ScatterDensity: Density Estimation and Visualization of 2D Scatter Plots

The user has the option to utilize the two-dimensional density estimation techniques called smoothed density published by Eilers and Goeman (2004) <doi:10.1093/bioinformatics/btg454>, and pareto density which was evaluated for univariate data by Thrun, Gehlert and Ultsch, 2020 <doi:10.1371/journal.pone.0238835>. Moreover, it provides visualizations of the density estimation in the form of two-dimensional scatter plots in which the points are color-coded based on increasing density. Colors are defined by the one-dimensional clustering technique called 1D distribution cluster algorithm (DDCAL) published by Lux and Rinderle-Ma (2023) <doi:10.1007/s00357-022-09428-6>.

Version: 0.0.3
Depends: methods, R (≥ 2.10)
Imports: Rcpp, pracma
LinkingTo: Rcpp, RcppArmadillo
Suggests: DataVisualizations, ggplot2, ggExtra, plotly, FCPS, parallelDist, secr, ClusterR
Published: 2023-07-18
Author: Michael Thrun ORCID iD [aut, cre, cph], Felix Pape [aut, rev], Luca Brinkman [aut], Quirin Stier ORCID iD [aut]
Maintainer: Michael Thrun <m.thrun at>
License: GPL-3
NeedsCompilation: yes
Citation: ScatterDensity citation info
CRAN checks: ScatterDensity results


Reference manual: ScatterDensity.pdf


Package source: ScatterDensity_0.0.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): ScatterDensity_0.0.3.tgz, r-oldrel (arm64): ScatterDensity_0.0.3.tgz, r-release (x86_64): ScatterDensity_0.0.3.tgz, r-oldrel (x86_64): ScatterDensity_0.0.3.tgz
Old sources: ScatterDensity archive

Reverse dependencies:

Reverse suggests: DataVisualizations


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