{"id":16735105,"url":"https://github.com/mamba413/bess","last_synced_at":"2025-03-21T21:31:35.726Z","repository":{"id":57414703,"uuid":"201882229","full_name":"Mamba413/bess","owner":"Mamba413","description":"Best Subset Selection algorithm for Regression, Classification, Count,  Survival analysis","archived":false,"fork":false,"pushed_at":"2021-02-24T09:14:33.000Z","size":21675,"stargazers_count":17,"open_issues_count":2,"forks_count":2,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-03-18T05:34:19.517Z","etag":null,"topics":["classification-model","feature-selection","poisson-regression","regression-models","sparse-linear-systems","survival-analysis","variable-selection"],"latest_commit_sha":null,"homepage":"","language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Mamba413.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2019-08-12T07:43:27.000Z","updated_at":"2025-03-14T02:01:39.000Z","dependencies_parsed_at":"2022-09-09T23:23:00.451Z","dependency_job_id":null,"html_url":"https://github.com/Mamba413/bess","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mamba413%2Fbess","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mamba413%2Fbess/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mamba413%2Fbess/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mamba413%2Fbess/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Mamba413","download_url":"https://codeload.github.com/Mamba413/bess/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244874292,"owners_count":20524577,"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":["classification-model","feature-selection","poisson-regression","regression-models","sparse-linear-systems","survival-analysis","variable-selection"],"created_at":"2024-10-13T00:04:57.698Z","updated_at":"2025-03-21T21:31:32.381Z","avatar_url":"https://github.com/Mamba413.png","language":"C++","readme":"# Python \u0026 R Packages for Best Subset Selection \u003cimg src='https://raw.githubusercontent.com/Mamba413/git_picture/master/BeSS.png' align=\"right\" height=\"120\" /\u003e\n\n\n## Introduction\n\nOne of the main tasks of statistical modeling is to exploit the association between\na response variable and multiple predictors. Linear model (LM), as a simple parametric\nregression model, is often used to capture linear dependence between response and\npredictors. Generalized linear model (GLM) can be considered as\nthe extensions of linear model, depending on the types of responses. Parameter estimation in these models\ncan be computationally intensive when the number of predictors is large. Meanwhile,\nOccam's razor is widely accepted as a heuristic rule for statistical modeling,\nwhich balances goodness of fit and model complexity. This rule leads to a relative \nsmall subset of important predictors. \n\n**BeSS** package provides solutions for best subset selection problem for sparse LM,\nand GLM models.\n\nWe consider a primal-dual active set (PDAS) approach to exactly solve the best subset\nselection problem for sparse LM and GLM models. \nIt utilizes an active set updating strategy and fits the sub-models through use of\ncomplementary primal and dual variables. We generalize the PDAS algorithm for \ngeneral convex loss functions with the best subset constraint.\n\n\n## Installation\n\n### Python \n\nThe package has been publish in PyPI. You can easy install by:\n```sh\n$ pip install bess\n```\n\n### R\n\nTo download and install **BeSS** from CRAN:\n\n```r\ninstall.packages(\"BeSS\")\n```\n\nOr try the development version on GitHub:\n\n```r\n# install.packages(\"devtools\")\ndevtools::install_github(\"Mamba413/bess/R\")\n```\n\n\n\n## Reference\n\n- Wen, C., Zhang, A., Quan, S., \u0026 Wang, X. (2020). BeSS: An R Package for Best Subset Selection in Linear, Logistic and Cox Proportional Hazards Models. Journal of Statistical Software, 94(4), 1 - 24. doi:http://dx.doi.org/10.18637/jss.v094.i04\n\n## Bug report\n\nPlease send an email to Jiang Kangkang(jiangkk3@mail2.sysu.edu.cn).\n\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmamba413%2Fbess","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmamba413%2Fbess","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmamba413%2Fbess/lists"}