tag:joss.theoj.org,2005:/papers/tagged/regression?page=3Journal of Open Source Software2021-06-06T09:37:22ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/23632021-06-06T09:37:22Z2021-06-07T00:01:24Zgmr: Gaussian Mixture Regressionaccepted1.42021-01-30 19:54:16 UTC622021-06-06 09:37:22 UTC620213054AlexanderFabischRobotics Innovation Center, DFKI GmbH, Bremen, Germany0000-0003-2824-795610.21105/joss.03054https://doi.org/10.5281/zenodo.4449631Pythonhttps://joss.theoj.org/papers/10.21105/joss.03054.pdfmachine learning, regressiontag:joss.theoj.org,2005:Paper/22142021-05-14T05:51:32Z2021-05-15T00:00:22Zmikropml: User-Friendly R Package for Supervised Machine Learning Pipelinesacceptedv0.0.12020-12-03 14:22:24 UTC612021-05-14 05:51:32 UTC620213073BegümD.TopçuoğluDepartment of Microbiology & Immunology, University of Michigan, Exploratory Science Center, Merck & Co., Inc., Cambridge, Massachusetts, USA.0000-0003-3140-537XZenaLappDepartment of Computational Medicine & Bioinformatics, University of Michigan0000-0003-4674-2176KellyL.SovacoolDepartment of Computational Medicine & Bioinformatics, University of Michigan0000-0003-3283-829XEvanSnitkinDepartment of Microbiology & Immunology, University of Michigan, Department of Internal Medicine/Division of Infectious Diseases, University of Michigan0000-0001-8409-278XJennaWiensDepartment of Electrical Engineering & Computer Science, University of Michigan0000-0002-1057-7722PatrickD.SchlossDepartment of Microbiology & Immunology, University of Michigan0000-0002-6935-427510.21105/joss.03073https://doi.org/10.5281/zenodo.4759346Rhttps://joss.theoj.org/papers/10.21105/joss.03073.pdfmachine learning, regression, classification, decision trees, random forest, xgboost, support vector machines, microbiologytag:joss.theoj.org,2005:Paper/25352021-04-21T09:33:12Z2021-04-22T00:02:22Zperformance: An R Package for Assessment, Comparison and Testing of Statistical Modelsaccepted0.7.12021-03-23 10:28:04 UTC602021-04-21 09:33:12 UTC620213139DanielLüdeckeUniversity Medical Center Hamburg-Eppendorf, Germany0000-0002-8895-3206MattanS.Ben-ShacharBen-Gurion University of the Negev, Israel0000-0002-4287-4801IndrajeetPatilCenter for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany0000-0003-1995-6531PhilipWaggonerUniversity of Chicago, USA0000-0002-7825-7573DominiqueMakowskiNanyang Technological University, Singapore0000-0001-5375-996710.21105/joss.03139https://doi.org/10.5281/zenodo.4700887Rhttps://joss.theoj.org/papers/10.21105/joss.03139.pdfeasystats, parameters, regression, linear models, coefficientstag:joss.theoj.org,2005:Paper/22372021-04-06T17:40:16Z2021-04-07T04:33:52ZNLSIG-COVID19Lab: A modern logistic-growth tool (nlogistic-sigmoid) for descriptively modelling the dynamics of the COVID-19 pandemic processacceptedv1.1.02020-12-16 06:55:27 UTC602021-04-06 17:40:16 UTC620213002OluwasegunA.SomefunFederal University of Technology Akure, Nigeria0000-0002-5171-8026KayodeF.AkingbadeFederal University of Technology Akure, NigeriaFolasadeM.DahunsiFederal University of Technology Akure, Nigeria10.21105/joss.03002https://doi.org/10.5281/zenodo.4662275Matlabhttps://joss.theoj.org/papers/10.21105/joss.03002.pdfCOVID-19, logistic function, machine learning, neural networks, optimization, regression, epidemiologytag:joss.theoj.org,2005:Paper/23652021-02-24T17:21:47Z2021-02-25T00:01:38ZGroupyr: Sparse Group Lasso in Pythonacceptedv0.2.02021-02-01 13:31:54 UTC582021-02-24 17:21:47 UTC620213024AdamRichie-HalfordeScience Institute, University of Washington0000-0001-9276-9084ManjariNarayanDepartment of Psychiatry and Behavioral