bayes4psy—An Open Source R Package for Bayesian Statistics in Psychology

Research in psychology generates complex data and often requires unique statistical analyses. These tasks are often very specific, so appropriate statistical models and methods cannot be found in accessible Bayesian tools. As a result, the use of Bayesian methods is limited to researchers and studen...

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Main Authors: Jure Demšar, Grega Repovš, Erik Štrumbelj
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-05-01
Series:Frontiers in Psychology
Subjects:
R
Online Access:https://www.frontiersin.org/article/10.3389/fpsyg.2020.00947/full
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spelling doaj-6f8b47adfa9d471f90ad61e8a6406d712020-11-25T02:09:51ZengFrontiers Media S.A.Frontiers in Psychology1664-10782020-05-011110.3389/fpsyg.2020.00947530848bayes4psy—An Open Source R Package for Bayesian Statistics in PsychologyJure Demšar0Jure Demšar1Grega Repovš2Erik Štrumbelj3Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, SloveniaMind & Brain Lab, Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, SloveniaMind & Brain Lab, Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, SloveniaFaculty of Computer and Information Science, University of Ljubljana, Ljubljana, SloveniaResearch in psychology generates complex data and often requires unique statistical analyses. These tasks are often very specific, so appropriate statistical models and methods cannot be found in accessible Bayesian tools. As a result, the use of Bayesian methods is limited to researchers and students that have the technical and statistical fundamentals that are required for probabilistic programming. Such knowledge is not part of the typical psychology curriculum and is a difficult obstacle for psychology students and researchers to overcome. The goal of the bayes4psy package is to bridge this gap and offer a collection of models and methods to be used for analysing data that arises from psychological experiments and as a teaching tool for Bayesian statistics in psychology. The package contains the Bayesian t-test and bootstrapping along with models for analysing reaction times, success rates, and tasks utilizing colors as a response. It also provides the diagnostic, analytic and visualization tools for the modern Bayesian data analysis workflow.https://www.frontiersin.org/article/10.3389/fpsyg.2020.00947/fullBayesian statisticsRpsychologyreaction timesuccess rateBayesian t-test
collection DOAJ
language English
format Article
sources DOAJ
author Jure Demšar
Jure Demšar
Grega Repovš
Erik Štrumbelj
spellingShingle Jure Demšar
Jure Demšar
Grega Repovš
Erik Štrumbelj
bayes4psy—An Open Source R Package for Bayesian Statistics in Psychology
Frontiers in Psychology
Bayesian statistics
R
psychology
reaction time
success rate
Bayesian t-test
author_facet Jure Demšar
Jure Demšar
Grega Repovš
Erik Štrumbelj
author_sort Jure Demšar
title bayes4psy—An Open Source R Package for Bayesian Statistics in Psychology
title_short bayes4psy—An Open Source R Package for Bayesian Statistics in Psychology
title_full bayes4psy—An Open Source R Package for Bayesian Statistics in Psychology
title_fullStr bayes4psy—An Open Source R Package for Bayesian Statistics in Psychology
title_full_unstemmed bayes4psy—An Open Source R Package for Bayesian Statistics in Psychology
title_sort bayes4psy—an open source r package for bayesian statistics in psychology
publisher Frontiers Media S.A.
series Frontiers in Psychology
issn 1664-1078
publishDate 2020-05-01
description Research in psychology generates complex data and often requires unique statistical analyses. These tasks are often very specific, so appropriate statistical models and methods cannot be found in accessible Bayesian tools. As a result, the use of Bayesian methods is limited to researchers and students that have the technical and statistical fundamentals that are required for probabilistic programming. Such knowledge is not part of the typical psychology curriculum and is a difficult obstacle for psychology students and researchers to overcome. The goal of the bayes4psy package is to bridge this gap and offer a collection of models and methods to be used for analysing data that arises from psychological experiments and as a teaching tool for Bayesian statistics in psychology. The package contains the Bayesian t-test and bootstrapping along with models for analysing reaction times, success rates, and tasks utilizing colors as a response. It also provides the diagnostic, analytic and visualization tools for the modern Bayesian data analysis workflow.
topic Bayesian statistics
R
psychology
reaction time
success rate
Bayesian t-test
url https://www.frontiersin.org/article/10.3389/fpsyg.2020.00947/full
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