kde1d-package: One-Dimensional Kernel Density Estimation

In kde1d: Univariate Kernel Density Estimation

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 [e-mail]

Authors:

• Thibault Vatter [e-mail]

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:

• https://tnagler.github.io/kde1d/

• Report bugs at https://github.com/tnagler/kde1d/issues/

kde1d documentation built on April 4, 2025, 5:13 a.m.