gaussian_kde: Gaussian Kernel Density Estimation

In BDIB: Bayesian Dynamic Information Borrowing for Longitudinal Data with Informative Dropout

Gaussian Kernel Density Estimation

Description

Gaussian Kernel Density Estimation

Usage

gaussian_kde(samples, eval_points, bandwidth = NULL)

Arguments

samples	Numeric vector of samples.
eval_points	Numeric vector of locations to evaluate.
bandwidth	Optional bandwidth; Silverman's rule if NULL.

Value

Numeric vector of density estimates.

Examples

set.seed(1000)
xs <- rnorm(1000, mean = -3, sd = 0.5)
pts <- seq(-4.5, -1.5, length.out = 5)
dens <- gaussian_kde(xs, pts)      # KDE values at 'pts'
cbind(pts, round(dens, 4))

BDIB documentation built on Oct. 18, 2026, 5:08 p.m.