dfoptim: Derivative-Free Optimization

#' Derivative-Free Optimization
#' 
#' Derivative-Free optimization algorithms. These algorithms do not require
#' gradient information.  More importantly, they can be used to solve
#' non-smooth optimization problems. They can also handle box constraints on
#' parameters.
#' 
#' \tabular{ll}{ Package: \tab dfoptim\cr Type: \tab Package\cr Version: \tab
#' 2016.7-1\cr Date: \tab 2016-07-08\cr License: \tab GPL-2 or greater\cr
#' LazyLoad: \tab yes\cr } Derivative-Free optimization algorithms. These
#' algorithms do not require gradient information.  More importantly, they can
#' be used to solve non-smooth optimization problems.  These algorithms were
#' translated from the Matlab code of Prof. C.T. Kelley, given in his book
#' "Iterative methods for optimization". However, there are some non-trivial
#' modifications of the algorithm. \cr
#' 
#' Currently, the Nelder-Mead and Hooke-Jeeves algorithms is implemented.  In
#' future, more derivative-free algorithms may be added.
#' 
#' @name dfoptim
#' @docType package
#' @author Ravi Varadhan, Johns Hopkins University \cr URL:
#' http://www.jhsph.edu/agingandhealth/People/Faculty_personal_pages/Varadhan.html
#' \cr Hans W. Borchers, ABB Corporate Research \cr Maintainer: Ravi Varadhan
#' <ravi.varadhan@@jhu.edu>
#' @references C.T. Kelley (1999), Iterative Methods for Optimization, SIAM.
#' @keywords optimize
NULL