lglasso - Graphical Lasso for Longitudinal Data
Estimate treatment-specific precision matrices (networks)
from longitudinal high-dimensional normal data. The
corresponding random effects are also estimated. It is
motivated by the analysis of omics data in clinical trials
where the longitudinal omics data becomes increasingly common.
It includes both one-stage models (without treatment) and
two-stage models (with one treatment). For details of the
algorithms, please check the materials on its GitHub repo. If
you have any questions, feel free to contact the maintainers
through the email below.
