% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/HookesJeeves.R
\name{HookeJeeves}
\alias{HookeJeeves}
\title{'nlminb' optimizer}
\usage{
HookeJeeves(ml, strt.lims)
}
\arguments{
\item{ml}{Either a Rdistance 'model frame' or an Rdistance 
'fitted object'.  Both are of class "dfunc". 
Rdistance 'model frames' are lists containing components 
necessary to estimate a distance function, but no estimates. 
Rdistance 'model frames' are typically
produced by calls to \code{\link{parseModel}}. 
Rdistance 'fitted objects'
are typically produced by calls to \code{\link{dfuncEstim}}.
'Fitted objects' are 'model frames'
with additional components such as the parameters estimates, 
log likelihood value, convergence information, and the variance-
covariance matrix of the parameters.}

\item{strt.lims}{A list containing start, low, and high limits for 
parameters of the requested likelihood. This list is typically produced
by a call to \code{\link{startLimits}}.}
}
\value{
A list with following named components:
\itemize{
  \item par = parameters
  \item loglik = objective function value at minimum
  \item convergence = 0 for yes, other for no
  \item iterations = number of iterations
  \item evaluations = function evaluations
  \item message = a convergence message
  \item varcovar = a variance covariance matrix of parameters
  \item limits = low and high limits
}
}
\description{
Call R native function 'nlminb' to perform optimization.
}
