{"id":16735110,"url":"https://github.com/mamba413/cdcsis","last_synced_at":"2025-03-21T21:31:35.330Z","repository":{"id":56936834,"uuid":"150571557","full_name":"Mamba413/cdcsis","owner":"Mamba413","description":"Conditional Distance Correlation based Statistical Method","archived":false,"fork":false,"pushed_at":"2024-08-23T09:47:37.000Z","size":176,"stargazers_count":7,"open_issues_count":0,"forks_count":2,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-03-18T05:34:19.277Z","etag":null,"topics":["conditional-dependence","feature-selection","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,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2018-09-27T10:44:43.000Z","updated_at":"2025-02-16T12:47:27.000Z","dependencies_parsed_at":"2024-10-28T11:34:53.667Z","dependency_job_id":"170b4bc1-b21f-4269-aa60-e2225f1c6c1c","html_url":"https://github.com/Mamba413/cdcsis","commit_stats":{"total_commits":78,"total_committers":3,"mean_commits":26.0,"dds":0.02564102564102566,"last_synced_commit":"29159b1257af059d2afa05a2c5cb7f602f241797"},"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mamba413%2Fcdcsis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mamba413%2Fcdcsis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mamba413%2Fcdcsis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mamba413%2Fcdcsis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Mamba413","download_url":"https://codeload.github.com/Mamba413/cdcsis/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":["conditional-dependence","feature-selection","variable-selection"],"created_at":"2024-10-13T00:04:58.613Z","updated_at":"2025-03-21T21:31:34.696Z","avatar_url":"https://github.com/Mamba413.png","language":"C++","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cimg src=https://github.com/Mamba413/git_picture/blob/master/scrcss.jpg width=135/\u003e CDC Statistics\n===========\n[![Travis build status](https://travis-ci.org/Mamba413/cdcsis.svg?branch=master)](https://travis-ci.org/Mamba413/cdcsis)\n[![CRAN Status Badge](http://www.r-pkg.org/badges/version/cdcsis)](https://CRAN.R-project.org/package=cdcsis)\n![CRAN Downloads overall](https://cranlogs.r-pkg.org/badges/grand-total/cdcsis?color=brightgreen)\n[![Codacy Badge](https://api.codacy.com/project/badge/Grade/645c7a47d9484abc80098d4426dd6a64)](https://app.codacy.com/app/Mamba413/cdcsis?utm_source=github.com\u0026utm_medium=referral\u0026utm_content=Mamba413/cdcsis\u0026utm_campaign=Badge_Grade_Dashboard)\n\nIntrodution\n----------\nThe fundamental problems for data mining and statistical/machine learning are:\n\n- how to select the important features for ultra high dimensional dataset?    \n- whether a statistical/machine learning model is sufficient (i.e. does not need to include additional variables)?    \n\nCDC Statistics based statistical method provides solutions for these issues. \n\nLicense\n----------\nGPL (\u003e= 2)\n\nReference\n----------    \n- Xueqin Wang, Wenliang Pan, Wenhao Hu, Yuan Tian \u0026 Heping Zhang (2015) Conditional Distance Correlation, Journal of the American Statistical Association, 110:512, 1726-1734, DOI: 10.1080/01621459.2014.993081    \n- Canhong Wen, Wenliang Pan, Mian Huang and Xueqin Wang (2018) Sure independence screening adjusted for confounding covariates with ultrahigh dimensional data, Statistica Sinica, 28 (2018), no. 1, 293--318, DOI:10.5705/ss.202014.0117     \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmamba413%2Fcdcsis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmamba413%2Fcdcsis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmamba413%2Fcdcsis/lists"}