graphiclasso: Graphical lasso for learning sparse inverse-covariance matrices
graphiclasso: Graphical lasso for learning sparse inverse-covariance matrices
In modern multivariate statistics, where high-dimensional datasets are ubiquitous, learning large (inverse-) covariance matrices is imperative for data analysis. A popular approach to estimating a large inverse-covariance matrix is to regularize the Gaussian log-likelihood function by imposing a convex penalty function. In a seminal article, Friedman, Hastie, and Tibshirani (2008, …