Software

• TransGraph [Link to CRAN]
  An R package for transfer graph learning. This package implements transfer learning for several complex graphical models, including Tensor Gaussian graphical models and non-Gaussian directed acyclic graph (DAG). Notably, this package promotes local transfer at node-level in DAG structural learning. As by-products, transfer learning for undirected graphical model (precision matrix) via D-trace loss, transfer learning for mean vector estimation, and single non-Gaussian learning via topological layer method are also included in this package. Moreover, the aggregation of auxiliary information is an important issue in transfer learning, and this package provides multiple user-friendly aggregation methods, including sample weighting, similarity weighting, and most informative selection.
  Reference:
  · Transfer learning for tensor Gaussian graphical models, Journal of Machine Learning Research, 2024.
  · Structural transfer learning of non-Gaussian DAG, arXiv, 2023.
  · Learning linear non-Gaussian directed acyclic graph with diverging number of nodes, Journal of Machine Learning Research, 2022.

• cencrne [Link to CRAN]
  An R package for consistent estimation of the number of communities via regularized network embedding.
  Reference:
  · Consistent estimation of the number of communities via regularized network embedding, Biometrics, 2023.

• HhP [Link to CRAN]
  An R package for hierarchical heterogeneity analysis. The current version contains only regression-based supervised hierarchical heterogeneity analysis and will be supplemented with Gaussian graphical model-based unsupervised hierarchical heterogeneity analysis in the next step.
  Reference:
  · Hierarchical cancer heterogeneity analysis based on histopathological imaging features, Biometrics, 2022.
  · Gaussian Graphical Model-based Hierarchical Cancer Heterogeneity Analysis via Integrating Pathological Imaging and Omics Data, Manuscript.

• HeteroGGM [Link to CRAN]
  An R package for Gaussian graphical model-based heterogeneity analysis.
  Reference:
  · Gaussian graphical model-based heterogeneity analysis via penalized fusion, Biometrics, 2022.
  · HeteroGGM: an R package for Gaussian graphical model-based heterogeneity analysis, Bioinformatics, 2021.