bayesics: Core Statistical Methods via Bayesian Inference in R

Daniel K. Sewell; Alan T. Arakkal

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arXiv · 2602.15150

bayesics: Core Statistical Methods via Bayesian Inference in R

Daniel K. Sewell·Alan T. Arakkal

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Abstract

Bayesian statistics is an integral part of contemporary applied science. bayesics provides a single framework, unified in syntax and output, for performing the most commonly used statistical procedures, ranging from one- and two-sample inference to general mediation analysis. bayesics leans hard away from the requirement that users be familiar with sampling algorithms by using closed-form solutions whenever possible, and automatically selecting the number of posterior samples required for accurate inference when such solutions are not possible. bayesics} focuses on providing key inferential quantities: point estimates, credible intervals, probability of direction, region of practical equivalance (ROPE), and, when applicable, Bayes factors. While algorithmic assessment is not required in bayesics, model assessment is still critical; towards that, bayesics provides diagnostic plots for parametric inference, including Bayesian p-values. Finally, bayesics provides extensions to models implemented in alternative R packages and, in the case of mediation analysis, correction to existing implementations.

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Daniel K. Sewell, Alan T. Arakkal. 2026-02-16. bayesics: Core Statistical Methods via Bayesian Inference in R. https://arxiv.org/abs/2602.15150

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