\name{T.Owen}
\alias{T.Owen}
\title{
Owen's function
}
\description{
Evaluates function \emph{T(h,a)} studied by D.B.Owen
}
\usage{
T.Owen(h, a, jmax=50, cut.point=6)
}
\arguments{
\item{h}{
a numerical vector. Missing values (\code{NA}s) and \code{Inf} are allowed.
}
\item{a}{
a numerical scalar. \code{Inf} is allowed.
}
\item{jmax}{
an integer scalar value which regulates the accuracy of the result.
See DETAILS below for explanation.
}
\item{cut.point}{
a scalar value which regulates the behaviour of the algorithm, as
explained by the details below.
}}
\value{
a numerical vector
}
\details{
If \code{a>1} and \code{0<h<=cut.point}, a series expansion is used,
truncated after \code{jmax} terms.
If \code{a>1} and \code{h>cut.point}, an asymptotic approximation is used.
In the other cases, various reflection properties of the function
are exploited. See the reference below for more information.
}
\section{Background}{
The function \emph{T(h,a)} is useful for the computation of the bivariate
normal distribution function and related quantities, including the 
distribution function of a skew-normal variate, \code{psn}.
See the reference below for more information on  \emph{T(h,a)}.
}
\references{
Owen, D. B. (1956).
Tables for computing bivariate normal probabilities.
\emph{Ann. Math. Statist.}
\bold{27}, 1075-1090.
}
\seealso{
\code{\link{pnorm2}}, \code{\link{psn}}
}
\examples{
owen <- T.Owen(1:10, 2)
}
\keyword{math}