I have a general question about appropriate design for a fairly complicated package, as I am redesigning and rebuilding the optimx package. It will get a new name as it appears that the calls will change in order to accommodate new functionality. I plan to make it fairly easy to add new optimization (function minimization) tools.
The query is whether to use an environment to hold all the common objects (functions, scaled functions, gradients, scaled gradients, hessians, scaled hessians, parameters, scaled parameters, program controls, algorithm tuning parameters, constraints, fixed parametes, some function value history, etc.) The alternative is a set of very long calls, especially when the function needing a quantity may be a couple of layers below the routine that creates or sets the values. The negative side is that the functions are not portable outside the package, so would not be exported. Before I dig into the heavy clay of code that this will need, I thought I should ask if there are issues I might be missing, as the local environment -- essentially "global" variables within the perimeter of the package -- would make the code a lot cleaner, and may offer some useful efficiencies in computation. Best, JN ______________________________________________ R-package-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-package-devel
