Kernel smoothers are essential tools for data analysis due to their ability to convey complex statistical information with concise graphical visualisations. Their inclusion in the base distribution and in the many user-contributed add-on packages of the R statistical analysis environment caters well to many practitioners. Though there remain some important gaps for specialised data types, most notably for tibbles (tidy data) within the tidyverse, and for simple features (geospatial data) within geospatial analysis. The proposed eks package fills in these gaps. In addition to kernel density estimation, this package also caters for more complex data analysis situations, such as density derivative estimation, density-based classification (supervised learning) and mean shift clustering (unsupervised learning). We illustrate with experimental data how to obtain and to interpret the statistical visualisations for these kernel smoothing methods.

翻译：核平滑器是数据分析的重要工具，因其能够通过简洁的图形可视化传递复杂的统计信息。这些工具已被纳入R统计计算环境的基础发行版及众多用户贡献的扩展包中，为众多实践者提供了良好支持。然而，在特定数据类型方面仍存在重要空白，其中最显著的是tidyverse中的tibble（整洁数据）以及地理空间分析中的simple features（地理空间数据）。所提出的eks包填补了这些空白。除核密度估计外，该包还支持更复杂的数据分析场景，例如密度导数估计、基于密度的分类（有监督学习）以及均值漂移聚类（无监督学习）。我们通过实验数据演示了如何获取并解读这些核平滑方法的统计可视化结果。