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\name{fitdstn}
\alias{fitdstn}
\title{Maximum-likelihood Fitting of Univariate Distributions}
\description{
Maximum-likelihood fitting of univariate distributions.
}
\usage{
fitdstn(x, densfun, \dots)
}
\arguments{
\item{x}{a numeric vector containing the sample.}
\item{densfun}{a character string naming the
distribution. Distributions \sQuote{gamma}, \sQuote{lognormal}, and
\sQuote{weibull} are supported.}
\item{\dots}{additional arguments are ignored.}
}
\details{
This function relies on the \code{\link[MASS]{fitdistr}} function for
the computations. The returned object is modified to support plotting
and comparison.
}
\value{
a list with class \dQuote{fitdstn} containing the following elements:
\item{estimate}{a named numeric vector containing the parameter estimates.}
\item{sd}{a named numeric vector containing the standard deviations of
the parameter estimates.}
\item{vcov}{a numeric matrix containing the variance-covariance matrix
of the estimated parameter vector.}
\item{n}{a single numeric value indicating the number of sample points in \code{x}.}
\item{loglik}{a single numeric value giving the maxized the log-likelihood.}
\item{call}{the matched call.}
\item{densfun}{the character string \code{densfun} provided in the arguments.}
\item{x}{the data provided in \code{x}.}
}
\note{
The \code{print} method displays the estimated parameters and their
standard errors (in parentheses).
}
\seealso{
An important goal here is the comparison with \emph{robust} fits to
the same distributions, see \code{\link{fitdstnRob}}.
\code{\link[MASS]{fitdistr}} which provides many more choices for
\code{densfun}.
}
\keyword{maximum-likelihood}
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