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mglasso

Multiscale Graphical Lasso

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) <doi:10.1109/TMI.2018.2829802> implemented in python.

Versions across snapshots

VersionRepositoryFileSize
0.1.2 rolling linux/jammy R-4.5 mglasso_0.1.2.tar.gz 70.5 KiB
0.1.2 rolling linux/noble R-4.5 mglasso_0.1.2.tar.gz 70.2 KiB
0.1.2 rolling source/ R- mglasso_0.1.2.tar.gz 28.4 KiB
0.1.2 latest linux/jammy R-4.5 mglasso_0.1.2.tar.gz 70.5 KiB
0.1.2 latest linux/noble R-4.5 mglasso_0.1.2.tar.gz 70.2 KiB
0.1.2 latest source/ R- mglasso_0.1.2.tar.gz 28.4 KiB
0.1.2 2026-04-26 source/ R- mglasso_0.1.2.tar.gz 28.4 KiB
0.1.2 2026-04-23 source/ R- mglasso_0.1.2.tar.gz 28.4 KiB
0.1.2 2026-04-09 windows/windows R-4.5 mglasso_0.1.2.zip 77.8 KiB
0.1.2 2025-04-20 source/ R- mglasso_0.1.2.tar.gz 28.4 KiB

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