tag:joss.theoj.org,2005:/papers/in/StanJournal of Open Source Software2023-11-06T16:28:16ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/46062023-11-06T16:28:16Z2023-11-07T00:00:17Zmeasr: Bayesian psychometric measurement using Stanacceptedv0.3.12023-07-14 20:06:08 UTC912023-11-06 16:28:16 UTC820235742W.JakeThompsonAccessible, Teaching, Learning, and Assessment Systems, University of Kansas0000-0001-7339-030010.21105/joss.05742https://doi.org/10.5281/zenodo.10058365R, Stan, C++https://joss.theoj.org/papers/10.21105/joss.05742.pdfbayesian-modeling, educational-assessment, psychometrics, diagnostic-modelingtag:joss.theoj.org,2005:Paper/45242023-09-26T10:32:27Z2023-10-24T13:30:07ZBernadette: Bayesian Inference and Model Selection for Stochastic Epidemics in Racceptedv.1.1.42023-06-04 21:22:05 UTC892023-09-26 10:32:27 UTC820235612LamprosBouranisDepartment of Statistics, Athens University of Economics and Business, Athens, Greece0000-0002-1291-219210.21105/joss.05612https://doi.org/10.5281/zenodo.8376673R, C++, Stanhttps://joss.theoj.org/papers/10.21105/joss.05612.pdfBayesian, Epidemicstag: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/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/33342022-09-12T18:22:12Z2022-10-11T07:59:08ZBCEA: An R Package for Cost-Effectiveness Analysisacceptedv2.4.12022-02-11 12:33:38 UTC772022-09-12 18:22:12 UTC720224206NathanGreenDepartment of Statistical Science, UCL, Torrington Place, UK.0000-0003-2745-1736AnnaHeathDepartment of Statistical Science, UCL, Torrington Place, UK., Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada., Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.0000-0002-7263-4251GianlucaBaioDepartment of Statistical Science, UCL, Torrington Place, UK.0000-0003-4314-257010.21105/joss.04206https://doi.org/10.5281/zenodo.7040450R, Stanhttps://joss.theoj.org/papers/10.21105/joss.04206.pdfHTA, health economics, cost-effectivenesstag:joss.theoj.org,2005:Paper/34082022-06-15T09:12:43Z2022-10-17T07:02:04Zrbmi: A R package for standard and reference-based multiple imputation methodsaccepted1.1.02022-03-02 09:12:01 UTC742022-06-15 09:12:43 UTC720224251CraigGower-PageData and Statistical Sciences, Pharma Development, Roche, Welwyn Garden City, UKAlessandroNociData and Statistical Sciences, Pharma Development, Roche, Basel, SwitzerlandMarcelWolbersData and Statistical Sciences, Pharma Development, Roche, Basel, Switzerland10.21105/joss.04251https://doi.org/10.5281/zenodo.6632154R, C++, Stan, SAShttps://joss.theoj.org/papers/10.21105/joss.04251.pdfBiostatistics, Clinical Trials, Estimands, Missing Data, Multiple Imputation, Reference-based Methodstag:joss.theoj.org,2005:Paper/25242022-03-21T18:04:18Z2022-03-22T00:00:30ZfishStan: Hierarchical Bayesian models for fisheriesacceptedv2.02021-03-16 16:45:40 UTC712022-03-21 18:04:18 UTC720223444RichardA.EricksonU.S. Geological Survey, Upper Midwest Environmental Sciences Center, La Crosse, WI, United States of America0000-0003-4649-482XDanielS.StichState University of New York at Oneonta, Oneonta, NY, United States of America0000-0002-8946-1115JillianL.HebertU.S. Geological Survey, Upper Midwest Environmental Sciences Center, La Crosse, WI, United States of America0000-0003-4893-828710.21105/joss.03444https://doi.org/10.5066/P9TT3ILOR, C++, Stanhttps://joss.theoj.org/papers/10.21105/joss.03444.pdfpopulation ecology, hierarchical Bayesian models, fisheries assessmenttag:joss.theoj.org,2005:Paper/28042021-08-08T13:54:05Z2021-08-09T00:02:17Zbmgarch: An R-Package for Bayesian Multivariate GARCH modelsacceptedv1.0.12021-06-19 03:59:32 UTC642021-08-08 13:54:05 UTC620213452PhilippeRastUniversity of California, Davis0000-0003-3630-6629StephenR.MartinComscore, Inc.0000-0001-8085-239010.21105/joss.03452https://doi.org/10.5281/zenodo.5168675R, Emacs Lisp, C++, Stanhttps://joss.theoj.org/papers/10.21105/joss.03452.pdfstan, GARCHtag:joss.theoj.org,2005:Paper/25632021-07-17T16:36:35Z2021-07-18T00:02:33Zpopsynth: A generic astrophysical population synthesis frameworkacceptedv1.0.12021-04-09 14:29:52 UTC632021-07-17 16:36:35 UTC620213257J.MichaelBurgessMax Planck Institute for Extraterrestrial Physics, Giessenbachstrasse, 85748 Garching, Germany0000-0003-3345-9515FrancescaCapelTechnical University of Munich, Boltzmannstrasse 2, 85748 Garching, Germany0000-0002-1153-213910.21105/joss.03257https://doi.org/10.5281/zenodo.5109590Python, Stanhttps://joss.theoj.org/papers/10.21105/joss.03257.pdfastronomy, population synthesis, cosmologytag:joss.theoj.org,2005:Paper/25772021-04-21T06:09:36Z2021-04-22T00:02:20ZThe stantargets R package: a workflow framework for efficient reproducible Stan-powered Bayesian data analysis pipelinesaccepted0.0.0.90032021-04-15 16:07:03 UTC602021-04-21 06:09:36 UTC620213193WilliamMichaelLandauEli Lilly and Company0000-0003-1878-325310.21105/joss.03193https://doi.org/10.5281/zenodo.4706113R, Stanhttps://joss.theoj.org/papers/10.21105/joss.03193.pdfreproducibility, high-performance computing, pipeline, workflow, Make, Bayesian