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.

Version:	0.2.1
Imports:	Rcpp, stats
LinkingTo:	Rcpp
Published:	2026-09-14
DOI:	10.32614/CRAN.package.matSPACE
Author:	Hyewon Kim [aut, cre], Seongoh Park [aut]
Maintainer:	Hyewon Kim <kimhw4126 at gmail.com>
BugReports:	https://github.com/kimhyew1/matSPACE/issues
License:	GPL (≥ 3)
URL:	https://github.com/kimhyew1/matSPACE
NeedsCompilation:	yes
Materials:	README
CRAN checks:	matSPACE results

Documentation:

Reference manual:

matSPACE.html , matSPACE.pdf

Downloads:

Package source:	matSPACE_0.2.1.tar.gz
Windows binaries:	r-devel: matSPACE_0.2.1.zip, r-release: matSPACE_0.2.1.zip, r-oldrel: matSPACE_0.2.1.zip
macOS binaries:	r-release (arm64): matSPACE_0.2.1.tgz, r-oldrel (arm64): matSPACE_0.2.1.tgz, r-release (x86_64): matSPACE_0.2.1.tgz, r-oldrel (x86_64): matSPACE_0.2.1.tgz
Old sources:	matSPACE archive

Linking:

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