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\name{goftest-package}
\alias{goftest-package}
\alias{goftest}
\docType{package}
\title{
Classical Goodness-of-Fit Tests
}
\description{
\ifelse{latex}{\out{Cram\'er}}{Cramer}-von Mises
and Anderson-Darling tests of goodness-of-fit
for continuous univariate distributions, using modern
algorithms to compute the null distributions.
}
\details{
The \pkg{goftest} package contains implementations of the
classical \ifelse{latex}{\out{Cram\'er}}{Cramer}-von Mises
and Anderson-Darling tests of goodness-of-fit
for continuous univariate distributions.
The \ifelse{latex}{\out{Cram\'er}}{Cramer}-von Mises test
is performed by \code{\link{cvm.test}}. The cumulative distribution
function of the null distribution of the test statistic
is computed by \code{\link{pCvM}}
using the algorithm of \ifelse{latex}{\out{Cs\"org\H{o}}}{Csorgo}
and Faraway (1996). The quantiles are computed by \code{\link{qCvM}}
by root-finding.
The Anderson-Darling test is performed by
\code{\link{ad.test}}. The cumulative distribution
function of the null distribution of the test statistic
is computed by \code{\link{pAD}}
using the algorithm of Marsaglia and Marsaglia (2004).
The quantiles are computed by \code{\link{qAD}} by root-finding.
By default, each test assumes that the parameters of the null
distribution are known (a \emph{simple} null hypothesis).
If the parameters were estimated (calculated from the data)
then the user should set \code{estimated=TRUE} which uses
the method of Braun (1980) to adjust for the effect of
estimating the parameters from the data.
}
\author{
Adrian Baddeley, Julian Faraway, John Marsaglia, George Marsaglia.
Maintainer: Adrian Baddeley <adrian.baddeley@uwa.edu.au>
}
\references{
Braun, H. (1980)
A simple method for testing goodness-of-fit in the presence of
nuisance parameters.
\emph{Journal of the Royal Statistical Society} \bold{42}, 53--63.
\ifelse{latex}{\out{Cs\"org\H{o}}}{Csorgo}, S. and Faraway, J.J. (1996)
The exact and asymptotic distributions of
\ifelse{latex}{\out{Cram\'er}}{Cramer}-von Mises statistics.
\emph{Journal of the Royal Statistical Society, Series B}
\bold{58}, 221--234.
Marsaglia, G. and Marsaglia, J. (2004)
Evaluating the Anderson-Darling Distribution.
\emph{Journal of Statistical Software} \bold{9} (2), 1--5.
February 2004.
\url{http://www.jstatsoft.org/v09/i02}
}
\keyword{package}
\keyword{htest}
\seealso{
\code{\link[stats]{ks.test}}
}
\examples{
x <- rnorm(30, mean=2, sd=1)
# default behaviour: parameters fixed: simple null hypothesis
cvm.test(x, "pnorm", mean=2, sd=1)
ad.test(x, "pnorm", mean=2, sd=1)
# parameters estimated: composite null hypothesis
mu <- mean(x)
sigma <- sd(x)
cvm.test(x, "pnorm", mean=mu, sd=sigma, estimated=TRUE)
ad.test(x, "pnorm", mean=mu, sd=sigma, estimated=TRUE)
}
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