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mglasso  

Multiscale Graphical Lasso
View on CRAN: Click here


Download and install mglasso package within the R console
Install from CRAN:
install.packages("mglasso")

Install from Github:
library("remotes")
install_github("cran/mglasso")

Install by package version:
library("remotes")
install_version("mglasso", "0.1.2")



Attach the package and use:
library("mglasso")
Maintained by
Edmond Sanou
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-02-21
Latest Update: 2022-09-08
Description:
Inference of Multiscale graphical models with neighborhood selection approach. The method is based on solving a convex optimization problem combining a Lasso and fused-group Lasso penalties. This allows to infer simultaneously a conditional independence graph and a clustering partition. The optimization is based on the Continuation with Nesterov smoothing in a Shrinkage-Thresholding Algorithm solver (Hadj-Selem et al. 2018) implemented in python.
How to cite:
Edmond Sanou (2022). mglasso: Multiscale Graphical Lasso. R package version 0.1.2, https://cran.r-project.org/web/packages/mglasso. Accessed 06 Mar. 2026.
Previous versions and publish date:
0.1.1 (2022-02-21 09:20)
Other packages that cited mglasso R package
View mglasso citation profile
Other R packages that mglasso depends, imports, suggests or enhances
Complete documentation for mglasso
Functions, R codes and Examples using the mglasso R package
Some associated functions: adj_mat . beta_ols . beta_to_vector . conesta . cost . dist_beta . fun_lines . image_sparse . install_pylearn_parsimony . merge_clusters . mglasso . plot_clusterpath . plot_mglasso . precision_to_regression . symmetrize . 
Some associated R codes: conesta.R . fun_lines.R . install_python_deps.R . mglasso.R . plot.R . utils.R . zzz.R .  Full mglasso package functions and examples
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