GraphSPME | Graphical Sparse Precision Matrix Estimation

by Blunde1 C++ Version: 0.0.2b0 License: GPL-3.0

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | GraphSPME Summary

GraphSPME is a C++ library. GraphSPME has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

Graphical Sparse Precision Matrix Estimation | For very high dimensions and with asymptotic regularization

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Support

GraphSPME has a low active ecosystem.

It has 3 star(s) with 2 fork(s). There are 2 watchers for this library.

It had no major release in the last 12 months.

There are 3 open issues and 1 have been closed. There are 3 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of GraphSPME is 0.0.2b0

Quality

GraphSPME has no bugs reported.

Security

GraphSPME has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

GraphSPME is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

GraphSPME releases are not available. You will need to build from source code and install.

Installation instructions, examples and code snippets are available.

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GraphSPME Key Features

No Key Features are available at this moment for GraphSPME.

GraphSPME Examples and Code Snippets

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Community Discussions

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Vulnerabilities

No vulnerabilities reported

Install GraphSPME

R: Install the development version from GitHub.

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Simulate a zero-mean AR1 process with a known graphical structure:. The graphical structure of the data is contained in Z, which shows the non-zero elements of the precision matrix. Such information is typically known in real-world problems. The exact dependence-structure is however typically unknown. GraphSPME therefore estimates a non-parametric estimate of the precision matrix using the prec_sparse() function.

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