tag:joss.theoj.org,2005:/papers/reviewed_by/@tomfaulkenberryJournal of Open Source Software2022-10-09T08:09:49ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/38042022-10-09T08:09:49Z2024-03-22T10:01:23Zdatawizard: An R Package for Easy Data Preparation and Statistical Transformationsaccepted0.5.02022-08-07 16:13:48 UTC782022-10-09 08:09:49 UTC720224684IndrajeetPatilcynkra Analytics GmbH, Germany0000-0003-1995-6531DominiqueMakowskiNanyang Technological University, Singapore0000-0001-5375-9967MattanS.Ben-ShacharBen-Gurion University of the Negev, Israel0000-0002-4287-4801BrentonM.WiernikIndependent Researcher0000-0001-9560-6336EtienneBacherLuxembourg Institute of Socio-Economic Research (LISER), Luxembourg0000-0002-9271-5075DanielLüdeckeUniversity Medical Center Hamburg-Eppendorf, Germany0000-0002-8895-320610.21105/joss.04684https://doi.org/10.5281/zenodo.714Rhttps://joss.theoj.org/papers/10.21105/joss.04684.pdfeasystatstag: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/18402020-08-31T19:08:01Z2021-02-15T11:30:19Zcausal-curve: A Python Causal Inference Package to Estimate Causal Dose-Response Curvesacceptedv0.3.22020-07-08 21:51:35 UTC522020-08-31 19:08:01 UTC520202523RoniW.KobroslyDepartment of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA, Flowcast, 44 Tehama St, San Francisco, CA, USA0000-0003-0363-966210.21105/joss.02523https://doi.org/10.5281/zenodo.3996524Pythonhttps://joss.theoj.org/papers/10.21105/joss.02523.pdfcausal inference, causality, machine learningtag:joss.theoj.org,2005:Paper/14522020-04-27T16:39:17Z2021-02-15T11:31:06Zhypr: An R package for hypothesis-driven contrast codingacceptedv0.1.62020-01-24 09:28:49 UTC482020-04-27 16:39:17 UTC520202134MaximilianM.RabeUniversity of Potsdam0000-0002-2556-5644ShravanVasishthUniversity of Potsdam0000-0003-2027-1994SvenHohensteinUniversity of Potsdam0000-0002-9708-1593ReinholdKlieglUniversity of Potsdam0000-0002-0180-8488DanielJ.SchadUniversity of Potsdam, Tilburg University0000-0003-2586-682310.21105/joss.02134https://doi.org/10.5281/zenodo.3765843Rhttps://joss.theoj.org/papers/10.21105/joss.02134.pdfpsychology, linguistics, linear regression, linear model, statistics, research methods, research hypothesestag:joss.theoj.org,2005:Paper/11322019-10-30T20:19:29Z2021-02-15T11:31:53ZDscoreApp: An user-friendly web application for computing the Implicit Association Test D-scoreacceptedv1.0.02019-07-26 14:41:08 UTC422019-10-30 20:19:29 UTC420191764OttaviaM.EpifaniaDepartment of Philosophy, Sociology, Pedagogy and Applied Psychology, University of Padova (IT)0000-0001-8552-568XPasqualeAnselmiDepartment of Philosophy, Sociology, Pedagogy and Applied Psychology, University of Padova (IT)0000-0003-2982-7178EgidioRobustoDepartment of Philosophy, Sociology, Pedagogy and Applied Psychology, University of Padova (IT)10.21105/joss.01764https://doi.org/10.5281/zenodo.3523063Rhttps://joss.theoj.org/papers/10.21105/joss.01764.pdfShiny, Implicit Association Test, D-score, User-friendlytag:joss.theoj.org,2005:Paper/11542019-10-23T16:20:29Z2021-02-15T11:31:48ZthurstonianIRT: Thurstonian IRT Models in Raccepted0.9.02019-08-11 20:19:20 UTC422019-10-23 16:20:29 UTC420191662Paul-ChristianBürknerAalto University, Department of Computer Science0000-0001-5765-899510.21105/joss.01662https://doi.org/10.5281/zenodo.3515749R, C++, Stanhttps://joss.theoj.org/papers/10.21105/joss.01662.pdfitem response theory, forced-choice data, lavaantag:joss.theoj.org,2005:Paper/5722018-11-26T11:27:55Z2021-02-15T11:33:09ZPymer4: Connecting R and Python for Linear Mixed Modelingaccepted0.5.02018-07-28 01:54:36 UTC312018-11-26 11:27:55 UTC32018862EshinJollyDartmouth College10.21105/joss.00862https://doi.org/10.5281/zenodo.1523205Pythonhttps://joss.theoj.org/papers/10.21105/joss.00862.pdfstatistics, multilevel models, R, lme4