This package contains routines and documentation for
        solving quadratic programming problems.

Derivative-free optimization algorithms by quadratic
approximation

Douglas Bates, Katharine M. Mullen, John C. Nash, Ravi Varadhan

Derivative-free optimization by quadratic approximation
        based on an interface to Fortran implementations by M. J. D.
        Powell.

Simple, Consistent Wrappers for Common String Operations

A consistent, simple and easy to use set of wrappers around the
    fantastic 'stringi' package. All function and argument names (and positions)
    are consistent, all functions deal with "NA"'s and zero length vectors
    in the same way, and the output from one function is easy to feed into
    the input of another.

c(
    person("Hadley", "Wickham", , "hadley@rstudio.com", c("aut", "cre", "cph")),
    person("RStudio", role = "cph")
    )

Hadley Wickham [aut, cre, cph],
  RStudio [cph]

Tools for Splitting, Applying and Combining Data

A set of tools that solves a common set of problems: you
    need to break a big problem down into manageable pieces, operate on each
    piece and then put all the pieces back together.  For example, you might
    want to fit a model to each spatial location or time point in your study,
    summarise data by panels or collapse high-dimensional arrays to simpler
    summary statistics. The development of 'plyr' has been generously supported
    by 'Becton Dickinson'.

person("Hadley", "Wickham", , "hadley@rstudio.com", c("aut", "cre"))

abind, testthat, tcltk, foreach, doParallel, itertools,
iterators

c( person(given = c("John", "C."), family = "Nash", role =
        c("aut", "cre"), email = "nashjc@uottawa.ca") )

Tools to assist in safely applying user-generated objective and 
   derivative functions to optimization programs. "Optimization" at the 
   time of writing refers primarily to function minimization methods with at most 
   bounds and masks on the parameters.
   Tools are provided to check the basic computation of objective functions that 
   the user provides, along with proposed gradient and Hessian functions, 
   as well as to wrap such functions to avoid failures when inadmissible parameters 
   are provided. Checks on bounds and masks (fixed parameters) are available, as
   are a check on scaling or optimality conditions. An axial search to seek lower
   points on the objective function surface is included. Though numDeriv is
   used as the primary tool for numerical derivative approximations, forward, 
   central and backward gradient approximation codes for convenience.