dist_kde: Create distributional object based on a kernel density...

In weird: Functions and Data Sets for "That's Weird: Anomaly Detection Using R" by Rob J Hyndman

Create distributional object based on a kernel density estimate

Description

Creates a distributional object using a kernel density estimate with a Gaussian kernel obtained from the kde() function. The bandwidth can be specified; otherwise the kde_bandwidth() function is used. The cdf, quantiles and moments are consistent with the kde. Generating random values from the kde is equivalent to a smoothed bootstrap.

Usage

dist_kde(
  y,
  h = NULL,
  H = NULL,
  method = c("normal", "robust", "plugin", "lookout"),
  ...
)

Arguments

y	Numerical vector or matrix of data, or a list of such objects. If a list is provided, then all objects should be of the same dimension. e.g., all vectors, or all matrices with the same number of columns.
h	Bandwidth for univariate distribution. Ignored if y has 2 or more columns. If NULL, the kde_bandwidth function is used.
H	Bandwidth matrix for multivariate distribution. If NULL, the kde_bandwidth function is used.
method	The method of bandwidth estimation to use. See kde_bandwidth() for details. Ignored if h or H are specified.
...	Other arguments are passed to kde.

References

Rob J Hyndman (2026) "That's weird: Anomaly detection using R", Section 2.7 and 3.9, https://OTexts.com/weird/.

Examples

dist_kde(c(rnorm(200), rnorm(100, 5)))
dist_kde(cbind(rnorm(200), rnorm(200, 5)))

weird documentation built on Jan. 27, 2026, 9:06 a.m.