Preprint
bayesics: Core Statistical Methods via Bayesian Inference in R
ArXiv.org
Cornell University
02/16/2026
DOI: 10.48550/arxiv.2602.15150
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.
Details
- Title: Subtitle
- bayesics: Core Statistical Methods via Bayesian Inference in R
- Creators
- Daniel K SewellAlan T Arakkal
- Resource Type
- Preprint
- Publication Details
- ArXiv.org
- DOI
- 10.48550/arxiv.2602.15150
- ISSN
- 2331-8422
- Publisher
- Cornell University; Ithaca, New York
- Language
- English
- Date posted
- 02/16/2026
- Academic Unit
- Biostatistics; Internal Medicine
- Record Identifier
- 9985141898502771
Metrics
1 Record Views