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here are a few good math programs:
Scilab, Octave, r-project, And for making webpages emacs works great ;)

can't someone figure out a smart solution for this without asking the user to modify the source themselves?
If you need more than 64k of data use a app made for scientific work, like R, mupad or Mathematica.

I've got the laptop in question right here, (I'm typing on it now) and yeah, I dual-boot Linux (Knoppix knx-hdinstall) and Windows 2000 SP4. I need to upgrade the hard drive to give both systems the space they need to coexist happily, but even now they both are happy together. The hard drive is 10GB, there is 228MB of RAM in here, and I have both a wired NIC and a Prism-based 802.11b card to use with it. It won't run Neverwinter Nights or Doom 3, or anything like that, but from what I understand Starcraft will probably run on this. I can certainly play KMahjongg on this until the cows come home.

However, I intend to use this machine primarily on Linux...*especially* when it is hooked up to the University network. Everyone knows just how good OpenOffice.org is as an Office alternative, and how much it needs to evolve, so I won't say much about that. However, the SPSS requirement is something that takes some thought.

After some judicious googling, I found two SPSS alternatives: The R Project and GNU/PSPP. I don't know much about either program, (nor do I know much about SPSS) but it's good to know there are at least two alternatives that leap out at you when you look for it.

Linux should be a supported alternative at all Universities and Colleges throughout the world. Actually, I think Linux should be promoted over Windows, and I am not alone in thinking this..

Linux solves a lot of problems that bedevil IT departments at Colleges and Universities. It comes with great Free/Open Source alternatives to widely-bootlegged proprietary software. It is less prone to malware, viruses and trojans. It is more secure than Windows. And if you look beyond full-figured GUIs like GNOME and KDE and use trim window managers like IceWM, BlackBox, XFCE and so on, you can run graphical Linux on modest computers. Linux + KDE is actually quite nimble on my 400MHz ThinkPad 600E, and I have seen it run OK on 233MHz Pentium systems with 128MB RAM or better. If Windows 2000 will run on a machine, Linux and KDE will also run.

All these problems the article we're discussing enumerates would be ameliorated if not completely sidestepped by encouraging alternatives to a Windows Monoculture.

For a start, they left out the S programming language (started in 1976), for which John Chambers won the ACM Software Systems Award. This, and its Libre dialect R (thanks to Robert Gentleman and Ross Ihaka at University of Aukland), are in daily use by folks who have to write programs to use data.

The R Project does a really good job too. It's an S-Plus clone, and meant for all things statistical, but even if you just use its graph features it kicks butt.

Just don't buy it if you're looking for an organiser, you'll be bitterly disappointed.

As the proud owner of a now obsolete Handspring Visor and having just purchased a "new" Zaurus 5500 on Ebay, I basically agree.

Everything on a Palm is instant. It may not have every feature you want, and until recently, the screens had pretty poor resolution (Sony is the exception). However, the applications worked well, and had reasonable interfaces so they could be used quickly.

I quickly abandoned the Sharp ROM for the Zaurus in favor of OpenZaurus, but I've certainly had my frustrations with bugs and missing features. Some small things, like the ability for the application buttons to turn on the device (like a Palm), and some larger things, like having it not reboot properly the second time if you haven't suspended in between (though there is an unofficial fix).

Why am I rebooting in the first place? Because of the Zaurus' greatest aspect - it's basically a fully-functional Linux system. As such, one can tweak, test, and otherwise poke around (sometimes needing a reboot if something got messed up or you're testing something). Some Zaurus applications I've found I've had no good free Palm equivalent. Zee Cookbook is a great, if somewhat slow (when editing), way to keep a database of recipes on hand. QTJournal is a great way to take notes that are categorized by date and subject. The ability to run just about any console-based Linux software (even the statistical software R) makes it very useful as a sysadmin tool. With a small, cheap wireless card from Ebay, it is often more convenient than lugging around a laptop.

Some of the things I've wanted to use my Zaurus for before I bought it work, and some don't (yet). I got xmms running and it plays OGG files well (but the included mediaplayer with openzaurus doesn't, and the Sharp ROM's media player has a horrendous interface). I can control the Zaurus remotely via ssh (VERY handy for exploring with a real keyboard) and VNC (with the framebuffer vnc package). However, the latter doesn't offer even basic security (and I haven't gotten iptables to work), so I'm reluctant to use it often, mostly out of principle.

