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\name{algorithms}
\alias{GenzBretz}
\alias{Miwa}
\alias{TVPACK}
\title{ Choice of Algorithm and Hyper Parameters }
\description{
Choose between three algorithms for evaluating normal (and t-)
distributions and define hyper parameters.
}
\usage{
GenzBretz(maxpts = 25000, abseps = 0.001, releps = 0)
Miwa(steps = 128, checkCorr = TRUE, maxval = 1e3)
TVPACK(abseps = 1e-6)
}
\arguments{
\item{maxpts}{maximum number of function values as integer. The internal
FORTRAN code always uses a minimum number depending on the dimension.
(for example 752 for three-dimensional problems).}
\item{abseps}{absolute error tolerance; for \code{TVPACK} only used
for dimension 3.}
\item{releps}{ relative error tolerance as double. }
\item{steps}{number of grid points to be evaluated; cannot be larger than
4097.}
\item{checkCorr}{logical indicating if a check for singularity of the
correlation matrix should be performed (once per function call to
\code{pmvt()} or \code{pmvnorm()}).}
\item{maxval}{replacement for \code{Inf} when non-orthant probabilities
involving \code{Inf} shall be computed.}
}
\details{
There are three algorithms available for evaluating normal
(and two algorithms for t-)
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 normal probabilities, smaller dimensions (up to 20) and non-singular
covariance matrices,
the algorithm by Miwa et al. (2003) can be used as well. This algorithm can
compute orthant probabilities (\code{lower} being \code{-Inf} or
\code{upper} equal to \code{Inf}). Non-orthant probabilities are computed
from the corresponding orthant probabilities, however, infinite limits are
replaced by \code{maxval} along with a warning.
For two- and three-dimensional problems and semi-infinite integration
region, \code{TVPACK} implements an interface to the methods described
by Genz (2004).
}
\value{
An object of class \code{"GenzBretz"}, \code{"Miwa"}, or \code{"TVPACK"}
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.
Genz, A. (2004), Numerical computation of rectangular bivariate and
trivariate normal and t-probabilities, \emph{Statistics and
Computing}, \bold{14}, 251--260.
Genz, A. and Bretz, F. (2009), \emph{Computation of Multivariate Normal and
t Probabilities}. Lecture Notes in Statistics, Vol. 195. Springer-Verlag,
Heidelberg.
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.
Mi, X., Miwa, T. and Hothorn, T. (2009).
\code{mvtnorm}: New numerical algorithm for multivariate normal probabilities.
\emph{The R Journal} \bold{1}(1): 37--39.
\url{https://journal.r-project.org/archive/2009-1/RJournal_2009-1_Mi+et+al.pdf}
}
\keyword{distribution}
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