A package for Quantile Regression calculations.


TITLE

SUBTITLE

DESCRIPTION

data

=begin data
{"type":"page","isPage":false,"publishUrl":"/mods/zef/Math::QuantileRegression/dist/README.md","file":"work_mods/zef/Math::QuantileRegression/dist/README.md","sources":[],"node":{"type":"block","id":"XYYG-GM_rcLweY6nPVr0M","margin":"","name":"root","content":[{"type":"block","id":"Math::QuantileRegression","margin":"","level":1,"name":"head","location":{"start":{"line":1,"column":1,"offset":0},"end":{"line":1,"column":27,"offset":26}},"content":[{"type":"text","value":"Math::QuantileRegression"}]},{"type":"para","id":"HqSeZMhMp64gBZwjl81K5","margin":"","location":{"start":{"line":3,"column":1,"offset":28},"end":{"line":3,"column":53,"offset":80}},"text":"text","content":[{"type":"text","value":"Raku package for basic Quantile Regression routines."}]},{"type":"block","id":"Installation","margin":"","level":2,"name":"head","location":{"start":{"line":7,"column":1,"offset":87},"end":{"line":7,"column":16,"offset":102}},"content":[{"type":"text","value":"Installation"}]},{"type":"para","id":"NMGe5YKxAsvh0D4swRAYI","margin":"","location":{"start":{"line":9,"column":1,"offset":104},"end":{"line":9,"column":107,"offset":210}},"text":"text","content":[{"type":"text","value":"Make sure the Raku package "},{"meta":"https://github.com/antononcube/Raku-COIN-OR","type":"fcode","name":"L","content":["\"COIN-OR\""]},{"type":"text","value":", [AAp1], is installed."}]},{"type":"para","id":"JtzzbSjrZfqf7YVlaVJqZ","margin":"","location":{"start":{"line":11,"column":1,"offset":212},"end":{"line":11,"column":28,"offset":239}},"text":"text","content":[{"type":"text","value":"Install from Zef ecosystem:"}]},{"type":"block","id":"RCpUdR05_ZthorsS34rIH","margin":"","name":"code","config":[],"location":{"start":{"line":13,"column":1,"offset":241},"end":{"line":15,"column":4,"offset":285}},"content":[{"type":"verbatim","value":"zef install Math::QuantileRegression"}]},{"type":"para","id":"EquApO6k7kZRYWaxNTAra","margin":"","location":{"start":{"line":17,"column":1,"offset":287},"end":{"line":17,"column":13,"offset":299}},"text":"text","content":[{"type":"text","value":"From GitHub:"}]},{"type":"block","id":"o-1CWcR3ikhiYEpclzzhR","margin":"","name":"code","config":[],"location":{"start":{"line":19,"column":1,"offset":301},"end":{"line":21,"column":4,"offset":384}},"content":[{"type":"verbatim","value":"zef install https://github.com/antononcube/Raku-Math-QuantileRegression.git"}]},{"type":"block","id":"References","margin":"","level":2,"name":"head","location":{"start":{"line":25,"column":1,"offset":391},"end":{"line":25,"column":14,"offset":404}},"content":[{"type":"text","value":"References"}]},{"type":"block","id":"Articles, books","margin":"","level":3,"name":"head","location":{"start":{"line":27,"column":1,"offset":406},"end":{"line":27,"column":20,"offset":425}},"content":[{"type":"text","value":"Articles, books"}]},{"type":"para","id":"8f9n_du1AvyeC5bmgW2Vl","margin":"","location":{"start":{"line":29,"column":1,"offset":427},"end":{"line":31,"column":34,"offset":583}},"text":"text","content":[{"type":"text","value":"[RK1] Roger Koenker,\n"},{"meta":"https://books.google.com/books/about/Quantile_Regression.html?id=hdkt7V4NXsgC","type":"fcode","name":"L","content":["Quantile Regression"]},{"type":"text","value":",\nCambridge University Press, 2005."}]},{"type":"para","id":"mjH30MnOn1FoY6ttW5dmT","margin":"","location":{"start":{"line":33,"column":1,"offset":585},"end":{"line":36,"column":37,"offset":759}},"text":"text","content":[{"type":"text","value":"[RK2] Roger Koenker,\n"},{"meta":"https://cran.r-project.org/web/packages/quantreg/vignettes/rq.pdf","type":"fcode","name":"L","content":["\"Quantile Regression in R: a vignette\""]},{"type":"text","value":",\n(2006),\n"},{"meta":"https://cran.r-project.org/","type":"fcode","name":"L","content":["CRAN"]},{"type":"text","value":"."}]},{"type":"para","id":"GkLnd1f-kA_Xc6slvs0Zh","margin":"","location":{"start":{"line":38,"column":1,"offset":761},"end":{"line":41,"column":95,"offset":1063}},"text":"text","content":[{"type":"text","value":"[AA1] Anton Antonov,\n"},{"meta":"https://github.com/antononcube/MathematicaForPrediction/blob/master/MarkdownDocuments/A-monad-for-Quantile-Regression-workflows.md","type":"fcode","name":"L","content":["\"A monad for Quantile Regression workflows\""]},{"type":"text","value":",\n(2018),\n"},{"meta":"https://github.com/antononcube/MathematicaForPrediction","type":"fcode","name":"L","content":["MathematicaForPrediction at GitHub"]},{"type":"text","value":"."}]},{"type":"block","id":"Packages, paclets","margin":"","level":3,"name":"head","location":{"start":{"line":43,"column":1,"offset":1065},"end":{"line":43,"column":22,"offset":1086}},"content":[{"type":"text","value":"Packages, paclets"}]},{"type":"para","id":"QAHS-VqLXCZLNbcuQZVXB","margin":"","location":{"start":{"line":45,"column":1,"offset":1088},"end":{"line":47,"column":37,"offset":1221}},"text":"text","content":[{"type":"text","value":"[RKp1] Roger Koenker,\n"},{"meta":"https://cran.r-project.org/web/packages/quantreg/index.html","type":"fcode","name":"L","content":[{"type":"fcode","name":"C","content":["quantreg"]}]},{"type":"text","value":",\n"},{"meta":"https://cran.r-project.org/","type":"fcode","name":"L","content":["CRAN"]},{"type":"text","value":"."}]},{"type":"para","id":"WMb7KQOf7dFcaWlaByMcT","margin":"","location":{"start":{"line":49,"column":1,"offset":1223},"end":{"line":52,"column":54,"offset":1406}},"text":"text","content":[{"type":"text","value":"[AAp1] Anton Antonov,\n"},{"meta":"https://github.com/antononcube/WL-QuantileRegression-paclet","type":"fcode","name":"L","content":["Quantile Regression, WL paclet"]},{"type":"text","value":",\n(2014-2023),\n"},{"meta":"https://github.com/antononcube","type":"fcode","name":"L","content":["GitHub/antononcube"]},{"type":"text","value":"."