tag:joss.theoj.org,2005:/papers/tagged/Bayesian%20statisticsJournal of Open Source Software2023-10-27T15:27:51ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/44482023-10-27T15:27:51Z2023-10-28T00:00:39Zbayes-toolbox: A Python package for Bayesian statisticsacceptedv0.1.02023-04-22 21:43:52 UTC902023-10-27 15:27:51 UTC820235526HyosubE.KimSchool of Kinesiology, The University of British Columbia, Canada, Department of Physical Therapy, University of Delaware, United States0000-0003-0109-593X10.21105/joss.05526https://doi.org/10.5281/zenodo.7849408Pythonhttps://joss.theoj.org/papers/10.21105/joss.05526.pdfBayesian statistics, psychology, neurosciencetag:joss.theoj.org,2005:Paper/42722023-09-22T03:44:04Z2023-09-28T19:34:21ZPreliZ: A tool-box for prior elicitationacceptedv0.2.02023-03-01 19:24:21 UTC892023-09-22 03:44:04 UTC820235499AlejandroIcazattiIMASL-CONICET. Universidad Nacional de San Luis. San Luis, Argentina0000-0003-1491-7330OriolAbril-PlaIndependent Researcher, Spain, Department of Computer Science, University of Helsinki, Finland0000-0002-1847-9481ArtoKlamiDepartment of Computer Science, University of Helsinki, Finland0000-0002-7950-1355OsvaldoA.MartinIMASL-CONICET. Universidad Nacional de San Luis. San Luis, Argentina0000-0001-7419-897810.21105/joss.05499https://doi.org/10.5281/zenodo.8368516Python, Jupyter Notebookhttps://joss.theoj.org/papers/10.21105/joss.05499.pdfBayesian statistics, prior elicitationtag:joss.theoj.org,2005:Paper/42902023-06-21T14:30:22Z2023-06-22T00:01:26Zminorbsem: An R package for structural equation models that account for the influence of minor factorsacceptedv0.1.02023-03-08 16:54:31 UTC862023-06-21 14:30:22 UTC820235292JamesOhiseiUanhoroDepartment of Educational Psychology, University of North Texas, USA0000-0002-4843-927X10.21105/joss.05292https://doi.org/10.5281/zenodo.8057759R, Stan, C++https://joss.theoj.org/papers/10.21105/joss.05292.pdfBayesian-statistics, latent-variable-models, structural-equation-modeling, psychometrics, meta-analytic-SEMtag:joss.theoj.org,2005:Paper/43142023-06-21T01:25:57Z2023-06-24T19:34:40ZPyVBMC: Efficient Bayesian inference in Pythonacceptedv1.0.02023-03-16 14:20:45 UTC862023-06-21 01:25:57 UTC820235428BobbyHugginsUniversity of Helsinki0009-0006-3475-5964ChengkunLiUniversity of Helsinki0000-0001-5848-910XMarlonTobabenUniversity of Helsinki0000-0002-9778-0853MikkoJ.AarnosUniversity of HelsinkiLuigiAcerbiUniversity of Helsinki0000-0001-7471-733610.21105/joss.05428https://doi.org/10.5281/zenodo.7966315Pythonhttps://joss.theoj.org/papers/10.21105/joss.05428.pdfBayesian statistics, Bayesian inference, Probabilistic programming, Model evidence, Machine learning, Simulator-based inferencetag:joss.theoj.org,2005:Paper/41442023-04-20T14:54:21Z2023-04-21T00:03:22ZRxInfer: A Julia package for reactive real-time Bayesian inferenceacceptedv2.4.12023-01-03 13:07:23 UTC842023-04-20 14:54:21 UTC820235161DmitryBagaevTechnical University of Eindhoven0000-0001-9655-7986AlbertPodusenkoTechnical University of Eindhoven0000-0003-0515-0465Bertde VriesTechnical University of Eindhoven0000-0003-0839-174X10.21105/joss.05161https://doi.org/10.5281/zenodo.7774921Jupyter Notebook, Juliahttps://joss.theoj.org/papers/10.21105/joss.05161.pdfstatistics, Bayesian inference, variational optimization, message passingtag:joss.theoj.org,2005:Paper/36252022-07-26T20:35:08Z2022-07-27T00:00:31ZGPJax: A Gaussian Process Framework in JAXacceptedv0.4.52022-05-24 16:24:45 UTC752022-07-26 20:35:08 UTC720224455ThomasPinderDepartment of Mathematics and Statistics, Lancaster University, United KingdomDanielDoddSTOR-i Centre for Doctoral Training, Lancaster University, United Kingdom10.21105/joss.04455https://doi.org/10.5281/zenodo.6882220Pythonhttps://joss.theoj.org/papers/10.21105/joss.04455.pdfGaussian processes, JAX, Bayesian inference, Machine learning, Statisticstag:joss.theoj.org,2005:Paper/15802021-12-01T22:44:54Z2021-12-06T10:17:14ZJASP for Audit: Bayesian Tools for the Auditing Practiceacceptedv1.0.02020-03-09 11:53:34 UTC682021-12-01 22:44:54 UTC620212733KoenDerksNyenrode Business University, the NetherlandsJacquesde SwartNyenrode Business University, the Netherlands, PwC Advisory, the NetherlandsEric-JanWagenmakersUniversity of Amsterdam, the NetherlandsJanWillePwC Advisory, the NetherlandsRuudWetzelsNyenrode Business University, the Netherlands, PwC Advisory, the Netherlands10.21105/joss.02733https://doi.org/10.5281/zenodo.5690283R, QMLhttps://joss.theoj.org/papers/10.21105/joss.02733.pdfaudit, Bayesian statistics, financial auditing, JfAtag:joss.theoj.org,2005:Paper/25452021-05-25T11:21:00Z2021-05-26T00:02:39ZVisualizations with statistical details: The 'ggstatsplot' approachaccepted0.7.12021-03-30 08:46:28 UTC612021-05-25 11:21:00 UTC620213167IndrajeetPatilCenter for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany0000-0003-1995-653110.21105/joss.03167https://doi.org/10.5281/zenodo.4681705Rhttps://joss.theoj.org/papers/10.21105/joss.03167.pdfparametric statistics, nonparametric statistics, robust statistics, Bayesian statistics, ggplot2, ggplot2-extensiontag:joss.theoj.org,2005:Paper/24872021-05-20T16:40:26Z2021-05-21T00:00:34ZstatsExpressions: R Package for Tidy Dataframes and Expressions with Statistical Detailsaccepted1.0.02021-03-12 20:09:05 UTC612021-05-20 16:40:26 UTC620213236IndrajeetPatilCenter for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany0000-0003-1995-653110.21105/joss.03236https://doi.org/10.5281/zenodo.4773886Rhttps://joss.theoj.org/papers/10.21105/joss.03236.pdfparametric statistics, nonparametric statistics, robust statistics, Bayesian statistics, tidytag:joss.theoj.org,2005:Paper/23702021-05-13T13:58:56Z2021-05-14T00:01:48ZPyGModels: A Python package for exploring Probabilistic Graphical Models with Graph Theoretical Structuresacceptedv0.1.0-beta2021-02-03 01:09:56 UTC612021-05-13 13:58:56 UTC620213115DoğuKaanEraslanÉcole Pratique des Hautes Études, Université PSL, Paris, France0000-0002-1552-893810.21105/joss.03115https://doi.org/10.5281/zenodo.4751740Pythonhttps://joss.theoj.org/papers/10.21105/joss.03115.pdfprobabilistic graphical models, Bayesian statistics, Probabilistic inference