rmvnorm

Simulate from a multiviate normal distribution

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, mixturegrams, 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 and the Chan Zuckerberg Initiative: Essential Open Source Software for Science (Grant No. 2020-255193).

Derek S. Young

mixtools

Tools for Analyzing Finite Mixture Models

Derek Young

Tatiana Benaglia

Didier Chauveau

David Hunter

Kedai Cheng

Ryan Elmore

Thomas Hettmansperger

Hoben Thomas

Fengjuan Xuan

rmvnorm function

<dl> <dt>n</dt>
<dd>Number of vectors to simulate</dd> <dt>mu</dt>
<dd>mean vector</dd> <dt>sigma</dt>
<dd>covariance matrix, assumed symmetric and nonnegative definite</dd></dl>

Arguments

Simulate from a Multivariate Normal Distribution — rmvnorm

<dl>

 <dt>n</dt>
<dd>Number of vectors to simulate</dd>

 <dt>mu</dt>
<dd>mean vector</dd>

 <dt>sigma</dt>
<dd>covariance matrix, assumed symmetric and nonnegative definite</dd>

</dl>

Simulate from a Multivariate Normal Distribution

rmvnorm: Simulate from a Multivariate Normal Distribution

Description

Usage

Value

Arguments

Details

See Also