}]},{"type":"para","id":"vaUT6l2I0HqDlciQ1HtcJ","margin":"","location":{"start":{"line":54,"column":1,"offset":1408},"end":{"line":57,"column":54,"offset":1606}},"text":"text","content":[{"type":"text","value":"[AAp2] Anton Antonov,\n"},{"meta":"https://github.com/antononcube/WL-MonadicQuantileRegression-paclet","type":"fcode","name":"L","content":["Monadic Quantile Regression, WL paclet"]},{"type":"text","value":",\n(2018-2024),\n"},{"meta":"https://github.com/antononcube","type":"fcode","name":"L","content":["GitHub/antononcube"]},{"type":"text","value":"."}]},{"type":"para","id":"aFdVyWLTVDNH_Cm-gc6Ml","margin":"","location":{"start":{"line":59,"column":1,"offset":1608},"end":{"line":62,"column":115,"offset":1860}},"text":"text","content":[{"type":"text","value":"[AAp3] Anton Antonov,\n"},{"meta":"https://resources.wolframcloud.com/FunctionRepository/resources/QuantileRegression","type":"fcode","name":"L","content":[{"type":"fcode","name":"C","content":["QuantileRegression"]}]},{"type":"text","value":",\n(2019),\n"},{"meta":"https://resources.wolframcloud.com/FunctionRepository/resources/QuantileRegression","type":"fcode","name":"L","content":["Wolfram Function Repository"]},{"type":"text","value":"."}]},{"type":"para","id":"DGn8grDmGI-7Wk2n4lh91","margin":"","location":{"start":{"line":64,"column":1,"offset":1862},"end":{"line":67,"column":60,"offset":2036}},"text":"text","content":[{"type":"text","value":"[AAp4] Anton Antonov,\n"},{"meta":"https://pypi.org/project/QuantileRegression","type":"fcode","name":"L","content":[{"type":"fcode","name":"C","content":["QuantileRegression"]},", Python package"]},{"type":"text","value":",\n(2024),\n"},{"meta":"https://pypi.org/user/antononcube/","type":"fcode","name":"L","content":["PyPI.org/antononcube"]},{"type":"text","value":"."}]},{"type":"block","id":"Repositories","margin":"","level":3,"name":"head","location":{"start":{"line":69,"column":1,"offset":2038},"end":{"line":69,"column":17,"offset":2054}},"content":[{"type":"text","value":"Repositories"}]},{"type":"para","id":"kPl7CiMLK0QkG7vap8u0W","margin":"","location":{"start":{"line":71,"column":1,"offset":2056},"end":{"line":74,"column":99,"offset":2314}},"text":"text","content":[{"type":"text","value":"[AAr1] Anton Antonov,\n"},{"meta":"https://github.com/antononcube/Raku-DSL-English-QuantileRegressionWorkflows","type":"fcode","name":"L","content":["DSL::English::QuantileRegressionWorkflows in Raku"]},{"type":"text","value":",\n(2020),\n"},{"meta":"https://github.com/antononcube/Raku-DSL-English-QuantileRegressionWorkflows","type":"fcode","name":"L","content":["GitHub/antononcube"]},{"type":"text","value":"."