Sparse Partial Correlation Estimation for Matrix-Variate Data R package matSPACE version 0.2.1 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. Software Imports: Rcpp, stats LinkingTo: Rcpp Hyewon Kim <kimhw4126@gmail.com> Comprehensive R Archive Network (CRAN) Hyewon Kim [aut, cre], Seongoh Park [aut] GPL (>= 3) 2026-09-14 application/tgz https://CRAN.R-project.org/package=matSPACE doi:10.32614/CRAN.package.matSPACE