Statistical visualisation for tidy and geospatial data in R via kernel smoothing methods in the eks package

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, which is the most widely implemented kernel smoother, this package also caters for more complex data analysis situations, such as density-based classification (supervised learning), mean shift clustering (unsupervised learning), density derivative estimation, density ridge estimation, and significance testing for density differences and for modal regions. We illustrate with experimental data how to obtain and to interpret the statistical graphical analyses for these kernel smoothing methods.