ChauBoxplot and AdaptiveBoxplot: Two R packages for boxplot-based outlier detection

TL;DR

This paper introduces two R packages, ChauBoxplot and AdaptiveBoxplot, which improve outlier detection in boxplots by providing more robust and statistically sound methods, demonstrated through simulations and real data analysis.

Contribution

The paper presents novel R packages that enhance boxplot-based outlier detection with more robust, statistically principled methods, addressing limitations of classic Tukey's boxplot.

Findings

ChauBoxplot and AdaptiveBoxplot outperform classic boxplot in outlier detection.

Simulation studies show improved accuracy and reliability.

Real-world data analysis demonstrates practical usefulness.

Abstract

Tukey's boxplot is widely used for outlier detection; however, its classic fixed-fence rule tends to flag an excessive number of outliers as the sample size grows. To address this, we introduce two new R packages, ChauBoxplot and AdaptiveBoxplot, which implement more robust and statistically principled outlier detection methods. We illustrate their advantages and practical implications through comprehensive simulation studies and a real-world analysis of provincial university admission rates from China's National College Entrance Examination. Based on these findings, we provide practical guidance to help practitioners select appropriate boxplot methods, achieving a balance between interpretability and statistical reliability.