kde1d-package {kde1d} | R Documentation |
One-Dimensional Kernel Density Estimation
Description
Provides an efficient implementation of univariate local polynomial kernel density estimators that can handle bounded, discrete, and zero-inflated data. The implementation utilizes spline interpolation to reduce memory usage and computational demand for large data sets.
Author(s)
Maintainer: Thomas Nagler mail@tnagler.com
Authors:
Thibault Vatter thibault.vatter@gmail.com
References
Geenens, G. (2014). Probit transformation for kernel density estimation on the unit interval. Journal of the American Statistical Association, 109:505, 346-358, arXiv:1303.4121
Geenens, G., Wang, C. (2018). Local-likelihood transformation kernel density estimation for positive random variables. Journal of Computational and Graphical Statistics, 27(4), 822-835. arXiv:1602.04862
Nagler, T. (2018a). A generic approach to nonparametric function estimation with mixed data. Statistics & Probability Letters, 137:326–330, arXiv:1704.07457
Nagler, T. (2018b). Asymptotic analysis of the jittering kernel density estimator. Mathematical Methods of Statistics, 27, 32-46. arXiv:1705.05431
See Also
Useful links: