{"api_uri":"/api/packages/CORElearn","uri":"/packages/CORElearn","name":"CORElearn","created_at":"2016-06-06T14:21:21.000Z","updated_at":"2018-09-29T13:30:04.000Z","latest_version_id":155711,"type_id":1,"versions":[{"uri":"/packages/CORElearn/versions/0.9.22","api_uri":"/api/packages/CORElearn/versions/0.9.22","canonicalLink":null,"id":17523,"package_name":"CORElearn","version":"0.9.22","title":"CORElearn - classification, regression, feature evaluation and\n ordinal evaluation","description":"CORElearn is machine learning suite ported to R from\n standalone C++ package. It contains several model learning\n techniques in classification and regression, for example\n decision and regression trees with optional constructive\n induction and models in the leafs, random forests, kNN, naive\n Bayes, and locally weighted regression. It is especially strong\n in feature evaluation algorithms where it contains several\n variants of Relief algorithm and many impurity based attribute\n evaluation functions, e.g., Gini, information gain, MDL, DKM,\n ... Its additional strength is ordEval algorithm and its\n visualization used for ordinal features and class. The top\n level documentation is reachable through ?CORElearn.","release_date":"2009-09-20T18:07:24.000Z","license":"GPL","url":"http://lkm.fri.uni-lj.si/rmarko/software/","copyright":null,"readmemd":null,"sourceJSON":"{\"Package\":\"CORElearn\",\"Title\":\"CORElearn - classification, regression, feature evaluation and\\n ordinal evaluation\",\"Version\":\"0.9.22\",\"Date\":\"2009-08-17\",\"Author\":\"Marko Robnik-Sikonja , Petr Savicky\\n \",\"Maintainer\":\"Marko Robnik-Sikonja \",\"Description\":\"CORElearn is machine learning suite ported to R from\\n standalone C++ package. It contains several model learning\\n techniques in classification and regression, for example\\n decision and regression trees with optional constructive\\n induction and models in the leafs, random forests, kNN, naive\\n Bayes, and locally weighted regression. It is especially strong\\n in feature evaluation algorithms where it contains several\\n variants of Relief algorithm and many impurity based attribute\\n evaluation functions, e.g., Gini, information gain, MDL, DKM,\\n ... Its additional strength is ordEval algorithm and its\\n visualization used for ordinal features and class. The top\\n level documentation is reachable through ?CORElearn.\",\"License\":\"GPL\",\"URL\":\"http://lkm.fri.uni-lj.si/rmarko/software/\",\"Packaged\":\"2009-09-20 19:06:04 UTC; rmarko\",\"Repository\":\"CRAN\",\"Date/Publication\":\"2009-09-20 20:07:24\",\"repoType\":\"cran\"}","created_at":"2016-06-06T15:30:06.000Z","updated_at":"2018-09-29T13:30:04.000Z","maintainer_id":539155},{"uri":"/packages/CORElearn/versions/0.9.24","api_uri":"/api/packages/CORElearn/versions/0.9.24","canonicalLink":null,"id":17478,"package_name":"CORElearn","version":"0.9.24","title":"CORElearn - classification, regression, feature evaluation and\n ordinal evaluation","description":"CORElearn is machine learning suite ported to R from\n standalone C++ package. It contains several model learning\n techniques in classification and regression, for example\n classification and regression trees with optional constructive\n induction and models in the leafs, random forests, kNN, naive\n Bayes, and locally weighted regression. It is especially strong\n in feature evaluation algorithms where it contains several\n variants of Relief algorithm and many impurity based attribute\n evaluation functions, e.g., Gini, information gain, MDL, DKM,\n ... Its additional strength is ordEval algorithm and its\n visualization used for ordinal features and class. The top\n level documentation is reachable through ?CORElearn.","release_date":"2009-12-06T09:29:00.000Z","license":"GPL (>= 3)","url":"http://lkm.fri.uni-lj.si/rmarko/software/","copyright":null,"readmemd":null,"sourceJSON":"{\"Package\":\"CORElearn\",\"Title\":\"CORElearn - classification, regression, feature evaluation and\\n ordinal evaluation\",\"Version\":\"0.9.24\",\"Date\":\"2009-12-06\",\"Author\":\"Marko Robnik-Sikonja , Petr Savicky\\n \",\"Maintainer\":\"Marko Robnik-Sikonja \",\"Description\":\"CORElearn is machine learning suite ported to R from\\n standalone C++ package. It contains several model learning\\n techniques in classification and regression, for example\\n classification and regression trees with optional constructive\\n induction and models in the leafs, random forests, kNN, naive\\n Bayes, and locally weighted regression. It is especially strong\\n in feature evaluation algorithms where it contains several\\n variants of Relief algorithm and many impurity based attribute\\n evaluation functions, e.g., Gini, information gain, MDL, DKM,\\n ... Its additional strength is ordEval algorithm and its\\n visualization used for ordinal features and class. The top\\n level documentation is reachable through ?CORElearn.\",\"License\":\"GPL (>= 3)\",\"URL\":\"http://lkm.fri.uni-lj.si/rmarko/software/\",\"Packaged\":\"2009-12-06 10:16:11 UTC; rmarko\",\"Repository\":\"CRAN\",\"Date/Publication\":\"2009-12-06 10:29:00\",\"repoType\":\"cran\"}","created_at":"2016-06-06T15:29:48.000Z","updated_at":"2018-09-29T13:30:04.000Z","maintainer_id":539155},{"uri":"/packages/CORElearn/versions/0.9.25","api_uri":"/api/packages/CORElearn/versions/0.9.25","canonicalLink":null,"id":17466,"package_name":"CORElearn","version":"0.9.25","title":"CORElearn - classification, regression, feature evaluation and\n ordinal evaluation","description":"CORElearn is machine learning suite ported to R from\n standalone C++ package. It contains several model learning\n techniques in classification and regression, for example\n classification and regression trees with optional constructive\n induction and models in the leafs, random forests, kNN, naive\n Bayes, and locally weighted regression. It is especially strong\n in feature evaluation algorithms where it contains several\n variants of Relief algorithm and many impurity based attribute\n evaluation functions, e.g., Gini, information gain, MDL, DKM,\n ... Its additional strength is ordEval algorithm and its\n visualization used for ordinal features and class. The top\n level documentation is reachable through ?CORElearn.","release_date":"2010-01-08T07:07:57.000Z","license":"GPL (>= 3)","url":"http://lkm.fri.uni-lj.si/rmarko/software/","copyright":null,"readmemd":null,"sourceJSON":"{\"Package\":\"CORElearn\",\"Title\":\"CORElearn - classification, regression, feature evaluation and\\n ordinal evaluation\",\"Version\":\"0.9.25\",\"Date\":\"2010-01-07\",\"Author\":\"Marko Robnik-Sikonja , Petr Savicky\\n \",\"Maintainer\":\"Marko Robnik-Sikonja \",\"Description\":\"CORElearn is machine learning suite ported to R from\\n standalone C++ package. It contains several model learning\\n techniques in classification and regression, for example\\n classification and regression trees with optional constructive\\n induction and models in the leafs, random forests, kNN, naive\\n Bayes, and locally weighted regression. It is especially strong\\n in feature evaluation algorithms where it contains several\\n variants of Relief algorithm and many impurity based attribute\\n evaluation functions, e.g., Gini, information gain, MDL, DKM,\\n ... Its additional strength is ordEval algorithm and its\\n visualization used for ordinal features and class. The top\\n level documentation is reachable through ?CORElearn.\",\"License\":\"GPL (>= 3)\",\"URL\":\"http://lkm.fri.uni-lj.si/rmarko/software/\",\"Packaged\":\"2010-01-07 19:20:11 UTC; rmarko\",\"Repository\":\"CRAN\",\"Date/Publication\":\"2010-01-08 08:07:57\",\"repoType\":\"cran\"}","created_at":"2016-06-06T15:29:44.000Z","updated_at":"2018-09-29T13:30:04.000Z","maintainer_id":539155},{"uri":"/packages/CORElearn/versions/0.9.26","api_uri":"/api/packages/CORElearn/versions/0.9.26","canonicalLink":null,"id":17440,"package_name":"CORElearn","version":"0.9.26","title":"CORElearn - classification, regression, feature evaluation and\n ordinal evaluation","description":"CORElearn is machine learning suite ported to R from\n standalone C++ package. It contains several model learning\n techniques in classification and regression, for example\n classification and regression trees with optional constructive\n induction and models in the leafs, random forests, kNN, naive\n Bayes, and locally weighted regression. It is especially strong\n in feature evaluation algorithms where it contains several\n variants of Relief algorithm and many impurity based attribute\n evaluation functions, e.g., Gini, information gain, MDL, DKM,\n ... Its additional strength is ordEval algorithm and its\n visualization used for ordinal features and class. The top\n level documentation is reachable through ?CORElearn.","release_date":"2010-01-11T06:42:34.000Z","license":"GPL (>= 3)","url":"http://lkm.fri.uni-lj.si/rmarko/software/","copyright":null,"readmemd":null,"sourceJSON":"{\"Package\":\"CORElearn\",\"Title\":\"CORElearn - classification, regression, feature evaluation and\\n ordinal evaluation\",\"Version\":\"0.9.26\",\"Date\":\"2010-01-10\",\"Author\":\"Marko Robnik-Sikonja , Petr Savicky\\n \",\"Maintainer\":\"Marko Robnik-Sikonja \",\"Description\":\"CORElearn is machine learning suite ported to R from\\n standalone C++ package. It contains several model learning\\n techniques in classification and regression, for example\\n classification and regression trees with optional constructive\\n induction and models in the leafs, random forests, kNN, naive\\n Bayes, and locally weighted regression. It is especially strong\\n in feature evaluation algorithms where it contains several\\n variants of Relief algorithm and many impurity based attribute\\n evaluation functions, e.g., Gini, information gain, MDL, DKM,\\n ... Its additional strength is ordEval algorithm and its\\n visualization used for ordinal features and class. The top\\n level documentation is reachable through ?CORElearn.