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  "Title": "Density Estimation and Visualization of 2D Scatter Plots",
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  "Date": "2025-05-15",
  "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(\"Luca\",\"Brinkman\",role=c(\"aut\")),person(\"Quirin\", \"Stier\",role=c(\"aut\"), comment = c(ORCID = \"0000-0002-7896-4737\")))",
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  "Description": "The user has the option to utilize the two-dimensional\ndensity estimation techniques called smoothed density published\nby Eilers and Goeman (2004)\n<doi:10.1093/bioinformatics/btg454>, and pareto density which\nwas evaluated for univariate data by Thrun, Gehlert and Ultsch,\n2020 <doi:10.1371/journal.pone.0238835>. Moreover, it provides\nvisualizations of the density estimation in the form of\ntwo-dimensional scatter plots in which the points are\ncolor-coded based on increasing density. Colors are defined by\nthe one-dimensional clustering technique called 1D distribution\ncluster algorithm (DDCAL) published by Lux and Rinderle-Ma\n(2023) <doi:10.1007/s00357-022-09428-6>.",
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  "License": "GPL-3",
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  "URL": "https://www.deepbionics.org/",
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  "Repository": "https://mthrun.r-universe.dev",
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        "ScatterDensity"
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        "Iterative Clustering",
        "Low-Variance Clustering"
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