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matSPACE  

Sparse Partial Correlation Estimation for Matrix-Variate Data
View on CRAN: Click here


Download and install matSPACE package within the R console
Install from CRAN:
install.packages("matSPACE")

Install from Github:
library("remotes")
install_github("cran/matSPACE")

Install by package version:
library("remotes")
install_version("matSPACE", "0.2.1")



Attach the package and use:
library("matSPACE")
Maintained by
Hyewon Kim
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-09-14
Latest Update: 2026-09-14
Description:
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.
How to cite:
Hyewon Kim (2026). matSPACE: Sparse Partial Correlation Estimation for Matrix-Variate Data. R package version 0.2.1, https://cran.r-project.org/web/packages/matSPACE. Accessed 05 Oct. 2026.
Previous versions and publish date:
(2026-09-14 09:00), 0.1.0 (2026-09-12 15:40)
Other packages that cited matSPACE R package
View matSPACE citation profile
Other R packages that matSPACE depends, imports, suggests or enhances
Complete documentation for matSPACE
Functions, R codes and Examples using the matSPACE R package
Full matSPACE package functions and examples
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