Block coordinate descent estimators to learn k-component, bipartite, and k-component bipartite graphs from data by imposing spectral constraints on the eigenvalues and eigenvectors of the Laplacian and adjacency matrices. Those estimators leverages spectral properties of the graphical models as a prior information, which turn out to play key roles in unsupervised machine learning tasks such as clustering and community detection. This package is based on the paper "A Unified Framework for Structured Graph Learning via Spectral Constraints" by S. Kumar et al (2019) <arXiv:1904.09792>.
| Version: | 0.1.1 |
| Imports: | Rcpp, osqp, MASS, Matrix, progress, rlist |
| LinkingTo: | Rcpp, RcppArmadillo, RcppEigen |
| Suggests: | bookdown, knitr, prettydoc, rmarkdown, R.rsp, testthat, patrick, corrplot, igraph, kernlab, pals, clusterSim, viridis, quadprog, matrixcalc, CVXR |
| Published: | 2019-06-03 |
| 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 |
| CRAN checks: | spectralGraphTopology results |
| Reference manual: | spectralGraphTopology.pdf |
| Vignettes: |
Learning graphs from data via spectral constraints (pdf) Learning graphs from data via spectral constraints (html) |
| Package source: | spectralGraphTopology_0.1.1.tar.gz |
| Windows binaries: | r-devel: spectralGraphTopology_0.1.1.zip, r-release: spectralGraphTopology_0.1.1.zip, r-oldrel: spectralGraphTopology_0.1.1.zip |
| OS X binaries: | r-release: not available, r-oldrel: not available |
| Old sources: | spectralGraphTopology archive |
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