Package: spectralGraphTopology 0.2.3

Ze Vinicius

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:Ze Vinicius [cre, aut], Daniel P. Palomar [aut]

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'))

Peer review:

Bug tracker:https://github.com/dppalomar/spectralgraphtopology/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

5.91 score 2 stars 1 packages 135 scripts 789 downloads 24 exports 31 dependencies

Last updated 2 years agofrom:97eee40c8a. Checks:ERROR: 1 WARNING: 8. Indexed: yes.

TargetResultDate
Doc / VignettesFAILOct 12 2024
R-4.5-win-x86_64WARNINGOct 12 2024
R-4.5-linux-x86_64WARNINGOct 12 2024
R-4.4-win-x86_64WARNINGOct 12 2024
R-4.4-mac-x86_64WARNINGOct 12 2024
R-4.4-mac-aarch64WARNINGOct 12 2024
R-4.3-win-x86_64WARNINGOct 12 2024
R-4.3-mac-x86_64WARNINGOct 12 2024
R-4.3-mac-aarch64WARNINGOct 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.asisusingR.rsp::asison Oct 12 2024.

Last update: 2019-04-28
Started: 2019-04-28