TransTGGM: Transfer Learning for Tensor Graphical Models

Tensor Gaussian graphical models (GGMs) have important applications in numerous areas, which can interpret conditional independence structures within tensor data. Yet, the available tensor data in one single study is often limited due to high acquisition costs. Although relevant studies can provide additional data, it remains an open question how to pool such heterogeneous data. This package implements a transfer learning framework for tensor GGMs, which takes full advantage of informative auxiliary domains even when non-informative auxiliary domains are present, benefiting from the carefully designed data-adaptive weights. Reference: Ren, M., Zhen Y., and Wang J. (2022). "Transfer learning for tensor graphical models" <doi:10.48550/arXiv.2211.09391>.

Version: 1.0.0
Depends: R (≥ 3.5.0)
Imports: MASS, Matrix, rTensor, Tlasso, glasso, doParallel, expm
Suggests: knitr, rmarkdown
Published: 2022-11-23
DOI: 10.32614/CRAN.package.TransTGGM
Author: Mingyang Ren ORCID iD [aut, cre], Yaoming Zhen [aut], Junhui Wang [aut]
Maintainer: Mingyang Ren <renmingyang17 at>
License: GPL-2
NeedsCompilation: no
CRAN checks: TransTGGM results


Reference manual: TransTGGM.pdf
Vignettes: TransTGGM


Package source: TransTGGM_1.0.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): TransTGGM_1.0.0.tgz, r-oldrel (arm64): TransTGGM_1.0.0.tgz, r-release (x86_64): TransTGGM_1.0.0.tgz, r-oldrel (x86_64): TransTGGM_1.0.0.tgz


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