{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/plmmr-an-r-package-to-fit-penalized-linear","title":"plmmr: an R package to fit penalized linear mixed models for genome-wide association data with complex correlation structure","arxiv_id":"2502.01577","date":"2025-02-03","proceeding":null,"authors":["Tabitha K. Peter","Anna C. Reisetter","Yujing Lu","Oscar A. Rysavy","Patrick J. Breheny"],"abstract":"Correlation among the observations in high-dimensional regression modeling can be a major source of confounding. We present a new open-source package, plmmr, to implement penalized linear mixed models in R. This R package estimates correlation among observations in high-dimensional data and uses those estimates to improve prediction with the best linear unbiased predictor. The package uses memory-mapping so that genome-scale data can be analyzed on ordinary machines even if the size of data exceeds RAM. We present here the methods, workflow, and file-backing approach upon which plmmr is built, and we demonstrate its computational capabilities with two examples from real GWAS data.","url_abs":"https://arxiv.org/abs/2502.01577v1","url_pdf":"https://arxiv.org/pdf/2502.01577v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"plmmr-an-r-package-to-fit-penalized-linear","repo_url":"https://github.com/pbreheny/plmmr","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}