick start, you can install the development version of FLAG
from GitHub with:
# install.packages("devtools")devtools::install_github("YangLabHKUST/FLAG")
Example
  • This is a basic example which shows you how to solve a common problem:
library(FLAG)
rm(list= ls())
set.seed(1234)
N=20P=10pi=0.2Pre=matrix(sample(c(0.2, 0.4), P*P, replace=T) * rbinom(P*P, 1, pi), nrow=P, ncol=P )
Pre[lower.tri(Pre)] = t(Pre)[lower.tri(Pre)]
diag(Pre) =1vals<- eigen(Pre)$valuesSigma= solve(Pre)
Z=MASS::mvrnorm(N, rep(0, P), Sigma)
Z.c= scale(Z, center=T, scale=F)
results= FLAG(Z.c)
results$precision.est
Real data analysis
Gene co-expression in the Human Brain
library(FLAG)

For the detailed experiments, please refer to NumericalResults/RealData/GeneExpression/gene_expression_data_analysis.ipynb
Term association in the university webpages
library(FLAG)

For the detailed experiments, please refer to NumericalResults/RealData/TermAssociation/meta_analysis.ipynb
Price comovement of S&P 100 stocks
library(FLAG)

For the detailed experiments, please refer to NumericalResults/RealData/StockComovement/network_measure.ipynb
Citing our work
If you find the FLAG package or any of the source code in this
repository useful for your work, please cite:
Qian, Y., Hu, X., & Yang, C. (2023).
Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications.
arXiv e-prints, arXiv-2306.
https://doi.org/10.48550/arXiv.2306.17584
Contact
Please feel free to contact Yueqi
Qian, Prof. Xianghong Hu, or Prof. Can
Yang if any inquiries.
About
Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications
Topics
empirical-bayesgaussian-graphical-modelsjoint-estimationlow-rank-updateprecision-matrixrrandom-effects-modelstatistical-inference-for-data-science
Resources
Readme
License
Activity
Custom properties
Stars
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Report repository
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GitHub - YangLabHKUST/FLAG: Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications · GitHub
Skip to content
BranchesTags
Repository files navigation
FLAG: The flexible and accurate Gaussian graphical model in R
The R package FLAG implements the methods based on the paper Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications.
FLAG aims to estimate precision matrix entries accurately and efficiently, and further quantify the uncertainty of each entry, which allows for better leveraging of the common structure across different groups through meta-analysis.
Installation
For a quick start, you can install the development version of FLAG
from GitHub with:
# install.packages("devtools")devtools::install_github("YangLabHKUST/FLAG")
Example
  • This is a basic example which shows you how to solve a common problem:
library(FLAG)
rm(list= ls())
set.seed(1234)
N=20P=10pi=0.2Pre=matrix(sample(c(0.2, 0.4), P*P, replace=T) * rbinom(P*P, 1, pi), nrow=P, ncol=P )
Pre[lower.tri(Pre)] = t(Pre)[lower.tri(Pre)]
diag(Pre) =1vals<- eigen(Pre)$valuesSigma= solve(Pre)
Z=MASS::mvrnorm(N, rep(0, P), Sigma)
Z.c= scale(Z, center=T, scale=F)
results= FLAG(Z.c)
results$precision.est
Real data analysis
Gene co-expression in the Human Brain
library(FLAG)

For the detailed experiments, please refer to NumericalResults/RealData/GeneExpression/gene_expression_data_analysis.ipynb
Term association in the university webpages
library(FLAG)

For the detailed experiments, please refer to NumericalResults/RealData/TermAssociation/meta_analysis.ipynb
Price comovement of S&P 100 stocks
library(FLAG)

