{"id":22885333,"url":"https://github.com/jkapila/vcvn","last_synced_at":"2025-06-20T03:39:25.958Z","repository":{"id":89228511,"uuid":"100487109","full_name":"jkapila/vcvn","owner":"jkapila","description":"R package for Variable  Selection, Curve Fitting, Variable Conversion and Normalisation","archived":false,"fork":false,"pushed_at":"2017-08-28T14:02:38.000Z","size":24,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-06-03T00:04:36.930Z","etag":null,"topics":["curve-fitting","measure","normalisation","scoring","variable-selection","voting"],"latest_commit_sha":null,"homepage":"","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jkapila.png","metadata":{"files":{"readme":"README.md","changelog":null,"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":"2017-08-16T12:31:08.000Z","updated_at":"2018-07-09T04:33:19.000Z","dependencies_parsed_at":"2023-05-06T09:46:56.698Z","dependency_job_id":null,"html_url":"https://github.com/jkapila/vcvn","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/jkapila/vcvn","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jkapila%2Fvcvn","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jkapila%2Fvcvn/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jkapila%2Fvcvn/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jkapila%2Fvcvn/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jkapila","download_url":"https://codeload.github.com/jkapila/vcvn/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jkapila%2Fvcvn/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":260874452,"owners_count":23075833,"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":["curve-fitting","measure","normalisation","scoring","variable-selection","voting"],"created_at":"2024-12-13T19:35:23.386Z","updated_at":"2025-06-20T03:39:20.950Z","avatar_url":"https://github.com/jkapila.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# vcvn\nR package for Variable  Selection, Curve Fitting, Variable Conversion, Normalisation and Accuracy Measures\n\nThis will include following:\n\n## Variable Selection:\n1) Information Value\n2) Gini Index\n3) Gini Impurity\n4) Entropy Gain\n5) Misclassification Error\n\n## To be Implemented under Variable Selection Methods:\n1) Variable Ranking Methods - voting / scoring / weighted scoring / weighted voting\n2) Generic Scoring Function (for Regressiona and Classification)\n3) Variable Inflation Factor\n4) Other Variable Impacts for regression\n\n## Curve Fitting(To be implemented):\n1) Template for Curve Fitting for Contineous and Categorical Variable\n2) Curve Comparision Methods\n3) Curve Indentification\n4) Curve Tuning\n5) Curve to Normal Conversion\n6) Non - Curve / Random / Many Matching curve Decision Criterion\n7) Goodness of Fit Test:\n  a) Kolomogorov- Simronov Test\n  b) Carmer-Von Mises Test\n  c) Anderson-Darling Test\n  d) Shapiro -Wilk Test\n  e) Chi-Squared Test\n  f) Akaike Information Criterion (AIC)\n  g) Hosmer - Lemeshow Test\n\n## Variable Conversion(To be implemented):\n1) Continuous to Categorical\n  a) Range Binning\n  b) WOE Criterion Binning\n  c) Dependent Binning\n\n2) Categrical to Contineous\n  a) One - Hot Encoding with and without reference\n  b) Label Encoding\n  c) Weightage Encoding\n  d) Boosted Encoding ( Based on CatBoost Methodology by Yandex)\n\n## Normalisation (To be Implemented):\n1) Unit Mean\n2) Unit SD\n3) Unit Mean And SD\n4) Min - Max\n5) Box-Cox\n6) Log\n7) Exponential\n8) Mean Difference\n9) Median Difference\n10) Mean Difference wiht SD\n11) Median Difference with SD\n\n**Will also try to include predict function for applying variable conversion and normalisation on raw data.\n\n## Measures (These are extensions for other calculations):\n1) RMSE\n2) MAE\n3) MAPE\n4) R-squared\n5) AIC\n6) BIC\n7) AUC\n\n## To be implemented Measures:\n1) Kendall's Tau\n2) Gini Index\n3) Weights\n4) Extension to caret's ConfusionMatrix\n\n**Will also try to include methods for finding best and/or biased limit for probablity cut-off of calssification problem\n\n## Updated as on 28th August, 2017","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjkapila%2Fvcvn","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjkapila%2Fvcvn","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjkapila%2Fvcvn/lists"}