\",\"License\":\"GPL (>= 3)\",\"URL\":\"http://lkm.fri.uni-lj.si/rmarko/software/\",\"Packaged\":\"2010-01-10 20:22:15 UTC; rmarko\",\"Repository\":\"CRAN\",\"Date/Publication\":\"2010-01-11 07:42:34\",\"repoType\":\"cran\"}","created_at":"2016-06-06T15:29:36.000Z","updated_at":"2018-09-29T13:30:04.000Z","maintainer_id":539155},{"uri":"/packages/CORElearn/versions/0.9.28","api_uri":"/api/packages/CORElearn/versions/0.9.28","canonicalLink":null,"id":17462,"package_name":"CORElearn","version":"0.9.28","title":"CORElearn - classification, regression, feature evaluation and\n ordinal evaluation","description":"CORElearn is machine learning suite ported to R from\n standalone C++ package. It contains several model learning\n techniques in classification and regression, for example\n classification and regression trees with optional constructive\n induction and models in the leafs, random forests, kNN, naive\n Bayes, and locally weighted regression. It is especially strong\n in feature evaluation algorithms where it contains several\n variants of Relief algorithm and many impurity based attribute\n evaluation functions, e.g., Gini, information gain, MDL, DKM,\n ... Its additional strength is ordEval algorithm and its\n visualization used for ordinal features and class. Several\n algorithms support parallel multithreaded execution via OpenMP.\n The top level documentation is reachable through ?CORElearn.","release_date":"2010-09-03T05:31:13.000Z","license":"GPL (>= 3)","url":"http://lkm.fri.uni-lj.si/rmarko/software/","copyright":null,"readmemd":null,"sourceJSON":"{\"Package\":\"CORElearn\",\"Title\":\"CORElearn - classification, regression, feature evaluation and\\n ordinal evaluation\",\"Version\":\"0.9.28\",\"Date\":\"2010-08-30\",\"Author\":\"Marko Robnik-Sikonja , Petr Savicky\\n \",\"Maintainer\":\"Marko Robnik-Sikonja \",\"Description\":\"CORElearn is machine learning suite ported to R from\\n standalone C++ package. It contains several model learning\\n techniques in classification and regression, for example\\n classification and regression trees with optional constructive\\n induction and models in the leafs, random forests, kNN, naive\\n Bayes, and locally weighted regression. It is especially strong\\n in feature evaluation algorithms where it contains several\\n variants of Relief algorithm and many impurity based attribute\\n evaluation functions, e.g., Gini, information gain, MDL, DKM,\\n ... Its additional strength is ordEval algorithm and its\\n visualization used for ordinal features and class. Several\\n algorithms support parallel multithreaded execution via OpenMP.\\n The top level documentation is reachable through ?CORElearn.\",\"License\":\"GPL (>= 3)\",\"URL\":\"http://lkm.fri.uni-lj.si/rmarko/software/\",\"Packaged\":\"2010-09-02 19:19:07 UTC; rmarko\",\"Repository\":\"CRAN\",\"Date/Publication\":\"2010-09-03 07:31:13\",\"repoType\":\"cran\"}","created_at":"2016-06-06T15:29:44.000Z","updated_at":"2018-09-29T13:30:04.000Z","maintainer_id":539155},{"uri":"/packages/CORElearn/versions/0.9.29","api_uri":"/api/packages/CORElearn/versions/0.9.29","canonicalLink":null,"id":17451,"package_name":"CORElearn","version":"0.9.29","title":"CORElearn - classification, regression, feature evaluation and\n ordinal evaluation","description":"CORElearn is machine learning suite ported to R from\n standalone C++ package. It contains several model learning\n techniques in classification and regression, for example\n classification and regression trees with optional constructive\n induction and models in the leafs, random forests, kNN, naive\n Bayes, and locally weighted regression. It is especially strong\n in feature evaluation algorithms where it contains several\n variants of Relief algorithm and many impurity based attribute\n evaluation functions, e.g., Gini, information gain, MDL, DKM,\n ... Its additional strength is ordEval algorithm and its\n visualization used for ordinal features and class. Several\n algorithms support parallel multithreaded execution via OpenMP.\n The top level documentation is reachable through ?CORElearn.","release_date":"2010-09-08T04:44:21.000Z","license":"GPL (>= 3)","url":"http://lkm.fri.uni-lj.si/rmarko/software/","copyright":null,"readmemd":null,"sourceJSON":"{\"Package\":\"CORElearn\",\"Title\":\"CORElearn - classification, regression, feature evaluation and\\n ordinal evaluation\",\"Version\":\"0.9.29\",\"Date\":\"2010-09-04\",\"Author\":\"Marko Robnik-Sikonja , Petr Savicky\\n \",\"Maintainer\":\"Marko Robnik-Sikonja \",\"Description\":\"CORElearn is machine learning suite ported to R from\\n standalone C++ package. It contains several model learning\\n techniques in classification and regression, for example\\n classification and regression trees with optional constructive\\n induction and models in the leafs, random forests, kNN, naive\\n Bayes, and locally weighted regression. It is especially strong\\n in feature evaluation algorithms where it contains several\\n variants of Relief algorithm and many impurity based attribute\\n evaluation functions, e.g., Gini, information gain, MDL, DKM,\\n ... Its additional strength is ordEval algorithm and its\\n visualization used for ordinal features and class. Several\\n algorithms support parallel multithreaded execution via OpenMP.\\n The top level documentation is reachable through ?CORElearn.\",\"License\":\"GPL (>= 3)\",\"URL\":\"http://lkm.fri.uni-lj.si/rmarko/software/\",\"Packaged\":\"2010-09-07 13:27:57 UTC; rmarko\",\"Repository\":\"CRAN\",\"Date/Publication\":\"2010-09-08 06:44:21\",\"repoType\":\"cran\"}","created_at":"2016-06-06T15:29:42.000Z","updated_at":"2018-09-29T13:30:04.000Z","maintainer_id":539155},{"uri":"/packages/CORElearn/versions/0.9.30","api_uri":"/api/packages/CORElearn/versions/0.9.30","canonicalLink":null,"id":17420,"package_name":"CORElearn","version":"0.9.30","title":"CORElearn - classification, regression, feature evaluation and\n ordinal evaluation","description":"CORElearn is machine learning suite ported to R from\n standalone C++ package. It contains several model learning\n techniques in classification and regression, for example\n classification and regression trees with optional constructive\n induction and models in the leafs, random forests, kNN, naive\n Bayes, and locally weighted regression. It is especially strong\n in feature evaluation algorithms where it contains several\n variants of Relief algorithm and many impurity based attribute\n evaluation functions, e.g., Gini, information gain, MDL, DKM,\n ... Its additional strength is ordEval algorithm and its\n visualization used for ordinal features and class. Several\n algorithms support parallel multithreaded execution via OpenMP.\n The top level documentation is reachable through ?CORElearn.","release_date":"2010-09-14T05:36:21.000Z","license":"GPL (>= 3)","url":"http://lkm.fri.uni-lj.si/rmarko/software/","copyright":null,"readmemd":null,"sourceJSON":"{\"Package\":\"CORElearn\",\"Title\":\"CORElearn - classification, regression, feature evaluation and\\n ordinal evaluation\",\"Version\":\"0.9.30\",\"Date\":\"2010-09-14\",\"Author\":\"Marko Robnik-Sikonja , Petr Savicky\\n \",\"Maintainer\":\"Marko Robnik-Sikonja \",\"Description\":\"CORElearn is machine learning suite ported to R from\\n standalone C++ package. It contains several model learning\\n techniques in classification and regression, for example\\n classification and regression trees with optional constructive\\n induction and models in the leafs, random forests, kNN, naive\\n Bayes, and locally weighted regression. It is especially strong\\n in feature evaluation algorithms where it contains several\\n variants of Relief algorithm and many impurity based attribute\\n evaluation functions, e.g., Gini, information gain, MDL, DKM,\\n ... Its additional strength is ordEval algorithm and its\\n visualization used for ordinal features and class. Several\\n algorithms support parallel multithreaded execution via OpenMP.\\n The top level documentation is reachable through ?CORElearn.\",\"License\":\"GPL (>= 3)\",\"URL\":\"http://lkm.fri.uni-lj.si/rmarko/software/\",\"Packaged\":\"2010-09-14 07:20:37 UTC; rmarko\",\"Repository\":\"CRAN\",\"Date/Publication\":\"2010-09-14 07:36:21\",\"repoType\":\"cran\"}","created_at":"2016-06-06T15:29:30.000Z","updated_at":"2018-09-29T13:30:04.000Z","maintainer_id":539155},{"uri":"/packages/CORElearn/versions/0.9.32","api_uri":"/api/packages/CORElearn/versions/0.9.32","canonicalLink":null,"id":17396,"package_name":"CORElearn","version":"0.9.32","title":"CORElearn - classification, regression, feature evaluation and\n ordinal evaluation","description":"CORElearn is machine learning suite ported to R from\n standalone C++ package. It contains several model learning\n techniques in classification and regression, for example\n classification and regression trees with optional constructive\n induction and models in the leafs, random forests, kNN, naive\n Bayes, and locally weighted regression. It is especially strong\n in feature evaluation algorithms where it contains several\n variants of Relief algorithm and many impurity based attribute\n evaluation functions, e.g., Gini, information gain, MDL, DKM,\n ... Its additional strength is ordEval algorithm and its\n visualization used for ordinal features and class. Several\n algorithms support parallel multithreaded execution via OpenMP.\n Windows binary versions supporting multithreading are available\n on package website, as CRAN uses different toolchain. The top\n level documentation is reachable through ?CORElearn.","release_date":"2010-12-01T13:29:44.000Z","license":"GPL-3","url":"http://lkm.fri.uni-lj.si/rmarko/software/","copyright":null,"readmemd":null,"sourceJSON":"{\"Package\":\"CORElearn\",\"Title\":\"CORElearn - classification, regression, feature evaluation and\\n ordinal evaluation\",\"Version\":\"0.9.32\",\"Date\":\"2010-11-22\",\"Author\":\"Marko Robnik-Sikonja , Petr Savicky\\n \",\"Maintainer\":\"Marko Robnik-Sikonja \",\"Description\":\"CORElearn is machine learning suite ported to R from\\n standalone C++ package. It contains several model learning\\n techniques in classification and regression, for example\\n classification and regression trees with optional constructive\\n induction and models in the leafs, random forests, kNN, naive\\n Bayes, and locally weighted regression. It is especially strong\\n in feature evaluation algorithms where it contains several\\n variants of Relief algorithm and many impurity based attribute\\n evaluation functions, e.g., Gini, information gain, MDL, DKM,\\n ... Its additional strength is ordEval algorithm and its\\n visualization used for ordinal features and class. Several\\n algorithms support parallel multithreaded execution via OpenMP.