tag:joss.theoj.org,2005:/papers/edited_by/@terrytangyuan?page=2Journal of Open Source Software2020-01-16T00:24:45ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/12172020-01-16T00:24:45Z2021-02-15T11:31:37ZLarq: An Open-Source Library for Training Binarized Neural Networksacceptedv0.7.12019-09-13 16:57:11 UTC452020-01-16 00:24:45 UTC520201746LukasGeigerPlumerai Research0000-0002-8697-9920PlumeraiTeamPlumerai Research10.21105/joss.01746https://doi.org/10.6084/m9.figshare.11619912.v1Pythonhttps://joss.theoj.org/papers/10.21105/joss.01746.pdfpython, tensorflow, keras, deep-learning, machine-learning, binarized-neural-networks, quantized-neural-networks, efficient-deep-learningtag:joss.theoj.org,2005:Paper/13322019-12-11T18:12:29Z2021-02-15T11:31:22Zmlr3: A modern object-oriented machine learning framework in Racceptedv0.1.42019-11-15 12:36:52 UTC442019-12-11 18:12:29 UTC420191903MichelLangTU Dortmund University, LMU Munich0000-0001-9754-0393MartinBinderLMU MunichJakobRichterTU Dortmund University0000-0003-4481-5554PatrickSchratzLMU Munich0000-0003-0748-6624FlorianPfistererLMU Munich0000-0001-8867-762XStefanCoorsLMU Munich0000-0002-7465-2146QuayAuLMU Munich0000-0002-5252-8902GiuseppeCasalicchioLMU Munich0000-0001-5324-5966LarsKotthoffUniversity of Wyoming0000-0003-4635-6873BerndBischlLMU Munich0000-0001-6002-698010.21105/joss.01903https://doi.org/10.5281/zenodo.3569963R, Rebolhttps://joss.theoj.org/papers/10.21105/joss.01903.pdfmachine learning, classification, regressiontag: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, interpretabilitytag:joss.theoj.org,2005:Paper/12782019-11-05T18:24:44Z2021-02-15T11:31:32ZmodelStudio: Interactive Studio with Explanations for ML Predictive Modelsacceptedv0.1.82019-10-05 14:30:57 UTC432019-11-05 18:24:44 UTC420191798HubertBanieckiFaculty of Mathematics and Information Science, Warsaw University of Technology0000-0001-6661-5364PrzemyslawBiecekFaculty of Mathematics and Information Science, Warsaw University of Technology0000-0001-8423-182310.21105/joss.01798https://doi.org/10.5281/zenodo.3527770R, JavaScripthttps://joss.theoj.org/papers/10.21105/joss.01798.pdfautomated data analysis, model visualization, explainable artificial intelligence, predictive modeling, interpretable machine learningtag:joss.theoj.org,2005:Paper/11032019-10-03T18:10:45Z2021-02-15T11:31:57Zkdensity: An R package for kernel density estimation with parametric starts and asymmetric kernelsacceptedv1.0.12019-07-11 20:19:52 UTC422019-10-03 18:10:45 UTC420191566JonasMossUniversity of Oslo0000-0002-6876-6964MartinTvetenUniversity of Oslo0000-0002-4236-633X10.21105/joss.01566https://doi.org/10.5281/zenodo.3466547Rhttps://joss.theoj.org/papers/10.21105/joss.01566.pdfstatistics, kernel density estimation, non-parametric statistics, non-parametrics, non-parametric density estimation, boundary biastag:joss.theoj.org,2005:Paper/12022019-10-01T17:27:15Z2021-02-15T11:31:40ZBayesPostEst: An R Package to Generate Postestimation Quantities for Bayesian MCMC Estimationaccepted0.1.02019-09-05 18:27:24 UTC422019-10-01 17:27:15 UTC420191722ShanaScoginUniversity of Notre Dame, South Bend, IN, USA0000-0002-7801-853XJohannesKarrethUrsinus College, Collegeville, PA, USA0000-0003-4586-7153AndreasBegerPredictive Heuristics, Bellevue, WA, USA0000-0003-1883-3169RobWilliamsWashington University in St. Louis, St. Louis, MO, USA0000-0001-9259-388310.21105/joss.01722https://doi.org/10.5281/zenodo.3464224Rhttps://joss.theoj.org/papers/10.21105/joss.01722.pdfMCMC, Bayesian methods, Visualization, ROC curves, Precision-Recall curves, Region of Practical Equivalencetag:joss.theoj.org,2005:Paper/11592019-09-02T00:00:00Z2021-02-15T11:31:47ZdeepCR: Cosmic Ray Rejection with Deep Learningaccepted0.1.52019-08-13 20:53:31 UTC412019-09-02 00:00:00 UTC420191651KemingZhangDepartment of Astronomy, University of California, Berkeley0000-0002-9870-5695JoshuaS.BloomDepartment of Astronomy, University of California, Berkeley, Lawrence Berkeley National Laboratory0000-0002-7777-216X10.21105/joss.01651https://doi.org/10.5281/zenodo.3383309Pythonhttps://joss.theoj.org/papers/10.21105/joss.01651.pdfPytorch, astronomy, image processing, cosmic ray, deep learningtag:joss.theoj.org,2005:Paper/11282019-08-21T17:12:06Z2021-02-15T11:31:54Zcontainerit: Generating Dockerfiles for reproducible research with Racceptedv0.5.02019-07-25 08:02:50 UTC402019-08-21 17:12:06 UTC420191603DanielNüstInstitute for Geoinformatics, University of Münster, Germany0000-0002-0024-5046MatthiasHinzProfessorship for Geoinformatics and Geodesy, Faculty of Agricultural and Environmental Sciences, University of Rostock, Germany0000-0001-6837-940610.21105/joss.01603https://doi.org/10.5281/zenodo.3373289R, Rebolhttps://joss.theoj.org/papers/10.21105/joss.01603.pdfcontainerisation, Docker, sandbox, reproducibility, reproducible researchtag:joss.theoj.org,2005:Paper/11312019-08-12T12:54:20Z2021-02-15T11:31:54Zgreta: simple and scalable statistical modelling in Raccepted0.3.02019-07-26 11:46:05 UTC402019-08-12 12:54:20 UTC420191601NickGoldingSchool of BioSciences, University of Melbourne0000-0001-8916-557010.21105/joss.01601https://doi.org/10.5281/zenodo.819476Rhttps://joss.theoj.org/papers/10.21105/joss.01601.pdfstatistics, statistical modelling, bayesian statistics, mcmc, hamiltonian monte carlo, tensorflowtag:joss.theoj.org,2005:Paper/11012019-07-30T16:20:45Z2021-02-15T11:31:58ZMemCNN: A Python/PyTorch package for creating memory-efficient invertible neural networksaccepted0.3.32019-07-10 22:18:15 UTC392019-07-30 16:20:45 UTC420191576SilC.van de LeemputRadboud University Medical Center, Department of Radiology and Nuclear Medicine, Nijmegen, The Netherlands0000-0001-6047-3051JonasTeuwenRadboud University Medical Center, Department of Radiology and Nuclear Medicine, Nijmegen, The NetherlandsBramvan GinnekenRadboud University Medical Center, Department of Radiology and Nuclear Medicine, Nijmegen, The NetherlandsRashindraManniesingRadboud University Medical Center, Department of Radiology and Nuclear Medicine, Nijmegen, The Netherlands10.21105/joss.01576https://doi.org/10.5281/zenodo.3353604Pythonhttps://joss.theoj.org/papers/10.21105/joss.01576.pdfMemCNN, PyTorch, machine learning, invertible networks, deep learning