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TransTGGM  

Transfer Learning for Tensor Graphical Models
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("TransTGGM", "1.0.0")



Attach the package and use:
library("TransTGGM")
Maintained by
Mingyang Ren
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-11-23
Latest Update: 2022-11-23
Description:
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>.
How to cite:
Mingyang Ren (2022). TransTGGM: Transfer Learning for Tensor Graphical Models. R package version 1.0.0, https://cran.r-project.org/web/packages/TransTGGM. Accessed 22 Dec. 2024.
Previous versions and publish date:
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Complete documentation for TransTGGM
Functions, R codes and Examples using the TransTGGM R package
Some associated functions: Theta.est . Theta.tuning . example.data . tensor.GGM.trans . 
Some associated R codes: Initial.R . Initial.aggr.R . S.est.R . Theta.est.R . Theta.tuning.R . Thetaest.admm.R . Thetaest.cd.R . delta.est.R . example.data.R . select.1.R . support_functions.R . tensor.GGM.trans.R . trans.estimate.R .  Full TransTGGM package functions and examples
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