gaussmixt

dgaussmixt

pgaussmixt

matrix of mixture parameters of dimension 3 x m, where m
 is the number of components, so that each column contains the
 mixture parameters (pi, mu, sigma) related to one component

params

logical, if TRUE, probabilities <code>p</code> are
 given as log(<code>p</code>).

logical, if TRUE (default), the Gaussian density is
 truncated below zero.

trunc

Density and distribution function for a mixture of Gaussians with
<code>m</code> components.

Neural network conditional mixtures are mixture models whose parameters are predicted by a neural network. The mixture model can thus change its parameters in response to changes in predictive covariates. Mixtures included are gaussian, log-normal and hybrid Pareto mixtures. The latter relies on the generalized Pareto distribution to account for the presence of large extreme events. The unconditional mixtures are also available.

gaussmixt: Mixture of Gaussians

Description

Usage

Arguments

Value

See Also