False discovery rates (FDR) are an essential component of statistical inference, representing the propensity for an observed result to be mistaken. FDR estimates should accompany observed results to help the user contextualize the relevance and potential impact of findings. This paper introduces a new user-friendly R package for computing FDRs and adjusting p-values for FDR control. These tools respect the critical difference between the adjusted p-value and the estimated FDR for a particular finding, which are sometimes numerically identical but are often confused in practice. Newly augmented methods for estimating the null proportion of findings - an important part of the FDR estimation procedure - are proposed and evaluated. The package is broad, encompassing a variety of methods for FDR estimation and FDR control, and includes plotting functions for easy display of results. Through extensive illustrations, we strongly encourage wider reporting of false discovery rates for observed findings.

翻译：虚假发现率(FDR)是统计推论的一个基本组成部分,代表了观察到的结果误差的倾向。FDR估计数应伴随观察结果,以帮助用户了解调查结果的相关性和潜在影响。本文件介绍了一个新的方便用户的R包,用于计算FDR和调整控制FDR的p价值。这些工具尊重调整后的p价值与特定调查结果的估计FDR之间的关键差异,这些差异有时在数字上相同,但在实践中往往被混淆。提出了新的扩大的估算结果的无效比例的方法,这是FDR估计程序的一个重要部分。这套方法范围很广,包括各种FDR估计和控制FDR的方法,包括易于显示结果的绘图功能。我们通过广泛的说明,大力鼓励更广泛地报告观察到的调查结果的虚假发现率。