Sciences, Stanford University0000-0001-5348-270XNoahSimonDepartment of Biostatistics, University of Washington0000-0002-8985-2474JasonYeatmanGraduate School of Education and Division of Developmental and Behavioral Pediatrics, Stanford University0000-0002-2686-1293ArielRokemDepartment of Psychology, University of Washington0000-0003-0679-198510.21105/joss.03024https://doi.org/10.5281/zenodo.4559599Pythonhttps://joss.theoj.org/papers/10.21105/joss.03024.pdfgroup lasso, penalized regression, classificationtag: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/22122021-01-05T11:31:37Z2021-02-15T11:29:41ZLinRegOutliers: A Julia package for detecting outliers in linear regressionacceptedv0.8.32020-12-02 18:04:24 UTC572021-01-05 11:31:37 UTC620212892MehmetHakanSatmanDepartment of Econometrics, Istanbul University, Istanbul, Turkey0000-0002-9402-1982ShreeshAdigaDepartment of Electronics and Communication Engineering, RV College of Engineering, Bengaluru, India0000-0002-1818-6961GuillermoAngerisDepartment of Electrical Engineering, Stanford University, Stanford, California, USA0000-0002-4950-3990EmreAkadalDepartment of Informatics, Istanbul University, Istanbul, Turkey0000-0001-6817-012710.21105/joss.02892https://doi.org/10.5281/zenodo.4419418Juliahttps://joss.theoj.org/papers/10.21105/joss.02892.pdflinear regression, outlier detection, robust statisticstag:joss.theoj.org,2005:Paper/21362020-12-23T14:17:27Z2021-02-15T11:29:47Zeffectsize: Estimation of Effect Size Indices and Standardized Parametersaccepted0.4.0.0012020-10-28 16:26:42 UTC562020-12-23 14:17:27 UTC520202815MattanS.Ben-ShacharBen-Gurion University of the Negev, Israel0000-0002-4287-4801DanielLüdeckeUniversity Medical Center Hamburg-Eppendorf, Germany0000-0002-8895-3206DominiqueMakowskiNanyang Technological University, Singapore0000-0001-5375-996710.21105/joss.02815https://doi.org/10.5281/zenodo.4384509Rhttps://joss.theoj.org/papers/10.21105/joss.02815.pdfeasystats, effect size, regression, linear models, standardized coefficientstag:joss.theoj.org,2005:Paper/18482020-10-10T17:18:47Z2021-02-15T11:30:17ZGENRE (GPU Elastic-Net REgression): A CUDA-Accelerated Package for Massively Parallel Linear Regression with Elastic-Net Regularizationacceptedv1.02020-07-10 23:13:10 UTC542020-10-10 17:18:47 UTC520202644ChristopherKhanVanderbilt University0000-0003-3201-3423BrettByramVanderbilt University0000-0003-3693-145910.21105/joss.02644https://doi.org/10.5281/zenodo.4076520Matlab, Cudahttps://joss.theoj.org/papers/10.21105/joss.02644.pdfCUDA, GPU computing, Cyclic coordinate descent, Elastic-net regularization, Linear regressiontag:joss.theoj.org,2005:Paper/17942020-09-26T07:36:15Z2021-02-15T11:30:26Zhal9001: Scalable highly adaptive lasso regression in Racceptedv0.2.62020-06-24 02:07:17 UTC532020-09-26 07:36:15 UTC520202526NimaS.HejaziGraduate Group in Biostatistics, University of California, Berkeley, Division of Biostatistics, School of Public Health, University of California, Berkeley, Center for Computational Biology, University of California, Berkeley0000-0002-7127-2789JeremyR.CoyleDivision of Biostatistics, School of Public Health, University of California, Berkeley0000-0002-9874-6649MarkJ.van der LaanDivision of Biostatistics, School of Public Health, University of California, Berkeley, Department of Statistics, University of California, Berkeley, Center for Computational Biology, University of California, Berkeley0000-0003-1432-551110.21105/joss.02526https://doi.org/10.5281/zenodo.4050561R, C++https://joss.theoj.org/papers/10.21105/joss.02526.pdfmachine learning, targeted learning, causal inference