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.

翻译：贝叶斯统计学是当代应用科学不可或缺的组成部分。bayesics 提供了一个语法与输出格式统一的单一框架，用于执行从单样本与双样本推断到一般中介分析的最常用统计流程。该框架尽可能采用闭式解，并在闭式解不可行时自动选择精确推断所需的后验样本数量，从而大幅降低用户需熟悉抽样算法的要求。bayesics 聚焦于提供关键推断量：点估计、可信区间、方向概率、实际等价区域（ROPE）以及适用时的贝叶斯因子。尽管 bayesics 无需算法评估，但模型评估仍至关重要；为此，该框架提供了参数推断的诊断图（包括贝叶斯 p 值）。最后，bayesics 支持对基于其他R包实现的模型进行扩展，并针对中介分析提供了对现有实现的修正。