\\n Windows binary versions supporting multithreading are available\\n on package website, as CRAN uses different toolchain. The top\\n level documentation is reachable through ?CORElearn.\",\"License\":\"GPL-3\",\"URL\":\"http://lkm.fri.uni-lj.si/rmarko/software/\",\"Packaged\":\"2010-11-23 07:32:57 UTC; rmarko\",\"Repository\":\"CRAN\",\"Date/Publication\":\"2010-12-01 14:29:44\",\"repoType\":\"cran\"}","created_at":"2016-06-06T15:29:12.000Z","updated_at":"2018-09-29T13:30:04.000Z","maintainer_id":539155},{"uri":"/packages/CORElearn/versions/0.9.33","api_uri":"/api/packages/CORElearn/versions/0.9.33","canonicalLink":null,"id":17389,"package_name":"CORElearn","version":"0.9.33","title":"CORElearn - classification, regression, feature evaluation and\n ordinal evaluation","description":"CORElearn is machine learning suite ported to R from\n standalone C++ package. It contains several model learning\n techniques in classification and regression, for example\n classification and regression trees with optional constructive\n induction and models in the leafs, random forests, kNN, naive\n Bayes, and locally weighted regression. It is especially strong\n in feature evaluation algorithms where it contains several\n variants of Relief algorithm and many impurity based attribute\n evaluation functions, e.g., Gini, information gain, MDL, DKM,\n ... Its additional strength is ordEval algorithm and its\n visualization used for ordinal features and class. Several\n algorithms support parallel multithreaded execution via OpenMP.\n Windows binary versions supporting multithreading are available\n on package website, as CRAN uses different toolchain. The top\n level documentation is reachable through ?CORElearn.","release_date":"2011-03-26T14:43:24.000Z","license":"GPL-3","url":"http://lkm.fri.uni-lj.si/rmarko/software/","copyright":null,"readmemd":null,"sourceJSON":"{\"Package\":\"CORElearn\",\"Title\":\"CORElearn - classification, regression, feature evaluation and\\n ordinal evaluation\",\"Version\":\"0.9.33\",\"Date\":\"2011-03-24\",\"Author\":\"Marko Robnik-Sikonja , Petr Savicky\\n \",\"Maintainer\":\"Marko Robnik-Sikonja \",\"Description\":\"CORElearn is machine learning suite ported to R from\\n standalone C++ package. It contains several model learning\\n techniques in classification and regression, for example\\n classification and regression trees with optional constructive\\n induction and models in the leafs, random forests, kNN, naive\\n Bayes, and locally weighted regression. It is especially strong\\n in feature evaluation algorithms where it contains several\\n variants of Relief algorithm and many impurity based attribute\\n evaluation functions, e.g., Gini, information gain, MDL, DKM,\\n ... Its additional strength is ordEval algorithm and its\\n visualization used for ordinal features and class. Several\\n algorithms support parallel multithreaded execution via OpenMP.\\n Windows binary versions supporting multithreading are available\\n on package website, as CRAN uses different toolchain. The top\\n level documentation is reachable through ?CORElearn.\",\"License\":\"GPL-3\",\"URL\":\"http://lkm.fri.uni-lj.si/rmarko/software/\",\"Packaged\":\"2011-03-24 12:09:06 UTC; rmarko\",\"Repository\":\"CRAN\",\"Date/Publication\":\"2011-03-26 15:43:24\",\"repoType\":\"cran\"}","created_at":"2016-06-06T15:29:06.000Z","updated_at":"2018-09-29T13:30:04.000Z","maintainer_id":539155},{"uri":"/packages/CORElearn/versions/0.9.34","api_uri":"/api/packages/CORElearn/versions/0.9.34","canonicalLink":null,"id":17390,"package_name":"CORElearn","version":"0.9.34","title":"CORElearn - classification, regression, feature evaluation and\n ordinal evaluation","description":"CORElearn is machine learning suite ported to R from\n standalone C++ package. It contains several model learning\n techniques in classification and regression, for example\n classification and regression trees with optional constructive\n induction and models in the leafs, random forests, kNN, naive\n Bayes, and locally weighted regression. It is especially strong\n in feature evaluation algorithms where it contains several\n variants of Relief algorithm and many impurity based attribute\n evaluation functions, e.g., Gini, information gain, MDL, DKM,\n ... Its additional strength is ordEval algorithm and its\n visualization used for ordinal features and class. Several\n algorithms support parallel multithreaded execution via OpenMP.\n Windows binary versions supporting multithreading are available\n on package website, as CRAN uses different toolchain. The top\n level documentation is reachable through ?CORElearn.","release_date":"2011-04-04T05:36:23.000Z","license":"GPL-3","url":"http://lkm.fri.uni-lj.si/rmarko/software/","copyright":null,"readmemd":null,"sourceJSON":"{\"Package\":\"CORElearn\",\"Title\":\"CORElearn - classification, regression, feature evaluation and\\n ordinal evaluation\",\"Version\":\"0.9.34\",\"Date\":\"2011-04-01\",\"Author\":\"Marko Robnik-Sikonja , Petr Savicky\\n \",\"Maintainer\":\"Marko Robnik-Sikonja \",\"Description\":\"CORElearn is machine learning suite ported to R from\\n standalone C++ package. It contains several model learning\\n techniques in classification and regression, for example\\n classification and regression trees with optional constructive\\n induction and models in the leafs, random forests, kNN, naive\\n Bayes, and locally weighted regression. It is especially strong\\n in feature evaluation algorithms where it contains several\\n variants of Relief algorithm and many impurity based attribute\\n evaluation functions, e.g., Gini, information gain, MDL, DKM,\\n ... Its additional strength is ordEval algorithm and its\\n visualization used for ordinal features and class. Several\\n algorithms support parallel multithreaded execution via OpenMP.\\n Windows binary versions supporting multithreading are available\\n on package website, as CRAN uses different toolchain. The top\\n level documentation is reachable through ?CORElearn.\",\"License\":\"GPL-3\",\"URL\":\"http://lkm.fri.uni-lj.si/rmarko/software/\",\"Packaged\":\"2011-04-01 12:04:38 UTC; rmarko\",\"Repository\":\"CRAN\",\"Date/Publication\":\"2011-04-04 07:36:23\",\"repoType\":\"cran\"}","created_at":"2016-06-06T15:29:06.000Z","updated_at":"2018-09-29T13:30:04.000Z","maintainer_id":539155},{"uri":"/packages/CORElearn/versions/0.9.35","api_uri":"/api/packages/CORElearn/versions/0.9.35","canonicalLink":null,"id":17374,"package_name":"CORElearn","version":"0.9.35","title":"CORElearn - classification, regression, feature evaluation and\n ordinal evaluation","description":"CORElearn is machine learning suite ported to R from\n standalone C++ package. It contains several model learning\n techniques in classification and regression, for example\n classification and regression trees with optional constructive\n induction and models in the leafs, random forests, kNN, naive\n Bayes, and locally weighted regression. It is especially strong\n in feature evaluation algorithms where it contains several\n variants of Relief algorithm and many impurity based attribute\n evaluation functions, e.g., Gini, information gain, MDL, DKM,\n ... Its additional strength is ordEval algorithm and its\n visualization used for ordinal features and class. Several\n algorithms support parallel multithreaded execution via OpenMP.\n Windows binary versions supporting multithreading are available\n on package website, as CRAN uses different toolchain. The top\n level documentation is reachable through ?CORElearn.","release_date":"2011-08-19T05:02:03.000Z","license":"GPL-3","url":"http://lkm.fri.uni-lj.si/rmarko/software/","copyright":null,"readmemd":null,"sourceJSON":"{\"Package\":\"CORElearn\",\"Title\":\"CORElearn - classification, regression, feature evaluation and\\n ordinal evaluation\",\"Version\":\"0.9.35\",\"Date\":\"2011-08-04\",\"Author\":\"Marko Robnik-Sikonja , Petr Savicky\\n \",\"Maintainer\":\"Marko Robnik-Sikonja \",\"Description\":\"CORElearn is machine learning suite ported to R from\\n standalone C++ package. It contains several model learning\\n techniques in classification and regression, for example\\n classification and regression trees with optional constructive\\n induction and models in the leafs, random forests, kNN, naive\\n Bayes, and locally weighted regression. It is especially strong\\n in feature evaluation algorithms where it contains several\\n variants of Relief algorithm and many impurity based attribute\\n evaluation functions, e.g., Gini, information gain, MDL, DKM,\\n ... Its additional strength is ordEval algorithm and its\\n visualization used for ordinal features and class. Several\\n algorithms support parallel multithreaded execution via OpenMP.\\n Windows binary versions supporting multithreading are available\\n on package website, as CRAN uses different toolchain. The top\\n level documentation is reachable through ?CORElearn.\",\"License\":\"GPL-3\",\"URL\":\"http://lkm.fri.uni-lj.si/rmarko/software/\",\"Depends\":\"cluster, rpart, stats\",\"Suggests\":\"lattice\",\"Packaged\":\"2011-08-19 06:11:01 UTC; rmarko\",\"Repository\":\"CRAN\",\"Date/Publication\":\"2011-08-19 07:02:03\",\"repoType\":\"cran\"}","created_at":"2016-06-06T15:28:55.000Z","updated_at":"2018-09-29T13:30:04.000Z","maintainer_id":539155},{"uri":"/packages/CORElearn/versions/0.9.36","api_uri":"/api/packages/CORElearn/versions/0.9.36","canonicalLink":null,"id":17307,"package_name":"CORElearn","version":"0.9.36","title":"CORElearn - classification, regression, feature evaluation and\n ordinal evaluation","description":"CORElearn is machine learning suite ported to R from standalone C++ package.\n It contains several model learning techniques in classification and regression,\n for example classification and regression trees with optional constructive induction and models in the leafs, \n random forests, kNN, naive Bayes, and locally weighted regression.\n It is especially strong in feature evaluation algorithms where it contains several variants of Relief\n algorithm and many impurity based attribute evaluation functions, e.g., Gini, information gain, MDL, DKM, ...