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.
| Version: |
0.2.3 |
| Imports: |
Rcpp (≥ 0.11.0), MASS, Matrix, progress, rlist |
| LinkingTo: |
Rcpp, RcppArmadillo, RcppEigen |
| Suggests: |
CVXR, bookdown, knitr, prettydoc, rmarkdown, R.rsp, testthat, patrick, corrplot, igraph, kernlab, pals, clusterSim, viridis, quadprog, matrixcalc |
| Published: |
2022-03-14 |
| DOI: |
10.32614/CRAN.package.spectralGraphTopology |
| Author: |
Ze Vinicius [cre, aut],
Daniel P. Palomar [aut] |
| Maintainer: |
Ze Vinicius <jvmirca at gmail.com> |
| BugReports: |
https://github.com/dppalomar/spectralGraphTopology/issues |
| License: |
GPL-3 |
| URL: |
https://github.com/dppalomar/spectralGraphTopology,
https://mirca.github.io/spectralGraphTopology/,
https://www.danielppalomar.com |
| NeedsCompilation: |
yes |
| Citation: |
spectralGraphTopology citation info |
| Materials: |
README NEWS |
| In views: |
GraphicalModels |
| CRAN checks: |
spectralGraphTopology results |
Documentation:
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