tag:joss.theoj.org,2005:/papers/tagged/sparse%20regressionJournal of Open Source Software2023-12-21T11:33:25ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/46872023-12-21T11:33:25Z2023-12-22T00:00:45Zsparse-lm: Sparse linear regression models in Pythonacceptedv0.5.12023-08-09 17:30:58 UTC922023-12-21 11:33:25 UTC820235867LuisBarroso-LuqueMaterials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley CA, 94720, USA, Department of Materials Science and Engineering, University of California Berkeley, Berkeley CA, 94720, USA0000-0002-6453-9545FengyuXieMaterials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley CA, 94720, USA, Department of Materials Science and Engineering, University of California Berkeley, Berkeley CA, 94720, USA0000-0002-1169-169010.21105/joss.05867https://doi.org/10.5281/zenodo.10246640Pythonhttps://joss.theoj.org/papers/10.21105/joss.05867.pdfscikit-learn, cvxpy, linear regression, regularization, structured sparsitytag:joss.theoj.org,2005:Paper/41462023-04-23T06:22:55Z2023-04-27T15:33:07Zpysr3: A Python Package for Sparse Relaxed Regularized Regressionacceptedv0.3.32023-01-04 18:36:29 UTC842023-04-23 06:22:55 UTC820235155AlekseiSholokhovDepartment of Applied Mathematics, University of Washington0000-0001-8173-6236PengZhengDepartment of Health Metrics Sciences, University of Washington0000-0003-3313-215XAleksandrAravkinDepartment of Applied Mathematics, University of Washington, Department of Health Metrics Sciences, University of Washington0000-0002-1875-180110.21105/joss.05155https://doi.org/10.5281/zenodo.7839335Pythonhttps://joss.theoj.org/papers/10.21105/joss.05155.pdffeature selection, linear models, mixed-effect models, regularizationtag:joss.theoj.org,2005:Paper/38312023-02-13T13:30:07Z2023-02-14T00:00:51ZenetLTS: Robust and Sparse Methods for High Dimensional Linear, Binary, and Multinomial Regressionaccepted1.1.02022-08-23 09:31:03 UTC822023-02-13 13:30:07 UTC820234773FatmaSevincKurnazDepartment of Statistics, Yildiz Technical University, Istanbul, Turkey0000-0002-5958-7366PeterFilzmoserInstitute of Statistics and Mathematical Methods in Economics, TU Wien, Vienna, Austria0000-0002-8014-468210.21105/joss.04773https://doi.org/10.5281/zenodo.7598948Rhttps://joss.theoj.org/papers/10.21105/joss.04773.pdfRobust regression, Elastic net, outlier detectiontag:joss.theoj.org,2005:Paper/31222022-01-29T01:16:01Z2022-01-30T00:01:12ZPySINDy: A comprehensive Python package for robust sparse system identificationacceptedv1.2.32021-10-21 18:46:09 UTC692022-01-29 01:16:01 UTC720223994AlanA.KaptanogluDepartment of Physics, University of WashingtonBrianM.de SilvaDepartment of Applied Mathematics, University of WashingtonUrbanFaselDepartment of Mechanical Engineering, University of WashingtonKadierdanKahemanDepartment of Mechanical Engineering, University of WashingtonAndyJ.GoldschmidtDepartment of Physics, University of WashingtonJaredCallahamDepartment of Mechanical Engineering, University of WashingtonCharlesB.DelahuntDepartment of Applied Mathematics, University of WashingtonZacharyG.NicolaouDepartment of Applied Mathematics, University of WashingtonKathleenChampionDepartment of Applied Mathematics, University of WashingtonJean-ChristopheLoiseauArts et Métiers Institute of Technology, CNAM, DynFluid, HESAM UniversitéJ.NathanKutzDepartment of Applied Mathematics, University of WashingtonStevenL.BruntonDepartment of Mechanical Engineering, University of Washington10.21105/joss.03994https://doi.org/10.5281/zenodo.5842612Pythonhttps://joss.theoj.org/papers/10.21105/joss.03994.pdfdynamical systems, sparse regression, model discovery, system identification, machine learningtag:joss.theoj.org,2005:Paper/21442021-01-17T09:12:07Z2021-02-15T11:29:46Zc-lasso - a Python package for constrained sparse and robust regression and classificationacceptedv1.02020-11-02 09:36:34 UTC572021-01-17 09:12:07 UTC620212844LéoSimpsonTechnische Universität MünchenPatrickL.CombettesDepartment of Mathematics, North Carolina State University, RaleighChristianL.MüllerCenter for Computational Mathematics, Flatiron Institute, New York, Institute of Computational Biology, Helmholtz Zentrum München, Department of Statistics, Ludwig-Maximilians-Universität München0000-0002-3821-708310.21105/joss.02844https://doi.org/10.6084/m9.figshare.13589585.v1Python, JavaScripthttps://joss.theoj.org/papers/10.21105/joss.02844.pdfregression, classification, constrained regression, Lasso, Huber function, Square Hinge SVM, convex optimization, perspective functiontag:joss.theoj.org,2005:Paper/15102020-05-18T20:15:54Z2021-02-15T11:31:00ZPySINDy: A Python package for the sparse identification of nonlinear dynamical systems from dataacceptedv0.12.02020-02-11 01:09:30 UTC492020-05-18 20:15:54 UTC520202104BrianM.de SilvaDepartment of Applied Mathematics, University of WashingtonKathleenChampionDepartment of Applied Mathematics, University of WashingtonMarkusQuadeAmbrosys GmbHJean-ChristopheLoiseauÉcole Nationale Supérieure des Arts et MétiersJ.NathanKutzDepartment of Applied Mathematics, University of WashingtonStevenL.BruntonDepartment of Mechanical Engineering, University of Washington, Department of Applied Mathematics, University of Washington10.21105/joss.02104https://doi.org/10.5281/zenodo.3832319Pythonhttps://joss.theoj.org/papers/10.21105/joss.02104.pdfdynamical systems, sparse regression, model discovery, system identification, machine learning