\n Its additional strength is ordEval algorithm and its visualization used for ordinal features and class. \n Several algorithms support parallel multithreaded execution via OpenMP. Windows binary versions \n supporting multithreading are available on package website, as CRAN uses different toolchain. \n The top level documentation is reachable through ?CORElearn.","release_date":"2012-01-03T15:20:46.000Z","license":"GPL-3","url":"http://lkm.fri.uni-lj.si/rmarko/software/","copyright":null,"readmemd":null,"sourceJSON":"{\"Package\":\"CORElearn\",\"Title\":\"CORElearn - classification, regression, feature evaluation and\\n ordinal evaluation\",\"Version\":\"0.9.36\",\"Date\":\"2012-01-03\",\"Author\":\"Marko Robnik-Sikonja ,\\n Petr Savicky \",\"Maintainer\":\"Marko Robnik-Sikonja \",\"Description\":\"CORElearn is machine learning suite ported to R from standalone C++ package.\\n It contains several model learning techniques in classification and regression,\\n for example classification and regression trees with optional constructive induction and models in the leafs, \\n random forests, kNN, naive Bayes, and locally weighted regression.\\n It is especially strong in feature evaluation algorithms where it contains several variants of Relief\\n algorithm and many impurity based attribute evaluation functions, e.g., Gini, information gain, MDL, DKM, ...\\n Its additional strength is ordEval algorithm and its visualization used for ordinal features and class. \\n Several algorithms support parallel multithreaded execution via OpenMP. Windows binary versions \\n supporting multithreading are available on package website, as CRAN uses different toolchain. \\n The top level documentation is reachable through ?CORElearn.\",\"License\":\"GPL-3\",\"URL\":\"http://lkm.fri.uni-lj.si/rmarko/software/\",\"Depends\":\"cluster, rpart, stats\",\"Suggests\":\"lattice\",\"Packaged\":\"2012-01-03 15:51:03 UTC; rmarko\",\"Repository\":\"CRAN\",\"Date/Publication\":\"2012-01-03 16:20:46\",\"repoType\":\"cran\"}","created_at":"2016-06-06T15:28:35.000Z","updated_at":"2018-09-29T13:30:04.000Z","maintainer_id":539155},{"uri":"/packages/CORElearn/versions/0.9.37","api_uri":"/api/packages/CORElearn/versions/0.9.37","canonicalLink":null,"id":17324,"package_name":"CORElearn","version":"0.9.37","title":"CORElearn - classification, regression, feature evaluation and\n ordinal evaluation","description":"CORElearn is machine learning suite ported to R from\n standalone C++ package. It contains several model learning\n techniques in classification and regression, for example\n classification and regression trees with optional constructive\n induction and models in the leafs, random forests, kNN, naive\n Bayes, and locally weighted regression. It is especially\n strong in feature evaluation algorithms where it contains\n several variants of Relief algorithm and many impurity based\n attribute evaluation functions, e.g., Gini, information gain,\n MDL, DKM, ... Its additional strength is ordEval algorithm and\n its visualization used for ordinal features and class. Several\n algorithms support parallel multithreaded execution via OpenMP.\n Windows binary versions supporting multithreading are available\n on package website, as CRAN uses different toolchain. The top\n level documentation is reachable through ?CORElearn.","release_date":"2012-01-17T13:11:11.000Z","license":"GPL-3","url":"http://lkm.fri.uni-lj.si/rmarko/software/","copyright":null,"readmemd":null,"sourceJSON":"{\"Package\":\"CORElearn\",\"Title\":\"CORElearn - classification, regression, feature evaluation and\\n ordinal evaluation\",\"Version\":\"0.9.37\",\"Date\":\"2012-01-16\",\"Author\":\"Marko Robnik-Sikonja , Petr Savicky\\n \",\"Maintainer\":\"Marko Robnik-Sikonja \",\"Description\":\"CORElearn is machine learning suite ported to R from\\n standalone C++ package. It contains several model learning\\n techniques in classification and regression, for example\\n classification and regression trees with optional constructive\\n induction and models in the leafs, random forests, kNN, naive\\n Bayes, and locally weighted regression. It is especially\\n strong in feature evaluation algorithms where it contains\\n several variants of Relief algorithm and many impurity based\\n attribute evaluation functions, e.g., Gini, information gain,\\n MDL, DKM, ... Its additional strength is ordEval algorithm and\\n its visualization used for ordinal features and class. Several\\n algorithms support parallel multithreaded execution via OpenMP.\\n Windows binary versions supporting multithreading are available\\n on package website, as CRAN uses different toolchain. The top\\n level documentation is reachable through ?CORElearn.\",\"License\":\"GPL-3\",\"URL\":\"http://lkm.fri.uni-lj.si/rmarko/software/\",\"Depends\":\"cluster, rpart, stats\",\"Suggests\":\"lattice\",\"Packaged\":\"2012-01-17 11:08:12 UTC; rmarko\",\"Repository\":\"CRAN\",\"Date/Publication\":\"2012-01-17 14:11:11\",\"repoType\":\"cran\"}","created_at":"2016-06-06T15:28:40.000Z","updated_at":"2018-09-29T13:30:04.000Z","maintainer_id":539155},{"uri":"/packages/CORElearn/versions/0.9.39","api_uri":"/api/packages/CORElearn/versions/0.9.39","canonicalLink":null,"id":13187,"package_name":"CORElearn","version":"0.9.39","title":"CORElearn - classification, regression, feature evaluation and\n ordinal evaluation","description":"CORElearn is machine learning suite ported to R from\n standalone C++ package. It contains several model learning\n techniques in classification and regression, for example\n classification and regression trees with optional constructive\n induction and models in the leafs, random forests, kNN, naive\n Bayes, and locally weighted regression. It is especially\n strong in feature evaluation algorithms where it contains\n several variants of Relief algorithm and many impurity based\n attribute evaluation functions, e.g., Gini, information gain,\n MDL, DKM, ... Its additional strength is ordEval algorithm and\n its visualization used for ordinal features and class. Several\n algorithms support parallel multithreaded execution via OpenMP.\n Windows binary versions supporting multithreading are available\n on package website, as CRAN uses different toolchain. The top\n level documentation is reachable through ?CORElearn.","release_date":"2012-01-28T04:02:53.000Z","license":"GPL-3","url":"http://lkm.fri.uni-lj.si/rmarko/software/","copyright":null,"readmemd":null,"sourceJSON":"{\"Package\":\"CORElearn\",\"Title\":\"CORElearn - classification, regression, feature evaluation and\\n ordinal evaluation\",\"Version\":\"0.9.39\",\"Date\":\"2012-01-27\",\"Author\":\"Marko Robnik-Sikonja , Petr Savicky\\n \",\"Maintainer\":\"Marko Robnik-Sikonja \",\"Description\":\"CORElearn is machine learning suite ported to R from\\n standalone C++ package. It contains several model learning\\n techniques in classification and regression, for example\\n classification and regression trees with optional constructive\\n induction and models in the leafs, random forests, kNN, naive\\n Bayes, and locally weighted regression. It is especially\\n strong in feature evaluation algorithms where it contains\\n several variants of Relief algorithm and many impurity based\\n attribute evaluation functions, e.g., Gini, information gain,\\n MDL, DKM, ... Its additional strength is ordEval algorithm and\\n its visualization used for ordinal features and class. Several\\n algorithms support parallel multithreaded execution via OpenMP.\\n Windows binary versions supporting multithreading are available\\n on package website, as CRAN uses different toolchain. The top\\n level documentation is reachable through ?CORElearn.\",\"License\":\"GPL-3\",\"URL\":\"http://lkm.fri.uni-lj.si/rmarko/software/\",\"Depends\":\"cluster, rpart, stats\",\"Suggests\":\"lattice\",\"Packaged\":\"2012-01-27 11:49:54 UTC; rmarko\",\"Repository\":\"CRAN\",\"Date/Publication\":\"2012-01-28 05:02:53\",\"repoType\":\"cran\"}","created_at":"2016-06-06T14:34:28.000Z","updated_at":"2018-09-29T13:30:04.000Z","maintainer_id":539155},{"uri":"/packages/CORElearn/versions/0.9.40","api_uri":"/api/packages/CORElearn/versions/0.9.40","canonicalLink":null,"id":13179,"package_name":"CORElearn","version":"0.9.40","title":"CORElearn - classification, regression, feature evaluation and\n ordinal evaluation","description":"CORElearn is machine learning suite ported to R from\n standalone C++ package. It contains several model learning\n techniques in classification and regression, for example\n classification and regression trees with optional constructive\n induction and models in the leafs, random forests, kNN, naive\n Bayes, and locally weighted regression. It is especially\n strong in feature evaluation algorithms where it contains\n several variants of Relief algorithm and many impurity based\n attribute evaluation functions, e.g., Gini, information gain,\n MDL, DKM, ... Its additional strength is ordEval algorithm and\n its visualization used for ordinal features and class. Several\n algorithms support parallel multithreaded execution via OpenMP.\n Windows binary versions supporting multithreading are available\n on package website, as CRAN uses different toolchain. The top\n level documentation is reachable through ?CORElearn.","release_date":"2012-07-10T03:46:13.000Z","license":"GPL-3","url":"http://lkm.fri.uni-lj.si/rmarko/software/","copyright":null,"readmemd":null,"sourceJSON":"{\"Package\":\"CORElearn\",\"Title\":\"CORElearn - classification, regression, feature evaluation and\\n ordinal evaluation\",\"Version\":\"0.9.40\",\"Date\":\"2012-07-06\",\"Author\":\"Marko Robnik-Sikonja , Petr Savicky\\n \",\"Maintainer\":\"Marko Robnik-Sikonja \",\"Description\":\"CORElearn is machine learning suite ported to R from\\n standalone C++ package. It contains several model learning\\n techniques in classification and regression, for example\\n classification and regression trees with optional constructive\\n induction and models in the leafs, random forests, kNN, naive\\n Bayes, and locally weighted regression. It is especially\\n strong in feature evaluation algorithms where it contains\\n several variants of Relief algorithm and many impurity based\\n attribute evaluation functions, e.g., Gini, information gain,\\n MDL, DKM, ... Its additional strength is ordEval algorithm and\\n its visualization used for ordinal features and class. Several\\n algorithms support parallel multithreaded execution via OpenMP.\\n Windows binary versions supporting multithreading are available\\n on package website, as CRAN uses different toolchain. The top\\n level documentation is reachable through ?CORElearn.