Package: TransGraph 1.0.1
Mingyang Ren
TransGraph: Transfer Graph Learning
Transfer learning, aiming to use auxiliary domains to help improve learning of the target domain of interest when multiple heterogeneous datasets are available, has always been a hot topic in statistical machine learning. The recent transfer learning methods with statistical guarantees mainly focus on the overall parameter transfer for supervised models in the ideal case with the informative auxiliary domains with overall similarity. In contrast, transfer learning for unsupervised graph learning is in its infancy and largely follows the idea of overall parameter transfer as for supervised learning. In this package, the transfer learning for several complex graphical models is implemented, including Tensor Gaussian graphical models, non-Gaussian directed acyclic graph (DAG), and Gaussian graphical mixture models. Notably, this package promotes local transfer at node-level and subgroup-level in DAG structural learning and Gaussian graphical mixture models, respectively, which are more flexible and robust than the existing overall parameter transfer. 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: Ren, M., Zhen Y., and Wang J. (2022) <arxiv:2211.09391> "Transfer learning for tensor graphical models". Ren, M., He X., and Wang J. (2023) <arxiv:2310.10239> "Structural transfer learning of non-Gaussian DAG". Zhao, R., He X., and Wang J. (2022) <https://jmlr.org/papers/v23/21-1173.html> "Learning linear non-Gaussian directed acyclic graph with diverging number of nodes".
Authors:
TransGraph_1.0.1.tar.gz
TransGraph_1.0.1.tar.gz(r-4.5-noble)TransGraph_1.0.1.tar.gz(r-4.4-noble)
TransGraph_1.0.1.tgz(r-4.4-emscripten)TransGraph_1.0.1.tgz(r-4.3-emscripten)
TransGraph.pdf |TransGraph.html✨
TransGraph/json (API)
# Install 'TransGraph' in R: |
install.packages('TransGraph', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 1 years agofrom:a556fa7ef9. Checks:OK: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 13 2024 |
R-4.5-linux | OK | Dec 13 2024 |
Exports:Evaluation.DAGEvaluation.GGMlayer_adjtensor.GGM.transTheta.estTheta.tuningTLLiNGAMtrans_GGMMtrans_meantrans_precisiontrans.local.DAG
Dependencies:cliclimecodetoolscpp11dcovdoParallelEvaluationMeasuresexpmforeachglassoglueHeteroGGMhugeigraphiteratorslatticelifecyclelpSolvemagrittrMASSMatrixpkgconfigRcppRcppArmadilloRcppEigenrlangrTensorTlassovctrs
Readme and manuals
Help Manual
Help page | Topics |
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Evaluation function for the estimated DAG. | Evaluation.DAG |
Evaluation function for the estimated GGM. | Evaluation.GGM |
The function of converting the adjacency matrix into the topological layer. | layer_adj |
Transfer learning for tensor graphical models. | tensor.GGM.trans |
Sparse precision matrix estimation. | Theta.est |
Sparse precision matrix estimation with tuning parameters. | Theta.tuning |
Learning linear non-Gaussian DAG via topological layers. | TLLiNGAM |
Transfer learning of high-dimensional Gaussian graphical mixture models. | trans_GGMM |
Transfer learning for mean estimation. | trans_mean |
Transfer learning for vector-valued precision matrix (graphical model). | trans_precision |
Structural transfer learning of non-Gaussian DAG. | trans.local.DAG |