For details, see the API documentation.
x <- rnorm(100) # simulate data
fit <- kde1d(x) # estimate density
dkde1d(0, fit) # evaluate density estimate
summary(fit) # information about the estimate
plot(fit) # plot the density estimate
curve(dnorm(x), add = TRUE, # add true density
col = "red")x <- rgamma(100, shape = 1) # simulate data
fit <- kde1d(x, xmin = 0, deg = 1) # estimate density
dkde1d(seq(0, 5, by = 1), fit) # evaluate density estimate
summary(fit) # information about the estimate
plot(fit) # plot the density estimate
curve(dgamma(x, shape = 1), # add true density
add = TRUE, col = "red",
from = 1e-3)x <- rbinom(100, size = 5, prob = 0.5) # simulate data
x <- ordered(x, levels = 0:5) # declare as ordered
fit <- kde1d(x) # estimate density
dkde1d(sort(unique(x)), fit) # evaluate density estimate
summary(fit) # information about the estimate
plot(fit) # plot the density estimate
points(ordered(0:5, 0:5), # add true density
dbinom(0:5, 5, 0.5), col = "red")x <- rnorm(100) # simulate data
weights <- rexp(100) # weights as in Bayesian bootstrap
fit <- kde1d(x, weights = weights) # weighted fit
plot(fit) # compare with unweighted fit
lines(kde1d(x), col = 2)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, to appear, arXiv:1602.04862
Loader, C. (2006). Local regression and likelihood. Springer Science & Business Media.
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, in press, arXiv:1705.05431