Reproducible research in computation aims to make the code needed to generate the results in a computational paper available to other researchers so that they can run this code to reproduce the results in that paper. I'd like to make all of my research reproducible, but I'm running into a bit of a snag: a couple papers I am working on use an in-house automatic differentiation package (called DAEPACK) on a proprietary library (CHEMKIN-II; license terms unclear).
It would be overly time-consuming to replace these software components with open-source versions. An open-source replacement for CHEMKIN-II exists called Cantera, but Cantera is in C++, whereas CHEMKIN-II is in Fortran 77. It would require a lot of effort to modify enough of the Cantera code so that it could be processed by automatic differentiation tools for C++.
Given that I need these proprietary packages, what is the best way to make my research as reproducible as possible, assuming that researchers may not have access to CHEMKIN-II? Since DAEPACK is a source-to-source translator, I don't necessarily need to distribute DAEPACK; I might be able to include its output, which would be Fortran source files that calculate derivatives.
More generally, if you require proprietary software in your work, and that proprietary software isn't widely available (i.e, isn't MATLAB, Mathematica, etc.), how do you make your work reproducible?