I got the xvnc server running, but the vncviewer client to view it simply will not connect to it, or any other vnc server. I've seen a few other reports of this behavior but no fix, and most people seem to have no problem. This combo is supposed to allow the use of any X11 application on the Zaurus itself, and more importantly for me, remote X applications (so I can control xmms on my music server with a wireless connection - the ultimate remote). If anyone has a suggestion about this, I'd be happy to hear it.

My other problem is mail - mailit (included with OpenZaurus) is simplistic, but more importantly doesn't work for one of my domains (not sure why this is). I can telnet manually to port 110 and execute pop commands fine, but this mail client barks about an unknown response from the server. QTmail doesn't work either - it gives host not found or something like that.

For the price I paid, I get far more functionality than I ever did from a Visor, but the Zaurus definitely has its frustrations. The PIM apps are nothing much to speak of, they function, but are slower than their Palm equivalents (this, again is on OpenZaurus). My greatest desire - the ability to have tree-view tasks, is not implemented on either my Handspring or any version of "todo" on the Zaurus that I've used.

So it's not perfect, but you can still pry it from my cold, dead hands.

However, making advanced graphs and plots with ROOT requires a whomping manual

Why not try R? There's not much point and click, but the command are quite ok, and as you can see from their page it generates some VERY good-looking graphs. Its GPL'ed :-)

R. It's the GNU version of S, which is a very good statistics tool.

It is a play on the name "S"/"SPlus."

http://www.r-project.org

Bah, We've allready made it all the way to R!

Having used a quite large number of statistics program, I would like to recommend R in addition to the ones in the list you linked to. R originally was a clone of S-plus, but I wouldn't be surprised if it has surpassed S-plus today. One of the nice features of R is that it's crossplatform (most Linux distros include it) and free.

The bad thing, at least for people used to excel, is that R has no fancy GUI.

what features is Excel's graphs missing, specifically?

Histograms. At least I never figured out how to make them :-( Excel made me misinterpret my results (initially) because they were displayed in some strange bar diagram which was the best possible approximation to a proper histogram. It's probably nice for quarterly reports, but I'd prefer R for scientific work. (I've heard nice things about Origin as well)

There was apparently some extra package one could purchase, but that was not possible. I may have overlooked something, but I searched around quite a bit, and it wasn't first time I used Excel.

My other peeve is that it is proprietary

You should try R. Free as in beer + speech, high level scripting, can link in compiled low level code (C, FORTRAN, maybe even Java), good graphics output, good matrix handling, lots of 3rd party extensions (most GPL'd). Not good for symbolic mathematics, though. Used heavily in the statistical community and actively developed by some very smart people.

I suspect you've got the name wrong.

The language/system called S or Splus is commonly used for such things by financial companies. There's a free gnu version called R with an enormous library of prediction software:

http://www.r-project.org/

Don't expect to predict the market with it though. Incidentally, there's an interesting book called The Predictors about a group of physicists including Stephan Wolfram who were funded by a major Wall Street firm to write market prediction software (as opposed to the more common option pricing and risk avoidance packages that assume market efficiency)

Computer benchmarking data are (usually) quite noisy. As such plain graphs, like those shown are not very useful. Simple statistical graphics like lowesss plots are much easier to interpret. Take a look at the for some ideas.

This stuff is easy to use, at least if you have a computer background, and would produce far better graphs.

I use emacs and LaTeX for writing. Those, together with AucTeX, the RefTeX mode, flyspell and the BibTeX modes, make Word look pretty primitive, for both input and results.

For my data analysis, I use Maxima and the R programming language, again in emacs, this time using the eMaxima and ESS modes, repsectively.

Notice that all this can be done, exactly the same way, with or without X. I prefer X for web browsing, and need it to see the finished product from LaTeX (the DVI files). I run X because it's prettier, and it's easier to keep track of different desktops than to keep track of the several consoles, each with a number of emacs buffers. X is nice for the kid's games, like jumpnbump, too.

I could do most of the writing using OpenOffice. I already use OO to open Word and Excel stuff that I bring home from work. OO writer is about good enough now, if Word is good enough for you. Unfortunately, Word just isn't good enough for me, and neither is OO. They seem incredibly primitive.

