I’ve been a Matlab user for 15 years, and over that time period I’ve of course become fairly dependent on it to get things done quickly. The downside? It’s expensive. It’s a pretty penny to buy the base package, toolboxes are extra, and there are recurrent “maintenance” costs each year to get upgrades.

Sure, that’s standard practice, but each year I have to stand up and justify to my boss why we need to pay these costs for our multiple Matlab users in our shop (a multi-user concurrent license is out of the question, don’t even ask). So what’s a user to do?

For years we’ve just bit the bullet and paid the fee, but with options such as R and Numpy/SciPy out there it may be time to loosen the chain a bit. Or maybe not.

A couple of possible alternatives to Matlab and their respective pros and cons:

R

R is a really nice statistical environment which has pretty much become the industry standard, replacing the very expensive S-Plus. It’s easy to install, has an excellent GUI on OS X, and has a ton of community released packages which are usually made during the preparation of scientific papers. There are some downsides, as there can be multiple (sometimes possibly conflicting) packages (e.g. gam vs mgcv) but choice is good, right? The cons for me are that it’s a new language to learn, and even though I write an m-script for everything, I find the scripting in R a bit clunky, even writing in TextWrangler and then hitting CTRL-R to have the SendToR script source the code for me. It’s just something new, and while the built in functions are really nice, the learning curve for coding things is higher, and will it be faster in the long run than just using Matlab?

Numpy/SciPy

The Numpy/SciPy combo in Python is a viable alternative to Matlab, even having a page dedicated to showing you how easy it is to transition from Matlab. As with R, it’s free, and there are a ton of functions available, but there is a downside for me. I’ve successfully installed it on CentOS 5.1 and OS X 10.5, but it was a bit complicated. I know that these are packaged in many distributions, but not in CentOS, and I had to install from either source or .egg files, which isn’t all that tough, but took some time. I’m not writing the 24.3 steps I did to get it installed because honestly, I didn’t write it down and I don’t remember what I did. Next time I promise to list it out! On OS X I did it all through MacPorts on the MP version of python 2.5. Again, it took some massaging to get it all set up since I was using the non-default install of python.

Overall though, the reason for this little diatribe is that while there are alternatives to Matlab, they all involve learning new ways to do things which, after I successfully learn them, may not be faster than just doing it in Matlab. Most of the time I just need to get things done, and the $7/day cost of Matlab may be well worth it if I’m saving more than 10 minutes of time during that day (assuming for a minute that I am earning $42/hour).

I’m rambling a bit here, but these are just questions that I ask myself as I code things up at the desk. For each of these tools has their place, and in terms of maximum comfort and speed, I use each of them for their strengths. The main dilemma is that in a perfect situation I would drop the commercial Matlab for the free/open source alternatives, but at a minimal cost in dollars and time.

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