Hilbert Similarity Index for High Dimensional Data R package hilbertSimilarity version 0.4.4 Quantifying similarity between high-dimensional single cell samples is challenging, and usually requires some simplifying hypothesis to be made. By transforming the high dimensional space into a high dimensional grid, the number of cells in each sub-space of the grid is characteristic of a given sample. Using a Hilbert curve each sample can be visualized as a simple density plot, and the distance between samples can be calculated from the distribution of cells using the Jensen-Shannon distance. Bins that correspond to significant differences between samples can identified using a simple bootstrap procedure. Software Imports: Rcpp, entropy LinkingTo: Rcpp Suggests: knitr, rmarkdown, ggplot2, dplyr, tidyr, reshape2, bodenmiller, abind Yann Abraham <yann.abraham@gmail.com> Comprehensive R Archive Network (CRAN) Yann Abraham [aut, cre], Marilisa Neri [aut], John Skilling [ctb] GPL (>= 3) 2026-01-14 application/tgz https://CRAN.R-project.org/package=hilbertSimilarity doi:10.32614/CRAN.package.hilbertSimilarity