CRAN/E | TransTGGM

TransTGGM

Transfer Learning for Tensor Graphical Models

Installation

About

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" .

Key Metrics

Version 1.0.0
R ≥ 3.5.0
Published 2022-11-23 491 days ago
Needs compilation? no
License GPL-2
CRAN checks TransTGGM results

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Maintainer

Maintainer

Mingyang Ren

renmingyang17@mails.ucas.ac.cn

Authors

Mingyang Ren

aut / cre

Yaoming Zhen

aut

Junhui Wang

aut

Material

Reference manual
Package source

Vignettes

TransTGGM

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-develnot available

x86_64

r-releasenot available

x86_64

r-oldrelnot available

x86_64

Depends

R ≥ 3.5.0

Imports

MASS
Matrix
rTensor
Tlasso
glasso
doParallel
expm

Suggests

knitr
rmarkdown