{"id":18311393,"url":"https://github.com/fabian-sp/gglasso","last_synced_at":"2025-04-06T16:14:04.510Z","repository":{"id":37263239,"uuid":"213875194","full_name":"fabian-sp/GGLasso","owner":"fabian-sp","description":"A Python package for General Graphical Lasso computation","archived":false,"fork":false,"pushed_at":"2025-02-28T10:37:01.000Z","size":127533,"stargazers_count":34,"open_issues_count":0,"forks_count":14,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-03-30T13:09:42.206Z","etag":null,"topics":["graphical-lasso","graphical-models","latent-variable-models","network-inference","optimization"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/fabian-sp.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE.txt","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2019-10-09T09:23:27.000Z","updated_at":"2025-03-25T07:22:32.000Z","dependencies_parsed_at":"2023-01-29T03:30:16.215Z","dependency_job_id":"a5862c1f-7fc6-4dcb-99fe-f12ff1a89d1e","html_url":"https://github.com/fabian-sp/GGLasso","commit_stats":{"total_commits":711,"total_committers":6,"mean_commits":118.5,"dds":"0.36708860759493667","last_synced_commit":"ea8319572cb519c1c2440e147144f040cb990cb1"},"previous_names":[],"tags_count":5,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fabian-sp%2FGGLasso","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fabian-sp%2FGGLasso/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fabian-sp%2FGGLasso/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fabian-sp%2FGGLasso/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/fabian-sp","download_url":"https://codeload.github.com/fabian-sp/GGLasso/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247509237,"owners_count":20950232,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["graphical-lasso","graphical-models","latent-variable-models","network-inference","optimization"],"created_at":"2024-11-05T16:17:31.274Z","updated_at":"2025-04-06T16:14:04.378Z","avatar_url":"https://github.com/fabian-sp.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# GGLasso\n\n[![PyPI version fury.io](https://badge.fury.io/py/gglasso.svg)](https://pypi.python.org/pypi/gglasso/)\n[![PyPI license](https://img.shields.io/pypi/l/gglasso.svg)](https://pypi.python.org/pypi/gglasso/)\n[![Documentation Status](https://readthedocs.org/projects/gglasso/badge/?version=latest)](http://gglasso.readthedocs.io/?badge=latest)\n[![DOI](https://joss.theoj.org/papers/10.21105/joss.03865/status.svg)](https://doi.org/10.21105/joss.03865)\n[![arXiv](https://img.shields.io/badge/arXiv-2011.00898-b31b1b.svg)](https://arxiv.org/abs/2110.10521)\n\n\nThis package contains algorithms for solving General Graphical Lasso (GGLasso) problems, including single, multiple, as well as latent \nGraphical Lasso problems. \u003cbr\u003e\n\n[Docs](https://gglasso.readthedocs.io/en/latest/) | [Examples](https://gglasso.readthedocs.io/en/latest/auto_examples/index.html)\n\n## Getting started\n\n### Install via pip/conda\n\nThe package is available on pip and conda and can be installed with\n\n    pip install gglasso\n\nor\n\n    conda install -c conda-forge gglasso\n\n\n### Developer installation\n\nIf you want to create a conda environment with full development dependencies (for building docs, testing,...), run:\n\n\tconda env create -f environment.yml\n\nTo install `gglasso` in developer mode run\n\n    python -m pip install --editable .\n\n\nTest your installation with \n\n    pytest tests/ -v\n\n\n\n\n## The `glasso_problem` class\n\n`GGLasso` can solve multiple problem forumulations, e.g. single and multiple Graphical Lasso problems as well as with and without latent factors. Therefore, the main entry point for the user is the `glasso_problem` class which chooses automatically the correct solver and model selection functionality. See [our documentation](https://gglasso.readthedocs.io/en/latest/problem-object.html) for all the details.\n\n\n## Algorithms\n\n`GGLasso` contains algorithms for solving a multitude of Graphical Lasso problem formulations. For all the details, we refer to the [solver overview in our documentation](https://gglasso.readthedocs.io/en/latest/solvers-overview.html).\n\nThe package includes solvers for the following problems:\u003cbr\u003e\n\n- **Single Graphical Lasso**\u003cbr\u003e\n\n- **Group and Fused Graphical Lasso**\u003cbr\u003e\nWe implemented the ADMM (see [2] and [3]) and a proximal point algorithm (see [4]). \n\n- **Non-conforming Group Graphical Lasso**\u003cbr\u003e\nA Group Graphical Lasso problem where not all variables exist in all instances/datasets.  \n\n- **Functional Graphical Lasso**\u003cbr\u003e\nA variant of Graphical Lasso where each variables has a functional representation (e.g. by Fourier coefficients).\n\nMoreover, for all problem formulation the package allows to model latent variables (Latent variable Graphical Lasso) in order to estimate a precision matrix of type *sparse - low rank*.\n\n## Citation\n\nIf you use `GGLasso`, please consider the following citation\n\n    @article{Schaipp2021,\n      doi = {10.21105/joss.03865},\n      url = {https://doi.org/10.21105/joss.03865},\n      year = {2021},\n      publisher = {The Open Journal},\n      volume = {6},\n      number = {68},\n      pages = {3865},\n      author = {Fabian Schaipp and Oleg Vlasovets and Christian L. Müller},\n      title = {GGLasso - a Python package for General Graphical Lasso computation},\n      journal = {Journal of Open Source Software}\n    }\n\n\n## Community Guidelines\n\n1)  Contributions and suggestions to the software are always welcome.\n    Please, consult our [contribution guidelines](CONTRIBUTING.md) prior\n    to submitting a pull request.\n2)  Report issues or problems with the software using github’s [issue\n    tracker](https://github.com/fabian-sp/GGLasso/issues).\n3)  Contributors must adhere to the [Code of\n    Conduct](CODE_OF_CONDUCT.md).\n\n\n## References\n*  [1] Friedman, J., Hastie, T., and Tibshirani, R. (2007).  Sparse inverse covariance estimation with the Graphical Lasso. Biostatistics, 9(3):432–441.\n*  [2] Danaher, P., Wang, P., and Witten, D. M. (2013). The joint graphical lasso for inverse covariance estimation across multiple classes. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76(2):373–397.\n* [3] Tomasi, F., Tozzo, V., Salzo, S., and Verri, A. (2018). Latent Variable Time-varying Network Inference. InProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery \u0026 Data Mining. ACM.\n* [4] Zhang, Y., Zhang, N., Sun, D., and Toh, K.-C. (2020). A proximal point dual Newton algorithm for solving group graphical Lasso problems. SIAM J. Optim., 30(3):2197–2220.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffabian-sp%2Fgglasso","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffabian-sp%2Fgglasso","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffabian-sp%2Fgglasso/lists"}