Description:

This package contains different methods to estimate the matrix of partial correlations based on a (n x p) matrix X. For p>n, the matrix of partial correlations can be estimated based on p least-squares regression fits. However, for p<n, theses least-squares problems are ill-posed and need to be regularized. This package contains four different regularized regression techniques for the estimation of the partial correlations: lasso, adaptive lasso, ridge regression, and partial least squares.

Changes to previous version:

Initial Announcement on mloss.org.

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Agnostic

Data Formats: None

Tags: Regression, Graphical Models, Sparse Learning

Archive: download here

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