}]},{"type":"block","id":"Videos","margin":"","level":3,"name":"head","location":{"start":{"line":76,"column":1,"offset":2316},"end":{"line":76,"column":11,"offset":2326}},"content":[{"type":"text","value":"Videos"}]},{"type":"para","id":"9r6WdX_rHcqZys3mfKNVe","margin":"","location":{"start":{"line":78,"column":1,"offset":2328},"end":{"line":81,"column":69,"offset":2529}},"text":"text","content":[{"type":"text","value":"[AAv1] Anton Antonov,\n"},{"meta":"https://www.youtube.com/watch?v=a_Dk25xarvE","type":"fcode","name":"L","content":["\"Boston useR! QuantileRegression Workflows 2019-04-18\""]},{"type":"text","value":",\n(2019),\n"},{"meta":"https://www.youtube.com/@AAA4Prediction","type":"fcode","name":"L","content":["Anton Antonov at YouTube"]},{"type":"text","value":"."}]},{"type":"para","id":"rrG7bxXiOY-4ErH9lN-QN","margin":"","location":{"start":{"line":83,"column":1,"offset":2531},"end":{"line":86,"column":85,"offset":2765}},"text":"text","content":[{"type":"text","value":"[AAv2] Anton Antonov,\n"},{"meta":"https://www.youtube.com/watch?v=b9Uu7gRF5KY","type":"fcode","name":"L","content":["\"useR! 2020: How to simplify Machine Learning workflows specifications\""]},{"type":"text","value":",\n(2020),\n"},{"meta":"https://www.youtube.com/channel/UC_R5smHVXRYGhZYDJsnXTwg","type":"fcode","name":"L","content":["R Consortium at YouTube"]},{"type":"text","value":"."}]},{"type":"para","id":"KH-NrZGMlG7mNoWAvIb5h","margin":"","location":{"start":{"line":88,"column":1,"offset":2767},"end":{"line":91,"column":69,"offset":2966}},"text":"text","content":[{"type":"text","value":"[AAv3] Anton Antonov,\n"},{"meta":"https://www.youtube.com/watch?v=mYXCynTPHsA","type":"fcode","name":"L","content":["\"Quantile Regression Workflows (PyData Global 2024)\""]},{"type":"text","value":",\n(2025),\n"},{"meta":"https://www.youtube.com/@AAA4Prediction","type":"fcode","name":"L","content":["Anton Antonov at YouTube"]},{"type":"text","value":"."}]}]},"pluginsData":{"moduleInfo":{"meta":{"api":"0","auth":"zef:antononcube","authors":["Anton Antonov"],"build-depends":[],"depends":["Data::Transformers:ver<0.0.4+>","Statistics::Distributions:ver<0.1.8+>","Math::Splines:ver<0.0.2+>","Math::Polynomial::Chebyshev:ver<0.0.2+>","Math::Optimization:ver<0.0.3+>","Math::SparseMatrix:ver<0.0.16+>"],"description":"A package for Quantile Regression calculations.","dist":"Math::QuantileRegression:ver<0.0.4>:auth<zef:antononcube>","license":"Artistic-2.0","name":"Math::QuantileRegression","path":"M/AT/MATH_QUANTILEREGRESSION/f991498a70d74d03c4548e8aa32f24c522cc09b0.tar.gz","provides":{"Math::QuantileRegression":"lib/Math/QuantileRegression.rakumod"},"tags":["quantile-regression","quantile","regression","b-splines","splines","chebyshev","chebyshev-polynomials","linear-programming"],"version":"0.0.4"},"files":[{"file":"work_mods/zef/Math::QuantileRegression/dist/README.md","publishUrl":"/mods/zef/Math::QuantileRegression/dist/README.md"},{"file":"work_mods/zef/Math::QuantileRegression/dist/docs/Quantile-Regression-with-B-splines.md","publishUrl":"/mods/zef/Math::QuantileRegression/dist/docs/Quantile-Regression-with-B-splines.md"}],"src":"zef","url":"/mods/zef/Math::QuantileRegression"}}}
=end data


