matSPACE - Sparse Partial Correlation Estimation for Matrix-Variate Data

Fits sparse partial correlation networks for matrix-variate data by extending the SPACE joint partial correlation estimation framework to a Kronecker-product covariance structure. All partial correlations are estimated simultaneously via an L1-penalized (lasso) shooting algorithm within a single optimization framework, which preserves symmetry of the estimated network and avoids the tuning-parameter selection difficulties of separate node-wise regressions. Optional features include column reweighting, residual variance re-estimation across outer iterations, and automatic generation of a lasso penalty sequence for tuning.