tag:joss.theoj.org,2005:/papers/tagged/normalizationJournal of Open Source Software2024-02-29T09:57:40ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/48482024-02-29T09:57:40Z2024-03-01T00:00:27ZSurjectors: surjection layers for density estimation with normalizing flowsacceptedv0.0.32023-10-14 23:34:59 UTC942024-02-29 09:57:40 UTC920246188SimonDirmeierSwiss Data Science Center, Zurich, Switzerland, ETH Zurich, Zurich, Switzerland10.21105/joss.06188https://doi.org/10.5281/zenodo.10679869Pythonhttps://joss.theoj.org/papers/10.21105/joss.06188.pdfJAX, Density estimation, Normalizing flow, Machine learning, Statisticstag:joss.theoj.org,2005:Paper/38102024-01-09T15:42:20Z2024-01-10T00:01:06ZScarlet: Scalable Anytime Algorithms for Learning Fragments of Linear Temporal Logicacceptedv0.0.12022-08-10 18:27:58 UTC932024-01-09 15:42:20 UTC920245052RitamRahaUniversity of Antwerp, Antwerp, Belgium, CNRS, LaBRI and Université de Bordeaux, France0000-0003-1467-1182RajarshiRoyMax Planck Institute for Software Systems, Kaiserslautern, Germany0000-0002-0202-1169NathanaëlFijalkowCNRS, LaBRI and Université de Bordeaux, France0000-0002-6576-4680DanielNeiderTU Dortmund University, Dortmund, Germany, Center for Trustworthy Data Science and Security, University Alliance Ruhr, Germany0000-0001-9276-634210.21105/joss.05052https://doi.org/10.5281/zenodo.10419514Pythonhttps://joss.theoj.org/papers/10.21105/joss.05052.pdflinear temporal logic (LTL), Explainable AI (XAI), specification mining, Formal Methodstag:joss.theoj.org,2005:Paper/42212023-06-24T19:39:19Z2023-06-25T00:01:02Znormflows: A PyTorch Package for Normalizing Flowsacceptedv1.62023-02-18 13:00:57 UTC862023-06-24 19:39:19 UTC820235361VincentStimperUniversity of Cambridge, Cambridge, United Kingdom, Max Planck Institute for Intelligent Systems, Tübingen, Germany0000-0002-4965-4297DavidLiuUniversity of Cambridge, Cambridge, United KingdomAndrewCampbellUniversity of Cambridge, Cambridge, United KingdomVincentBerenzMax Planck Institute for Intelligent Systems, Tübingen, GermanyLukasRyllUniversity of Cambridge, Cambridge, United KingdomBernhardSchölkopfMax Planck Institute for Intelligent Systems, Tübingen, Germany0000-0002-8177-0925JoséMiguelHernández-LobatoUniversity of Cambridge, Cambridge, United Kingdom10.21105/joss.05361https://doi.org/10.5281/zenodo.8027667Pythonhttps://joss.theoj.org/papers/10.21105/joss.05361.pdfPyTorch, Machine Learning, Normalizing Flows, Density Estimationtag:joss.theoj.org,2005:Paper/41872023-05-30T10:10:11Z2023-05-31T00:01:19ZproteoDA: a package for quantitative proteomicsacceptedv1.0.02023-02-02 15:47:05 UTC852023-05-30 10:10:11 UTC820235184TimothyJ.ThurmanDepartment of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, USA0000-0002-9602-6226CharityL.WashamDepartment of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, USA0000-0001-5761-9304DuahAlkamDepartment of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, USA0000-0002-5965-7694JordanT.BirdDepartment of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, USA0000-0001-5753-6058AllenGiesDepartment of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, USA0000-0003-2492-0429KalyaniDhusiaDepartment of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, USA0000-0002-8803-1295MichaelS.RobesonDepartment of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA0000-0001-7119-6301StephanieD.ByrumDepartment of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, USA, Arkansas Children's Research Institute, Little Rock, AR, USA0000-0002-1783-361010.21105/joss.05184https://doi.org/10.5281/zenodo.7962306R, JavaScripthttps://joss.theoj.org/papers/10.21105/joss.05184.pdfmass spectrometry, intensity data, normalization, linear modelstag:joss.theoj.org,2005:Paper/39952023-03-09T21:00:11Z2023-03-10T00:04:43ZflowMC: Normalizing flow enhanced sampling package for probabilistic inference in JAXacceptedv0.0.72022-11-10 08:41:32 UTC832023-03-09 21:00:11 UTC820235021KazeW. k.WongCenter for Computational Astrophysics, Flatiron Institute, New York, NY 10010, US0000-0001-8432-7788MarylouGabriéÉcole Polytechnique, Palaiseau 91120, France, Center for Computational Mathematics, Flatiron Institute, New York, NY 10010, US0000-0002-5989-1018DanielForeman-MackeyCenter for Computational Astrophysics, Flatiron Institute, New York, NY 10010, US0000-0002-9328-565210.21105/joss.05021https://doi.org/10.5281/zenodo.7706605Pythonhttps://joss.theoj.org/papers/10.21105/joss.05021.pdfBayesian