The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
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 |
Maintainer: | Mingyang Ren <renmingyang17 at mails.ucas.ac.cn> |
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: TransTGGM_1.0.0.zip, r-release: TransTGGM_1.0.0.zip, r-oldrel: TransTGGM_1.0.0.zip |
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 |
Please use the canonical form https://CRAN.R-project.org/package=TransTGGM to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.