README.md
In gleday/beam: Fast Bayesian Inference in Large Gaussian Graphical Models
beam
This R package implements the method described in
Leday, G.G.R. and Richardson, S. (2019).
Fast Bayesian inference in large Gaussian graphical models.
Biometrics. 75(4), 1288--1298.
Description
beam:
- enables the reconstruction of marginal and conditional independence
graphs from high-dimensional data
- is computationally very efficient, addressing problems with
thousands of variables in just a few seconds.
- outperforms popular Bayesian and non-Bayesian methods
Installation
If you wish to install beam from R:
# Install/load R package devtools
install.packages("devtools")
library(devtools)
# Install/load R package beam from github
install_github("gleday/beam")
library(beam)
gleday/beam documentation built on June 10, 2025, 9:41 a.m.
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beam
This R package implements the method described in
Leday, G.G.R. and Richardson, S. (2019). Fast Bayesian inference in large Gaussian graphical models. Biometrics. 75(4), 1288--1298.
Description
beam:
- enables the reconstruction of marginal and conditional independence graphs from high-dimensional data
- is computationally very efficient, addressing problems with thousands of variables in just a few seconds.
- outperforms popular Bayesian and non-Bayesian methods
Installation
If you wish to install beam from R:
# Install/load R package devtools
install.packages("devtools")
library(devtools)
# Install/load R package beam from github
install_github("gleday/beam")
library(beam)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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