React

    =begin React :component<RenderItem>
    =begin data
    {"type":"page","isPage":false,"publishUrl":"/mods/zef/Math::QuantileRegression/dist/README.md","file":"work_mods/zef/Math::QuantileRegression/dist/README.md","sources":[],"node":{"type":"block","id":"XYYG-GM_rcLweY6nPVr0M","margin":"","name":"root","content":[{"type":"block","id":"Math::QuantileRegression","margin":"","level":1,"name":"head","location":{"start":{"line":1,"column":1,"offset":0},"end":{"line":1,"column":27,"offset":26}},"content":[{"type":"text","value":"Math::QuantileRegression"}]},{"type":"para","id":"HqSeZMhMp64gBZwjl81K5","margin":"","location":{"start":{"line":3,"column":1,"offset":28},"end":{"line":3,"column":53,"offset":80}},"text":"text","content":[{"type":"text","value":"Raku package for basic Quantile Regression routines."}]},{"type":"block","id":"Installation","margin":"","level":2,"name":"head","location":{"start":{"line":7,"column":1,"offset":87},"end":{"line":7,"column":16,"offset":102}},"content":[{"type":"text","value":"Installation"}]},{"type":"para","id":"NMGe5YKxAsvh0D4swRAYI","margin":"","location":{"start":{"line":9,"column":1,"offset":104},"end":{"line":9,"column":107,"offset":210}},"text":"text","content":[{"type":"text","value":"Make sure the Raku package "},{"meta":"https://github.com/antononcube/Raku-COIN-OR","type":"fcode","name":"L","content":["\"COIN-OR\""]},{"type":"text","value":", [AAp1], is installed."}]},{"type":"para","id":"JtzzbSjrZfqf7YVlaVJqZ","margin":"","location":{"start":{"line":11,"column":1,"offset":212},"end":{"line":11,"column":28,"offset":239}},"text":"text","content":[{"type":"text","value":"Install from Zef ecosystem:"}]},{"type":"block","id":"RCpUdR05_ZthorsS34rIH","margin":"","name":"code","config":[],"location":{"start":{"line":13,"column":1,"offset":241},"end":{"line":15,"column":4,"offset":285}},"content":[{"type":"verbatim","value":"zef install Math::QuantileRegression"}]},{"type":"para","id":"EquApO6k7kZRYWaxNTAra","margin":"","location":{"start":{"line":17,"column":1,"offset":287},"end":{"line":17,"column":13,"offset":299}},"text":"text","content":[{"type":"text","value":"From GitHub:"}]},{"type":"block","id":"o-1CWcR3ikhiYEpclzzhR","margin":"","name":"code","config":[],"location":{"start":{"line":19,"column":1,"offset":301},"end":{"line":21,"column":4,"offset":384}},"content":[{"type":"verbatim","value":"zef install https://github.com/antononcube/Raku-Math-QuantileRegression.git"}]},{"type":"block","id":"References","margin":"","level":2,"name":"head","location":{"start":{"line":25,"column":1,"offset":391},"end":{"line":25,"column":14,"offset":404}},"content":[{"type":"text","value":"References"}]},{"type":"block","id":"Articles, books","margin":"","level":3,"name":"head","location":{"start":{"line":27,"column":1,"offset":406},"end":{"line":27,"column":20,"offset":425}},"content":[{"type":"text","value":"Articles, books"}]},{"type":"para","id":"8f9n_du1AvyeC5bmgW2Vl","margin":"","location":{"start":{"line":29,"column":1,"offset":427},"end":{"line":31,"column":34,"offset":583}},"text":"text","content":[{"type":"text","value":"[RK1] Roger Koenker,\n"},{"meta":"https://books.google.com/books/about/Quantile_Regression.html?id=hdkt7V4NXsgC","type":"fcode","name":"L","content":["Quantile Regression"]},{"type":"text","value":",\nCambridge University Press, 2005."}]},{"type":"para","id":"mjH30MnOn1FoY6ttW5dmT","margin":"","location":{"start":{"line":33,"column":1,"offset":585},"end":{"line":36,"column":37,"offset":759}},"text":"text","content":[{"type":"text","value":"[RK2] Roger Koenker,\n"},{"meta":"https://cran.r-project.org/web/packages/quantreg/vignettes/rq.pdf","type":"fcode","name":"L","content":["\"Quantile Regression in R: a vignette\""]},{"type":"text","value":",\n(2006),\n"},{"meta":"https://cran.r-project.org/","type":"fcode","name":"L","content":["CRAN"]},{"type":"text","value":"."