- implements a univariate kernel density estimator that can handle bounded and discrete data.
- provides classical kernel density as well as log-linear and log-quadratic methods.
- is highly efficient due to the Fast Fourier Transform, spline interpolation, and a C++ backend.

For details, see the API documentation.

- the stable release from CRAN:

install.packages("kde1d")

- the latest development version:

# install.packages("devtools") devtools::install_github("tnagler/kde1d@dev")

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")

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