{ "_id": "6a251d994b233be19838f07c", "Package": "Kernelheaping", "Type": "Package", "Title": "Kernel Density Estimation for Heaped and Rounded Data", "Authors@R": "c(person(\"Marcus\", \"Gross\",\nrole = c(\"aut\", \"cre\"), email = \"marcus.gross@inwt-statistics.de\"),\nperson(\"Lukas\", \"Fuchs\", role = \"aut\",\nemail = \"f.lukas@live.de\"),\nperson(\"Kerstin\", \"Erfurth\", role = \"ctb\",\nemail = \"keri.erfurth@gmail.com\"))", "Version": "2.3.0", "Date": "2022-01-26", "Author": "Marcus Gross [aut, cre], Lukas Fuchs [aut], Kerstin Erfurth\n[ctb]", "Maintainer": "Marcus Gross ", "Description": "In self-reported or anonymised data the user often\nencounters heaped data, i.e. data which are rounded (to a\npossibly different degree of coarseness). While this is mostly\na minor problem in parametric density estimation the bias can\nbe very large for non-parametric methods such as kernel density\nestimation. This package implements a partly Bayesian algorithm\ntreating the true unknown values as additional parameters and\nestimates the rounding parameters to give a corrected kernel\ndensity estimate. It supports various standard bandwidth\nselection methods. Varying rounding probabilities (depending on\nthe true value) and asymmetric rounding is estimable as well:\nGross, M. and Rendtel, U. (2016) ().\nAdditionally, bivariate non-parametric density estimation for\nrounded data, Gross, M. et al. (2016)\n(), as well as data aggregated on areas\nis supported.", "License": "GPL-2 | GPL-3", "RoxygenNote": "7.1.0", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-07 07:14:26 UTC", "User": "root" }, "Config/pak/sysreqs": "make pari-gp", "Repository": "https://mgross.r-universe.dev", "Date/Publication": "2022-01-26 17:42:52 UTC", "RemoteUrl": "https://github.com/cran/Kernelheaping", "RemoteRef": "HEAD", "RemoteSha": "d916c0c7286124af4c9f4c5094ede84a7e3f9b2a", "MD5sum": "5c1e6efe7e3fae37969f292a058059f1", "_user": "mgross", "_type": "src", "_file": "Kernelheaping_2.3.0.tar.gz", "_fileid": "f8ffacc18bca39d31c6a5afc0ba26448e3941603184c7f3fd48d2d8289f3aadf", "_filesize": 152075, "_sha256": "f8ffacc18bca39d31c6a5afc0ba26448e3941603184c7f3fd48d2d8289f3aadf", "_created": "2026-06-07T07:14:26.000Z", "_published": "2026-06-07T07:28:25.725Z", "_distro": "noble", "_jobs": [ { "job": 79940104831, "time": 175, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": 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"role": "Imports" }, { "package": "mvtnorm", "role": "Imports" } ], "_owner": "cran", "_selfowned": true, "_usedby": 0, "_updates": [], "_tags": [], "_stars": 0, "_contributors": [ { "user": "mgross", "count": 6, "uuid": 5138071 } ], "_userbio": { "uuid": 5138071, "type": "user", "name": "Marcus Groß" }, "_downloads": { "count": 275, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/Kernelheaping" }, "_searchresults": 11, "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/Kernelheaping.html", "manual.pdf" ], "_realowner": "mgross", "_cranurl": false, "_releases": [ { "version": "0.2", "date": "2015-01-27" }, { "version": "0.5", "date": "2015-04-27" }, { "version": "0.9", "date": "2015-06-13" }, { "version": "1.0", "date": "2015-07-31" }, { "version": "1.2", "date": "2015-12-07" }, { "version": "1.5", "date": "2016-03-21" }, { "version": "1.6", "date": "2016-04-15" }, { 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"dclass", "title": "Kernel density estimation for classified data", "topics": [ "dclass" ] }, { "page": "dheaping", "title": "Kernel density estimation for heaped data", "topics": [ "dheaping" ] }, { "page": "dshape3dProp", "title": "3d Kernel density estimation for data classified in polygons or shapes", "topics": [ "dshape3dProp" ] }, { "page": "dshapebivr", "title": "Bivariate Kernel density estimation for data classified in polygons or shapes", "topics": [ "dshapebivr" ] }, { "page": "dshapebivrProp", "title": "Bivariate Kernel density estimation for data classified in polygons or shapes", "topics": [ "dshapebivrProp" ] }, { "page": "Kernelheaping", "title": "Kernel Density Estimation for Heaped Data", "topics": [ "Kernelheaping" ] }, { "page": "plot.bivrounding", "title": "Plot Kernel density estimate of heaped data naively and corrected by partly bayesian model", "topics": [ "plot.bivrounding" ] }, { "page": "plot.Kernelheaping", "title": "Plot Kernel density estimate of heaped data naively and corrected by partly bayesian model", "topics": [ "plot.Kernelheaping" ] }, { "page": "sim.Kernelheaping", "title": "Simulation of heaping correction method", "topics": [ "sim.Kernelheaping" ] }, { "page": "simSummary.Kernelheaping", "title": "Simulation Summary", "topics": [ "simSummary.Kernelheaping" ] }, { "page": "students", "title": "Student0405", "topics": [ "students" ] }, { "page": "summary.Kernelheaping", "title": "Prints some descriptive statistics (means and quantiles) for the estimated rounding, bias and acceleration (beta) parameters", "topics": [ "summary.Kernelheaping" ] }, { "page": "toOtherShape", "title": "Transfer observations to other shape", "topics": [ "toOtherShape" ] }, { "page": "tracePlots", "title": "Plots some trace plots for the rounding, bias and acceleration (beta) parameters", "topics": [ "tracePlots" ] } ], "_rundeps": [ "abind", "boot", "cli", "codetools", "contfrac", "cubature", "DBI", "deldir", "deSolve", "doParallel", "dplyr", 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