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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 |
| Reference manual: | finbipartite.pdf |
| 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 |
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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.