Inference, Machine Learning, JAXtag:joss.theoj.org,2005:Paper/38482022-12-26T14:37:35Z2022-12-28T19:01:29ZMParT: Monotone Parameterization Toolkitaccepted1.0.02022-08-30 22:22:24 UTC802022-12-26 14:37:35 UTC720224843MatthewParnoDartmouth College, Hanover, NH USA, Solea Energy, Overland Park, KS USA0000-0002-9419-2693Paul-BaptisteRubioMassachusetts Institute of Technology, Cambridge, MA USA0000-0002-9765-1162DanielSharpMassachusetts Institute of Technology, Cambridge, MA USA0000-0002-0439-5084MichaelBrennanMassachusetts Institute of Technology, Cambridge, MA USA0000-0001-7812-9347RicardoBaptistaMassachusetts Institute of Technology, Cambridge, MA USA0000-0002-0421-890XHenningBonartMassachusetts Institute of Technology, Cambridge, MA USA, Technische Universität Darmstadt, Darmstadt, Germany0000-0002-5026-4499YoussefMarzoukMassachusetts Institute of Technology, Cambridge, MA USA0000-0001-8242-329010.21105/joss.04843https://doi.org/10.5281/zenodo.7435142C++, Objective-C, MATLABhttps://joss.theoj.org/papers/10.21105/joss.04843.pdfPython, Julia, measure transport, transport map, density estimation, Bayesian inference, normalizing flows, machine learningtag:joss.theoj.org,2005:Paper/34202022-12-08T21:58:29Z2022-12-09T03:31:51ZPyNM: a Lightweight Python implementation of Normative Modelingaccepted1.0.0b12022-03-11 18:20:42 UTC802022-12-08 21:58:29 UTC720224321AnnabelleHarveyCentre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Université de Montréal, QC, Canada, Centre de Recherche du CHU Sainte-Justine, Université de Montréal, QC, Canada0000-0002-9940-8799GuillaumeDumasCentre de Recherche du CHU Sainte-Justine, Université de Montréal, QC, Canada, Mila - Quebec AI Institute, Université de Montréal, QC, Canada0000-0002-2253-184410.21105/joss.04321https://doi.org/10.5281/zenodo.7396721Python, Jupyter Notebookhttps://joss.theoj.org/papers/10.21105/joss.04321.pdfNormative Modeling, Heterogeneity, Heteroskedasticity, Big Data, Centiles, LOESS, Gaussian Process, Stochastic Variational Gaussian Process, GAMLSS, Computational Psychiatry, Neurosciencetag:joss.theoj.org,2005:Paper/30062022-05-07T21:21:13Z2022-05-08T00:01:23ZROCK: digital normalization of whole genome sequencing dataacceptedv_1.9.42021-09-01 12:54:20 UTC732022-05-07 21:21:13 UTC720223790VéroniqueNbspLegrandInstitut Pasteur, Université Paris Cité, Plateforme HPC, F-75015 Paris, FranceThomasNbspKergrohenPrédicteurs moléculaires et nouvelles cibles en oncologie, INSERM, Gustave Roussy, Université Paris-Saclay, Villejuif, France, Département de Cancérologie de l'Enfant et de l'Adolescent, Gustave Roussy, Université Paris-Saclay, Villejuif, FranceNicolasNbspJolyInstitut Pasteur, Université Paris Cité, Plateforme HPC, F-75015 Paris, FranceAlexisNbspCriscuoloInstitut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, F-75015 Paris, France, Institut Pasteur, Université Paris Cité, Plateforme de Microbiologie Mutualisée (P2M), F-75015 Paris, France0000-0002-8212-521510.21105/joss.03790https://doi.org/10.5281/zenodo.6527091M4, C++https://joss.theoj.org/papers/10.21105/joss.03790.pdfhigh-throughput sequencing, digital normalization, _k_-mertag:joss.theoj.org,2005:Paper/33422022-03-30T22:01:18Z2022-03-31T16:26:13Zmxnorm: An R Package to Normalize Multiplexed Imaging Dataacceptedv1.0.02022-02-15 16:56:41 UTC712022-03-30 22:01:18 UTC720224180ColemanHarrisDepartment of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA0000-0002-6325-0694JuliaWrobelDepartment of Biostatistics & Informatics, Colorado School of Public Health, Aurora, CO, USASimonVandekarDepartment of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA10.21105/joss.04180https://doi.org/10.5281/zenodo.6390746Rhttps://joss.theoj.org/papers/10.21105/joss.04180.pdfmultiplexed imaging, normalization, statisticstag:joss.theoj.org,2005:Paper/11482019-09-25T22:29:09Z2021-02-15T11:31:49ZvirtualNicheR: generating virtual fundamental and realised niches for use in virtual ecology experimentsacceptedv1.02019-08-06 04:18:42 UTC412019-09-25 22:29:09 UTC420191661ThomasR.EtheringtonManaaki Whenua -- Landcare Research0000-0002-3187-075XO.PascalOmondiagbeManaaki Whenua -- Landcare Research0000-0002-9267-483210.21105/joss.01661https://doi.org/10.7931/cae9-bt94Rhttps://joss.theoj.org/papers/10.21105/joss.01661.pdfniche, ecology, modelling, multivariate normal