But if you look at Emacs-as-IDE route taken by EDE/CEDET suite, it's a kludgy beast... (I don't know how large the EDE/CEDET user community is, but most people I know forsake it in favor of the simpler facilities in Emacs).
that's just one plan. a lot of emacs people think that's a good plan, a lot of emacs people think that's a bad plan. I'm in the latter camp and prefer CommonLisp. But, hey, whatever works.
I was under the impression that while a lot of people think CL is a better plan, the work is actually under way for Guile (I guess more by the Guile team than the Emacs team). But, I feel like you would know better than I do.
Can you get it through a host like sourceforge/github, or through a package manager like macports/homebrew? I can only find a zipped file on the web...
The original statement could be interpreted in this way: the former operation 'returns a function' and in the latter, it 'bind[s] [...] arguments' - suggesting a form of destructive updating of the original function. But I agree with you that in this context, since both return functions it's an implementation detail.
I wouldn't discourage them uniquivocally - for instance, 'subset' is an idiom often used in the context of data/relational tables. That matters a little bit (enough to pay a little penalty in raw performance), I would think.
Game theory doesn't become computationally intensive until you reach mixed strategies, which start about half way through the first chapter. (If this comment is referring to the PDF preview on my website, the PDF only contains the first lesson.)
How is this better than attaching data to objects and operating on them with its methods? (Seriously, I would like to know). The syntax does not look all that different and with classes you can additionally have inheritance by which you can "get" a lot of relevant methods for free...
I entirely agree though my personal preference is for lattice over ggplot as it is faster and more flexible at the present time (the "limitations" of ggplot reflect the preference of the creator; e.g. not being able to have two axes for the same plot). But reshape and also plyr are quite useful.
R does have surprising capability for shell scripting and text processing, albeit slower than Python. I also use Python for the rapid text-processing necessary (possibly populating an SQL database) for R to eventually use.
I've been keeping an eye on SciPy, but there still seems to be a lot of "the source code is the documentation", whereas in R the documentation is usually superb and well-structured. And Matplotlib, while beautiful, seems to be more verbose than Matlab or R when it comes to customizing details of the graphics (e.g., axes, etc.). That's just my impression, but I wouldn't mind being shown otherwise.
I've built the equivalent to most of the plyr and reshape/reshape2 packages inside pandas (http://pandas.sourceforge.net, note I am in the midst of overhauling the documentation for the upcoming release). I plan to write a decent amount of side-by-side code comparisons, should definitely be useful for folks with R experience wishing to use Python for data analysis / statistics. Feedback from savvier R users than myself on pandas would also be extremely helpful.
Building a plotting library with the ease, sophistication, and beauty of ggplot2 in Python would be a big deal. A number of people I know are interested in venturing down that path (ggpy, anyone?).