For the detailed experiments, please refer to NumericalResults/RealData/StockComovement/network_measure.ipynb
Citing our work
If you find the FLAG package or any of the source code in this
repository useful for your work, please cite:
Qian, Y., Hu, X., & Yang, C. (2023).
Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications.
arXiv e-prints, arXiv-2306.
https://doi.org/10.48550/arXiv.2306.17584
Contact
Please feel free to contact Yueqi
Qian, Prof. Xianghong Hu, or Prof. Can
Yang if any inquiries.
About
Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications
Topics
empirical-bayesgaussian-graphical-modelsjoint-estimationlow-rank-updateprecision-matrixrrandom-effects-modelstatistical-inference-for-data-science
Resources
Readme
License
Activity
Custom properties
Stars
5 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
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GitHub - YangLabHKUST/FLAG: Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications · GitHub
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Repository files navigation
FLAG: The flexible and accurate Gaussian graphical model in R
The R package FLAG implements the methods based on the paper Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications.
FLAG aims to estimate precision matrix entries accurately and efficiently, and further quantify the uncertainty of each entry, which allows for better leveraging of the common structure across different groups through meta-analysis.
Installation
For a quick start, you can install the development version of FLAG
from GitHub with:
# install.packages("devtools")devtools::install_github("YangLabHKUST/FLAG")
Example
  • This is a basic example which shows you how to solve a common problem:
library(FLAG)
rm(list= ls())
set.seed(1234)
N=20P=10pi=0.2Pre=matrix(sample(c(0.2, 0.4), P*P, replace=T) * rbinom(P*P, 1, pi), nrow=P, ncol=P )
Pre[lower.tri(Pre)] = t(Pre)[lower.tri(Pre)]
diag(Pre) =1vals<- eigen(Pre)$valuesSigma= solve(Pre)
Z=MASS::mvrnorm(N, rep(0, P), Sigma)
Z.c= scale(Z, center=T, scale=F)
results= FLAG(Z.c)
results$precision.est
Real data analysis
Gene co-expression in the Human Brain
library(FLAG)

For the detailed experiments, please refer to NumericalResults/RealData/GeneExpression/gene_expression_data_analysis.ipynb
Term association in the university webpages
library(FLAG)

For the detailed experiments, please refer to NumericalResults/RealData/TermAssociation/meta_analysis.ipynb
Price comovement of S&P 100 stocks
library(FLAG)

For the detailed experiments, please refer to NumericalResults/RealData/StockComovement/network_measure.ipynb
Citing our work
If you find the FLAG package or any of the source code in this
repository useful for your work, please cite:
Qian, Y., Hu, X., & Yang, C. (2023).
Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications.
arXiv e-prints, arXiv-2306.
https://doi.org/10.48550/arXiv.2306.17584
Contact
Please feel free to contact Yueqi
Qian, Prof. Xianghong Hu, or Prof. Can
Yang if any inquiries.
About
Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications
Topics
empirical-bayesgaussian-graphical-modelsjoint-estimationlow-rank-updateprecision-matrixrrandom-effects-modelstatistical-inference-for-data-science
Resources
Readme
License
Activity
Custom properties
Stars
5 stars
Watchers
1 watching
Forks
0 forks
Report repository
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Contributors
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GitHub - YangLabHKUST/FLAG: Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications · GitHub
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BranchesTags
Repository files navigation
FLAG: The flexible and accurate Gaussian graphical model in R
The R package FLAG implements the methods based on the paper Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications.
FLAG aims to estimate precision matrix entries accurately and efficiently, and further quantify the uncertainty of each entry, which allows for better leveraging of the common structure across different groups through meta-analysis.
Installation
For a quick start, you can install the development version of FLAG
from GitHub with:
# install.packages("devtools")devtools::install_github("YangLabHKUST/FLAG")
Example
  • This is a basic example which shows you how to solve a common problem:
library(FLAG)
rm(list= ls())
set.seed(1234)
N=20P=10pi=0.2Pre=matrix(sample(c(0.2, 0.4), P*P, replace=T) * rbinom(P*P, 1, pi), nrow=P, ncol=P )
Pre[lower.tri(Pre)] = t(Pre)[lower.tri(Pre)]
diag(Pre) =1vals<- eigen(Pre)$valuesSigma= solve(Pre)
Z=MASS::mvrnorm(N, rep(0, P), Sigma)
Z.c= scale(Z, center=T, scale=F)
results= FLAG(Z.c)
results$precision.est
Real data analysis
Gene co-expression in the Human Brain
library(FLAG)

