Package: spectralGraphTopology
Title: Learning Graphs from Data via Spectral Constraints
Version: 0.1.0
Description: 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>.
Authors@R: c(
  person("Ze", "Vinicius", role = c("cre", "aut"), email = "jvmirca@gmail.com"),
  person(c("Daniel", "P."), "Palomar", role = "aut", email = "daniel.p.palomar@gmail.com")
  )
Maintainer: Ze Vinicius <jvmirca@gmail.com>
URL: https://github.com/dppalomar/spectralGraphTopology,
        https://mirca.github.io/spectralGraphTopology,
        https://www.danielppalomar.com
BugReports: https://github.com/dppalomar/spectralGraphTopology/issues
Depends:
License: GPL-3
Encoding: UTF-8
LazyData: true
LinkingTo: Rcpp, RcppArmadillo, RcppEigen
Imports: Rcpp, osqp, MASS, Matrix, progress, rlist
RoxygenNote: 6.1.1
Suggests: bookdown, knitr, prettydoc, rmarkdown, R.rsp, testthat,
        patrick, corrplot, igraph, kernlab, pals, clusterSim, viridis,
        quadprog, matrixcalc, CVXR
VignetteBuilder: bookdown, knitr, prettydoc, rmarkdown, R.rsp
NeedsCompilation: yes
Packaged: 2019-05-04 11:46:48 UTC; jvmirca
Author: Ze Vinicius [cre, aut],
  Daniel P. Palomar [aut]
Repository: CRAN
Date/Publication: 2019-05-08 10:30:03 UTC
