\name{RRgauss}
\alias{RRgauss}
\title{Vector Of Independent Gaussian Random Variables}
\description{
  \command{RRgauss} defines
  the d-dimensional vector of independent Gaussian random
 variables. 
 }
 \usage{
RRgauss(mu, sd, log) 
}

\arguments{
 \item{mu, sd, log}{see \link[stats]{Normal}. Here, the components can be
 vectors, leading to multivariate distibution with independent components.}
}

\value{
 \command{\link{RRgauss}} returns an object of class \code{\link[=RMmodel-class]{RMmodel}}.

}

\details{
  It has the same effect as
  \code{\link{RRdistr}(\link[=rnorm]{norm}(mu=mu, sd=sd, log=log))}.
}

\me
\seealso{
 \command{\link{RMmodel}},
 \command{\link{RRdistr}},
 \command{\link{RRunif}}.

 Do not mix up \command{RRgauss} with \command{\link{RMgauss}} or
 \command{\link{RPgauss}}. 
 }

\examples{\dontshow{StartExample()}
RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
##                   RFoptions(seed=NA) to make them all random again
r <- RFrdistr(RRgauss(mu=c(1,5)), n=1000, dim=2)
plot(r[1,], r[2, ])
\dontshow{FinalizeExample()}}

 
\keyword{spatial}
\keyword{models}