The problem isn't that they require X or Windows, or that they can't run on the old 486DX I occasionally use, the problem is that I don't like that way of working. I struggle so hard, and my results look so much worse than when I let LaTeX typeset my work.

Maxima has a GUI, called XMaxima, and there is a Windows version of that. There just isn't a GUI for R, but there are several project working on putting one together. Given that we have emacs and ESS, that seems like a big step down, but to each his own.

The question here isn't CLI versus GUI, though I can see why it looks that way. The question here is are you using the tools which have been developed over the years to fit the platform you're on? On Unix, those happen to be mostly CLI, and they are incredibly sophisticated. On Windows, they are entirely GUI, and they may, someday, approach LaTeX for quality and ease of use (which is different from ease of learning). LaTeX and emacs are a pain to use on Windows (I use Windows at work, since it's my employer's time I'm wasting, rather than my own). Even with Cygwin, a lot of things just aren't quite right on Windows, if you're trying to do things the Unix way.

my_plot(my_data)

Xix.

I did an assingment this week for my comparative vertebrate morphology class. [...] The instructor showed us how to do the plots in Excel.

If you are working in the sciences, you should be using a decent, scientific data analysis and plotting package. That means something like Splus/R. SPSS and Minitab are also commonly used (I don't have first hand experience with those). Matlab/Octave and Mathematica also have lots of plotting and data analysis functions. For plotting, GNUplot is also pretty decent, and for calculations, Perl/PDL and Numerical Python are other good choices. Excel, on the other hand, is hardly ever the right thing to use for scientific work; its feature set, user community, and testing is oriented towards business applications, not the sciences.

You can get "R" for free here.

If you want statistically sound data analysis and graphing, look no further than the R Project. It's a complete programming environment and is extensively used by working statisticians.

If you want statistically sound data analysis and graphing, look no further than the R Project. It's a complete programming environment and is extensively used by working statisticians.

Python, Numerical Python, various Python plot packages, and VTK also make for very powerful visualizations if you want something more do-it-yourself.

To use an example I'm familiar with, S-plus costs US$1200 and up. The libre equivalent costs US$0.00. How much do they pay you per hour?

Or were you talking about searching for cracked copies of Reader Rabbit Kindergarten?

Or use R . . I find it better than Gnuplot.

Another open-source statistical language is R. Its commercial cousin is S-Plus.

GNU R an implementation of the "S" language is about as good as it gets for statistics.

For a matlab type clone there is octave, which won't run all the fancy toolkits, but then it doesn't cost $15,000 either.

If you need to do some heavy algebraic formula crunching to solve model equations, YACAS, Maxima, and Jacal are all good.

If anything, the data analysis arena is where Linux is completely taking over many labs. It's the hardware that's hard to talk with.

Oh and not to mention that Matlab, Mathematica, and S-Plus were all available under Linux last I checked. (proprietary of course)

The carbonised version of gnuplot does work well. However, it is based on 3.7.1 which is getting on a bit now. 3.8 (the pre 4.0 version) has some very nice extra features including mouse interaction for zooming and 3d plot rotation. And some very nice surface mapping and image handling functions.
I have had no problems building version 3.8 under OSX 10.2.4. The aqua term works fine but you only get the mouse interaction when running under X11 which also works perfectly well in my experience.

On a related point you can use gnuplot from within octave a matlab-like environment which is open source. This also works fine under OSX 10.2.4 and can make use of the VecLib BLAS and Lapack accelerate libraries under OSX - details on HPC for OSX. However, I must admit my preference is with R for data analysis outside of my own code (several others have already mentioned R) purely because of the wealth of statistical functionaility available.

Especially if you're doing policy analysis (i.e., you're not data mining a 10 million record database) R is a great environment to work in. Hmm, it might be possible to process 10 million record databases with R, I just haven't tried it.

R has more or less equivalent functionality to Octave, but the programming language (S) is more interesting, and plotting functions are more sophisticated. For matrix operations, both R and Octave use BLAS.

My advice is to stay away from SAS and SPSS. These two were invented back when "everybody knows using a computer is hard". Both have GUI interfaces now, but that's just an attempt to hide the essential ugliness of their command languages. If you must use a commercial product then S+ is my recommendation.

Good luck & have fun!

