{"id":19863815,"url":"https://github.com/sandialabs/xpcar","last_synced_at":"2025-06-13T10:09:48.328Z","repository":{"id":215507236,"uuid":"658975580","full_name":"sandialabs/xpcaR","owner":"sandialabs","description":"Implementation of XPCA in R (https://arxiv.org/abs/1808.07510)","archived":false,"fork":false,"pushed_at":"2023-06-26T22:31:16.000Z","size":54,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-06-13T10:09:44.154Z","etag":null,"topics":["scr-2407","snl-comp-science-libs","snl-data-analysis"],"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/sandialabs.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2023-06-26T22:31:03.000Z","updated_at":"2024-02-15T20:51:52.000Z","dependencies_parsed_at":"2024-01-04T20:57:39.667Z","dependency_job_id":null,"html_url":"https://github.com/sandialabs/xpcaR","commit_stats":null,"previous_names":["sandialabs/xpcar"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/sandialabs/xpcaR","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sandialabs%2FxpcaR","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sandialabs%2FxpcaR/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sandialabs%2FxpcaR/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sandialabs%2FxpcaR/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sandialabs","download_url":"https://codeload.github.com/sandialabs/xpcaR/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sandialabs%2FxpcaR/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":259624744,"owners_count":22886331,"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","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":["scr-2407","snl-comp-science-libs","snl-data-analysis"],"created_at":"2024-11-12T15:16:12.174Z","updated_at":"2025-06-13T10:09:48.264Z","avatar_url":"https://github.com/sandialabs.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"## XPCA\n\n## Table of Contents\n\n* [Background](#background)\n* [Install](#install)\n\n## Background\nXPCA factors an observed data matrix X (m rows x n columns) into two factors, A (m x k) and B (n x k) where rank = k. Each row in X corresponds to an observed element, and each column corresponds to a feature of the observed element. These columns can be of mixed variable types - continuous, count, binary, etc. \n\nThis is the R version of the library. The most recent version of the library is\nimplemented in python.\n\nxpcaR implements 3 capabilities: xpca, pca, and coca. \n\n### coca\n`coca` is from work done by:\n\n1. Fang Han and Han Liu. Semiparametric principal component analysis. In\nNIPS’12: Proceedings of the 26th Annual Conference on Neural Information Pro- cessing Systems, pages 171–179, 2012. \nURL http://papers.nips.cc/paper/4809-semiparametric-principal-component-analysis.\n\n2. Bernhard Egger, Dinu Kaufmann, Sandro Sch ̈onborn, Volker Roth, and Thomas Vetter. Copula eigenfaces — semiparametric principal component analysis for facial appearance modeling. \nIn VISIGRAPP’16: Proceedings of the 11th Joint Conference on Computer Vi- sion, Imaging and Computer Graphics Theory and Applications, pages 50–58. SciTePress, 2016. 10.5220/0005718800480056.\n\n\n### xpca\n`xpca` is Sandia-built work done by Cliff Anderson-Bergman, Tamara Kolda, and Kina Kincher-Winoto.\nPaper is in review and available on arXiv:\n\nC. Anderson-Bergman, T. G. Kolda, K. Kincher-Winoto. XPCA: Extending PCA for a Combination of Discrete and Continuous Variables. arXiv:1808.07510, 2018.\n\n### pca\n`pca` is the well-known algorithm. For reference: \n\nMichael E. Tipping and Christopher M. Bishop. Probabilistic principal component analysis. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 61(3):611– 622, August 1999. 10.1111/1467-9868.00196.\n\n\n## Install\n\nFrom R:\n```\nR\u003e devtools::install_gitlab(\"xpca/xpcar/xpcaR\")\n```\n\nLocally from command line:\n```\n$ R CMD INSTALL xpcaR\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsandialabs%2Fxpcar","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsandialabs%2Fxpcar","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsandialabs%2Fxpcar/lists"}