bayestestR: Describing Effects and their Uncertainty, Existence and Significance within the Bayesian Framework
By Dominique Makowski, Mattan S. Ben-Shachar, Daniel Lüdecke. Journal of Open Source Software, 2019
1,222 citations recorded by the source corpus.
The Bayesian framework for statistics is quickly gaining in popularity among scientists, for reasons such as reliability and accuracy (particularly in noisy data and small samples), the possibility of incorporating prior knowledge into the analysis, and the intuitive interpretation of results (Andrews & Baguley, 2013; Etz & Vandekerckhove, 2016; Kruschke, 2010; Kruschke, Aguinis, & Joo, 2012; Wagenmakers et al., 2017). Adopting the Bayesian framework is more of a shift in the paradigm than a change in the methodology; all the common statistical procedures (t-tests, correlations, ANOVAs, regressions, etc.) can also be achieved within the Bayesian framework. One of the core difference is that in the frequentist view, the effects are fixed (but unknown) and data are random. On the other hand, instead of having single estimates of the “true effect”, the Bayesian inference process computes the probability of different effects given the observed data, resulting in a distribution of possible values for the parameters, called the posterior distribution. The bayestestR package provides tools to describe these posterior distributions.
Subfields: Artificial Intelligence, Information Systems, Information Systems and Management
View Paper: https://www.semanticscholar.org/paper/bfa693e9b6f583983cd900d1ddfc0c779aa342a3
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Citations in the corpus
- performance: An R Package for Assessment, Comparison and Testing of Statistical Models (2021, methodology): “…of the easystats ecosystem, which is a collaborative project focused on facilitating simple and intuitive usage of R for statistical analysis (Ben-Shachar et al., 2020; Lüdecke, Ben-Shachar, Patil, Waggoner, et al., 2020; Lüdecke, Ben-Shachar, Patil, & Makowski, 2020; Makowski et al., 2019, 2020).”
- Visualizations with statistical details: The 'ggstatsplot' approach (2021, methodology): “Internally, data cleaning is carried out using the tidyverse (Wickham et al., 2019), while statistical analysis is carried out via the statsExpressions (Patil, 2021) and easysta ts (Ben-Shachar et al., 2020; Lüdecke et al., 2019, 2020, 2021; Makowski et al., 2019, 2020) packages.”