knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "README-" )
The goal of kader is to supply functions to compute nonparametric kernel estimators for
The functions are based on the theory introduced in
A very brief summary of the theory and sort of a vignette is presented in Eichner, G. (2017): Kader - An R package for nonparametric kernel adjusted density estimation and regression. In: Ferger, D., et al. (eds.): From Statistics to Mathematical Finance, Festschrift in Honour of Winfried Stute. Springer International Publishing. To appear in Jan. 2018. DOI then(!) presumably: 10.1007/978-3-319-50986-0.
You can install kader from CRAN with:
install.packages("kader")
or from github with:
# install.packages("devtools") devtools::install_github("GerritEichner/kader")
This example shows you how to estimate at $x_0 = 2$ the value of the density
function of the probability distribution underlying Old-Faithful's eruptions data
using the (nonrobust) method of Srihera & Stute (2011). The initial grid
(given to Sigma
) on which the minimization of the estimated MSE as a function
of a (kernel-adjusting) scale parameter $\sigma$ is started is rather coarse here
to save computing time.
library(kader) x0 <- 2 sigma <- seq(0.01, 10, length = 21) fit <- kade(x = x0, data = faithful$eruptions, method = "nonrobust", Sigma = sigma, ticker = TRUE) print(fit)
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