Year: 2020
Type: article
Abstract: 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.
Source: Frontiers in Psychology
Authors: Jure Demšar, Grega Repovš, Erik Štrumbelj
Institution: University of Ljubljana
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,950
Awards: Psychological and neuroscientific aspects of cognition, Digitalization as Driving Force for Sustainability of Individuals, Organizations, and Society, Advancement of computationally intensive methods for efficient modern general-purpose statistical analysis and inference, Decomposing cognition: Working memory mechanism and representations, Physiological mechanisms of neurological disorers and diseases
OpenAlex ID: W2954648409