tag:joss.theoj.org,2005:/papers/tagged/inferenceJournal of Open Source Software2024-03-27T23:48:03ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/51402024-03-27T23:48:03Z2024-03-28T15:06:00ZTDApplied: An R package for machine learning and inference with persistence diagramsacceptedv3.0.22024-01-25 19:46:31 UTC952024-03-27 23:48:03 UTC920246321ShaelBrownDepartment of Quantitative Life Sciences, McGill University, Montreal, Canada0000-0001-8868-2867RezaFarivar-MohseniMcGill Vision Research, Department of Opthamology, McGill University, Montreal, Canada0000-0002-3123-262710.21105/joss.06321https://doi.org/10.5281/zenodo.10814141R, C++, Chttps://joss.theoj.org/papers/10.21105/joss.06321.pdftopological data analysis, persistent homologytag:joss.theoj.org,2005:Paper/43602024-03-13T22:02:50Z2024-03-20T22:49:13ZGPCERF - An R package for implementing Gaussian processes for estimating causal exposure response curvesacceptedv0.2.12023-03-23 15:46:21 UTC952024-03-13 22:02:50 UTC920245465NaeemKhoshnevisUniversity Research Computing and Data Services, Harvard University, Cambridge, Massachusetts, United States of America0000-0003-4315-1426BoyuRenMcLean Hospital, Belmont, Massachusetts, United States of America0000-0002-5300-1184DanielleBraunDepartment of Biostatistics, Harvard School of Public Health, Cambridge, Massachusetts, United States of America0000-0002-5177-859810.21105/joss.05465https://doi.org/10.5281/zenodo.10757333R, C++https://joss.theoj.org/papers/10.21105/joss.05465.pdfcausal inference, Gaussian Processes, causal exposure response functiontag:joss.theoj.org,2005:Paper/46362024-03-09T11:15:15Z2024-03-10T00:01:20Zkonfound: An R Sensitivity Analysis Package to Quantify the Robustness of Causal Inferencesaccepted0.4.02023-07-31 19:56:25 UTC952024-03-09 11:15:15 UTC920245779SarahNarvaizUniversity of Tennessee, Knoxville, Knoxville, TN, USAQinyunLinUniversity of Gothenburg, Gothenburg, SEJoshuaM.RosenbergUniversity of Tennessee, Knoxville, Knoxville, TN, USAKennethA.FrankMichigan State University, East Lansing, MI, USASpiroJ.MaroulisArizona State University, Tempe, AZ, USAWeiWangUniversity of Tennessee, Knoxville, Knoxville, TN, USARanXuUniversity of Connecticut, Hartford, CT, USA10.21105/joss.05779https://doi.org/10.5281/zenodo.10708094Rhttps://joss.theoj.org/papers/10.21105/joss.05779.pdfSensitivity analysis, Causal inferencetag:joss.theoj.org,2005:Paper/46792024-02-07T22:25:26Z2024-02-12T11:55:04ZChi: A Python package for treatment response modellingacceptedv0.2.32023-08-06 13:35:22 UTC942024-02-07 22:25:26 UTC920245925DavidAugustinDepartment of Computer Science, University of Oxford, Oxford, United Kingdom0000-0002-4885-108810.21105/joss.05925https://doi.org/10.5281/zenodo.10510572Pythonhttps://joss.theoj.org/papers/10.21105/joss.05925.pdfpkpd, treatment planning, inference, Bayesian inferencetag:joss.theoj.org,2005:Paper/45712024-02-02T16:32:04Z2024-02-03T00:00:40ZFEniCS-arclength: A numerical continuation package in FEniCS for nonlinear problems in solid mechanicsacceptedv0.1.02023-06-29 15:19:07 UTC942024-02-02 16:32:04 UTC920245727PeerasaitPrachasereeDepartment of Mechanical Engineering, Boston University, Massachusetts, the United States of America0009-0000-3325-1410SaeedMohammadzadehDepartment of Systems Engineering, Boston University, Massachusetts, the United States of America0000-0001-9879-044XBerkinDortdivanliogluDepartment of Civil, Architectural and Environmental Engineering, The University of Texas at Austin, Austin, the United States of America, Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, the United States of America0000-0001-7105-1452EmmaLejeuneDepartment of Mechanical Engineering, Boston University, Massachusetts, the United States of America0000-0001-8099-346810.21105/joss.05727https://doi.org/10.5281/zenodo.10563095Pythonhttps://joss.theoj.org/papers/10.21105/joss.05727.pdfFEniCS, Finite Element Analysis, Solid Mechanicstag:joss.theoj.org,2005:Paper/43192023-12-15T18:30:03Z2023-12-16T00:01:06ZCRE: An R package for interpretable discovery and inference of heterogeneous treatment effectsacceptedv0.2.12023-03-17 18:39:28 UTC922023-12-15 18:30:03 UTC820235587RiccardoCadeiDepartment of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America, Department