For the detailed experiments, please refer to NumericalResults/RealData/GeneExpression/gene_expression_data_analysis.ipynb
Term association in the university webpages
library(FLAG)

For the detailed experiments, please refer to NumericalResults/RealData/TermAssociation/meta_analysis.ipynb
Price comovement of S&P 100 stocks
library(FLAG)

For the detailed experiments, please refer to NumericalResults/RealData/StockComovement/network_measure.ipynb
Citing our work
If you find the FLAG package or any of the source code in this
repository useful for your work, please cite:
Qian, Y., Hu, X., & Yang, C. (2023).
Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications.
arXiv e-prints, arXiv-2306.
https://doi.org/10.48550/arXiv.2306.17584
Contact
Please feel free to contact Yueqi
Qian, Prof. Xianghong Hu, or Prof. Can
Yang if any inquiries.
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Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications
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empirical-bayesgaussian-graphical-modelsjoint-estimationlow-rank-updateprecision-matrixrrandom-effects-modelstatistical-inference-for-data-science
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GitHub - YangLabHKUST/FLAG: Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications · GitHub
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FLAG: The flexible and accurate Gaussian graphical model in R
The R package FLAG implements the methods based on the paper Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications.
FLAG aims to estimate precision matrix entries accurately and efficiently, and further quantify the uncertainty of each entry, which allows for better leveraging of the common structure across different groups through meta-analysis.
Installation
For a quick start, you can install the development version of FLAG
from GitHub with:
# install.packages("devtools")devtools::install_github("YangLabHKUST/FLAG")
Example
  • This is a basic example which shows you how to solve a common problem:
library(FLAG)
rm(list= ls())
set.seed(1234)
N=20P=10pi=0.2Pre=matrix(sample(c(0.2, 0.4), P*P, replace=T) * rbinom(P*P, 1, pi), nrow=P, ncol=P )
Pre[lower.tri(Pre)] = t(Pre)[lower.tri(Pre)]
diag(Pre) =1vals<- eigen(Pre)$valuesSigma= solve(Pre)
Z=MASS::mvrnorm(N, rep(0, P), Sigma)
Z.c= scale(Z, center=T, scale=F)
results= FLAG(Z.c)
results$precision.est
Real data analysis
Gene co-expression in the Human Brain
library(FLAG)

For the detailed experiments, please refer to NumericalResults/RealData/GeneExpression/gene_expression_data_analysis.ipynb
Term association in the university webpages
library(FLAG)

For the detailed experiments, please refer to NumericalResults/RealData/TermAssociation/meta_analysis.ipynb
Price comovement of S&P 100 stocks
library(FLAG)

For the detailed experiments, please refer to NumericalResults/RealData/StockComovement/network_measure.ipynb
Citing our work
If you find the FLAG package or any of the source code in this
repository useful for your work, please cite:
Qian, Y., Hu, X., & Yang, C. (2023).
Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications.
arXiv e-prints, arXiv-2306.
https://doi.org/10.48550/arXiv.2306.17584
Contact
Please feel free to contact Yueqi
Qian, Prof. Xianghong Hu, or Prof. Can
Yang if any inquiries.
About
Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications
Topics
empirical-bayesgaussian-graphical-modelsjoint-estimationlow-rank-updateprecision-matrixrrandom-effects-modelstatistical-inference-for-data-science
Resources
Readme
License
Activity
Custom properties
Stars
5 stars
Watchers
1 watching
Forks
0 forks
Report repository
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