Here we introduce graphPAF, a comprehensive R package designed for estimation, inference and display of population attributable fractions (PAF) and impact fractions. In addition to allowing inference for standard population attributable fractions and impact fractions, graphPAF facilitates display of attributable fractions over multiple risk factors using fan-plots and nomograms, calculations of attributable fractions for continuous exposures, inference for attributable fractions appropriate for specific risk factor \(\rightarrow \) mediator \(\rightarrow \) outcome pathways (pathway-specific attributable fractions) and Bayesian network-based calculations and inference for joint, sequential and average population attributable fractions in multi-risk factor scenarios. This article can be used as both a guide to the theory of attributable fraction estimation and a tutorial regarding how to use graphPAF in practical examples.

在这里，我们介绍 graphPAF，这是一个综合性的 R 包，旨在估计、推断和显示总体归因分数 (PAF) 和影响分数。除了允许推断标准人群归因分数和影响分数之外，graphPAF 还可以使用扇形图和列线图显示多个风险因素的归因分数、计算连续暴露的归因分数、推断适合特定风险因素的归因分数\( \rightarrow \)中介\(\rightarrow \)结果路径（路径特定的归因分数）和基于贝叶斯网络的计算和多风险因素场景中联合、连续和平均群体归因分数的推断。本文既可以用作归因分数估计理论的指南，也可以用作有关如何在实际示例中使用 graphPAF 的教程。