dmgh: Density of multivariate Generalized Hyperbolic distribution

In mixture: Mixture Models for Clustering and Classification

Density of multivariate Generalized Hyperbolic distribution

Description

Computes the density of an observation for a multivariate Generalized Hyperbolic distribution.

Usage

dmgh(x, mu, alpha, Sig, omega, lambda, LOG = FALSE)

Arguments

x	A numeric vector of dimension (1 x p).
mu	A (1 x p) numeric vector of location values.
alpha	A (1 x p) numeric vector of skewness values.
Sig	A (p x p) numeric covariance matrix.
omega	A numeric value for the first gamma parameter.
lambda	A numeric value for the second gamma parameter.
LOG	A logical value indicating if the logarithm of the density is returned (default: LOG = FALSE).

Value

A numeric value of the density of the observation x for the multivariate Generalized Hyperbolic distribution with parameters mean, alpha, Sig, omega and lambda.

Examples

x = c(1.2, 0.4, 0.8)
mu = c(1, 0, 2)
alpha = c(0.2, -0.1, 0.3)
Sig = matrix(c(1.0, 0.5, 0.5,
                0.5, 1.0, 0.5,
                0.5, 0.5, 1.0), nrow = 3, ncol = 3)
omega = 1
lambda = 2
dens = dmgh(x, mu, alpha, Sig, omega, lambda, LOG = FALSE)
dens

mixture documentation built on Dec. 19, 2025, 1:06 a.m.