{"id":32200886,"url":"https://github.com/longhaisk/htlr","last_synced_at":"2026-03-01T01:33:35.562Z","repository":{"id":56936093,"uuid":"192601562","full_name":"longhaiSK/HTLR","owner":"longhaiSK","description":"Bayesian Logistic Regression with Hyper-LASSO priors","archived":false,"fork":false,"pushed_at":"2025-10-21T04:05:51.000Z","size":35769,"stargazers_count":10,"open_issues_count":5,"forks_count":1,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-10-22T03:56:48.009Z","etag":null,"topics":["bayesian","classification","high-dimensional-data","machine-learning","mcmc"],"latest_commit_sha":null,"homepage":"https://longhaisk.github.io/HTLR","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/longhaiSK.png","metadata":{"files":{"readme":"README.Rmd","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}},"created_at":"2019-06-18T19:36:30.000Z","updated_at":"2025-07-22T06:48:16.000Z","dependencies_parsed_at":"2024-08-15T05:50:03.018Z","dependency_job_id":null,"html_url":"https://github.com/longhaiSK/HTLR","commit_stats":{"total_commits":291,"total_committers":3,"mean_commits":97.0,"dds":"0.030927835051546393","last_synced_commit":"c7fac4ed05b5889f50de2cecf8dbe0140b4805fe"},"previous_names":[],"tags_count":5,"template":false,"template_full_name":null,"purl":"pkg:github/longhaiSK/HTLR","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/longhaiSK%2FHTLR","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/longhaiSK%2FHTLR/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/longhaiSK%2FHTLR/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/longhaiSK%2FHTLR/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/longhaiSK","download_url":"https://codeload.github.com/longhaiSK/HTLR/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/longhaiSK%2FHTLR/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":280376547,"owners_count":26320275,"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":"2025-10-22T02:00:06.515Z","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":["bayesian","classification","high-dimensional-data","machine-learning","mcmc"],"created_at":"2025-10-22T03:56:57.627Z","updated_at":"2025-10-22T03:56:59.162Z","avatar_url":"https://github.com/longhaiSK.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"---\noutput: github_document\n---\n\n\u003c!-- README.md is generated from README.Rmd. Please edit that file --\u003e\n\n```{r, include = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\u003e\",\n  fig.path = \"man/figures/README-\",\n  out.width = \"100%\"\n)\n```\n## HTLR: Bayesian Logistic Regression with Heavy-tailed Priors\n\n### a test\n\u003c!-- badges: start --\u003e\n[![CRAN status](https://www.r-pkg.org/badges/version/HTLR)](https://CRAN.R-project.org/package=HTLR)\n[![build](https://github.com/longhaiSK/HTLR/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/longhaiSK/HTLR/actions/workflows/R-CMD-check.yaml)\n[![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable)\n[![downloads](https://cranlogs.r-pkg.org/badges/grand-total/HTLR)](https://cran.r-project.org/package=HTLR)\n\n\u003c!-- badges: end --\u003e\n\n*HTLR* performs classification and feature selection by fitting Bayesian polychotomous (multiclass, multinomial) logistic regression models based on heavy-tailed priors with small degree freedom. This package is suitable for classification with high-dimensional features, such as gene expression profiles. Heavy-tailed priors can impose stronger shrinkage (compared to Guassian and Laplace priors) to the coefficients associated with a large number of useless features, but still allow coefficients of a small number of useful features to stand out with little punishment. Heavy-tailed priors can also automatically make selection within a large number of correlated features. The posterior of coefficients and hyperparameters is sampled with resitricted Gibbs sampling for leveraging high-dimensionality and Hamiltonian Monte Carlo for handling high-correlations among coefficients. \n\n## Installation\n\n[CRAN](https://CRAN.R-project.org) version (recommended):\n\n``` r\ninstall.packages(\"HTLR\")\n```\n\nDevelopment version on [GitHub](https://github.com/):\n\n``` r\n# install.packages(\"devtools\")\ndevtools::install_github(\"longhaiSK/HTLR\")\n\n```\n\nThis package uses library [Armadillo](https://arma.sourceforge.net/) for carrying out most of matrix operations, you may get speed benefits from using an alternative BLAS library such as [ATLAS](https://math-atlas.sourceforge.net/), [OpenBLAS](https://www.openblas.net/) or Intel MKL. Check out this [post](https://brettklamer.com/diversions/statistical/faster-blas-in-r/) for the comparison and the installation guide. Windows users may consider installing [Microsoft R Open](https://mran.microsoft.com/open).\n\n## Reference\n\nLonghai Li and Weixin Yao (2018). Fully Bayesian Logistic Regression with Hyper-Lasso Priors for High-dimensional Feature Selection. \\emph{Journal of Statistical Computation and Simulation} 2018, 88:14, 2827-2851, [the published version](https://www.tandfonline.com/doi/full/10.1080/00949655.2018.1490418), or [arXiv version](https://arxiv.org/pdf/1405.3319.pdf).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flonghaisk%2Fhtlr","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flonghaisk%2Fhtlr","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flonghaisk%2Fhtlr/lists"}