}]},{"type":"para","id":"GkLnd1f-kA_Xc6slvs0Zh","margin":"","location":{"start":{"line":38,"column":1,"offset":761},"end":{"line":41,"column":95,"offset":1063}},"text":"text","content":[{"type":"text","value":"[AA1] Anton Antonov,\n"},{"meta":"https://github.com/antononcube/MathematicaForPrediction/blob/master/MarkdownDocuments/A-monad-for-Quantile-Regression-workflows.md","type":"fcode","name":"L","content":["\"A monad for Quantile Regression workflows\""]},{"type":"text","value":",\n(2018),\n"},{"meta":"https://github.com/antononcube/MathematicaForPrediction","type":"fcode","name":"L","content":["MathematicaForPrediction at GitHub"]},{"type":"text","value":"."}]},{"type":"block","id":"Packages, paclets","margin":"","level":3,"name":"head","location":{"start":{"line":43,"column":1,"offset":1065},"end":{"line":43,"column":22,"offset":1086}},"content":[{"type":"text","value":"Packages, paclets"}]},{"type":"para","id":"QAHS-VqLXCZLNbcuQZVXB","margin":"","location":{"start":{"line":45,"column":1,"offset":1088},"end":{"line":47,"column":37,"offset":1221}},"text":"text","content":[{"type":"text","value":"[RKp1] Roger Koenker,\n"},{"meta":"https://cran.r-project.org/web/packages/quantreg/index.html","type":"fcode","name":"L","content":[{"type":"fcode","name":"C","content":["quantreg"]}]},{"type":"text","value":",\n"},{"meta":"https://cran.r-project.org/","type":"fcode","name":"L","content":["CRAN"]},{"type":"text","value":"."}]},{"type":"para","id":"WMb7KQOf7dFcaWlaByMcT","margin":"","location":{"start":{"line":49,"column":1,"offset":1223},"end":{"line":52,"column":54,"offset":1406}},"text":"text","content":[{"type":"text","value":"[AAp1] Anton Antonov,\n"},{"meta":"https://github.com/antononcube/WL-QuantileRegression-paclet","type":"fcode","name":"L","content":["Quantile Regression, WL paclet"]},{"type":"text","value":",\n(2014-2023),\n"},{"meta":"https://github.com/antononcube","type":"fcode","name":"L","content":["GitHub/antononcube"]},{"type":"text","value":"."}]},{"type":"para","id":"vaUT6l2I0HqDlciQ1HtcJ","margin":"","location":{"start":{"line":54,"column":1,"offset":1408},"end":{"line":57,"column":54,"offset":1606}},"text":"text","content":[{"type":"text","value":"[AAp2] Anton Antonov,\n"},{"meta":"https://github.com/antononcube/WL-MonadicQuantileRegression-paclet","type":"fcode","name":"L","content":["Monadic Quantile Regression, WL paclet"]},{"type":"text","value":",\n(2018-2024),\n"},{"meta":"https://github.com/antononcube","type":"fcode","name":"L","content":["GitHub/antononcube"]},{"type":"text","value":"."}]},{"type":"para","id":"aFdVyWLTVDNH_Cm-gc6Ml","margin":"","location":{"start":{"line":59,"column":1,"offset":1608},"end":{"line":62,"column":115,"offset":1860}},"text":"text","content":[{"type":"text","value":"[AAp3] Anton Antonov,\n"},{"meta":"https://resources.wolframcloud.com/FunctionRepository/resources/QuantileRegression","type":"fcode","name":"L","content":[{"type":"fcode","name":"C","content":["QuantileRegression"]}]},{"type":"text","value":",\n(2019),\n"},{"meta":"https://resources.wolframcloud.com/FunctionRepository/resources/QuantileRegression","type":"fcode","name":"L","content":["Wolfram Function Repository"]},{"type":"text","value":"."}]},{"type":"para","id":"DGn8grDmGI-7Wk2n4lh91","margin":"","location":{"start":{"line":64,"column":1,"offset":1862},"end":{"line":67,"column":60,"offset":2036}},"text":"text","content":[{"type":"text","value":"[AAp4] Anton Antonov,\n"},{"meta":"https://pypi.org/project/QuantileRegression","type":"fcode","name":"L","content":[{"type":"fcode","name":"C","content":["QuantileRegression"]},", Python package"]},{"type":"text","value":",\n(2024),\n"},{"meta":"https://pypi.org/user/antononcube/","type":"fcode","name":"L","content":["PyPI.org/antononcube"]},{"type":"text","value":"."