tag:joss.theoj.org,2005:/papers/tagged/density%20estimationJournal 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/39352023-08-25T03:11:58Z2023-08-26T08:10:23ZMetrics As Scores: A Tool- and Analysis Suite and Interactive Application for Exploring Context-Dependent Distributionsacceptedv1.0.82022-10-03 14:45:19 UTC882023-08-25 03:11:58 UTC820234913SebastianHönelDepartment of Computer Science and Media Technology, Linnaeus University, Sweden0000-0001-7937-1645MorganEricssonDepartment of Computer Science and Media Technology, Linnaeus University, Sweden0000-0003-1173-5187WelfLöweDepartment of Computer Science and Media Technology, Linnaeus University, Sweden0000-0002-7565-3714AnnaWingkvistDepartment of Computer Science and Media Technology, Linnaeus University, Sweden0000-0002-0835-823X10.21105/joss.04913https://doi.org/10.5281/zenodo.8202326Python, Jupyter Notebookhttps://joss.theoj.org/papers/10.21105/joss.04913.pdfMultiple ANOVA, Distribution fitting, Inverse sampling, Empirical distributions, Kernel density estimationtag: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/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/36132022-09-23T14:45:34Z2022-09-25T16:58:01Zhaldensify: Highly adaptive lasso conditional density estimation in Racceptedv0.2.52022-05-18 15:31:04 UTC772022-09-23 14:45:34 UTC720224522NimaS.HejaziDepartment of Biostatistics, T.H. Chan School of Public Health, Harvard University0000-0002-7127-2789MarkJ.van der LaanDivision of Biostatistics, School of Public Health, University of California, Berkeley, Department of Statistics, University of California, Berkeley0000-0002-1019-8343DavidBenkeserDepartment of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University0000-0002-1019-834310.21105/joss.04522https://doi.org/10.5281/zenodo.7089147Rhttps://joss.theoj.org/papers/10.21105/joss.04522.pdfmachine learning, causal inference, conditional density estimation, generalized propensity score, inverse probability weighting, semiparametric inferencetag:joss.theoj.org,2005:Paper/20732021-01-22T08:36:17Z2021-02-15T11:29:51Zkalepy: a Python package for kernel density estimation, sampling and plottingacceptedv1.02020-10-13 04:15:46 UTC572021-01-22 08:36:17 UTC620212784LukeZoltanKelleyCenter for Interdisciplinary Exploration and Research in Astrophysics (CIERA), Northwestern University, USA, Physics & Astronomy, Northwestern University, USA0000-0002-6625-645010.21105/joss.02784https://doi.org/10.5281/zenodo.4456130Python, Jupyter Notebookhttps://joss.theoj.org/papers/10.21105/joss.02784.pdfastronomy, statistics, monte carlo methodstag:joss.theoj.org,2005:Paper/11832019-12-19T19:52:15Z2021-02-15T11:31:43Zkramersmoyal: Kramers--Moyal coefficients for stochastic processesacceptedv0.312019-08-23 12:27:34 UTC442019-12-19 19:52:15 UTC420191693LeonardoRydinGorjãoDepartment of Epileptology, University of Bonn, Venusberg Campus 1, 53127 Bonn, Germany, Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Nußallee 14--16, 53115 Bonn, Germany, Forschungszentrum Jülich, Institute for Energy and Climate Research - Systems Analysis and Technology Evaluation (IEK-STE), 52428 Jülich, Germany, Institute for Theoretical Physics, University of Cologne, 50937 Köln, Germany0000-0001-5513-0580FranciscoMeirinhosPhysikalisches Institut and Bethe Center for Theoretical Physics, Universität Bonn, Nussallee 12, 53115 Bonn, Germany0000-0002-3864-756910.21105/joss.01693https://doi.org/10.21105/joss.01693Pythonhttps://joss.theoj.org/papers/10.21105/joss.01693.pdfKramers--Moyal coefficents, Kernel-density estimators, N-dimensional stochastic processes, Markovian processes, Master equation, Fokker--Planck, Ornstein--Uhlenbeck processtag:joss.theoj.org,2005:Paper/13172019-12-04T15:38:58Z2021-02-15T11:31:25ZunivariateML: An R package for maximum likelihood estimation of univariate densitiesacceptedv1.0.02019-11-01 20:49:26 UTC442019-12-04 15:38:58 UTC420191863JonasMossUniversity of Oslo0000-0002-6876-696410.21105/joss.01863https://doi.org/10.5281/zenodo.3562385Rhttps://joss.theoj.org/papers/10.21105/joss.01863.pdfstatistics, maximum likelihood, density estimationtag: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/5682018-08-06T14:48:45Z2021-02-15T11:33:10ZlogKDE: log-transformed kernel density estimationacceptedv0.3.12018-07-24 10:43:23 UTC282018-08-06 14:48:45 UTC32018870AndrewT.JonesSchool of Mathematics and Physics, University of Queensland, St. Lucia 4072, Queensland AustraliaHienD.NguyenDepartment of Mathematics and Statistics, La Trobe University, Bundoora 3086, Victoria Australia0000-0002-9958-432XGeoffreyJ.McLachlanSchool of Mathematics and Physics, University of Queensland, St. Lucia 4072, Queensland Australia10.21105/joss.00870https://doi.org/10.5281/zenodo.1339352R, C++https://joss.theoj.org/papers/10.21105/joss.00870.pdfdata visualization, exploratory data analysis, non-parametric, positive data, probability density function