glasso: Graphical lasso for learning sparse inverse covariance matrices

In modern multivariate statistics, where high-dimensional datasets are ubiquitous, learning large inverse covariance matrices is a fundamental problem. A popular approach is to apply a penalty on the Gaussian log-likelihood and solve the convex optimization problem. Graphical lasso (Glasso) (Friedman et al. 2008) is one of the efficient and popular algorithms for imposing sparsity on the inverse covariance matrix. In this article, we introduce a corresponding new command glasso and explore the details of the algorithm. Moreover, we discuss widely used criteria for tuning parameter selection, such as the extended Bayesian information criterion (eBIC) and cross-validation (CV), and introduce corresponding commands. Simulation results and real data analysis illustrate the use of the Glasso.