}]},{"type":"block","id":"Repositories","margin":"","level":3,"name":"head","location":{"start":{"line":69,"column":1,"offset":2038},"end":{"line":69,"column":17,"offset":2054}},"content":[{"type":"text","value":"Repositories"}]},{"type":"para","id":"kPl7CiMLK0QkG7vap8u0W","margin":"","location":{"start":{"line":71,"column":1,"offset":2056},"end":{"line":74,"column":99,"offset":2314}},"text":"text","content":[{"type":"text","value":"[AAr1] Anton Antonov,\n"},{"meta":"https://github.com/antononcube/Raku-DSL-English-QuantileRegressionWorkflows","type":"fcode","name":"L","content":["DSL::English::QuantileRegressionWorkflows in Raku"]},{"type":"text","value":",\n(2020),\n"},{"meta":"https://github.com/antononcube/Raku-DSL-English-QuantileRegressionWorkflows","type":"fcode","name":"L","content":["GitHub/antononcube"]},{"type":"text","value":"."}]},{"type":"block","id":"Videos","margin":"","level":3,"name":"head","location":{"start":{"line":76,"column":1,"offset":2316},"end":{"line":76,"column":11,"offset":2326}},"content":[{"type":"text","value":"Videos"}]},{"type":"para","id":"9r6WdX_rHcqZys3mfKNVe","margin":"","location":{"start":{"line":78,"column":1,"offset":2328},"end":{"line":81,"column":69,"offset":2529}},"text":"text","content":[{"type":"text","value":"[AAv1] Anton Antonov,\n"},{"meta":"https://www.youtube.com/watch?v=a_Dk25xarvE","type":"fcode","name":"L","content":["\"Boston useR! QuantileRegression Workflows 2019-04-18\""]},{"type":"text","value":",\n(2019),\n"},{"meta":"https://www.youtube.com/@AAA4Prediction","type":"fcode","name":"L","content":["Anton Antonov at YouTube"]},{"type":"text","value":"."}]},{"type":"para","id":"rrG7bxXiOY-4ErH9lN-QN","margin":"","location":{"start":{"line":83,"column":1,"offset":2531},"end":{"line":86,"column":85,"offset":2765}},"text":"text","content":[{"type":"text","value":"[AAv2] Anton Antonov,\n"},{"meta":"https://www.youtube.com/watch?v=b9Uu7gRF5KY","type":"fcode","name":"L","content":["\"useR! 2020: How to simplify Machine Learning workflows specifications\""]},{"type":"text","value":",\n(2020),\n"},{"meta":"https://www.youtube.com/channel/UC_R5smHVXRYGhZYDJsnXTwg","type":"fcode","name":"L","content":["R Consortium at YouTube"]},{"type":"text","value":"."}]},{"type":"para","id":"KH-NrZGMlG7mNoWAvIb5h","margin":"","location":{"start":{"line":88,"column":1,"offset":2767},"end":{"line":91,"column":69,"offset":2966}},"text":"text","content":[{"type":"text","value":"[AAv3] Anton Antonov,\n"},{"meta":"https://www.youtube.com/watch?v=mYXCynTPHsA","type":"fcode","name":"L","content":["\"Quantile Regression Workflows (PyData Global 2024)\""]},{"type":"text","value":",\n(2025),\n"},{"meta":"https://www.youtube.com/@AAA4Prediction","type":"fcode","name":"L","content":["Anton Antonov at YouTube"]},{"type":"text","value":"."}]}]},"pluginsData":{"moduleInfo":{"meta":{"api":"0","auth":"zef:antononcube","authors":["Anton Antonov"],"build-depends":[],"depends":["Data::Transformers:ver<0.0.4+>","Statistics::Distributions:ver<0.1.8+>","Math::Splines:ver<0.0.2+>","Math::Polynomial::Chebyshev:ver<0.0.2+>","Math::Optimization:ver<0.0.3+>","Math::SparseMatrix:ver<0.0.16+>"],"description":"A package for Quantile Regression calculations.","dist":"Math::QuantileRegression:ver<0.0.4>:auth<zef:antononcube>","license":"Artistic-2.0","name":"Math::QuantileRegression","path":"M/AT/MATH_QUANTILEREGRESSION/f991498a70d74d03c4548e8aa32f24c522cc09b0.tar.gz","provides":{"Math::QuantileRegression":"lib/Math/QuantileRegression.rakumod"},"tags":["quantile-regression","quantile","regression","b-splines","splines","chebyshev","chebyshev-polynomials","linear-programming"],"version":"0.0.4"},"files":[{"file":"work_mods/zef/Math::QuantileRegression/dist/README.md","publishUrl":"/mods/zef/Math::QuantileRegression/dist/README.md"},{"file":"work_mods/zef/Math::QuantileRegression/dist/docs/Quantile-Regression-with-B-splines.md","publishUrl":"/mods/zef/Math::QuantileRegression/dist/docs/Quantile-Regression-with-B-splines.md"}],"src":"zef","url":"/mods/zef/Math::QuantileRegression"}}}
    =end data
    =end React


