P.r.s

Arguments:
 @param dat features x samples data matrix for mixture modeling
 @param pars Gaussian mixture model parameters (diagonal covariances); list with elements mu (mean vectors), sd (covariance diagonals), w (weights). The mu and sd are component x features matrices, w is vector giving weight for each component.
 @param log Logical. Return densities in log domain.
 @param scaling Try to avoid floating errors. To be improved later.

internal

utilities

Algorithms for functional network analysis. Includes an
    implementation of a variational Dirichlet process Gaussian
    mixture model for nonparametric mixture modeling.

Leo Lahti

netresponse

Functional Network Analysis

P.r.s function

Description: Probabiity of mode given a sample (a data vector)
Mainly for internal use; documentation will be provided later. Tools for calculating densities with Gaussian mixture models.

P.r.s: Description: Probabiity of mode given a sample (a data vector) Mainly for internal use; documentation will be provided later. Tools for calculating densities with Gaussian mixture models.

Description

Usage

Arguments

Details

References