\",\"License\":\"GPL-3\",\"URL\":\"http://lkm.fri.uni-lj.si/rmarko/software/\",\"Depends\":\"cluster, rpart, stats\",\"Suggests\":\"lattice\",\"Packaged\":\"2012-07-06 19:36:06 UTC; rmarko\",\"Repository\":\"CRAN\",\"Date/Publication\":\"2012-07-10 05:46:13\",\"repoType\":\"cran\"}","created_at":"2016-06-06T14:34:27.000Z","updated_at":"2018-09-29T13:30:04.000Z","maintainer_id":539155},{"uri":"/packages/CORElearn/versions/0.9.41","api_uri":"/api/packages/CORElearn/versions/0.9.41","canonicalLink":null,"id":13146,"package_name":"CORElearn","version":"0.9.41","title":"CORElearn - classification, regression, feature evaluation and\n ordinal evaluation","description":"CORElearn is machine learning suite ported to R from\n standalone C++ package. It contains several model learning\n techniques in classification and regression, for example\n classification and regression trees with optional constructive\n induction and models in the leafs, random forests, kNN, naive\n Bayes, and locally weighted regression. It is especially\n strong in feature evaluation algorithms where it contains\n several variants of Relief algorithm and many impurity based\n attribute evaluation functions, e.g., Gini, information gain,\n MDL, DKM, ... Its additional strength is ordEval algorithm and\n its visualization used for ordinal features and class. Several\n algorithms support parallel multithreaded execution via OpenMP.\n Windows binary versions supporting multithreading are available\n on package website, as CRAN uses different toolchain. The top\n level documentation is reachable through ?CORElearn.","release_date":"2013-01-04T11:07:40.000Z","license":"GPL-3","url":"http://lkm.fri.uni-lj.si/rmarko/software/","copyright":null,"readmemd":null,"sourceJSON":"{\"Package\":\"CORElearn\",\"Title\":\"CORElearn - classification, regression, feature evaluation and\\n ordinal evaluation\",\"Version\":\"0.9.41\",\"Date\":\"2013-01-02\",\"Author\":\"Marko Robnik-Sikonja , Petr Savicky\\n \",\"Maintainer\":\"Marko Robnik-Sikonja \",\"Description\":\"CORElearn is machine learning suite ported to R from\\n standalone C++ package. It contains several model learning\\n techniques in classification and regression, for example\\n classification and regression trees with optional constructive\\n induction and models in the leafs, random forests, kNN, naive\\n Bayes, and locally weighted regression. It is especially\\n strong in feature evaluation algorithms where it contains\\n several variants of Relief algorithm and many impurity based\\n attribute evaluation functions, e.g., Gini, information gain,\\n MDL, DKM, ... Its additional strength is ordEval algorithm and\\n its visualization used for ordinal features and class. Several\\n algorithms support parallel multithreaded execution via OpenMP.\\n Windows binary versions supporting multithreading are available\\n on package website, as CRAN uses different toolchain. The top\\n level documentation is reachable through ?CORElearn.\",\"License\":\"GPL-3\",\"URL\":\"http://lkm.fri.uni-lj.si/rmarko/software/\",\"Depends\":\"cluster, rpart, stats\",\"Suggests\":\"lattice\",\"Packaged\":\"2013-01-04 10:14:34 UTC; rmarko\",\"Repository\":\"CRAN\",\"Date/Publication\":\"2013-01-04 12:07:40\",\"repoType\":\"cran\"}","created_at":"2016-06-06T14:34:06.000Z","updated_at":"2018-09-29T13:30:04.000Z","maintainer_id":539155},{"uri":"/packages/CORElearn/versions/0.9.42","api_uri":"/api/packages/CORElearn/versions/0.9.42","canonicalLink":null,"id":13059,"package_name":"CORElearn","version":"0.9.42","title":"CORElearn - classification, regression, feature evaluation and\n ordinal evaluation","description":"CORElearn is machine learning suite ported to R from standalone C++ package.\n It contains several model learning techniques in classification and regression,\n for example classification and regression trees with optional constructive induction and models in the leafs, \n random forests, kNN, naive Bayes, and locally weighted regression.\n It is especially strong in feature evaluation algorithms where it contains several variants of Relief\n algorithm and many impurity based attribute evaluation functions, e.g., Gini, information gain, MDL, DKM, ...\n Its additional strength is ordEval algorithm and its visualization used for ordinal features and class. \n Several algorithms support parallel multithreaded execution via OpenMP. \n The top level documentation is reachable through ?CORElearn.","release_date":"2013-10-18T08:24:09.000Z","license":"GPL-3","url":"http://lkm.fri.uni-lj.si/rmarko/software/","copyright":null,"readmemd":null,"sourceJSON":"{\"Package\":\"CORElearn\",\"Title\":\"CORElearn - classification, regression, feature evaluation and\\n ordinal evaluation\",\"Version\":\"0.9.42\",\"Date\":\"2013-10-17\",\"Author\":\"Marko Robnik-Sikonja ,\\n Petr Savicky \",\"Maintainer\":\"Marko Robnik-Sikonja \",\"Description\":\"CORElearn is machine learning suite ported to R from standalone C++ package.\\n It contains several model learning techniques in classification and regression,\\n for example classification and regression trees with optional constructive induction and models in the leafs, \\n random forests, kNN, naive Bayes, and locally weighted regression.\\n It is especially strong in feature evaluation algorithms where it contains several variants of Relief\\n algorithm and many impurity based attribute evaluation functions, e.g., Gini, information gain, MDL, DKM, ...\\n Its additional strength is ordEval algorithm and its visualization used for ordinal features and class. \\n Several algorithms support parallel multithreaded execution via OpenMP. \\n The top level documentation is reachable through ?CORElearn.\",\"License\":\"GPL-3\",\"URL\":\"http://lkm.fri.uni-lj.si/rmarko/software/\",\"Depends\":\"cluster, rpart, stats\",\"Suggests\":\"lattice\",\"Packaged\":\"2013-10-18 07:36:11 UTC; rmarko\",\"NeedsCompilation\":\"yes\",\"Repository\":\"CRAN\",\"Date/Publication\":\"2013-10-18 10:24:09\",\"repoType\":\"cran\"}","created_at":"2016-06-06T14:33:51.000Z","updated_at":"2018-09-29T13:30:04.000Z","maintainer_id":539155},{"uri":"/packages/CORElearn/versions/0.9.43","api_uri":"/api/packages/CORElearn/versions/0.9.43","canonicalLink":null,"id":13049,"package_name":"CORElearn","version":"0.9.43","title":"Classification, regression, feature evaluation and ordinal\n evaluation","description":"CORElearn is machine learning suite ported to R from standalone C++ package.\n It contains several model learning techniques in classification and regression,\n for example classification and regression trees with optional constructive induction and models in the leafs, \n random forests, kNN, naive Bayes, and locally weighted regression.\n It is especially strong in feature evaluation where it contains several variants of Relief\n algorithm and many impurity based attribute evaluation functions, e.g., Gini, information gain, MDL, DKM...\n Its additional strength is ordEval algorithm and its visualization used for evaluation of data sets with ordinal features and class. \n Several algorithms support parallel multithreaded execution via OpenMP. \n The top level documentation is reachable through ?CORElearn.","release_date":"2014-05-12T05:51:11.000Z","license":"GPL-3","url":"http://lkm.fri.uni-lj.si/rmarko/software/","copyright":null,"readmemd":null,"sourceJSON":"{\"Package\":\"CORElearn\",\"Title\":\"Classification, regression, feature evaluation and ordinal\\n evaluation\",\"Version\":\"0.9.43\",\"Date\":\"2014-05-11\",\"Author\":\"Marko Robnik-Sikonja , \\n Petr Savicky \",\"Maintainer\":\"Marko Robnik-Sikonja \",\"Description\":\"CORElearn is machine learning suite ported to R from standalone C++ package.\\n It contains several model learning techniques in classification and regression,\\n for example classification and regression trees with optional constructive induction and models in the leafs, \\n random forests, kNN, naive Bayes, and locally weighted regression.\\n It is especially strong in feature evaluation where it contains several variants of Relief\\n algorithm and many impurity based attribute evaluation functions, e.g., Gini, information gain, MDL, DKM...\\n Its additional strength is ordEval algorithm and its visualization used for evaluation of data sets with ordinal features and class. \\n Several algorithms support parallel multithreaded execution via OpenMP. \\n The top level documentation is reachable through ?CORElearn.\",\"License\":\"GPL-3\",\"URL\":\"http://lkm.fri.uni-lj.si/rmarko/software/\",\"Imports\":\"cluster, rpart, stats\",\"Suggests\":\"lattice,MASS\",\"Packaged\":\"2014-05-11 21:42:29 UTC; rmarko\",\"NeedsCompilation\":\"yes\",\"Repository\":\"CRAN\",\"Date/Publication\":\"2014-05-12 07:51:11\",\"repoType\":\"cran\"}","created_at":"2016-06-06T14:33:47.000Z","updated_at":"2018-09-29T13:30:04.000Z","maintainer_id":539155},{"uri":"/packages/CORElearn/versions/0.9.44","api_uri":"/api/packages/CORElearn/versions/0.9.44","canonicalLink":null,"id":13023,"package_name":"CORElearn","version":"0.9.44","title":"Classification, Regression and Feature Evaluation","description":"CORElearn is machine learning suite ported to R from standalone C++ package.\n It contains several model learning techniques in classification and regression,\n for example classification and regression trees with optional constructive induction and models in the leafs, \n random forests, kNN, naive Bayes, and locally weighted regression.\n It is especially strong in feature evaluation where it contains several variants of Relief\n algorithm and many impurity based attribute evaluation functions, e.g., Gini, information gain, MDL, DKM...\n Its additional strength is OrdEval algorithm and its visualization used for evaluation of data sets with ordinal features and class. \n Several algorithms support parallel multithreaded execution via OpenMP. \n The top level documentation is reachable through ?CORElearn.","release_date":"2014-12-24T05:22:17.000Z","license":"GPL-3","url":"http://lkm.fri.uni-lj.si/rmarko/software/","copyright":null,"readmemd":null,"sourceJSON":"{\"Package\":\"CORElearn\",\"Title\":\"Classification, Regression and Feature Evaluation\",\"Version\":\"0.9.44\",\"Date\":\"2014-12-08\",\"Author\":\"Marko Robnik-Sikonja and Petr Savicky with contributions from John Adeyanju Alao\",\"Maintainer\":\"\\\"Marko Robnik-Sikonja\\\" \",\"Description\":\"CORElearn is machine learning suite ported to R from standalone C++ package.