I'm very interested in what you're saying in the post about multidimensional optimizers, because I do lots of statistics work, and have been recently trying to decide whether or not to write a program in C++.

I was assuming that I'd have to write my own minimization routines, but you bring up an interesting alternative.

I've never really looked much for general-purpose open source multidimensional optimizers. They must be out there, though.

A quick thought--I believe that the R language has a multidimensional optimizer. Of course, you might not want to do whatever you do directly in R, but R is open source, and you might be able to find the code somewhere.

Not particularly helpful, I know.

Does anyone else know of any good libraries for C/C++ or Java? So far the discussion has focused mostly on Maxima/Maple/Mathematica/Matlab.

At UCLA's math department, the applied people have a QUIST related research program that uses Fortran77 and shell scripts, and C++ for various parts of their code to implement the Level-Set method to simulate the growth of thin films (atom by atom construction of electronic devices). The language choice seems to be due to legacy reasons: the grad student that started it so long ago used it, and the code has continued to grow ever since(ie. legacy reasons).

Although I wouldn't call it *math* research, here at MBI, bioinformatics programs run on the cluster *seem* to be written in C or C++ for the most part. I thinks it's more of the former because that's the interface for a number of bioinformatics libraries that we have licenced. Also, these things tend to be mixed heavily with shell and perl scripts; so the language is only for ease of integration with support libraries.

For most all of my undergrad work, I saw everyone use matlab, mathimatica, and their relatives for their work. In grad school, it seems to depend more on the class and the religious leanings of the mathematician involved.

There's a class on scientific computing that uses VC++ with fortran libraries from netlib (leveraged by f2c) solve some math implementation problem (tends to vary from year to year). Prof Anderson tends to by a junkyard warrior when it comes to math code generation. But then he's the mathematician's MacGuyver. (side note: Prof Anderson is a wonderful teacher and researcher - check out his page for some handy software tools and papers. Also, look at 270B for tidbits of linear algebra optimizations).

The benifit of matlab-ish programs is that you can usually implement your math structure quickly. The down side is that if you want to use any advance optimization then it near impossible. On the other hand, if you don't have a numerical analysis background, then many of the things you try to do to optimize your code in more mundane languages are probably going to be *much* slower then matlab, et al.

All of this is assuming you'r doing numerical analysis. If you're interested in abstract algebra , then I think you're stuck with maple. good program, but I don't have a review on it since I did most of my work by pencil and paper. I did use it for one of my crypto classes and found its implementation of Z_n groups very nice... although I ended up just coding it in C++ anyway :)

Also, check out the R project as it is GNU matlab.

I have gone several ways on this question. Mathematica for symbolic solving, coding my own in C for mathematical modeling. But I did once use S-plus which is a fairly nifty matrix algebra system that might be useful to you. An open source variation is R

Octave is a nice MATLAB clone, developed from chemical engineers in the beginning, but now used extensively in virtually any area that math is usefull.

Many packages have their open source counterparts: Octave for MATLAB, R-system for SPLUS (statistics algebra system), and so forth. But IMHO you raise another issue: you can use each of these packages to do whatever calculations you want, since all of them are extended in the C/Fortran end, i.e. they can use programs written in these languages. Custom code is readily integrated. And above all, the GNU Scientific Library. If you don't like or you don't trust the numerical solvers integrated in MATLAB, you can investigate the source in the GSL.

And yes, you can use all of these together. So, what is the question again?

First, a shameless plug: I'm working on a couple of C++ applications that are currently using FOX for the GUI (and other parts). The first one is a front end to the open source stats language R and it is called Obversive. This is the most active project, but it is still real tiny. The second project is a little chat client I'm writing called Bombyx which will work on the SILC and hopefully IRC. In addition to Bombyx, I've also developed a little GUI generator with Lua that makes FOX widgets pretty quickly. It's called GIG and you can get it from the Bombyx site mentioned previously. The intention with GIG is to make it work for any toolkit, but right now it does FOX only.

When I was working on these projects, I went through a huge number of toolkits and actually tried developing fast "mock-ups". My test was to see if I could get the first window I wanted in each project using available resources. If that worked, then I would try a couple of other windows. I also reviewed the designs of each and then went with what worked best at the time.

Here's my review of each of the toolkits I used. These are just my opinions, but they are based on actually using them in various platforms (or trying to).

