{"id":31556902,"url":"https://github.com/cherylisabella/leakr","last_synced_at":"2025-10-04T23:19:34.855Z","repository":{"id":316655543,"uuid":"1063449632","full_name":"cherylisabella/leakR","owner":"cherylisabella","description":"R package for detecting data leakage in machine learning workflows, ensuring model integrity and reliability.","archived":false,"fork":false,"pushed_at":"2025-09-25T21:45:50.000Z","size":89,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-09-25T23:41:29.375Z","etag":null,"topics":["data-leakage","data-science","leakage-detection","machine-learning","r"],"latest_commit_sha":null,"homepage":"","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/cherylisabella.png","metadata":{"files":{"readme":"README.md","changelog":"NEWS.md","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,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-09-24T16:34:57.000Z","updated_at":"2025-09-25T22:12:09.000Z","dependencies_parsed_at":"2025-09-26T03:01:36.834Z","dependency_job_id":null,"html_url":"https://github.com/cherylisabella/leakR","commit_stats":null,"previous_names":["cherylisabella/leakr"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/cherylisabella/leakR","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cherylisabella%2FleakR","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cherylisabella%2FleakR/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cherylisabella%2FleakR/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cherylisabella%2FleakR/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/cherylisabella","download_url":"https://codeload.github.com/cherylisabella/leakR/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cherylisabella%2FleakR/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":278386457,"owners_count":25978175,"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":"2025-10-04T02:00:05.491Z","response_time":63,"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":["data-leakage","data-science","leakage-detection","machine-learning","r"],"created_at":"2025-10-04T23:19:31.978Z","updated_at":"2025-10-04T23:19:34.846Z","avatar_url":"https://github.com/cherylisabella.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# leakR: Universal Data Leakage Detector for R  \n\nWelcome to **leakR**, an R package designed to help researchers, data scientists, and machine learning practitioners rigorously detect and diagnose **data leakage** in their workflows.  \n\nData leakage is a pervasive yet often overlooked issue that undermines the integrity and reproducibility of predictive models by allowing unintended information to “leak” between training and testing phases.  \n\n**leakR** provides a modular, extensible toolkit for detecting the most common and impactful forms of leakage, starting with tabular data contamination, target leakage, and temporal misalignments, while laying the foundation for a universal leakage detection framework across diverse data domains.\n\n---\n\n## Why leakR?  \n\n- Automates leakage detection, filling a key methodological gap.  \n- Designed for clarity, reproducibility, and transparent ML research.  \n- Modular architecture supports gradual expansion (time series, NLP, images).  \n- Useful for both academic and industry workflows.  \n\n---\n\n## Features  \n\n- Detects:  \n  - Train/test contamination  \n  - Target leakage  \n  - Duplicate rows/records  \n  - Temporal misalignments  \n- Provides **visual summaries** of suspicious patterns.  \n- Generates **detailed leakage reports** for audits or publications.  \n- Offers clean APIs for seamless integration into ML workflows.  \n- Includes **example vignettes** demonstrating leakage phenomena with code illustrations.  \n\n---\n\n## Roadmap  \n\n- **Phase 1**: Core tabular leakage detectors.  \n- **Phase 2**: Time series leakage detection.  \n- **Phase 3**: Domain-specific extensions (NLP, image pipelines).  \n- **Phase 4**: Pipeline integration and multi-language support.  \n\n---\n\n## Installation  \n\nleakR is currently under **active development**. Installation instructions will be provided once the first release is available on CRAN or GitHub.  \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcherylisabella%2Fleakr","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcherylisabella%2Fleakr","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcherylisabella%2Fleakr/lists"}