{"id":32598371,"url":"https://github.com/msesia/conditional-conformal-pvalues","last_synced_at":"2025-10-30T05:28:24.188Z","repository":{"id":47548902,"uuid":"322662432","full_name":"msesia/conditional-conformal-pvalues","owner":"msesia","description":"Conditional calibration of conformal p-values for outlier detection.","archived":false,"fork":false,"pushed_at":"2022-11-15T07:09:08.000Z","size":312,"stargazers_count":17,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2023-05-10T14:18:11.728Z","etag":null,"topics":["conformal-prediction","false-discovery-rate","machine-learning","outlier-detection","statistics"],"latest_commit_sha":null,"homepage":"","language":"Python","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/msesia.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}},"created_at":"2020-12-18T17:31:49.000Z","updated_at":"2023-05-10T13:06:23.000Z","dependencies_parsed_at":"2023-01-23T03:46:16.738Z","dependency_job_id":null,"html_url":"https://github.com/msesia/conditional-conformal-pvalues","commit_stats":null,"previous_names":[],"tags_count":null,"template":null,"template_full_name":null,"purl":"pkg:github/msesia/conditional-conformal-pvalues","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/msesia%2Fconditional-conformal-pvalues","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/msesia%2Fconditional-conformal-pvalues/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/msesia%2Fconditional-conformal-pvalues/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/msesia%2Fconditional-conformal-pvalues/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/msesia","download_url":"https://codeload.github.com/msesia/conditional-conformal-pvalues/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/msesia%2Fconditional-conformal-pvalues/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":281749663,"owners_count":26555056,"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":"2025-10-30T02:00:06.501Z","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":["conformal-prediction","false-discovery-rate","machine-learning","outlier-detection","statistics"],"created_at":"2025-10-30T05:28:20.863Z","updated_at":"2025-10-30T05:28:24.182Z","avatar_url":"https://github.com/msesia.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Testing for Outliers with Conformal p-values\n\nWe study the construction of p-values for nonparametric outlier detection, taking a multiple-testing perspective.  The framework is that of conformal prediction, which wraps around any machine-learning algorithm to provide finite-sample guarantees regarding the validity of predictions for future testpoints.  In this setting, existing methods can compute p-values that are marginally valid but mutually dependent for different future test points. \n\nThis repository contains a software implementation and guided examples for the methodology developed in the [accompanying paper](https://arxiv.org/abs/2104.08279), which provides a new method to  compute p-values that are both conditionally valid and independent of each other for different future test points, thus allowing multiple testing with stronger stronger type-I error guarantees.\n  ```\n  \"Testing for Outliers with Conformal p-values\"\n  Stephen Bates, Emmanuel Candes, Lihua Lei, Yaniv Romano, and Matteo Sesia. \n  accepted in Annals of Statistics (2022)\n  arXiv pre-print: https://arxiv.org/abs/2104.08279\n  ```\n  \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmsesia%2Fconditional-conformal-pvalues","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmsesia%2Fconditional-conformal-pvalues","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmsesia%2Fconditional-conformal-pvalues/lists"}