\\n It contains several model learning techniques in classification and regression,\\n for example classification and regression trees with optional constructive induction and models in the leafs, \\n random forests, kNN, naive Bayes, and locally weighted regression.\\n It is especially strong in feature evaluation where it contains several variants of Relief\\n algorithm and many impurity based attribute evaluation functions, e.g., Gini, information gain, MDL, DKM...\\n Its additional strength is OrdEval algorithm and its visualization used for evaluation of data sets with ordinal features and class. \\n Several algorithms support parallel multithreaded execution via OpenMP. \\n The top level documentation is reachable through ?CORElearn.\",\"License\":\"GPL-3\",\"URL\":\"http://lkm.fri.uni-lj.si/rmarko/software/\",\"Imports\":\"cluster, rpart, stats\",\"Suggests\":\"lattice,MASS\",\"Packaged\":\"2014-12-23 18:29:47 UTC; rmarko\",\"NeedsCompilation\":\"yes\",\"Repository\":\"CRAN\",\"Date/Publication\":\"2014-12-24 06:22:17\",\"repoType\":\"cran\"}","created_at":"2016-06-06T14:33:27.000Z","updated_at":"2018-09-29T13:30:04.000Z","maintainer_id":539155},{"uri":"/packages/CORElearn/versions/0.9.45","api_uri":"/api/packages/CORElearn/versions/0.9.45","canonicalLink":null,"id":12651,"package_name":"CORElearn","version":"0.9.45","title":"Classification, Regression and Feature Evaluation","description":"The package is a port of stand-alone C++ software to R. \n It contains several machine learning model learning techniques in classification and regression,\n for example classification and regression trees with optional constructive induction and models in the leafs, \n random forests, kNN, naive Bayes, and locally weighted regression.\n It is especially strong in feature evaluation where it contains several variants of Relief\n algorithm and many impurity based attribute evaluation functions, e.g., Gini, information gain, MDL, DKM.\n Its additional strength is OrdEval algorithm and its visualization used for evaluation of data sets with ordinal features and class. \n Several algorithms support parallel multithreaded execution via OpenMP. \n The top-level documentation is reachable through ?CORElearn.","release_date":"2015-01-27T09:08:52.000Z","license":"GPL-3","url":"http://lkm.fri.uni-lj.si/rmarko/software/","copyright":null,"readmemd":null,"sourceJSON":"{\"Package\":\"CORElearn\",\"Title\":\"Classification, Regression and Feature Evaluation\",\"Version\":\"0.9.45\",\"Date\":\"2015-01-28\",\"Author\":\"Marko Robnik-Sikonja and Petr Savicky with contributions from John Adeyanju Alao\",\"Maintainer\":\"\\\"Marko Robnik-Sikonja\\\" \",\"Description\":\"The package is a port of stand-alone C++ software to R. \\n It contains several machine learning model learning techniques in classification and regression,\\n for example classification and regression trees with optional constructive induction and models in the leafs, \\n random forests, kNN, naive Bayes, and locally weighted regression.\\n It is especially strong in feature evaluation where it contains several variants of Relief\\n algorithm and many impurity based attribute evaluation functions, e.g., Gini, information gain, MDL, DKM.\\n Its additional strength is OrdEval algorithm and its visualization used for evaluation of data sets with ordinal features and class. \\n Several algorithms support parallel multithreaded execution via OpenMP. \\n The top-level documentation is reachable through ?CORElearn.\",\"License\":\"GPL-3\",\"URL\":\"http://lkm.fri.uni-lj.si/rmarko/software/\",\"Imports\":\"cluster, rpart, stats\",\"Suggests\":\"lattice,MASS,rpart.plot\",\"Packaged\":\"2015-01-27 08:27:51 UTC; rmarko\",\"NeedsCompilation\":\"yes\",\"Repository\":\"CRAN\",\"Date/Publication\":\"2015-01-27 10:08:52\",\"repoType\":\"cran\"}","created_at":"2016-06-06T14:27:07.000Z","updated_at":"2018-09-29T13:30:04.000Z","maintainer_id":539155},{"uri":"/packages/CORElearn/versions/0.9.46","api_uri":"/api/packages/CORElearn/versions/0.9.46","canonicalLink":null,"id":11976,"package_name":"CORElearn","version":"0.9.46","title":"Classification, Regression and Feature Evaluation","description":"This is a suite of machine learning algorithms written in C++ with R interface. \n It contains several machine learning model learning techniques in classification and regression,\n for example classification and regression trees with optional constructive induction and models in the leaves, \n random forests, kNN, naive Bayes, and locally weighted regression.\n It is especially strong in feature evaluation where it contains several variants of Relief\n algorithm and many impurity based attribute evaluation functions, e.g., Gini, information gain, MDL, DKM.\n These methods can be used for example to discretize numeric attributes.\n Its additional strength is OrdEval algorithm and its visualization used for evaluation of data sets with \n ordinal features and class enabling analysis according to the Kano model. \n Several algorithms support parallel multithreaded execution via OpenMP. \n The top-level documentation is reachable through ?CORElearn.","release_date":"2015-06-03T17:06:30.000Z","license":"GPL-3","url":"http://lkm.fri.uni-lj.si/rmarko/software/","copyright":null,"readmemd":null,"sourceJSON":"{\"Package\":\"CORElearn\",\"Title\":\"Classification, Regression and Feature Evaluation\",\"Version\":\"0.9.46\",\"Date\":\"2015-05-29\",\"Author\":\"Marko Robnik-Sikonja and Petr Savicky with contributions from John Adeyanju Alao\",\"Maintainer\":\"\\\"Marko Robnik-Sikonja\\\" \",\"Description\":\"This is a suite of machine learning algorithms written in C++ with R interface. \\n It contains several machine learning model learning techniques in classification and regression,\\n for example classification and regression trees with optional constructive induction and models in the leaves, \\n random forests, kNN, naive Bayes, and locally weighted regression.\\n It is especially strong in feature evaluation where it contains several variants of Relief\\n algorithm and many impurity based attribute evaluation functions, e.g., Gini, information gain, MDL, DKM.\\n These methods can be used for example to discretize numeric attributes.\\n Its additional strength is OrdEval algorithm and its visualization used for evaluation of data sets with \\n ordinal features and class enabling analysis according to the Kano model. \\n Several algorithms support parallel multithreaded execution via OpenMP. \\n The top-level documentation is reachable through ?CORElearn.\",\"License\":\"GPL-3\",\"URL\":\"http://lkm.fri.uni-lj.si/rmarko/software/\",\"Imports\":\"cluster, rpart, stats\",\"Suggests\":\"lattice,MASS,rpart.plot\",\"NeedsCompilation\":\"yes\",\"Packaged\":\"2015-06-02 12:04:19 UTC; rmarko\",\"Repository\":\"CRAN\",\"Date/Publication\":\"2015-06-03 19:06:30\",\"repoType\":\"cran\"}","created_at":"2016-06-06T14:21:35.000Z","updated_at":"2018-09-29T13:30:04.000Z","maintainer_id":539155},{"uri":"/packages/CORElearn/versions/1.47.1","api_uri":"/api/packages/CORElearn/versions/1.47.1","canonicalLink":null,"id":11940,"package_name":"CORElearn","version":"1.47.1","title":"Classification, Regression and Feature Evaluation","description":"This is a suite of machine learning algorithms written in C++ with R\ninterface. It contains several machine learning model learning techniques in\nclassification and regression, for example classification and regression trees with\noptional constructive induction and models in the leaves, random forests, kNN,\nnaive Bayes, and locally weighted regression. All predictions obtained with these\nmodels can be explained and visualized with ExplainPrediction package.\nThe package is especially strong in feature evaluation where it contains several variants of\nRelief algorithm and many impurity based attribute evaluation functions, e.g., Gini,\ninformation gain, MDL, and DKM. These methods can be used for example to discretize\nnumeric attributes.\nIts additional feature is OrdEval algorithm and its visualization used for evaluation\nof data sets with ordinal features and class, enabling analysis according to the\nKano model of customer satisfaction.\nSeveral algorithms support parallel multithreaded execution via OpenMP.\nThe top-level documentation is reachable through ?CORElearn.","release_date":"2015-09-04T05:35:24.000Z","license":"GPL-3","url":"http://lkm.fri.uni-lj.si/rmarko/software/","copyright":null,"readmemd":"","sourceJSON":"{\"Package\":\"CORElearn\",\"Title\":\"Classification, Regression and Feature Evaluation\",\"Version\":\"1.47.1\",\"Date\":\"2015-09-03\",\"Author\":\"Marko Robnik-Sikonja and Petr Savicky with contributions from John Adeyanju Alao\",\"Maintainer\":\"\\\"Marko Robnik-Sikonja\\\" \",\"Description\":\"This is a suite of machine learning algorithms written in C++ with R\\ninterface. It contains several machine learning model learning techniques in\\nclassification and regression, for example classification and regression trees with\\noptional constructive induction and models in the leaves, random forests, kNN,\\nnaive Bayes, and locally weighted regression. All predictions obtained with these\\nmodels can be explained and visualized with ExplainPrediction package.\\nThe package is especially strong in feature evaluation where it contains several variants of\\nRelief algorithm and many impurity based attribute evaluation functions, e.g., Gini,\\ninformation gain, MDL, and DKM. These methods can be used for example to discretize\\nnumeric attributes.\\nIts additional feature is OrdEval algorithm and its visualization used for evaluation\\nof data sets with ordinal features and class, enabling analysis according to the\\nKano model of customer satisfaction.\\nSeveral algorithms support parallel multithreaded execution via OpenMP.\\nThe top-level documentation is reachable through ?CORElearn.