FOX After all of this evaluation, I found that FOX was pretty much the best at the moment for clean, simple GUI building. It has pretty good docs (way better than wxWindows), and is fairly natural for coding. It also has some nice language bindings for various languages like Python, Ruby, and Eiffel. It doesn't have quite as many controls as wxWindows, and it doesn't work natively on as many platforms, but it is much easier to build and work with and much smaller. If you are porting a Windows application to another platform, you will probably be better off with wxWindows since they try to mimick the API design as much as possible (and of course, all it's problems too). Also, if you hate macros then you'll have a problem with FOX (and wxWindows too). A testimony to how easy FOX is comes from Obversive, where one developer had no C++ or FOX experience and has already created several complicated controls. Another developer has good C++ skills, but only worked with KDE previously. He not only developed an excellent Twin Table control, but also added lots of great features to it and in very little time.

FLTK I did the original work on Bombyx with FLTK. FLTK is a nice library, but it was just missing too many widgets to be useful. It was also rather buggy and it was a real pain to do delegation. There are, however, two really nice things about FLTK: no macros, excellent GUI builder. One of huge problems with ALL other toolkits I've ran into is that, when the developers start to make their GUI builder, the decide they need an entire IDE. I already have one thank you. I doubt some OSS project is really going to out-do my tool chain right now, especially if it's also working on the widgets. Just give me a simple builder please. FLTK got it right with FLUID--their GUI builder. It's real easy to use, super fast, and does one thing well: build interfaces. It was a little weird at times, but I checked out the latest and it worked great. Also, FLTK does not use macros for messaging which is nice for the C++ purists.

Cocoa I love Cocoa, but, Apple made a huge mistake by making it a MacOSX only beast. I think Apple could rule the world if I could spend my time writing my apps with a nice UI, great tools, and an easy language (Objective-C), and then click a button and have a Windows, Linux, HP-UX, and Solaris binary. You could do this with the original NeXT stuff, but Apple crippled Cocoa and friends. So, I reserve Cocoa for other useless programming that I only do on my MacOSX. There is GNUstep, but I couldn't get anything to work with it, and half the fun of Cocoa development is using Interface Builder (IB). BTW, IB is another GUI builder that is excellent, and also does just GUI building. Let's hope the wxWindows guys figure out this pattern and write a small GUI builder rather than their entire IDE which nobody seems to be able to compile but three dudes inside the cabal (sorry, it really burns me).

wxWindows I must say that wxWindows has a ton of nice widgets (I may steal their HTML widget) and works on a huge number of platforms. But, I kept getting burned in three areas: docs, dependencies, and bugs. Their docs are horrible. You have to hunt around the net looking for tutorials which are not very good anyway. Half the example applications in the distro don't even work so you can't rely on them, and it's just really hard to figure out what to do. I also hated having to add a billion other libraries to my build just to get wxWindows to work. I found it a problem when linking my application took longer than compiling all the files from scratch. Finally, just about everytime I went to use some widget, I found some bug or limitation that made it unusable. They have improved quite a lot from what I have heard, and people really rave about it, so I think everyone should give it a try. One thing also that annoyed me about wxWindows is that it does not support delegation very well. All the other toolkits I used either support delegation easily or practically require it. In wxWindows, I spent the better part of a week just trying to get one simple delegation design working and couldn't do it despite lots of help from people on various IRC channels. To me, things are broken if I have to subclass every time I want a widget. The wxWindows XML based resource files are supposed to fix this, but I won't hold my breath.

JFC/Swing The original version of Obversive was done in Swing with lots of other goodies. Java is my primary work language, and I really wanted to use it on the Obversive project, but we just kept running into performance problems. Frankly, Swing is just a P.I.G. and is really hard to code. None of the GUI builders available we tried proved to be that useful, and eventually we dropped it becuase it was just too huge. Ironically, we were able to prototype an almost complete GUI using FOX which runs on Linux, *BSD, Windows, and MacOSX (through the XDarwin or Apple X ports) in about 3 weeks time. This was mainly in part due to the slick design of FOX and simplicity, and partly in part because of the GIG code generator I put together. Oh, and we looked at SWT, but balked at the Linux or Windows only (and the fact that the flagship, Eclipse, ALWAYS crashes on Linux does not make me happy).

