Demšar Jure, Repovš Grega, Štrumbelj Erik
Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
Mind & Brain Lab, Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia.
Front Psychol. 2020 May 12;11:947. doi: 10.3389/fpsyg.2020.00947. eCollection 2020.
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 -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.
心理学研究产生复杂的数据，并且常常需要独特的统计分析。这些任务通常非常具体，因此在现有的贝叶斯工具中找不到合适的统计模型和方法。结果，贝叶斯方法的使用仅限于具备概率编程所需技术和统计基础的研究人员和学生。此类知识并非典型心理学课程的一部分，是心理学学生和研究人员难以克服的障碍。bayes4psy软件包的目标是弥合这一差距，提供一组模型和方法，用于分析心理实验产生的数据，并作为心理学中贝叶斯统计的教学工具。该软件包包含贝叶斯检验和自展法，以及用于分析反应时间、成功率和以颜色作为反应的任务的模型。它还为现代贝叶斯数据分析工作流程提供诊断、分析和可视化工具。