\",\"License\":\"GPL-3\",\"URL\":\"http://lkm.fri.uni-lj.si/rmarko/software/\",\"Imports\":\"cluster,rpart, stats\",\"Suggests\":\"lattice,MASS,rpart.plot\",\"NeedsCompilation\":\"yes\",\"Packaged\":\"2015-09-03 21:19:25 UTC; rmarko\",\"Repository\":\"CRAN\",\"Date/Publication\":\"2015-09-04 07:35:24\",\"readme\":\"\",\"repoType\":\"cran\"}","created_at":"2016-06-06T14:21:22.000Z","updated_at":"2018-09-29T13:30:04.000Z","maintainer_id":539155},{"uri":"/packages/CORElearn/versions/1.48.0","api_uri":"/api/packages/CORElearn/versions/1.48.0","canonicalLink":null,"id":65962,"package_name":"CORElearn","version":"1.48.0","title":"Classification, Regression and Feature Evaluation","description":"This is a suite of machine learning algorithms written in C++ with R\ninterface. It contains several machine learning model learning techniques in\nclassification and regression, for example classification and regression trees with\noptional constructive induction and models in the leaves, random forests, kNN,\nnaive Bayes, and locally weighted regression. All predictions obtained with these\nmodels can be explained and visualized with ExplainPrediction package.\nThe package is especially strong in feature evaluation where it contains several variants of\nRelief algorithm and many impurity based attribute evaluation functions, e.g., Gini,\ninformation gain, MDL, and DKM. These methods can be used for example to discretize\nnumeric attributes.\nIts additional feature is OrdEval algorithm and its visualization used for evaluation\nof data sets with ordinal features and class, enabling analysis according to the\nKano model of customer satisfaction.\nSeveral algorithms support parallel multithreaded execution via OpenMP.\nThe top-level documentation is reachable through ?CORElearn.","release_date":"2016-07-25T18:39:07.000Z","license":"GPL-3","url":"http://lkm.fri.uni-lj.si/rmarko/software/","copyright":null,"readmemd":"","sourceJSON":"{\"Package\":\"CORElearn\",\"Title\":\"Classification, Regression and Feature Evaluation\",\"Version\":\"1.48.0\",\"Date\":\"2016-07-23\",\"Author\":\"Marko Robnik-Sikonja and Petr Savicky with contributions from John Adeyanju Alao\",\"Maintainer\":\"\\\"Marko Robnik-Sikonja\\\" \",\"Description\":\"This is a suite of machine learning algorithms written in C++ with R\\ninterface. It contains several machine learning model learning techniques in\\nclassification and regression, for example classification and regression trees with\\noptional constructive induction and models in the leaves, random forests, kNN,\\nnaive Bayes, and locally weighted regression. All predictions obtained with these\\nmodels can be explained and visualized with ExplainPrediction package.\\nThe package is especially strong in feature evaluation where it contains several variants of\\nRelief algorithm and many impurity based attribute evaluation functions, e.g., Gini,\\ninformation gain, MDL, and DKM. These methods can be used for example to discretize\\nnumeric attributes.\\nIts additional feature is OrdEval algorithm and its visualization used for evaluation\\nof data sets with ordinal features and class, enabling analysis according to the\\nKano model of customer satisfaction.\\nSeveral algorithms support parallel multithreaded execution via OpenMP.\\nThe top-level documentation is reachable through ?CORElearn.\",\"License\":\"GPL-3\",\"URL\":\"http://lkm.fri.uni-lj.si/rmarko/software/\",\"Imports\":\"cluster,rpart, stats\",\"Suggests\":\"lattice,MASS,rpart.plot,ExplainPrediction\",\"NeedsCompilation\":\"yes\",\"Packaged\":\"2016-07-22 21:57:41 UTC; rmarko\",\"Repository\":\"CRAN\",\"Date/Publication\":\"2016-07-25 20:39:07\",\"readme\":\"\",\"repoType\":\"cran\"}","created_at":"2016-07-25T19:00:34.000Z","updated_at":"2018-09-29T13:30:04.000Z","maintainer_id":539155},{"uri":"/packages/CORElearn/versions/1.50.3","api_uri":"/api/packages/CORElearn/versions/1.50.3","canonicalLink":null,"id":86066,"package_name":"CORElearn","version":"1.50.3","title":"Classification, Regression and Feature Evaluation","description":"A suite of machine learning algorithms written in C++ with R\ninterface contains several learning techniques for classification and regression,\nPredictive models include e.g., classification and regression trees with\noptional constructive induction and models in the leaves, random forests, kNN,\nnaive Bayes, and locally weighted regression. All predictions obtained with these\nmodels can be explained and visualized with ExplainPrediction package.\nThe package is especially strong in feature evaluation where it contains several variants of\nRelief algorithm and many impurity based attribute evaluation functions, e.g., Gini,\ninformation gain, MDL, and DKM. These methods can be used for feature selection\nor discretization of numeric attributes.\nThe OrdEval algorithm and its visualization is used for evaluation\nof data sets with ordinal features and class, enabling analysis according to the\nKano model of customer satisfaction.\nSeveral algorithms support parallel multithreaded execution via OpenMP.\nThe top-level documentation is reachable through ?CORElearn.","release_date":"2017-03-28T15:27:04.000Z","license":"GPL-3","url":"http://lkm.fri.uni-lj.si/rmarko/software/","copyright":null,"readmemd":"","sourceJSON":"{\"Package\":\"CORElearn\",\"Title\":\"Classification, Regression and Feature Evaluation\",\"Version\":\"1.50.3\",\"Date\":\"2017-03-28\",\"Author\":\"Marko Robnik-Sikonja and Petr Savicky\",\"Maintainer\":\"\\\"Marko Robnik-Sikonja\\\" \",\"Description\":\"A suite of machine learning algorithms written in C++ with R\\ninterface contains several learning techniques for classification and regression,\\nPredictive models include e.g., classification and regression trees with\\noptional constructive induction and models in the leaves, random forests, kNN,\\nnaive Bayes, and locally weighted regression. All predictions obtained with these\\nmodels can be explained and visualized with ExplainPrediction package.\\nThe package is especially strong in feature evaluation where it contains several variants of\\nRelief algorithm and many impurity based attribute evaluation functions, e.g., Gini,\\ninformation gain, MDL, and DKM. These methods can be used for feature selection\\nor discretization of numeric attributes.\\nThe OrdEval algorithm and its visualization is used for evaluation\\nof data sets with ordinal features and class, enabling analysis according to the\\nKano model of customer satisfaction.\\nSeveral algorithms support parallel multithreaded execution via OpenMP.\\nThe top-level documentation is reachable through ?CORElearn.\",\"License\":\"GPL-3\",\"URL\":\"http://lkm.fri.uni-lj.si/rmarko/software/\",\"Imports\":\"cluster,rpart, stats\",\"Suggests\":\"lattice,MASS,rpart.plot,ExplainPrediction\",\"NeedsCompilation\":\"yes\",\"Packaged\":\"2017-03-28 13:58:56 UTC; rmarko\",\"Repository\":\"CRAN\",\"Date/Publication\":\"2017-03-28 15:27:04 UTC\",\"repoType\":\"cran\",\"tarballUrl\":\"ftp://cran.r-project.org/pub/R/src/contrib/CORElearn_1.50.3.tar.gz\",\"readme\":\"\"}","created_at":"2017-08-08T14:45:59.000Z","updated_at":"2018-09-29T13:30:04.000Z","maintainer_id":539155},{"uri":"/packages/CORElearn/versions/1.51.2","api_uri":"/api/packages/CORElearn/versions/1.51.2","canonicalLink":null,"id":86061,"package_name":"CORElearn","version":"1.51.2","title":"Classification, Regression and Feature Evaluation","description":"A suite of machine learning algorithms written in C++ with the R\ninterface contains several learning techniques for classification and regression.\nPredictive models include e.g., classification and regression trees with\noptional constructive induction and models in the leaves, random forests, kNN,\nnaive Bayes, and locally weighted regression. All predictions obtained with these\nmodels can be explained and visualized with the 'ExplainPrediction' package.\nThis package is especially strong in feature evaluation where it contains several variants of\nRelief algorithm and many impurity based attribute evaluation functions, e.g., Gini,\ninformation gain, MDL, and DKM. These methods can be used for feature selection\nor discretization of numeric attributes.\nThe OrdEval algorithm and its visualization is used for evaluation\nof data sets with ordinal features and class, enabling analysis according to the\nKano model of customer satisfaction.\nSeveral algorithms support parallel multithreaded execution via OpenMP.\nThe top-level documentation is reachable through ?CORElearn.","release_date":"2017-08-08T14:00:15.000Z","license":"GPL-3","url":"http://lkm.fri.uni-lj.si/rmarko/software/","copyright":null,"readmemd":"","sourceJSON":"{\"Package\":\"CORElearn\",\"Title\":\"Classification, Regression and Feature Evaluation\",\"Version\":\"1.51.2\",\"Date\":\"2017-08-08\",\"Author\":\"Marko Robnik-Sikonja and Petr Savicky\",\"Maintainer\":\"\\\"Marko Robnik-Sikonja\\\" \",\"Description\":\"A suite of machine learning algorithms written in C++ with the R\\ninterface contains several learning techniques for classification and regression.\\nPredictive models include e.g., classification and regression trees with\\noptional constructive induction and models in the leaves, random forests, kNN,\\nnaive Bayes, and locally weighted regression. All predictions obtained with these\\nmodels can be explained and visualized with the 'ExplainPrediction' package.\\nThis package is especially strong in feature evaluation where it contains several variants of\\nRelief algorithm and many impurity based attribute evaluation functions, e.g., Gini,\\ninformation gain, MDL, and DKM. These methods can be used for feature selection\\nor discretization of numeric attributes.\\nThe OrdEval algorithm and its visualization is used for evaluation\\nof data sets with ordinal features and class, enabling analysis according to the\\nKano model of customer satisfaction.\\nSeveral algorithms support parallel multithreaded execution via OpenMP.\\nThe top-level documentation is reachable through ?CORElearn.\",\"License\":\"GPL-3\",\"URL\":\"http://lkm.fri.uni-lj.si/rmarko/software/\",\"Imports\":\"cluster,rpart, stats,nnet\",\"Suggests\":\"lattice,MASS,rpart.plot,ExplainPrediction\",\"NeedsCompilation\":\"yes\",\"Packaged\":\"2017-08-08 11:39:12 UTC; rmarko\",\"Repository\":\"CRAN\",\"Date/Publication\":\"2017-08-08 14:00:15 UTC\",\"repoType\":\"cran\",\"tarballUrl\":\"ftp://cran.r-project.org/pub/R/src/contrib/CORElearn_1.51.2.tar.gz\",\"readme\":\"\"}","created_at":"2017-08-08T14:44:07.000Z","updated_at":"2018-09-29T13:30:04.000Z","maintainer_id":539155},{"uri":"/packages/CORElearn/versions/1.52.0","api_uri":"/api/packages/CORElearn/versions/1.52.0","canonicalLink":null,"id":90528,"package_name":"CORElearn","version":"1.52.0","title":"Classification, Regression and Feature Evaluation","description":"A suite of machine learning algorithms written in C++ with the R\ninterface contains several learning techniques for classification and regression.