So, that's my experiences with these toolkits. I hope that helps answer the question, and I hope the reply wasn't too long. To summarize: Use FOX if you need C++ and good design. Use wxWindows if porting from MFC or if native controls are important. Use Swing if speed is not a problem, but cross platform really is. Use Cocoa if you only care about MacOSX and need to get something out quick. I actually prototype some UIs in Interface Builder on MacOSX, which says a lot.

There are a lot of packages out there that will do bayesian analysis for you, if you have a fairly standard type of problem (ie you'll be doing EM with exact inference via junction tree or some approximate algorithm). A good list is here. Many of these packages have source available.

In general, as has been noted by everyone else, Mathematica is great for symbolic analysis, and Matlab is great for numeric analysis. Most people I've talked to in the field use Matlab.

I've been doing some Bayesian analysis recently and I've been writing everything from scratch in R, since it's free so I can use it at home. If you will be doing the whole shebang yourself, and you have the funds, I'd advise going with Matlab, since it tends to be more stable and well supported than R.

If you are interested in R, here is a page about the project to integrate graphical models into R.

There are a lot of packages out there that will do bayesian analysis for you, if you have a fairly standard type of problem (ie you'll be doing EM with exact inference via junction tree or some approximate algorithm). A good list is here. Many of these packages have source available.

In general, as has been noted by everyone else, Mathematica is great for symbolic analysis, and Matlab is great for numeric analysis. Most people I've talked to in the field use Matlab.

I've been doing some Bayesian analysis recently and I've been writing everything from scratch in R, since it's free so I can use it at home. If you will be doing the whole shebang yourself, and you have the funds, I'd advise going with Matlab, since it tends to be more stable and well supported than R.

If you are interested in R, here is a page about the project to integrate graphical models into R.

Matlab is probably more useful to you than Mathematica since you'll be working with simulations and/or data it sounds like. You might also want to check out R, which is designed as a statistical analysis environment and has a large number of packages---including machine learning and whatnot. Its also Open Source and GPL if you care about that sort of thing and runs on pretty much any platform you could potentially care about.

Mercifully, when I had to install Excel on Wine because OpenOffice doesn't do something as fscking simple as a polynomial regression, the damned paperclip didn't work.


Well, when you need math there's for example R and Octave instead of that excel crap.

In my field(s) of study (bioinformatics, genetics, evolution, organismal and molecular biology), I can say that the open source movement has NOT been adopted by the majority. However, I have recently come across the R project (particularly Bioconductor.org ) which is HIGHLY respected by many MS-oriented biologists from a wide scope of fields. Many computational tasks which are limited to those who can fork out $6000 per year for a license, can be performed on R (for free). Although the learning curve is somewhat steep, I've found the documentation to be exceptional and have been able to execute my needs within a day or two. It has also come to my attention that many graduate students and professors across the country are actively learning R. Granted, many of the users of R are probably "sophisticated"- familar with SAS, C, or UNIX- but it seems to me that open-source is congruent with the peer-review process that so many academics are familiar with and value. I think, if we can introduce it to enough people, and explain to them WHY the open-source method (as opposed to proprietary software) is invaluable, it will become mainstream. R seems to be leading the way in academia at least...

For statistics, at least, R can't be beat.

It's not exactly like MATLAB, Maple, or Mathematica, because it's so statistically oriented. However, it does have matrix computation and pretty good graphical capabilities.

But if you're doing statistics, you almost can't go wrong.

I haven't seen this being suggested here yet, but R for statistical computing (link) (GNU 'S') is not only open-source, but also used a lot in several scientific fields, such as statistics and machine learning (books have accompanying source code in R). It has loads of packages which allows you to do all kinds of stuff.

I haven't seen this being suggested here yet, but R for statistical computing (link) (GNU 'S') is not only open-source, but also used a lot in several scientific fields, such as statistics and machine learning (books have accompanying source code in R). It has loads of packages which allows you to do all kinds of stuff.

Oh, by the way it was the New Kind of Science book, not the Mathematica book that I read ;)

At any rate, I found some cool analysis tools that people should check out as alternatives to Mathematica for analysis and visualization of everything from battlebots to cellular automata. Without further ado:

1. PDL
2. R
3. PGPlot
4. GRASS

PDL is the most directly analagous to Mathematica or Matlab. R is, of course, like S/S+. PGPlot is for visualization. Grass is mostly for geostatistics/GIS. But it's cool enough to throw in the mix.

