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\name{algorithms}
\alias{GenzBretz}
\alias{Miwa}
\title{ Choice of Algorithm and Hyper Parameters }
\description{
Choose between two algorithms for evaluating normal distributions
and define hyper parameters.
}
\usage{
GenzBretz(maxpts = 25000, abseps = 0.001, releps = 0)
Miwa(steps = 128)
}
\arguments{
\item{maxpts}{ maximum number of function values as integer. }
\item{abseps}{ absolute error tolerance as double. }
\item{releps}{ relative error tolerance as double. }
\item{steps}{ number of grid points to be evaluated. }
}
\details{
There are two algorithms available for evaluating normal
probabilities: The default is the randomized Quasi-Monte-Carlo procedure
by Genz (1992, 1993) and Genz and Bretz (2002) applicable to
arbitrary covariance structures and dimensions up to 1000.
For smaller dimensions (up to 20) and non-singular covariance matrices,
the algorithm by Miwa et al. (2003) can be used as well.
}
\value{
An object of class \code{GenzBretz} or \code{Miwa}
defining hyper parameters.
}
\references{
Genz, A. (1992). Numerical computation of multivariate normal probabilities.
\emph{Journal of Computational and Graphical Statistics}, \bold{1}, 141--150.
Genz, A. (1993). Comparison of methods for the computation of multivariate
normal probabilities. \emph{Computing Science and Statistics}, \bold{25},
400--405.
Genz, A. and Bretz, F. (2002), Methods for the computation of multivariate
t-probabilities. \emph{Journal of Computational and Graphical Statistics},
\bold{11}, 950--971.
Miwa, A., Hayter J. and Kuriki, S. (2003).
The evaluation of general non-centred orthant probabilities.
\emph{Journal of the Royal Statistical Society}, Ser. B, 65, 223--234.
}
\keyword{distribution}
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