kedd - Kernel Estimator and Bandwidth Selection for Density and Its
Derivatives
Kernel estimation of a univariate probability density and
of its derivatives of any order r, with data-driven bandwidth
selection. Provides the kernel estimator of the derivative of
order r, the kernels, their derivatives and convolutions, and
eight bandwidth selectors: the asymptotic MISE rule (AMISE),
and unbiased (UCV), biased (BCV1, BCV2), complete (CCV),
modified (MCV), trimmed (TCV), smoothed (SCV) and
maximum-likelihood (MLCV) cross-validation. Supports ten named
kernels (Gaussian, Epanechnikov, uniform, triangular, biweight,
triweight, tricube, cosine, Silverman, logistic) and the power
family Pow(s,t), c(1-|x|^s)^t on [-1,1]. Results are S4 objects
with 'ggplot2' graphics; the O(n^2) criteria are computed in
C++ ('Rcpp') with 'OpenMP' parallelism. Includes five example
samples from Marron and Wand (1992) normal mixtures, a vignette
and demos. Companion to Guidoum (2020)
<doi:10.48550/arXiv.2012.06102>.
