tag:joss.theoj.org,2005:/papers/tagged/sparsityJournal 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/30902022-03-16T15:43:08Z2022-03-17T00:01:34ZSISSO++: A C++ Implementation of the Sure-Independence Screening and Sparsifying Operator Approachacceptedv1.0.02021-10-01 10:23:53 UTC712022-03-16 15:43:08 UTC720223960ThomasA. r.PurcellNOMAD Laboratory at the Fritz Haber Institute of the Max Planck Society and Humboldt University, Berlin, Germany0000-0003-4564-7206MatthiasSchefflerNOMAD Laboratory at the Fritz Haber Institute of the Max Planck Society and Humboldt University, Berlin, GermanyChristianCarbognoNOMAD Laboratory at the Fritz Haber Institute of the Max Planck Society and Humboldt University, Berlin, Germany0000-0003-0635-8364LucaM.GhiringhelliNOMAD Laboratory at the Fritz Haber Institute of the Max Planck Society and Humboldt University, Berlin, Germany0000-0001-5099-302910.21105/joss.03960https://doi.org/10.5281/zenodo.6245396Python, C++https://joss.theoj.org/papers/10.21105/joss.03960.pdfSISSO, Symbolic Regression, Physicstag:joss.theoj.org,2005:Paper/31162021-12-10T15:24:09Z2021-12-11T00:01:44ZGGLasso - a Python package for General Graphical Lasso computationacceptedv0.1.42021-10-18 16:15:11 UTC682021-12-10 15:24:09 UTC620213865FabianSchaippTechnische Universität München0000-0002-0673-9944OlegVlasovetsInstitute of Computational Biology, Helmholtz Zentrum München, Department of Statistics, Ludwig-Maximilians-Universität MünchenChristianL.MüllerInstitute of Computational Biology, Helmholtz Zentrum München, Department of Statistics, Ludwig-Maximilians-Universität München, Center for Computational Mathematics, Flatiron Institute, New York0000-0002-3821-708310.21105/joss.03865https://doi.org/10.5281/zenodo.5718440Jupyter Notebook, Pythonhttps://joss.theoj.org/papers/10.21105/joss.03865.pdfgraphical lasso, latent graphical model, structured sparsity, convex optimization, ADMMtag:joss.theoj.org,2005:Paper/14572020-02-25T00:42:46Z2021-02-15T11:31:05ZscPCA: A toolbox for sparse contrastive principal component analysis in Racceptedv1.1.62020-01-28 17:27:57 UTC462020-02-25 00:42:46 UTC520202079PhilippeBoileauGraduate Group in Biostatistics, University of California, Berkeley0000-0002-4850-2507NimaS.HejaziGraduate Group in Biostatistics, University of California, Berkeley, Center for Computational Biology, University of California, Berkeley0000-0002-7127-2789SandrineDudoitCenter for Computational Biology, University of California, Berkeley, Department of Statistics, University of California, Berkeley, Division of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley0000-0002-6069-862910.21105/joss.02079https://doi.org/10.5281/zenodo.3686201Rhttps://joss.theoj.org/papers/10.21105/joss.02079.pdfdimensionality reduction, principal component analysis, computational biology, unwanted variation, sparsitytag:joss.theoj.org,2005:Paper/12752019-12-06T14:47:37Z2021-02-15T11:31:33ZPyUoI: The Union of Intersections Framework in Pythonaccepted1.0.02019-10-04 17:50:39 UTC442019-12-06 14:47:37 UTC420191799PratikS.SachdevaRedwood Center for Theoretical Neuroscience, University of California, Berkeley, Berkeley, California, USA, Department of Physics, University of California, Berkeley, Berkeley, California, USA, Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA0000-0002-6809-2437JesseA.LivezeyRedwood Center for Theoretical Neuroscience, University of California, Berkeley, Berkeley, California, USA, Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA0000-0003-0494-8758AndrewJ.TrittComputational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA0000-0002-1617-449XKristoferE.BouchardRedwood Center for Theoretical Neuroscience, University of California, Berkeley, Berkeley, California, USA, Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA, Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, USA0000-0002-1974-460310.21105/joss.01799https://doi.org/10.5281/zenodo.3563147C, Pythonhttps://joss.theoj.org/papers/10.21105/joss.01799.pdfgeneralized linear models, dimensionality reduction, sparsity, interpretability