{"id":51512943,"url":"https://github.com/computorg/published-202407-susmann-adaptive-conformal","last_synced_at":"2026-07-08T08:02:51.019Z","repository":{"id":249016494,"uuid":"827888123","full_name":"computorg/published-202407-susmann-adaptive-conformal","owner":"computorg","description":null,"archived":false,"fork":false,"pushed_at":"2025-07-29T07:44:34.000Z","size":10973,"stargazers_count":0,"open_issues_count":1,"forks_count":1,"subscribers_count":4,"default_branch":"main","last_synced_at":"2025-07-29T09:42:42.660Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"http://computo-journal.org/published-202407-susmann-adaptive-conformal/","language":"TeX","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"cc-by-4.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/computorg.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,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2024-07-12T15:42:33.000Z","updated_at":"2025-07-29T07:44:38.000Z","dependencies_parsed_at":"2024-08-25T03:23:57.732Z","dependency_job_id":"af4597e4-1be2-456c-95c3-2169e84eb71b","html_url":"https://github.com/computorg/published-202407-susmann-adaptive-conformal","commit_stats":null,"previous_names":["computorg/published-202407-susmann-adaptive-conformal"],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/computorg/published-202407-susmann-adaptive-conformal","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/computorg%2Fpublished-202407-susmann-adaptive-conformal","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/computorg%2Fpublished-202407-susmann-adaptive-conformal/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/computorg%2Fpublished-202407-susmann-adaptive-conformal/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/computorg%2Fpublished-202407-susmann-adaptive-conformal/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/computorg","download_url":"https://codeload.github.com/computorg/published-202407-susmann-adaptive-conformal/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/computorg%2Fpublished-202407-susmann-adaptive-conformal/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":35257172,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-07-08T02:00:06.796Z","response_time":61,"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":[],"created_at":"2026-07-08T08:02:50.008Z","updated_at":"2026-07-08T08:02:51.011Z","avatar_url":"https://github.com/computorg.png","language":"TeX","funding_links":[],"categories":[],"sub_categories":[],"readme":"# AdaptiveConformal: An `R` Package for Adaptive Conformal Inference\nHerbert Susmann, Antoine Chambaz, Julie Josse\n2024-07-18\n\n### Citation\n\nHerbert Susmann, Antoine Chambaz and Julie Josse (July 2024). AdaptiveConformal: An R Package for Adaptive Conformal Inference. Computo.\n\u003chttps://doi.org/10.57750/edan-5f53\u003e\n\n### Badges\n\n[![build and\npublish](https://github.com/computorg/published-202407-susmann-adaptive-conformal/actions/workflows/build.yml/badge.svg)](https://github.com/computorg/published-202407-susmann-adaptive-conformal/actions/workflows/build.yml)\n[![reviews](https://img.shields.io/badge/review-report-blue)](https://github.com/computorg/published-202407-susmann-adaptive-conformal/issues?q=is%3Aopen+is%3Aissue+label%3Areview)\n[![SWH](https://archive.softwareheritage.org/badge/origin/https://github.com/computorg/published-202407-susmann-adaptive-conformal)](https://archive.softwareheritage.org/browse/origin/?origin_url=https://github.com/computorg/published-202407-susmann-adaptive-conformal)\n[![DOI:10.57750/edan-5f53](https://img.shields.io/badge/DOI-10.57750%2Fedan--5f53-034E79.svg)](https://doi.org/10.57750/edan-5f53)\n[![Creative Commons\nLicense](https://i.creativecommons.org/l/by/4.0/80x15.png)](http://creativecommons.org/licenses/by/4.0/)\n\n### Authors’ affiliations\n\n- [Herbert Susmann](https://herbsusmann.com) (CEREMADE (UMR 7534), Université Paris-Dauphine PSL, Place du Maréchal de Lattre de Tassigny, Paris, 75016, France)\n- [Antoine Chambaz](https://helios2.mi.parisdescartes.fr/~chambaz/) (Université Paris Cité, CNRS, MAP5, F-75006 Paris, France)\n- [Julie Josse](http://juliejosse.com/) (Inria PreMeDICaL team, Idesp, Université de Montpellier)\n\n### Abstract\n\nConformal Inference (CI) is a popular approach for generating finite\nsample prediction intervals based on the output of any point prediction\nmethod when data are exchangeable. Adaptive Conformal Inference (ACI)\nalgorithms extend CI to the case of sequentially observed data, such as\ntime series, and exhibit strong theoretical guarantees without having to\nassume exchangeability of the observed data. The common thread that\nunites algorithms in the ACI family is that they adaptively adjust the\nwidth of the generated prediction intervals in response to the observed\ndata. We provide a detailed description of five ACI algorithms and their\ntheoretical guarantees, and test their performance in simulation\nstudies. We then present a case study of producing prediction intervals\nfor influenza incidence in the United States based on black-box point\nforecasts. Implementations of all the algorithms are released as an\nopen-source `R` package, `AdaptiveConformal`, which also includes tools\nfor visualizing and summarizing conformal prediction intervals.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcomputorg%2Fpublished-202407-susmann-adaptive-conformal","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcomputorg%2Fpublished-202407-susmann-adaptive-conformal","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcomputorg%2Fpublished-202407-susmann-adaptive-conformal/lists"}