Anyhow, hope this helps someone out. Go forth and make a battlebot.

the "confirmation of results" & peer review point I was making had to do with crypto and offsite backup software more than with statistical software. When we're talking about crypto or storing someone else's data, it's super important to be transparent. Re: stats, well, one of my slides pointed out that up 'til now, I've always hacked numbers & graphs in Stata, which is proprietary (though most of the really good stuff is published freely, but that's another matter). We should use R, but for cost & "who controls the license" reasons as much as (if not more) than verifiability.

While we're on the verifiability point, human rights data organization techniques tend to be pretty complicated, and it helps to be able to use free software. While distributing the data (via XML) may or may not be useful, it is very important to open the data specifications. I think that means opening SQL scripts, too, and all the database software (in our current mix, the backend is postgres, the front end is Java). That's coming in about a month.

But human rights folks are pretty underfunded, and the "free as in beer" part of open source and free software is a big help, too.

slashdotters might be interested in the International Covenant on Economic, Social, and Cultural Rights, Article 15(b), which states that everyone has the right "To enjoy the benefits of scientific progress and its applications." This is a real, live, human right.

But the real bottom line to human rights and free software has to do with power. Our core rights -- to freedom of speech and free association -- are increasingly exercised in electronic media. Who controls the online world? Can any contractual obligation resulting from a license abridge your human rights? IMHO, these questions make software a human rights concern.

-- PB.

The basic point of the article still stands, you can't verify things yourself. For any scientist, that should be bad. And for a Human Rights worker, who quite certainly has someone powerful who doesn't want him to do whatever he's doing, it is of paramount importance.

Really, I find it strange that in science, you are supposed to openly document all you're doing except the software implementation of what you did... I don't think that can last.

The R Project
A statistical programming language. If some of your friends get burned in Excel by having data over 64K or whatever their limit is, try this. Grandma my not "get it", but Josh College will. Try it.

You mention something like Matlab as an alternative if it did not have poor performance. There is a very high quality free (beer, speech) program for Windows/Linux/OSX called R that is a matrix oriented language based on the S language, whose commercial analog is S-Plus. One of the really nice features is that you can write iteration or computation heavy routines in C or Fortran and dynamically load them into your R program/script.. The benefits are that you can take advantage of the high-level nature and easy scriptability of R (not to mention publication quality graphing capabilities), while taking advantage of the speed of Fortran/C.

I am always shocked at how little attention R gets considering how good it is.

For data visualization you may also want to take a look at R. It has much better support for plotting than Octave.

How could you mention feng shui, brag about your belief in a western mythology and yet get modded as insightful?

Probably because those things were irrelevant, and the posting was actually insightful.

I'm using a Crusoe-based laptop right now. It weighs about 3 pounds, gets about 7 hours of battery life in real life use. I may have bought it a little early (before they get heavily discounted if the Crusoe is discontinued), but maybe I've got a collector's item here!

The fact is, it's nice to have a laptop small enough to carry with me all the time. It's about the speed of a 500 MHz PIII, and that's fast enough for just about anything I'd want to do on it. I used it to build the Windows distribution of a well-known statistical software package. If I'd had a machine that was 3 times faster, I probably wouldn't have had it with me to do that build.

I have no idea whether OpenOffice suffers similar flaws. Perhaps I'll get ambitious enough to run some of these tests on it myself, someday. In the mean time, I do my number crunching in R. I find that LaTeX provides far more professional-looking reports than any combination of MS products.

-Carter

Social Networks has been pretty slow to come to open source world. One of the few pieces of software I know that uses them is the R project, which now has some social network analysis tools.

For visualization, though, I'm currently unaware of any open-source tools. Krackplot has a free web interface, and there is a simple Java program that uses spring-based algorithms for node positioning, but I know of nothing open-source that uses Krackplot's simulated annealing algorithm.

In general, social network analysis can be very useful, but it's results are often subject to misinterpretation. For example, a social isolate in a business might be isolated for a good reason (they are doing research, for example), so you wouldn't want to tell them to integrate themselves more. But in general, it's a great tool to get another look at data you would not normally find out about.

Also check out R. It has, IMHO, somewhat more advanced graphing stuff than gnuplot.