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finbipartite: Learning Bipartite Graphs: Heavy Tails and Multiple Components

Learning bipartite and k-component bipartite graphs from financial datasets. This package contains implementations of the algorithms described in the paper: Cardoso JVM, Ying J, and Palomar DP (2022). <https://openreview.net/pdf?id=WNSyF9qZaMd> "Learning bipartite graphs: heavy tails and multiple components, Advances in Neural Informations Processing Systems" (NeurIPS).

Version: 0.1.0
Depends: spectralGraphTopology, quadprog
Imports: MASS, stats, progress, mvtnorm, CVXR
Suggests: testthat, igraph
Published: 2023-02-22
DOI: 10.32614/CRAN.package.finbipartite
Author: Ze Vinicius [cre, aut]
Maintainer: Ze Vinicius <jvmirca at gmail.com>
BugReports: https://github.com/convexfi/bipartite/issues
License: GPL-3
URL: https://github.com/convexfi/bipartite/
NeedsCompilation: no
Materials: README
CRAN checks: finbipartite results

Documentation:

Reference manual: finbipartite.pdf

Downloads:

Package source: finbipartite_0.1.0.tar.gz
Windows binaries: r-devel: finbipartite_0.1.0.zip, r-release: finbipartite_0.1.0.zip, r-oldrel: finbipartite_0.1.0.zip
macOS binaries: r-release (arm64): finbipartite_0.1.0.tgz, r-oldrel (arm64): finbipartite_0.1.0.tgz, r-release (x86_64): finbipartite_0.1.0.tgz, r-oldrel (x86_64): finbipartite_0.1.0.tgz

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.