Package: DataVisualizations
Type: Package
Title: Visualizations of High-Dimensional Data
Version: 1.5.0
Date: 2026-09-24
Authors@R: c(person("Michael", "Thrun", email= "m.thrun@gmx.net",role=c("aut","cre","cph"), comment = c(ORCID = "0000-0001-9542-5543")),person("Felix", "Pape",role=c("aut","rev")),person("Onno", "Hansen-Goos",role=c("ctr","ctb")),person("Quirin", "Stier",role=c("ctb","rev"), comment = c(ORCID = "0000-0002-7896-4737")),person("Hamza", "Tayyab",role=c("ctr","ctb")),person("Luca", "Brinkmann",role=c("ctr","ctb")),person("Dirk", "Eddelbuettel",role=c("ctb")),person("Winston", "Chang",role=c("ctb")),person("Craig", "Varrichio",role=c("ctb")),person("Alfred", "Ultsch",role=c("dtc","ctb","ctr")))
Maintainer: Michael Thrun <m.thrun@gmx.net>
Description: Gives access to data visualisation methods that are
        relevant from the data scientist's point of view. The flagship
        idea of 'DataVisualizations' is the mirrored density plot
        (MD-plot) for either classified or non-classified multivariate
        data published in Thrun, M.C. et al.: "Analyzing the Fine
        Structure of Distributions" (2020), PLoS ONE,
        <DOI:10.1371/journal.pone.0238835>. The MD-plot outperforms the
        box-and-whisker diagram (box plot), violin plot and bean plot
        and geom_violin plot of ggplot2. Furthermore, a collection of
        various visualization methods for univariate data is provided.
        In the case of exploratory data analysis, 'DataVisualizations'
        makes it possible to inspect the distribution of each feature
        of a dataset visually through a combination of four methods.
        One of these methods is the Pareto density estimation (PDE) of
        the probability density function (pdf). Additionally,
        visualizations of the distribution of distances using PDE, the
        scatter-density plot using PDE for two variables as well as the
        Shepard density plot and the Bland-Altman plot are presented
        here. Pertaining to classified high-dimensional data, a number
        of visualizations are described, such as f.ex. the heat map and
        silhouette plot. A political map of the world or Germany can be
        visualized with the additional information defined by a
        classification of countries or regions. By extending the
        political map further, an uncomplicated function for a
        Choropleth map can be used which is useful for measurements
        across a geographic area. For categorical features, the Pie
        charts, slope charts and fan plots, improved by the ABC
        analysis, become usable. More detailed explanations are found
        in the book by Thrun, M.C.: "Projection-Based Clustering
        through Self-Organization and Swarm Intelligence" (2018)
        <DOI:10.1007/978-3-658-20540-9>.
License: GPL-3
Imports: Rcpp (>= 0.12.12), RcppParallel (>= 5.1.4), ggplot2, sp,
        pracma, reshape2, grid
Suggests: plyr, MBA, ggmap, plotrix, rworldmap, rgl, ABCanalysis,
        parallelDist, knitr (>= 1.12), rmarkdown (>= 0.9), vioplot,
        ggExtra, plotly, htmlwidgets, diptest, moments, signal,
        ggrepel, MASS, ROCit, ScatterDensity (>= 0.0.3), colorspace,
        viridis, gridExtra
LinkingTo: Rcpp, RcppParallel
Depends: R (>= 3.5)
SystemRequirements: GNU make, C++17
LazyLoad: yes
LazyData: TRUE
LazyDataCompression: xz
URL: https://www.deepbionics.org/
VignetteBuilder: knitr
BugReports: https://github.com/Mthrun/DataVisualizations/issues
NeedsCompilation: yes
Config/pak/sysreqs: cmake make libicu-dev
Repository: https://mthrun.r-universe.dev
Date/Publication: 2026-09-24 12:41:54 UTC
RemoteUrl: https://github.com/mthrun/datavisualizations
RemoteRef: HEAD
RemoteSha: 7aa4eb4a2e198f414c94611025342cba04f29263
Packaged: 2026-09-24 15:22:07 UTC; root
Author: Michael Thrun [aut, cre, cph] (ORCID:
    <https://orcid.org/0000-0001-9542-5543>),
  Felix Pape [aut, rev],
  Onno Hansen-Goos [ctr, ctb],
  Quirin Stier [ctb, rev] (ORCID:
    <https://orcid.org/0000-0002-7896-4737>),
  Hamza Tayyab [ctr, ctb],
  Luca Brinkmann [ctr, ctb],
  Dirk Eddelbuettel [ctb],
  Winston Chang [ctb],
  Craig Varrichio [ctb],
  Alfred Ultsch [dtc, ctb, ctr]
Built: R 4.6.0; wasm32-unknown-emscripten; 2026-09-24 15:25:47 UTC; unix