\nPredictive models include e.g., classification and regression trees with\noptional constructive induction and models in the leaves, random forests, kNN,\nnaive Bayes, and locally weighted regression. All predictions obtained with these\nmodels can be explained and visualized with the 'ExplainPrediction' package.\nThis package is especially strong in feature evaluation where it contains several variants of\nRelief algorithm and many impurity based attribute evaluation functions, e.g., Gini,\ninformation gain, MDL, and DKM. These methods can be used for feature selection\nor discretization of numeric attributes.\nThe OrdEval algorithm and its visualization is used for evaluation\nof data sets with ordinal features and class, enabling analysis according to the\nKano model of customer satisfaction.\nSeveral algorithms support parallel multithreaded execution via OpenMP.\nThe top-level documentation is reachable through ?CORElearn.","release_date":"2018-01-04T15:45:56.000Z","license":"GPL-3","url":"http://lkm.fri.uni-lj.si/rmarko/software/","copyright":null,"readmemd":"","sourceJSON":"{\"Package\":\"CORElearn\",\"Title\":\"Classification, Regression and Feature Evaluation\",\"Version\":\"1.52.0\",\"Date\":\"2017-12-27\",\"Author\":\"Marko Robnik-Sikonja and Petr Savicky\",\"Maintainer\":\"\\\"Marko Robnik-Sikonja\\\" \",\"Description\":\"A suite of machine learning algorithms written in C++ with the R\\ninterface contains several learning techniques for classification and regression.\\nPredictive models include e.g., classification and regression trees with\\noptional constructive induction and models in the leaves, random forests, kNN,\\nnaive Bayes, and locally weighted regression. All predictions obtained with these\\nmodels can be explained and visualized with the 'ExplainPrediction' package.\\nThis package is especially strong in feature evaluation where it contains several variants of\\nRelief algorithm and many impurity based attribute evaluation functions, e.g., Gini,\\ninformation gain, MDL, and DKM. These methods can be used for feature selection\\nor discretization of numeric attributes.\\nThe OrdEval algorithm and its visualization is used for evaluation\\nof data sets with ordinal features and class, enabling analysis according to the\\nKano model of customer satisfaction.\\nSeveral algorithms support parallel multithreaded execution via OpenMP.\\nThe top-level documentation is reachable through ?CORElearn.\",\"License\":\"GPL-3\",\"URL\":\"http://lkm.fri.uni-lj.si/rmarko/software/\",\"Imports\":\"cluster,rpart, stats,nnet\",\"Suggests\":\"lattice,MASS,rpart.plot,ExplainPrediction\",\"NeedsCompilation\":\"yes\",\"Packaged\":\"2017-12-27 16:58:01 UTC; rmarko\",\"Repository\":\"CRAN\",\"Date/Publication\":\"2018-01-04 15:45:56 UTC\",\"repoType\":\"cran\",\"tarballUrl\":\"ftp://cran.r-project.org/pub/R/src/contrib/CORElearn_1.52.0.tar.gz\",\"readme\":\"\",\"jobInfo\":{\"package\":\"CORElearn\",\"version\":\"1.52.0\",\"parsingStatus\":\"success\",\"parserVersion\":1,\"parsedAt\":\"2018-04-02T17:29:52+0000\"}}","created_at":"2018-01-08T10:10:31.000Z","updated_at":"2018-09-29T13:30:04.000Z","maintainer_id":539155},{"uri":"/packages/CORElearn/versions/1.52.1","api_uri":"/api/packages/CORElearn/versions/1.52.1","canonicalLink":null,"id":93675,"package_name":"CORElearn","version":"1.52.1","title":"Classification, Regression and Feature Evaluation","description":"A suite of machine learning algorithms written in C++ with the R\ninterface contains several learning techniques for classification and regression.\nPredictive models include e.g., classification and regression trees with\noptional constructive induction and models in the leaves, random forests, kNN,\nnaive Bayes, and locally weighted regression. All predictions obtained with these\nmodels can be explained and visualized with the 'ExplainPrediction' package.\nThis package is especially strong in feature evaluation where it contains several variants of\nRelief algorithm and many impurity based attribute evaluation functions, e.g., Gini,\ninformation gain, MDL, and DKM. These methods can be used for feature selection\nor discretization of numeric attributes.\nThe OrdEval algorithm and its visualization is used for evaluation\nof data sets with ordinal features and class, enabling analysis according to the\nKano model of customer satisfaction.\nSeveral algorithms support parallel multithreaded execution via OpenMP.\nThe top-level documentation is reachable through ?CORElearn.","release_date":"2018-04-02T14:31:48.000Z","license":"GPL-3","url":"http://lkm.fri.uni-lj.si/rmarko/software/","copyright":null,"readmemd":"","sourceJSON":"{\"Package\":\"CORElearn\",\"Title\":\"Classification, Regression and Feature Evaluation\",\"Version\":\"1.52.1\",\"Date\":\"2018-04-02\",\"Author\":\"Marko Robnik-Sikonja and Petr Savicky\",\"Maintainer\":\"\\\"Marko Robnik-Sikonja\\\" \",\"Description\":\"A suite of machine learning algorithms written in C++ with the R\\ninterface contains several learning techniques for classification and regression.\\nPredictive models include e.g., classification and regression trees with\\noptional constructive induction and models in the leaves, random forests, kNN,\\nnaive Bayes, and locally weighted regression. All predictions obtained with these\\nmodels can be explained and visualized with the 'ExplainPrediction' package.\\nThis package is especially strong in feature evaluation where it contains several variants of\\nRelief algorithm and many impurity based attribute evaluation functions, e.g., Gini,\\ninformation gain, MDL, and DKM. These methods can be used for feature selection\\nor discretization of numeric attributes.\\nThe OrdEval algorithm and its visualization is used for evaluation\\nof data sets with ordinal features and class, enabling analysis according to the\\nKano model of customer satisfaction.\\nSeveral algorithms support parallel multithreaded execution via OpenMP.\\nThe top-level documentation is reachable through ?CORElearn.\",\"License\":\"GPL-3\",\"URL\":\"http://lkm.fri.uni-lj.si/rmarko/software/\",\"Imports\":\"cluster,rpart, stats,nnet\",\"Suggests\":\"lattice,MASS,rpart.plot,ExplainPrediction\",\"NeedsCompilation\":\"yes\",\"Packaged\":\"2018-04-02 10:07:28 UTC; rmarko\",\"Repository\":\"CRAN\",\"Date/Publication\":\"2018-04-02 14:31:48 UTC\",\"repoType\":\"cran\",\"tarballUrl\":\"ftp://cran.r-project.org/pub/R/src/contrib/CORElearn_1.52.1.tar.gz\",\"readme\":\"\",\"jobInfo\":{\"package\":\"CORElearn\",\"version\":\"1.52.1\",\"parsingStatus\":\"success\",\"parserVersion\":1,\"parsedAt\":\"2018-09-29T13:29:46+0000\"}}","created_at":"2018-04-02T15:30:02.000Z","updated_at":"2018-09-29T13:30:04.000Z","maintainer_id":539155},{"uri":"/packages/CORElearn/versions/1.53.1","api_uri":"/api/packages/CORElearn/versions/1.53.1","canonicalLink":null,"id":155711,"package_name":"CORElearn","version":"1.53.1","title":"Classification, Regression and Feature Evaluation","description":"A suite of machine learning algorithms written in C++ with the R\ninterface contains several learning techniques for classification and regression.\nPredictive models include e.g., classification and regression trees with\noptional constructive induction and models in the leaves, random forests, kNN,\nnaive Bayes, and locally weighted regression. All predictions obtained with these\nmodels can be explained and visualized with the 'ExplainPrediction' package.\nThis package is especially strong in feature evaluation where it contains several variants of\nRelief algorithm and many impurity based attribute evaluation functions, e.g., Gini,\ninformation gain, MDL, and DKM. These methods can be used for feature selection\nor discretization of numeric attributes.\nThe OrdEval algorithm and its visualization is used for evaluation\nof data sets with ordinal features and class, enabling analysis according to the\nKano model of customer satisfaction.\nSeveral algorithms support parallel multithreaded execution via OpenMP.\nThe top-level documentation is reachable through ?CORElearn.","release_date":"2018-09-29T10:30:03.000Z","license":"GPL-3","url":"http://lkm.fri.uni-lj.si/rmarko/software/","copyright":null,"readmemd":"","sourceJSON":"{\"Package\":\"CORElearn\",\"Title\":\"Classification, Regression and Feature Evaluation\",\"Version\":\"1.53.1\",\"Date\":\"2018-09-29\",\"Author\":\"Marko Robnik-Sikonja and Petr Savicky\",\"Maintainer\":\"\\\"Marko Robnik-Sikonja\\\" \",\"Description\":\"A suite of machine learning algorithms written in C++ with the R\\ninterface contains several learning techniques for classification and regression.\\nPredictive models include e.g., classification and regression trees with\\noptional constructive induction and models in the leaves, random forests, kNN,\\nnaive Bayes, and locally weighted regression. All predictions obtained with these\\nmodels can be explained and visualized with the 'ExplainPrediction' package.\\nThis package is especially strong in feature evaluation where it contains several variants of\\nRelief algorithm and many impurity based attribute evaluation functions, e.g., Gini,\\ninformation gain, MDL, and DKM. These methods can be used for feature selection\\nor discretization of numeric attributes.\\nThe OrdEval algorithm and its visualization is used for evaluation\\nof data sets with ordinal features and class, enabling analysis according to the\\nKano model of customer satisfaction.\\nSeveral algorithms support parallel multithreaded execution via OpenMP.\\nThe top-level documentation is reachable through ?CORElearn.\",\"License\":\"GPL-3\",\"URL\":\"http://lkm.fri.uni-lj.si/rmarko/software/\",\"Imports\":\"cluster,rpart, stats,nnet\",\"Suggests\":\"lattice,MASS,rpart.plot,ExplainPrediction\",\"NeedsCompilation\":\"yes\",\"Packaged\":\"2018-09-29 10:05:52 UTC; rmarko\",\"Repository\":\"CRAN\",\"Date/Publication\":\"2018-09-29 10:30:03 UTC\",\"repoType\":\"cran\",\"tarballUrl\":\"ftp://cran.r-project.org/pub/R/src/contrib/CORElearn_1.53.1.tar.gz\",\"readme\":\"\",\"jobInfo\":{\"package\":\"CORElearn\",\"version\":\"1.53.1\",\"parsingStatus\":\"success\",\"parserVersion\":1,\"parsedAt\":\"2018-09-29T13:29:55+0000\"}}","created_at":"2018-09-29T11:30:08.000Z","updated_at":"2018-09-29T13:30:04.000Z","maintainer_id":539155}],"type":"package"}