{"id":20693446,"url":"https://github.com/nanxstats/ohpl","last_synced_at":"2025-04-22T17:42:58.766Z","repository":{"id":142658939,"uuid":"91051008","full_name":"nanxstats/OHPL","owner":"nanxstats","description":"📈 Ordered Homogeneity Pursuit Lasso for Group Variable Selection","archived":false,"fork":false,"pushed_at":"2024-07-21T18:30:30.000Z","size":4201,"stargazers_count":7,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-04-10T07:18:48.591Z","etag":null,"topics":["chemometrics","high-dimensional-data","homogeneity-pursuit","lasso","partial-least-squares-regression","spectroscopy","variable-selection"],"latest_commit_sha":null,"homepage":"https://OHPL.io","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/nanxstats.png","metadata":{"files":{"readme":"README.md","changelog":"NEWS.md","contributing":"CONTRIBUTING.md","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}},"created_at":"2017-05-12T04:22:18.000Z","updated_at":"2024-07-21T18:30:33.000Z","dependencies_parsed_at":null,"dependency_job_id":"5acdda7f-4003-460d-b793-329d5e251c94","html_url":"https://github.com/nanxstats/OHPL","commit_stats":null,"previous_names":[],"tags_count":5,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nanxstats%2FOHPL","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nanxstats%2FOHPL/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nanxstats%2FOHPL/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nanxstats%2FOHPL/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/nanxstats","download_url":"https://codeload.github.com/nanxstats/OHPL/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248981228,"owners_count":21193149,"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":["chemometrics","high-dimensional-data","homogeneity-pursuit","lasso","partial-least-squares-regression","spectroscopy","variable-selection"],"created_at":"2024-11-16T23:26:43.748Z","updated_at":"2025-04-22T17:42:58.737Z","avatar_url":"https://github.com/nanxstats.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Ordered Homogeneity Pursuit Lasso \u003cimg src=\"man/figures/logo.png\" align=\"right\" width=\"120\" /\u003e\n\n\u003c!-- badges: start --\u003e\n[![R-CMD-check](https://github.com/nanxstats/OHPL/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/nanxstats/OHPL/actions/workflows/R-CMD-check.yaml)\n[![CRAN Version](https://www.r-pkg.org/badges/version/OHPL)](https://cran.r-project.org/package=OHPL)\n[![Downloads from the RStudio CRAN mirror](https://cranlogs.r-pkg.org/badges/OHPL)](https://cran.r-project.org/package=OHPL)\n\u003c!-- badges: end --\u003e\n\n## Introduction\n\nImplements the ordered homogeneity pursuit lasso (OHPL) algorithm for group variable selection proposed in Lin et al. (2017) \u003c[DOI:10.1016/j.chemolab.2017.07.004](https://doi.org/10.1016/j.chemolab.2017.07.004)\u003e ([PDF](https://nanx.me/papers/OHPL.pdf)). The OHPL method exploits the homogeneity structure in high-dimensional data and enjoys the grouping effect to select groups of important variables automatically. This feature makes it particularly useful for high-dimensional datasets with strongly correlated variables, such as spectroscopic data.\n\n## Paper citation\n\nFormatted citation:\n\nYou-Wu Lin, Nan Xiao, Li-Li Wang, Chuan-Quan Li, and Qing-Song Xu (2017). Ordered homogeneity pursuit lasso for group variable selection with applications to spectroscopic data. _Chemometrics and Intelligent Laboratory Systems_ 168, 62-71.\n\nBibTeX entry:\n\n```\n@article{lin2017ordered,\n  title   = {Ordered homogeneity pursuit lasso for group variable selection with applications to spectroscopic data},\n  author  = {You-Wu Lin and Nan Xiao and Li-Li Wang and Chuan-Quan Li and Qing-Song Xu},\n  journal = {Chemometrics and Intelligent Laboratory Systems},\n  year    = {2017},\n  volume  = {168},\n  pages   = {62--71},\n  doi     = {10.1016/j.chemolab.2017.07.004}\n}\n```\n\n## Installation\n\nYou can install OHPL from CRAN:\n\n```r\ninstall.packages(\"OHPL\")\n```\n\nOr try the development version on GitHub:\n\n```r\n# install.packages(\"remotes\")\nremotes::install_github(\"nanxstats/OHPL\")\n```\n\nTo get started, try the examples in `OHPL()`:\n\n```r\nlibrary(\"OHPL\")\n?OHPL\n```\n\n[Browse the package documentation](https://ohpl.io/doc/) for more information.\n\n## Contribute\n\nTo contribute to this project, please take a look at the [Contributing Guidelines](https://ohpl.io/doc/CONTRIBUTING.html) first.\nPlease note that the OHPL project is released with a\n[Contributor Code of Conduct](https://ohpl.io/doc/CODE_OF_CONDUCT.html).\nBy contributing to this project, you agree to abide by its terms.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnanxstats%2Fohpl","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnanxstats%2Fohpl","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnanxstats%2Fohpl/lists"}