I have to use Matlab professionally in my organization for financial risk modelling and there are some larger legacy codebases. However, I wouldn't share your o

I have to use Matlab professionally in my organization for financial risk modelling and there are some larger legacy codebases. However, I wouldn't share your opinion about maintenance issues. Like with every other language, if the code is well organized, everything is fine. Matlab integrates with SVN or GIT, you have got the functionality for unit-testing, proper debugging and profiling and namespaces through packages. OOP works also well although you will pay for it in performance. Additionally, you can easily use Java packages and interface with .NET and COM. Compared to how R is used here, the whole Matlab environment feels much more complete and solid prototyping is certainly an advantage. Nevertheless, we feel the pain when it comes to datasets of 10-100 GB for which we don't want to set up a database. We haven't bought the Parallel toolbox and without this, processing is just very slow. Here R shines with e.g. data.table. If I would rewrite the applications, I would however not do it in Matlab, because the audience of people looking at the code is more familiar with open source alternatives.