tag:joss.theoj.org,2005:/papers/tagged/efficient%20estimationJournal of Open Source Software2023-11-05T15:58:05ZJournal of Open Source Softwarehttps://joss.theoj.orgtag: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/33232023-02-21T17:40:46Z2023-02-22T00:00:54ZCVtreeMLE: Efficient Estimation of Mixed Exposures using Data Adaptive Decision Trees and Cross-Validated Targeted Maximum Likelihood Estimation in Racceptedv1.0.02022-02-04 17:57:11 UTC822023-02-21 17:40:46 UTC820234181DavidMcCoyDivision of Environmental Health Sciences, University of California, Berkeley, CA, United States of America0000-0002-5515-6307AlanHubbardDepartment of Biostatistics, University of California, Berkeley, CA, United States of America0000-0002-3769-0127MarkVan der LaanDepartment of Biostatistics, University of California, Berkeley, CA, United States of America0000-0003-1432-551110.21105/joss.04181https://doi.org/10.5281/zenodo.7651354Rhttps://joss.theoj.org/papers/10.21105/joss.04181.pdfcausal inference, machine learning, decision trees, efficient estimation, targeted learning, iterative backfitting, mixed exposurestag:joss.theoj.org,2005:Paper/22332021-12-08T09:57:12Z2022-01-10T13:58:06Zsamplics: a Python Package for selecting, weighting and analyzing data from complex sampling designs.accepted0.2.62020-12-13 00:23:03 UTC682021-12-08 09:57:12 UTC620213376MamadouS.DialloUNICEF, The United Nations Children's Fund0000-0002-0376-363110.21105/joss.03376https://doi.org/10.5281/zenodo.5750761Pythonhttps://joss.theoj.org/papers/10.21105/joss.03376.pdfSurvey sampling, small area estimation, Official statisticstag:joss.theoj.org,2005:Paper/16222020-10-07T21:19:35Z2021-02-15T11:30:45Ztxshift: Efficient estimation of the causal effects of stochastic interventions in Raccepted0.3.32020-05-20 18:51:38 UTC542020-10-07 21:19:35 UTC520202447NimaS.HejaziGraduate Group in Biostatistics, University of California, Berkeley, Center for Computational Biology, University of California, Berkeley0000-0002-7127-2789DavidBenkeserDepartment of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University0000-0002-1019-834310.21105/joss.02447https://doi.org/10.5281/zenodo.4070043Rhttps://joss.theoj.org/papers/10.21105/joss.02447.pdfcausal inference, machine learning, two-phase sampling, efficient estimation, targeted learning, stochastic intervention, modified treatment policytag:joss.theoj.org,2005:Paper/11612019-10-13T12:40:41Z2021-02-15T11:31:47Zfeign: a Python package to estimate geometric efficiency in passive gamma spectroscopy measurements of nuclear fuelacceptedv1.0.02019-08-15 11:24:28 UTC422019-10-13 12:40:41 UTC420191650ZsoltElterUppsala University, Division of Applied Nuclear Physics0000-0003-2339-4340AronCserkaszkyPazmany Peter Catholic University, Faculty of Information Technology0000-0001-5708-9999SophieGrapeUppsala University, Division of Applied Nuclear Physics0000-0002-5133-682910.21105/joss.01650https://doi.org/10.5281/zenodo.3480082Pythonhttps://joss.theoj.org/papers/10.21105/joss.01650.pdfnuclear safeguards, gamma spectroscopy, ray tracingtag:joss.theoj.org,2005:Paper/6152018-09-20T17:38:43Z2021-02-15T11:33:02Zungroup: An R package for efficient estimation of smooth distributions from coarsely binned dataacceptedv1.0.32018-09-10 12:57:29 UTC292018-09-20 17:38:43 UTC32018937MariusD.PascariuInstitute of Public Health, Center on Population Dynamics, University of Southern Denmark, Odense, Denmark0000-0002-2568-6489MaciejJ.DańkoMax Planck Institute for Demographic Research, Rostock, Germany0000-0002-7924-9022JonasSchöleyInstitute of Public Health, Center on Population Dynamics, University of Southern Denmark, Odense, Denmark0000-0002-3340-8518SilviaRizziInstitute of Public Health, Unit of Epidemiology Biostatistics and Biodemography, University of Southern Denmark, Odense, Denmark10.21105/joss.00937https://doi.org/10.5281/zenodo.1421648R, C++https://joss.theoj.org/papers/10.21105/joss.00937.pdfcomposite link model, GLM, histogram, binned data, smoothing