tag:joss.theoj.org,2005:/papers/tagged/inference?page=3Journal of Open Source Software2022-12-26T14:40:55ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/39182022-12-26T14:40:55Z2023-01-03T02:48:24Zrigr: Regression, Inference, and General Data Analysis Tools in Racceptedv1.0.42022-09-22 23:13:55 UTC802022-12-26 14:40:55 UTC720224847YiqunT.ChenDepartment of Biostatistics, University of Washington, Seattle, USA, Data Science Institute & Department of Biomedical Data Science, Stanford University, Stanford, USA0000-0002-4100-1507BrianD.WilliamsonKaiser Permanente Washington Health Research Institute, Seattle, USA0000-0002-7024-548XTaylorOkonekDepartment of Biostatistics, University of Washington, Seattle, USACharlesJ.WolockDepartment of Biostatistics, University of Washington, Seattle, USAAndrewJ.SpiekerVanderbilt University Medical Center, Nashville, USATravisY. HeeWaiVA Puget Sound Health Care System, Seattle, USAJamesP.HughesDepartment of Biostatistics, University of Washington, Seattle, USAScottS.EmersonDepartment of Biostatistics, University of Washington, Seattle, USAAmyD.WillisDepartment of Biostatistics, University of Washington, Seattle, USA0000-0002-2802-431710.21105/joss.04847https://doi.org/10.5281/zenodo.7456326Rhttps://joss.theoj.org/papers/10.21105/joss.04847.pdfinference, regression analysis, data analysis, robust standard errorstag: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/39642022-12-16T13:14:37Z2022-12-17T00:01:04Zhstrat: a Python Package for phylogenetic inference on distributed digital evolution populationsacceptedv1.0.32022-10-16 23:14:14 UTC802022-12-16 13:14:37 UTC720224866MatthewAndresMorenoMichigan State University0000-0003-4726-4479EmilyDolsonMichigan State University0000-0001-8616-4898CharlesOfriaMichigan State University0000-0003-2924-173210.21105/joss.04866https://doi.org/10.5281/zenodo.7416592Pythonhttps://joss.theoj.org/papers/10.21105/joss.04866.pdfartificial life, digital evolution, distributed computingtag:joss.theoj.org,2005:Paper/39372022-12-13T13:38:32Z2022-12-14T00:01:24Zcosasi: Graph Diffusion Source Inference in Pythonaccepted0.0.12022-10-03 18:27:51 UTC802022-12-13 13:38:32 UTC720224894LucasH.McCabeDigital and Analytic Solutions, Logistics Management Institute, Department of Mathematics, The George Washington University0000-0002-7383-282310.21105/joss.04894https://doi.org/10.5281/zenodo.7430558Pythonhttps://joss.theoj.org/papers/10.21105/joss.04894.pdfnetwork science, graph algorithms, network analysis, epidemics, simulation, communication, information theorytag:joss.theoj.org,2005:Paper/37192022-11-16T16:19:19Z2022-11-17T00:01:38Zgeostan: An R package for Bayesian spatial analysisacceptedV0.3.02022-07-14 01:08:25 UTC792022-11-16 16:19:19 UTC720224716ConnorDoneganGeography and Geospatial Information Sciences, The University of Texas at Dallas, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center0000-0002-9698-544310.21105/joss.04716https://doi.org/10.5281/zenodo.7311716R, C++, Stanhttps://joss.theoj.org/papers/10.21105/joss.04716.pdfspatial data, survey data, Bayesian inferencetag:joss.theoj.org,2005:Paper/32352022-11-11T22:07:21Z2022-11-12T00:00:47ZDataAssimilationBenchmarks.jl: a data assimilation research framework.acceptedv0.2.02021-12-03 00:04:43 UTC792022-11-11 22:07:21 UTC720224129ColinGrudzienCW3E - Scripps Institution of Oceanography, University of California, San Diego, USA, Department of Mathematics and Statistics, University of Nevada, Reno, USA0000-0002-3084-3178CharlotteMerchantCW3E - Scripps Institution of Oceanography, University of California, San Diego, USA, Department of Computer Science, Princeton University, USASukhreenSandhuDepartment of Computer Science and Engineering, University of Nevada, Reno, USA10.21105/joss.04129https://doi.org/10.5281/zenodo.7311847Julia, Pythonhttps://joss.theoj.org/papers/10.21105/joss.04129.pdfData Assimilation, Bayesian Inference, Optimization, Machine learningtag:joss.theoj.org,2005:Paper/37142022-11-09T17:49:38Z2022-11-10T00:01:16ZpocoMC: A Python package for accelerated Bayesian inference in astronomy and cosmologyaccepted0.1.02022-07-12 16:05:01 UTC792022-11-09 17:49:38 UTC720224634MinasKaramanisInstitute for Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill, Edinburgh EH9 3HJ, UK, Physics Department, University of California and Lawrence Berkeley National Laboratory Berkeley, CA 94720, USA0000-0001-9489-4612DavidNabergojFaculty of Computer and Information Science, University of Ljubljana, Ve\v{c}na pot 113, 1000 Ljubljana, Slovenia0000-0001-6882-627XFlorianBeutlerInstitute for Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill, Edinburgh EH9 3HJ, UK0000-0003-0467-5438JohnA.PeacockInstitute for Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill, Edinburgh EH9 3HJ, UK0000-0002-1168-8299UrošSeljakPhysics Department, University of California and Lawrence Berkeley National Laboratory Berkeley, CA 94720, USA0000-0003-2262-356X10.21105/joss.04634https://doi.org/10.5281/zenodo.7308533Pythonhttps://joss.theoj.org/papers/10.21105/joss.04634.pdfastronomytag:joss.theoj.org,2005:Paper/36552022-09-29T21:26:26Z2022-09-30T00:00:33ZCWInPy: A Python package for inference with continuous gravitational-wave signals from pulsarsacceptedv0.8.02022-06-14 08:03:13 UTC772022-09-29 21:26:26 UTC720224568MatthewPitkinDepartment of Physics, Lancaster University, Lancaster, UK, LA1 4YB, School of Physics and Astronomy, University of Glasgow, University Avenue, Glasgow, UK, G12 8QQ0000-0003-4548-526X10.21105/joss.04568https://doi.org/10.5281/zenodo.7121400Python, Cython, Jupyter Notebookhttps://joss.theoj.org/papers/10.21105/joss.04568.pdfgravitational waves, pulsarstag: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/35922022-09-05T11:14:39Z2023-10-08T07:24:41Ztipr: An R package for sensitivity analyses for unmeasured confoundersacceptedv0.4.12022-05-06 16:58:03 UTC772022-09-05 11:14:39 UTC720224495LucyD\'AgostinoMcGowanWake Forest University, USA0000-0001-7297-935910.21105/joss.04495https://doi.org/10.5281/zenodo.6958926Rhttps://joss.theoj.org/papers/10.21105/joss.04495.pdfstatistics, epidemiology, sensitivity analyses, causal inference, confounding