# MVNorm: The Multivariate Normal Distribution In DIRECT: Bayesian Clustering of Multivariate Data Under the Dirichlet-Process Prior

## Description

Functions to compute the density of a multivariate normal distribution and to generate random realizations from such a distribution.

## Usage

 ```1 2 3``` ```dMVNorm (x, mean, sigma, log = FALSE) rMVNorm (n, mean = rep(0, nrow(sigma)), sigma = diag(length(mean)), method=c("eigen", "svd", "chol")) ```

## Arguments

 `x` Vector or matrix of quantiles. If `x` is a matrix, each row is taken to be a quantile. `n` Number of realizations. `mean` Mean vector, default is `rep(0, length = ncol(x))`. `sigma` Covariance matrix, default is `diag(ncol(x))`. `log` Logical; if `TRUE`, densities are log-transformed. `method` Matrix decomposition used to determine the matrix root of `sigma`, possible methods are eigenvalue decomposition (`"eigen"`, default), singular value decomposition (`"svd"`), and Cholesky decomposition (`"chol"`).

## Value

`rMVNorm` returns a vector of the same length as `mean` if `n`=1, or a matrix with each row being an independent realization otherwise.

## Author(s)

The code for both functions is taken from similar functions written by Friedrich Leisch and Fabian Scheipl in R package `mvtnorm`. Audrey Q. Fu modified `dMVNorm` to use a different method to compute the matrix determinants.

## Examples

 ```1 2 3 4 5``` ```## Not run: x <- rMVNorm (10, mean=rep(0,3), method="svd") dMVNorm (x, mean=rep(0,3), log=TRUE) ## End(Not run) ```

DIRECT documentation built on May 1, 2019, 8:08 p.m.