bayesics: Core Statistical Methods via Bayesian Inference in R

TL;DR

bayesics is an R package that simplifies Bayesian statistical analysis by providing a unified, user-friendly interface with automatic sampling decisions, focusing on key inferential outputs and model diagnostics.

Contribution

It introduces a comprehensive R framework that automates Bayesian inference procedures, reducing the need for sampling expertise and extending existing models with corrections.

Findings

Supports a wide range of statistical procedures in a unified syntax

Automatically determines the number of posterior samples needed for accurate inference

Provides diagnostic tools and extensions for model assessment and correction

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.