if the scientific programming libraries in Python might eventually be a better environment.

Well... there is R.py http://rpy.sourceforge.net/ to give you most (if not all) the functionality of R in conjunction with all the scipy goodness.

There are a number of packages for relatively obscure statistical techniques (say, nonmetric multidimensional scaling) that are available for R, but not for python (that I have seen).

I also believe R is easier for statisticians who cannot and do not want to know how to program. There are many advanced techniques that are one-liners.

You've hit the nail on the head there. A programmer looks at R (or MATLAB) and says "this sucks as a programming language". An engineer or a scientist or a statistician says "so what, I'm not a programmer, nor do I want to be one, I've got actual work to do".

Must be a slow news day.

In other news: Programmers Captivated by Power of C

To some people C is just the 3rd letter of the alphabet. To others, it’s the grade they got in journalism, a measure of bust size or what pirates in movies sail across.

I find these kinds of comments hilarious. I'm scanning the comments, there's a 3, there's a 4, oh, there's a 7. Ho hum, 4, 3, 37, 2...37, wha!?

Then "Men Captivated by Power of C" would clearly appeal to a larger audience.

Having only very minimal experience with R, I get the impression that while python is fantastic for programmers, and the tools are probably close to or better than R's (no idea about library support), it's still a programming language, and requires one to think and structure things like a programmer.

R, at least in the beginning, comes across more like a powerful set of Excel formulas, so I think a non-programmer might pick it up faster without having good programming form.

(Not to say one is more powerful than the other, or that R programmers aren't as good as Python programmers, etc, I just think it's a difference in community viewpoints)

R is not at all like Excel, in form or function. R is actually a complete programming language, much more similar to Python in that respect. It supports two types of OOP (S3 and S4), has hundreds (if not more) of contributed packages that do things like survival analysis, 2D/3D plotting, bioinformatics, machine learning, Bayesian statistics, econometrics, numerical integration, spatial statistics, and more (http://cran.r-project.org/web/packages/)

One major advantage over Python is that it's vectorised, so you can say things like A + 1 when A is a vector (or matrix). A bit like Matlab in that regard, only that it doesn't suck as much as the Matlab ``language''.

"One major advantage over Python is that it's vectorised, so you can say things like A + 1 when A is a vector (or matrix). A bit like Matlab in that regard, only that it doesn't suck as much as the Matlab ``language''."

This is not very accurate. NumPy / SciPy provide vectorized matrix libraries, significantly faster than both R and Matlab for matrix operations. No argument though that Matlab as a language truly sucks =)

Well, I don't how NumPy can be significantly faster than R, as R basically passes most linear algebra down to BLAS and LAPACK. NumPy does the same, no?

Yes, NumPy does the same.

it strikes me that it would be pretty trivial to implement such syntax in smalltalk seeing as whitespace is not an issue and you can implement operators how you please per class. Or even better yet an "array based language" like Nial would be a good fit as well (think apl/j but with out the "thats just noise" readability issue)

back in uni, i used matlab everyday couple years ago, it was that function is file, so if i have 100 functions, i must have 100 files in a directory

i find that too limiting, found python+numarray+matplotlib and never looked at matlab ever since ... never regret

ironically i got a phone interview request from matlab at the end that year (i was on vacation, never got to that)

Way offtopic, but can you tell more about that conometrics/philosophy course? sounds interesting.

The full course title was - deep breath - The Philosophical Foundations of Statistical Modeling and Causal Inference. It was a economics professor and a visiting philosophy professor teaching their research.

And it turns out I still have the syllabus lying around, so I don't need to try to explain it myself: http://www.cs.vt.edu/~scschnei/syllabus.pdf

Thats great stuff! thanks for sharing. Any hindsigths? something rigth out of your head about the subject???

Sad to say, my only real takeaway lessons were:

- All statistical models have assumptions. Even if a model looks like it fits the data, make sure the data doesn't violate those assumptions. If it does, the model doesn't fit.

- Causation can be inferred, with confidence, just by analyzing data.

Honestly, the econometrics stuff was presented poorly. What looked like pages from a book were put up on the projector (and in some cases, I think they were book pages), and the professor would just talk through the page. Picking up anything worthwhile from his lectures was hard - he knew the class had a varied background (some CS, some philosophy, some economics, even one person from marketing), but he still went faster than my prob/stat background could keep up.

The causal inference stuff was presented better, but I think the subject matter is more intuitive in general. His (the philosophy professor) math was graph theory, which I have a firmer grasp of.

More along the lines of philosophy of science then; great stuff!!!

Economics is hot right now?

In my opinion it is. I have anticipated this downturn for the rich historical perspective on depressions it will offer.