{"id":31724976,"url":"https://github.com/abess-team/slide","last_synced_at":"2025-10-09T05:18:27.693Z","repository":{"id":317517683,"uuid":"996429893","full_name":"abess-team/SLIDE","owner":"abess-team","description":"[JASA] Reconstruct Ising Model with Global Optimality via SLIDE","archived":false,"fork":false,"pushed_at":"2025-10-01T10:57:58.000Z","size":828,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-10-01T12:36:30.563Z","etag":null,"topics":["binary-random-vector","coupling","ising-model","pseudo-likelihood","sparse-learning","spin","subset-selection"],"latest_commit_sha":null,"homepage":"","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/abess-team.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"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,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-06-05T00:09:51.000Z","updated_at":"2025-10-01T10:58:01.000Z","dependencies_parsed_at":"2025-10-01T12:36:34.058Z","dependency_job_id":null,"html_url":"https://github.com/abess-team/SLIDE","commit_stats":null,"previous_names":["abess-team/slide"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/abess-team/SLIDE","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/abess-team%2FSLIDE","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/abess-team%2FSLIDE/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/abess-team%2FSLIDE/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/abess-team%2FSLIDE/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/abess-team","download_url":"https://codeload.github.com/abess-team/SLIDE/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/abess-team%2FSLIDE/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279000742,"owners_count":26082933,"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","status":"online","status_checked_at":"2025-10-09T02:00:07.460Z","response_time":59,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["binary-random-vector","coupling","ising-model","pseudo-likelihood","sparse-learning","spin","subset-selection"],"created_at":"2025-10-09T05:18:25.438Z","updated_at":"2025-10-09T05:18:27.689Z","avatar_url":"https://github.com/abess-team.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Instructions for Reproducible Materials\n\n## Organization\n\n- **Bash script** (`batch.sh`): automate the execution of all numerical simulation studies\n- **R scripts** (`.R`): implement baseline methods, evaluation metrics, simulation studies, real-data analysis of the congressional voting dataset\n- **Data files** (`.csv`):  for real-world data analysis. It is available at [Dropbox](https://www.dropbox.com/scl/fo/zbfrhxm60y8hhrzufhno2/AJzjVAZiJHK8AhrrBS6xxUw?rlkey=fpjf3h5awrki1cik5ypy5pqg9\u0026st=8qwgpufi\u0026dl=0). \n\n## File Descriptions\n\n### Main R scripts\n- `simu_degree.R` — empirical sample complexity analysis with respect to the degree.  \n- `simu_beta.R` — empirical sample complexity analysis with respect to the maximum signal.  \n- `simu_high.R` — experiments for high-dimensional cases.  \n- `simu_p.R` — empirical sample complexity analysis with respect to the dimension.  \n- `simu_ws.R` — empirical sample complexity analysis with respect to the weakest signal.  \n- `DataAnalysis.R` — real-data analysis: data cleaning, estimation of the graphical structure among senators, and visualization.  \n\n#### Utility R scripts (automatically used by the main scripts)\n- `simulation_main.R` — runs one method on a given simulated dataset.  \n- `method_implementation.R` — implementations of baseline methods (RPLE, RISE, logRISE, ELASSO, RLRF).  \n- `evaluation.R` — evaluation metrics (e.g., Frobenius norm, true positive rate).  \n\n## Reproducing Results\n\nThe scripts reproduce the results presented in the paper as follows:  \n\n- **Figure 1 and Table S1** → `simu_degree.R`  \n- **Figure 2 and Figure S1** → `simu_beta.R`  \n- **Figure 3 and Figure S2** → `simu_high.R`  \n- **Figure S3** → `simu_p.R`  \n- **Figure S4** → `simu_ws.R`  \n- **Figure 4** → `DataAnalysis.R`  \n\nThe simplest procedure on reproduction:\n  1. Use the provided bash scripts (`batch.sh`) to execute the full set of simulation automatically.  \n  2. Run `DataAnalysis.R` to reproduce the real-data analysis (Figure 4). \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabess-team%2Fslide","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fabess-team%2Fslide","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabess-team%2Fslide/lists"}