rmvnorm

<p>covariance matrix, assumed symmetric and nonnegative definite</p>

sigma

<p>Simulate from a multiviate normal distribution</p>

distribution

Analyzes finite mixture models for various parametric and semiparametric settings.  This includes mixtures of parametric distributions (normal, multivariate normal, multinomial, gamma), various Reliability Mixture Models (RMMs), mixtures-of-regressions settings (linear regression, logistic regression, Poisson regression, linear regression with changepoints, predictor-dependent mixing proportions, random effects regressions, hierarchical mixtures-of-experts), and tools for selecting the number of components (bootstrapping the likelihood ratio test statistic and model selection criteria).  Bayesian estimation of mixtures-of-linear-regressions models is available as well as a novel data depth method for obtaining credible bands.  This package is based upon work supported by the National Science Foundation under Grant No. SES-0518772.

Derek S. Young

mixtools

Tools for Analyzing Finite Mixture Models

Derek Young

Tatiana Benaglia

Didier Chauveau

David Hunter

Ryan Elmore

Thomas Hettmansperger

Hoben Thomas

Fengjuan Xuan

rmvnorm function

Simulate from a Multivariate Normal Distribution — rmvnorm

Simulate from a Multivariate Normal Distribution

rmvnorm: Simulate from a Multivariate Normal Distribution

Description

Usage

Arguments

Value

Details

See Also