rquest: An R package for hypothesis tests and confidence intervals for quantiles and summary measures based on quantiles

TL;DR

The rquest package in R offers straightforward tools for hypothesis testing and confidence interval estimation for quantiles and related measures, enhancing statistical analysis of data distributions.

Contribution

It introduces a user-friendly R package that implements recent distribution-free methods for quantile-based inference, including custom measures and comparisons between samples.

Findings

Provides reliable confidence intervals for quantiles with moderate sample sizes

Enables hypothesis testing for quantiles and their linear combinations

Includes tools for quantile-based inequality measures

Abstract

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.