Sample quantiles, such as the median, are often better suited than the sample mean for summarising location characteristics of a data set. Similarly, linear combinations of sample quantiles and ratios of such linear combinations, e.g. the interquartile range and quantile-based skewness measures, are often used to quantify characteristics such as spread and skew. While often reported, it is uncommon to accompany quantile estimates with confidence intervals or standard errors. The rquest package provides a simple way to conduct hypothesis tests and derive confidence intervals for quantiles, linear combinations of quantiles, ratios of dependent linear combinations (e.g., Bowley's measure of skewness) and differences and ratios of all of the above for comparisons between independent samples. Many commonly used measures based on quantiles are included, although it is also very simple for users to define their own. Additionally, quantile-based measures of inequality are also considered. The methods are based on recent research showing that reliable distribution-free confidence intervals can be obtained, even for moderate sample sizes. Several examples are provided herein.

翻译：样本分位数（如中位数）在概括数据集的位置特征时通常比样本均值更为适用。类似地，样本分位数的线性组合及其比值（例如四分位距和基于分位数的偏度度量）常被用于量化数据的离散程度与偏斜特征。尽管分位数估计被广泛报告，但为其配备置信区间或标准误的做法并不常见。rquest包提供了一种简便方法，可用于对以下对象进行假设检验并推导置信区间：分位数、分位数线性组合、相依线性组合的比值（如鲍利偏度度量），以及所有上述指标在独立样本间比较时的差异与比值。该包包含许多常用的基于分位数的度量指标，同时也允许用户轻松自定义指标。此外，该包还考虑了基于分位数的不平等性度量方法。这些方法基于最新研究成果，表明即使在中等样本量下也能获得可靠的无分布置信区间。本文提供了若干应用示例。