root

Hyper-complex numbers as objects with operators


Math::Quaternion


Raku package with functionalities based on Chebyshev polynomials.


Math::Polynomial::Chebyshev


para

=begin React :component<HeaderCol> :id<menu> 
=para L<Documentation|/doc/introduction>
=para L<Modules| /mods>
=para L<Examples| file:../examples/index.podlite>
=para L<AI|file:../mcp/index.podlite>
=para L<Download |file:../download/index.podlite>
=para L<About|file:../about/index.podlite>
=para 🔍 K<⌘K>/K<ctrl-K>
=end React


=begin React :component<HeaderCol> :id<footer>  
=begin nested :!nested
=item1 B<Language>
    =item2 L<Get started|file:../getting-started/getting-started.podlite>
    =item2 L<Why Raku?|/doc/language/faq#Why-should-I-learn-Raku-What's-so-great-about-it>
    =item2 L<Try Raku|https://glot.io/new/raku>
    =item2 L<Download | file:../download/index.podlite>
    =item2 L<Raku cheat sheet|https://github.com/Raku/mu/blob/master/docs/Perl6/Cheatsheet/cheatsheet.txt>
    =item2 L<Raku on Exercism|https://exercism.org/tracks/raku>
    =item2 L<Wikipedia|https://en.wikipedia.org/wiki/Raku_(programming_language)>

=end nested
=begin nested :!nested

=item1 B<L<Documentation|/doc>>
    =item2 L<Getting started, Migration guides from other languages, & Tutorials | file:../doc/introduction.podlite>
    =item2 L<Language References|file:../doc/reference.podlite>
    =item2 L<Type Reference| file:../doc/types.podlite>
    =item2 L<Miscellaneous| file:../doc/miscellaneous.podlite>
    =item2 L<FAQs (Frequently Asked Questions)|/doc/language/faq>
    =item2 L<Community|/doc/language/community>
    =item2 L<The list of all documents|file:../doc/index.podlite>
    =item2 L<Hand these documents to an AI agent|file:../mcp/index.podlite>

=end nested
=begin nested :!nested
=item1 B<Learning>
    =item2 L<The Raku Guide|https://raku.guide/>
    =item2 L<Wikibook |https://en.wikibooks.org/wiki/Raku_Programming>
    =item2 L<Books |https://perl6book.com/>
    =item2 L<Rosetta Code | https://www.raku.org/community/rosettacode>
    =item2 L<Learn Raku in Y minutes|https://learnxinyminutes.com/docs/raku/>
    =item2 L<Golfing |https://github.com/AlexDaniel/raku-golf-cheatsheet>
=end nested
=begin nested :!nested

=item1 B<Explore>
    =item2 L<Raku Blog Aggregator|https://planet.raku.org/>
    =item2 L<Rakudo Weekly|https://rakudoweekly.blog/>
    =item2 L<The Weekly Challenge |https://perlweeklychallenge.org/>
    =item2 L<Raku Advent Calendar|https://raku-advent.blog/>

=end nested
=end React


Built with L<Podlite|https://podlite.org> — the markup and publishing tools behind this site.


=begin React :component<CookieConsent> :id<CookieConsent> :buttonCaption("Got it!")
=para
This website uses cookies for analytics.
=end React


Math::QuantileRegression

Math::QuantileRegression

Installation

References

Articles, books

Packages, paclets

Repositories

Videos

Math::QuantileRegression v0.0.4

Authors

License

Dependencies

Test Dependencies

Provides

Documentation