Package: spectralGraphTopology 0.2.3
spectralGraphTopology: Learning Graphs from Data via Spectral Constraints
In the era of big data and hyperconnectivity, learning high-dimensional structures such as graphs from data has become a prominent task in machine learning and has found applications in many fields such as finance, health care, and networks. 'spectralGraphTopology' is an open source, documented, and well-tested R package for learning graphs from data. It provides implementations of state of the art algorithms such as Combinatorial Graph Laplacian Learning (CGL), Spectral Graph Learning (SGL), Graph Estimation based on Majorization-Minimization (GLE-MM), and Graph Estimation based on Alternating Direction Method of Multipliers (GLE-ADMM). In addition, graph learning has been widely employed for clustering, where specific algorithms are available in the literature. To this end, we provide an implementation of the Constrained Laplacian Rank (CLR) algorithm.
Authors:
spectralGraphTopology_0.2.3.tar.gz
spectralGraphTopology_0.2.3.zip(r-4.5)spectralGraphTopology_0.2.3.zip(r-4.4)spectralGraphTopology_0.2.3.zip(r-4.3)
spectralGraphTopology_0.2.3.tgz(r-4.4-x86_64)spectralGraphTopology_0.2.3.tgz(r-4.4-arm64)spectralGraphTopology_0.2.3.tgz(r-4.3-x86_64)spectralGraphTopology_0.2.3.tgz(r-4.3-arm64)
spectralGraphTopology_0.2.3.tar.gz(r-4.5-noble)spectralGraphTopology_0.2.3.tar.gz(r-4.4-noble)
spectralGraphTopology_0.2.3.tgz(r-4.4-emscripten)spectralGraphTopology_0.2.3.tgz(r-4.3-emscripten)
|spectralGraphTopology.html✨
spectralGraphTopology/json (API)
NEWS
# Install 'spectralGraphTopology' in R: |
install.packages('spectralGraphTopology', repos = c('https://dppalomar.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/dppalomar/spectralgraphtopology/issues
Last updated 2 years agofrom:97eee40c8a. Checks:ERROR: 1 WARNING: 8. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | FAIL | Oct 12 2024 |
R-4.5-win-x86_64 | WARNING | Oct 12 2024 |
R-4.5-linux-x86_64 | WARNING | Oct 12 2024 |
R-4.4-win-x86_64 | WARNING | Oct 12 2024 |
R-4.4-mac-x86_64 | WARNING | Oct 12 2024 |
R-4.4-mac-aarch64 | WARNING | Oct 12 2024 |
R-4.3-win-x86_64 | WARNING | Oct 12 2024 |
R-4.3-mac-x86_64 | WARNING | Oct 12 2024 |
R-4.3-mac-aarch64 | WARNING | Oct 12 2024 |
Exports:AaccuracyAstarblock_diagcluster_k_component_graphDDstarfdrfscoreLlearn_bipartite_graphlearn_bipartite_k_component_graphlearn_combinatorial_graph_laplacianlearn_graph_sigreplearn_k_component_graphlearn_laplacian_gle_admmlearn_laplacian_gle_mmlearn_smooth_approx_graphlearn_smooth_graphLstarnpvrecallrelative_errorspecificity
Dependencies:bitbit64clarabelclicrayonCVXRdata.tableECOSolveRgluegmphmsjsonlitelatticelifecycleMASSMatrixosqppkgconfigprettyunitsprogressR6RcppRcppArmadilloRcppEigenrlangrlistRmpfrscsvctrsXMLyaml
Learning graphs from data via spectral constraints (html)
Rendered fromSpectralGraphTopology.html.asis
usingR.rsp::asis
on Oct 12 2024.Last update: 2019-04-28
Started: 2019-04-28
Readme and manuals
Help Manual
Help page | Topics |
---|