通过eks包中的核平滑方法在R中实现整洁数据与地理空间数据的统计可视化
Statistical visualisation for tidy and geospatial data in R via kernel smoothing methods in the eks package
AI总结：
针对R环境中tidy与地理空间数据的核平滑分析空白，提出eks包，支持核密度估计、密度导数估计、基于密度的分类和均值漂移聚类，并通过实验展示其统计可视化的获取与解读方法。
摘要：
核平滑器是数据分析的必备工具，因其能以简洁的图形可视化传达复杂的统计信息。R统计分析环境的基础发行版及众多用户贡献的附加包中均包含核平滑器，很好地满足了许多从业者的需求。不过，针对特殊数据仍存在一些重要空白，最突出的是整洁（tidy）数据和地理空间（geospatial）数据。本文提出的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（监督学习）和均值漂移（mean shift）聚类（无监督学习）。我们通过实验数据说明了如何获取并解读这些核平滑方法的统计可视化结果。
英文摘要：
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, most notably for tidy and geospatial data. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
