Package: Kernelheaping 2.3.0

Kernelheaping: Kernel Density Estimation for Heaped and Rounded Data

In self-reported or anonymised data the user often encounters heaped data, i.e. data which are rounded (to a possibly different degree of coarseness). While this is mostly a minor problem in parametric density estimation the bias can be very large for non-parametric methods such as kernel density estimation. This package implements a partly Bayesian algorithm treating the true unknown values as additional parameters and estimates the rounding parameters to give a corrected kernel density estimate. It supports various standard bandwidth selection methods. Varying rounding probabilities (depending on the true value) and asymmetric rounding is estimable as well: Gross, M. and Rendtel, U. (2016) (<doi:10.1093/jssam/smw011>). Additionally, bivariate non-parametric density estimation for rounded data, Gross, M. et al. (2016) (<doi:10.1111/rssa.12179>), as well as data aggregated on areas is supported.

Authors:Marcus Gross [aut, cre], Lukas Fuchs [aut], Kerstin Erfurth [ctb]

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11 exports 0.00 score 71 dependencies 8 scripts 418 downloads

Last updated 3 years agofrom:d916c0c728. Checks:OK: 7. Indexed: yes.

Exports:createSim.Kernelheapingdbivrdclassdheapingdshape3dPropdshapebivrdshapebivrPropsim.KernelheapingsimSummary.KernelheapingtoOtherShapetracePlots

Dependencies:abindbootclicodetoolscontfraccubatureDBIdeldirdeSolvedoParalleldplyrellipticfansifastmatchfitdistrplusFNNforeachGB2genericsgluegoftesthypergeoiteratorskernlabKernSmoothkslaekenlatticelifecyclemagrittrMASSMatrixmclustmgcvminqamisc3dmitoolsmulticoolmvtnormnlmenumDerivpillarpkgconfigplyrpolyclippracmaR6RcppRcppArmadillorlangrpartspsparrspatstatspatstat.dataspatstat.explorespatstat.geomspatstat.linnetspatstat.modelspatstat.randomspatstat.sparsespatstat.univarspatstat.utilssurveysurvivaltensortibbletidyselectutf8vctrswithr