\name{skewness}
\alias{skewness}

\alias{skewness.default}
\alias{skewness.data.frame}
\alias{skewness.POSIXct}
\alias{skewness.POSIXlt}


\title{Skewness}

\description{
  Functions to compute skewness.
}

\usage{
skewness(x, \dots)

\method{skewness}{default}(x, na.rm = FALSE, method = c("moment", "fisher"), \dots)

\method{skewness}{data.frame}(x, na.rm = FALSE, method = c("moment", "fisher"), \dots)

\method{skewness}{POSIXct}(x, \dots)

\method{skewness}{POSIXlt}(x, \dots)
}

\arguments{
  
  \item{x}{
    a numeric vector or object.      
  }
  \item{na.rm}{
    a logical. Should missing values be removed?
  }
  \item{method}{
    
    a character string, the method of computation, see section
    \sQuote{Detaials}.
    
  }
  \item{\dots}{
    arguments to be passed.
  }
}

\details{

  Argument \code{method} can be one of \code{"moment"} or
  \code{"fisher"}.  The \code{"moment"} method is based on the
  definitions of skewness for distributions and this should be used when
  resampling (bootstrap or jackknife). The \code{"fisher"} method
  correspond to the usual "unbiased" definition of sample variance,
  although in the case of skewness exact unbiasedness is not possible.

}

\value{
    
  a numeric value or vector with attribute \code{"method"} indicating
  the method. 
    
}

\seealso{
  \code{\link{kurtosis}}
}

\examples{   
r = rnorm(100)
mean(r)
var(r)
   
## skewness
skewness(r)  
}

\keyword{univar}
