## Kernelheaping: Kernel Density Estimation for Heaped Data

In self-reported data the user often encounters heaped data, i.e. data which are rounded to a 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.
Additionally varying rounding probabilities (with the true value) and asymmetric rounding is estimable as well.

Version: |
0.2 |

Depends: |
R (≥ 2.15.0), plyr, evmix, MASS |

Published: |
2015-01-27 |

Author: |
Marcus Gross |

Maintainer: |
Marcus Gross <marcus.gross at fu-berlin.de> |

License: |
GPL-2 |

NeedsCompilation: |
no |

CRAN checks: |
Kernelheaping results |

#### Downloads: