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Learning graphs for financial markets with optimization algorithms. This package contains implementations of the algorithms described in the paper: Cardoso JVM, Ying J, and Palomar DP (2021) <https://papers.nips.cc/paper/2021/hash/a64a034c3cb8eac64eb46ea474902797-Abstract.html> "Learning graphs in heavy-tailed markets", Advances in Neural Informations Processing Systems (NeurIPS).
| Version: | 0.1.0 |
| Depends: | spectralGraphTopology |
| Imports: | MASS, stats, progress, mvtnorm |
| Suggests: | testthat |
| Published: | 2023-02-14 |
| DOI: | 10.32614/CRAN.package.fingraph |
| Author: | Ze Vinicius [cre, aut] |
| Maintainer: | Ze Vinicius <jvmirca at gmail.com> |
| BugReports: | https://github.com/convexfi/fingraph/issues |
| License: | GPL-3 |
| URL: | https://github.com/convexfi/fingraph/ |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | fingraph results |
| Reference manual: | fingraph.pdf |
| Package source: | fingraph_0.1.0.tar.gz |
| Windows binaries: | r-devel: fingraph_0.1.0.zip, r-release: fingraph_0.1.0.zip, r-oldrel: fingraph_0.1.0.zip |
| macOS binaries: | r-release (arm64): fingraph_0.1.0.tgz, r-oldrel (arm64): fingraph_0.1.0.tgz, r-release (x86_64): fingraph_0.1.0.tgz, r-oldrel (x86_64): fingraph_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.