tag:joss.theoj.org,2005:/papers/tagged/parameter%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/33132023-04-21T10:26:19Z2023-04-24T20:59:17ZAsimov: A framework for coordinating parameter estimation workflowsacceptedv0.3.32022-02-01 11:26:06 UTC842023-04-21 10:26:19 UTC820234170DanielWilliamsSchool of Physics and Astronomy, University of Glasgow, Glasgow, G12 8QQ, United Kingdom0000-0003-3772-198XJohnVeitchSchool of Physics and Astronomy, University of Glasgow, Glasgow, G12 8QQ, United Kingdom0000-0002-6508-0713MariaLuisaChiofaloDepartment of Physics "Enrico Fermi", University of Pisa, and INFN, Largo Bruno Pontecorvo 3 I-56126 Pisa, Italy0000-0002-6992-5963PatriciaSchmidtSchool of Physics and Astronomy and Institute for Gravitational Wave Astronomy, University of Birmingham, Edgbaston, Birmingham, B15 9TT, United Kingdom0000-0003-1542-1791RhiannonP.UdallLIGO Laboratory, California Institute of Technology0000-0001-6877-3278AviVajpejiSchool of Physics and Astronomy, Monash University, Clayton VIC 3800, Australia, OzGrav: The ARC Centre of Excellence for Gravitational Wave Discovery, Clayton VIC 3800, Australia0000-0002-4146-1132CharlieHoyCardiff University, Cardiff CF24 3AA, UK0000-0002-8843-671910.21105/joss.04170https://doi.org/10.5281/zenodo.7843573https://joss.theoj.org/papers/10.21105/joss.04170.pdfastronomy, gravitational waves, Pythontag:joss.theoj.org,2005:Paper/40212023-03-09T10:43:20Z2023-03-10T00:04:42ZFast and flexible simulation and parameter estimation for synthetic biology using bioscrapeacceptedv1.22022-11-23 08:27:41 UTC832023-03-09 10:43:20 UTC820235057AyushPandeyControl and Dynamical Systems, California Institute of Technology, Pasadena, CA, USA0000-0003-3590-4459WilliamPooleAltos Labs, San Francisco, CA, USA0000-0002-2958-6776AnandhSwaminathanGhost Locomotion, Mountain View, CA, USA0000-0001-9935-6530VictoriaHsiaoAmyris, Emeryville, CA, USA0000-0001-9297-1522RichardM.MurrayControl and Dynamical Systems and Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA0000-0002-5785-748110.21105/joss.05057https://doi.org/10.5281/zenodo.7677726Python, Cython, Jupyter Notebookhttps://joss.theoj.org/papers/10.21105/joss.05057.pdfsynthetic biology, systems biology, deterministic and stochastic simulations, parameter inferencetag:joss.theoj.org,2005:Paper/27562021-08-11T13:49:00Z2022-01-18T12:05:15ZSurPyval: Survival Analysis with Pythonaccepted0.4.02021-06-13 09:16:35 UTC642021-08-11 13:49:00 UTC620213484DerrynKnifeIndependent researcher10.21105/joss.03484https://doi.org/10.5281/zenodo.5177222Pythonhttps://joss.theoj.org/papers/10.21105/joss.03484.pdfsurvival analysis, parameter estimation, censored data, truncated data, maximum likelihood, product spacing estimation, method of moments, mean square error, probability plotting, probability plotting parameter estimationtag:joss.theoj.org,2005:Paper/21362020-12-23T14:17:27Z2021-02-15T11:29:47Zeffectsize: Estimation of Effect Size Indices and Standardized Parametersaccepted0.4.0.0012020-10-28 16:26:42 UTC562020-12-23 14:17:27 UTC520202815MattanS.Ben-ShacharBen-Gurion University of the Negev, Israel0000-0002-4287-4801DanielLüdeckeUniversity Medical Center Hamburg-Eppendorf, Germany0000-0002-8895-3206DominiqueMakowskiNanyang Technological University, Singapore0000-0001-5375-996710.21105/joss.02815https://doi.org/10.5281/zenodo.4384509Rhttps://joss.theoj.org/papers/10.21105/joss.02815.pdfeasystats, effect size, regression, linear models, standardized coefficientstag: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 bias