of Computer and Communication Science, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland0000-0003-2416-8943NaeemKhoshnevisResearch Computing, Harvard University, Cambridge, Massachusetts, United States of America0000-0003-4315-1426KwonsangLeeDepartment of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America0000-0002-5823-4331DanielaMariaGarciaDepartment of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America0000-0003-3226-3561FalcoJ. BargagliStoffiDepartment of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America0000-0002-6131-816510.21105/joss.05587https://doi.org/10.5281/zenodo.10278296Rhttps://joss.theoj.org/papers/10.21105/joss.05587.pdfcausal inference, heterogeneous effect, interpretability, machine learningtag:joss.theoj.org,2005:Paper/42122023-11-05T15:58:05Z2023-11-06T00:00:26ZSuperNOVA: Semi-Parametric Identification and Estimation of Interaction and Effect Modification in Mixed Exposures using Stochastic Interventions in Racceptedv1.0.02023-02-15 18:37:21 UTC912023-11-05 15:58:05 UTC820235422DavidMcCoyDepartment of Biostatistics, University of California Berkeley, Berkeley, CA 94704, U.S.A.0000-0002-5515-6307AlejandroSchulerDepartment of Biostatistics, University of California Berkeley, Berkeley, CA 94704, U.S.A.0000-0003-4853-6130AlanHubbardDepartment of Biostatistics, University of California Berkeley, Berkeley, CA 94704, U.S.A.0000-0002-3769-0127Markvan der LaanDepartment of Biostatistics, University of California Berkeley, Berkeley, CA 94704, U.S.A.0000-0003-1432-551110.21105/joss.05422https://doi.org/10.5281/zenodo.10038794Rhttps://joss.theoj.org/papers/10.21105/joss.05422.pdfcausal inference, machine learning, stochastic interventions, efficient estimation, targeted learning, mixed exposurestag:joss.theoj.org,2005:Paper/44392023-10-05T09:52:45Z2023-10-06T00:01:05ZNORDic: a Network-Oriented package for the Repurposing of Drugsacceptedv2.4.22023-04-18 11:32:05 UTC902023-10-05 09:52:45 UTC820235532ClémenceRédaUniversité Paris Cité, Neurodiderot, Inserm, F-75019 Paris, France0000-0003-3238-0258AndréeDelahaye-DuriezUniversité Paris Cité, Neurodiderot, Inserm, F-75019 Paris, France, Université Sorbonne Paris Nord, UFR de santé, médecine et biologie humaine, F-93000 Bobigny, France, Unité fonctionnelle de médecine génomique et génétique clinique, Hôpital Jean Verdier, AP-HP, F-93140 Bondy, France0000-0003-4324-737210.21105/joss.05532https://doi.org/10.5281/zenodo.8355529Jupyter Notebook, Pythonhttps://joss.theoj.org/papers/10.21105/joss.05532.pdfnetwork analysis, boolean network, network inference, biomarker identification, drug repurposingtag:joss.theoj.org,2005:Paper/46202023-10-03T09:14:22Z2023-10-12T14:13:09ZTensorInference: A Julia package for tensor-based probabilistic inferenceaccepted0.2.02023-07-22 22:27:34 UTC902023-10-03 09:14:22 UTC820235700MartinRoa-VillescasEindhoven University of Technology0009-0009-0291-503XJin-GuoLiuHong Kong University of Science and Technology (Guangzhou)0000-0003-1635-267910.21105/joss.05700https://doi.org/10.5281/zenodo.8399580Juliahttps://joss.theoj.org/papers/10.21105/joss.05700.pdfprobabilistic graphical models, tensor networks, probabilistic inferencetag:joss.theoj.org,2005:Paper/46082023-09-28T19:37:54Z2023-09-29T11:54:28ZBlackBIRDS: Black-Box Inference foR Differentiable Simulatorsacceptedv1.02023-07-17 13:26:44 UTC892023-09-28 19:37:54 UTC820235776ArnauQuera-BofarullDepartment of Computer Science, University of Oxford, UK, Institute for New Economic Thinking, University of Oxford, UK0000-0001-5055-9863JoelDyerDepartment of Computer Science, University of Oxford, UK, Institute for New Economic Thinking, University of Oxford, UK0000-0002-8304-8450AnisoaraCalinescuDepartment of Computer Science, University of Oxford, UK0000-0003-2082-734XJ.DoyneFarmerInstitute for New Economic Thinking, University of Oxford, UK, Mathematical Institute, University of Oxford, UK, Santa Fe Institute, USA0000-0001-7871-073XMichaelWooldridgeDepartment of Computer Science, University of Oxford, UK0000-0002-9329-841010.21105/joss.05776https://doi.org/10.5281/zenodo.8377044Pythonhttps://joss.theoj.org/papers/10.21105/joss.05776.pdfBayesian inference, differentiable simulators, variational inference, Markov chain Monte Carlo