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unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["julia","l1","lasso","regularized-linear-regression"],"created_at":"2025-12-30T00:09:25.588Z","updated_at":"2026-02-21T20:01:14.743Z","avatar_url":"https://github.com/JuliaStats.png","language":"Julia","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Lasso\n\n| **Documentation**                                                               | **Build Status**                                                                                |\n|:-------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------:|\n| [![][docs-stable-img]][docs-stable-url] [![][docs-dev-img]][docs-dev-url] | [![][actions-img]][actions-url] [![][codecov-img]][codecov-url] |\n\n[docs-dev-img]: https://img.shields.io/badge/docs-dev-blue.svg\n[docs-dev-url]: https://juliastats.github.io/Lasso.jl/latest\n\n[docs-stable-img]: https://img.shields.io/badge/docs-stable-blue.svg\n[docs-stable-url]: https://juliastats.github.io/Lasso.jl/stable\n\n[actions-img]: https://github.com/JuliaStats/Lasso.jl/workflows/CI/badge.svg\n[actions-url]: https://github.com/JuliaStats/Lasso.jl/actions?query=workflow%3ACI+branch%3Amaster\n\n[codecov-img]: http://codecov.io/github/JuliaStats/Lasso.jl/coverage.svg?branch=master\n[codecov-url]: http://codecov.io/github/JuliaStats/Lasso.jl?branch=master\n\nLasso.jl is a pure Julia implementation of the glmnet coordinate\ndescent algorithm for fitting linear and generalized linear Lasso and\nElastic Net models, as described in:\n\nFriedman, J., Hastie, T., \u0026 Tibshirani, R. (2010). Regularization paths\nfor generalized linear models via coordinate descent. Journal of\nStatistical Software, 33(1), 1. http://www.jstatsoft.org/v33/i01/\n\nLasso.jl also includes an implementation of the O(n) fused Lasso\nimplementation described in:\n\nJohnson, N. A. (2013). A dynamic programming algorithm for the fused\nlasso and L0-segmentation. Journal of Computational and Graphical\nStatistics, 22(2), 246–260. doi:10.1080/10618600.2012.681238\n\nAs well as an implementation of polynomial trend filtering based on:\n\nRamdas, A., \u0026 Tibshirani, R. J. (2014). Fast and flexible ADMM\nalgorithms for trend filtering. arXiv Preprint arXiv:1406.2082.\nRetrieved from http://arxiv.org/abs/1406.2082\n\nAlso implements the Gamma Lasso, a concave regularization path glmnet variant:\nTaddy, M. (2017) One-Step Estimator Paths for Concave Regularization\nJournal of Computational and Graphical Statistics, 26:3, 525-536\nhttp://dx.doi.org/10.1080/10618600.2016.1211532\n\n\n## Quick start\n\nTo fit a Lasso path with default parameters:\n\n```julia\nfit(LassoPath, X, y, dist, link)\n```\n\n`dist` is any distribution supported by GLM.jl and `link` defaults to\nthe canonical link for that distribution.\n\nTo fit a fused Lasso model:\n\n```julia\nfit(FusedLasso, y, λ)\n```\n\nTo fit a polynomial trend filtering model:\n\n```julia\nfit(TrendFilter, y, order, λ)\n```\nTo fit a Gamma Lasso path:\n\n```julia\nfit(GammaLassoPath, X, y, dist, link; γ=1.0)\n```\nIt supports the same parameters as fit(LassoPath...), plus γ which controls\nthe concavity of the regularization path. γ=0.0 is the Lasso. Higher values\ntend to result in sparser coefficient estimates.\n\nMore documentation is available at [![][docs-stable-img]][docs-stable-url].\n\n## TODO\n\n - User-specified weights are untested\n - Maybe integrate LARS.jl\n\n## See also\n\n - [LassoPlot.jl](https://github.com/AsafManela/LassoPlot.jl), a package for\n   plotting regularization paths.\n - [GLMNet.jl](https://github.com/JuliaStats/GLMNet.jl), a wrapper for the\n   glmnet Fortran code.\n - [LARS.jl](https://github.com/simonster/LARS.jl), an implementation\n   of least angle regression for fitting entire linear (but not\n   generalized linear) Lasso and Elastic Net coordinate paths.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjuliastats%2Flasso.jl","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjuliastats%2Flasso.jl","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjuliastats%2Flasso.jl/lists"}