\name{ghtFit}


\alias{ghtFit}


\alias{ghtFit}


\title{GHT Distribution Fit}


\description{

    Estimates the distributional parameters for a 
    generalized hyperbolic Student-t distribution.

}


\usage{ 
ghtFit(x, beta = 0.1, delta = 1, mu = 0, nu = 10, 
    scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE, 
    title = NULL, description = NULL, \dots) 
}


\arguments{
  
    \item{beta, delta, mu}{
        numeric values.
        \code{beta} is the skewness parameter in the range \code{(0, alpha)};
        \code{delta} is the scale parameter, must be zero or positive; 
        \code{mu} is the location parameter, by default 0.
        These are the parameters in the first parameterization.
        }
    \item{nu}{
        defines the number of degrees of freedom. 
        Note, \code{alpha} takes the limit of \code{abs(beta)}, 
        and \code{lambda=-nu/2}.
        }
    \item{x}{
        a numeric vector. 
        }
    \item{scale}{
        a logical flag, by default \code{TRUE}. Should the time series
        be scaled by its standard deviation to achieve a more stable
        optimization?
        }
    \item{doplot}{
        a logical flag. Should a plot be displayed?
        }        
    \item{span}{
        x-coordinates for the plot, by default 100 values 
        automatically selected and ranging between the 0.001, 
        and 0.999 quantiles. Alternatively, you can specify
        the range by an expression like \code{span=seq(min, max,
        times = n)}, where, \code{min} and \code{max} are the 
        left and right endpoints of the range, and \code{n} gives 
        the number of the intermediate points.
        }        
    \item{trace}{
        a logical flag. Should the parameter estimation process be
        traced?
        }
    \item{title}{
        a character string which allows for a project title.
        }     
    \item{description}{
        a character string which allows for a brief description.
        }
    \item{\dots}{
        parameters to be parsed.
        }

}


\value{
  
    returns a list with the following components:
  
    \item{estimate}{
        the point at which the maximum value of the log liklihood 
        function is obtained.
        }
    \item{minimum}{
        the value of the estimated maximum, i.e. the value of the
        log liklihood function.
        }
    \item{code}{
        an integer indicating why the optimization process terminated.\cr
        1: relative gradient is close to zero, current iterate is probably 
            solution; \cr
        2: successive iterates within tolerance, current iterate is probably 
            solution; \cr
        3: last global step failed to locate a point lower than \code{estimate}. 
            Either \code{estimate} is an approximate local minimum of the 
            function or \code{steptol} is too small; \cr
        4: iteration limit exceeded; \cr
        5: maximum step size \code{stepmax} exceeded five consecutive times. 
            Either the function is unbounded below, becomes asymptotic to a 
            finite value from above in some direction or \code{stepmax} 
            is too small.
            }
    \item{gradient}{
        the gradient at the estimated maximum.
        }
    \item{steps}{
        number of function calls.
        }
              
}


\details{

    The function \code{\link{nlm}} is used to minimize the "negative" 
    maximum log-likelihood function. \code{nlm} carries out a minimization 
    using a Newton-type algorithm.
       
}


\examples{    
## ghtFit -
   # Simulate Random Variates:
   set.seed(1953)
   
## ghtFit -  
   # Fit Parameters:
}


\keyword{distribution}