{"id":19839845,"url":"https://github.com/qdata/diffee","last_synced_at":"2026-05-10T12:14:29.567Z","repository":{"id":56937216,"uuid":"123168153","full_name":"QData/DIFFEE","owner":"QData","description":"AISTAT2018: Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure","archived":false,"fork":false,"pushed_at":"2019-08-28T16:29:38.000Z","size":28072,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":5,"default_branch":"master","last_synced_at":"2025-01-11T11:26:37.927Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://cran.r-project.org/web/packages/diffee/index.html","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/QData.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2018-02-27T18:04:47.000Z","updated_at":"2019-08-28T16:29:41.000Z","dependencies_parsed_at":"2022-08-21T01:10:09.881Z","dependency_job_id":null,"html_url":"https://github.com/QData/DIFFEE","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/QData%2FDIFFEE","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/QData%2FDIFFEE/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/QData%2FDIFFEE/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/QData%2FDIFFEE/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/QData","download_url":"https://codeload.github.com/QData/DIFFEE/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241209584,"owners_count":19927736,"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":[],"created_at":"2024-11-12T12:24:34.456Z","updated_at":"2026-05-10T12:14:24.527Z","avatar_url":"https://github.com/QData.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# DIFFEE\n\n\n\n##  R package \"diffee\":  @ [CRAN Website](https://cran.r-project.org/web/packages/diffee/index.html)\n\n```R\ninstall.packages(\"diffee\")\nlibrary(diffee)\ndemo(diffeeDemo)\n```\n\n## Reference\n\n```latex\n@InProceedings{pmlr-v84-wang18f,\n  title = \t {Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure},\n  author = \t {Beilun Wang and arshdeep Sekhon and Yanjun Qi},\n  booktitle = \t {Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics},\n  pages = \t {1691--1700},\n  year = \t {2018},\n  editor = \t {Amos Storkey and Fernando Perez-Cruz},\n  volume = \t {84},\n  series = \t {Proceedings of Machine Learning Research},\n  address = \t {Playa Blanca, Lanzarote, Canary Islands},\n  month = \t {09--11 Apr},\n  publisher = \t {PMLR},\n  pdf = \t {http://proceedings.mlr.press/v84/wang18f/wang18f.pdf},\n  url = \t {http://proceedings.mlr.press/v84/wang18f.html},\n  abstract = \t {We focus on the problem of estimating the change in the dependency structures of two $p$-dimensional Gaussian Graphical models (GGMs). Previous studies for sparse change estimation in GGMs involve expensive and difficult non-smooth optimization. We propose a novel method, DIFFEE for estimating DIFFerential networks via an Elementary Estimator under a high-dimensional situation. DIFFEE is solved through a faster and closed form solution that enables it to work in large-scale settings. We conduct a rigorous statistical analysis showing that surprisingly DIFFEE achieves the same asymptotic convergence rates as the state-of-the-art estimators that are much more difficult to compute. Our experimental results on multiple synthetic datasets and one real-world data about brain connectivity show strong performance improvements over baselines, as well as significant computational benefits.}\n}\n```\n\n##  more details in project [website](http://jointggm.org/)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqdata%2Fdiffee","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fqdata%2Fdiffee","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqdata%2Fdiffee/lists"}