The language should

- Have an intepreter for rapid prototyping, ease of debugging, and maximum fun.
- Have a native code (not just byte code) compiler that produces fast code that can be run stand-alone or be called from the interactive environment.
- Have good support for vectors, multi-dimensional arrays, strings, hash tables, etc. in the standard library.
- Have a free implementation.
- Work under linux and windows (so I can transfer code easily between my desktop and my laptop).

- Computer language shootout benchmarks
- An empirical comparison of C, C++, Java, Perl, Python, Rexx and Tcl for a search/ string-processing program, Lutz Prechelt, TR 2000
- Revenge of the Nerds, Paul Graham, 2003. Interesting discussion of C++, Java, Lisp, Python and Perl.
- Why I hate (programming language) advocacy, by Mark-Jason Dominus (2000).
- Religious zealotry for Python and other languages
- Keith Waclena's programming language comparison (1997)
- Java vs Lisp, JPL study
- Lisp's macros, a response to Graham's "Beating the averages" article
- Numerical benchmark of C++, Java and Fortran, 4 Jan 2003
- Lisp vs Ocaml vs C++.
- Why Ocaml?

- Comparison of
mathematical programs for data analysis,
Stefan Steinhaus, tech report, 2000.

This is a very detailed comparison of features and speed of several interactive scientific programming environments, e.g. Matlab, Mathematica, Splus. - User comparisons of several interactive langauges
- Matlab vs R discussion, April 2004. Note: you can call matlab from R and vice versa.
- Short but sensible comparison of Splus and Matlab, 2003.
- Econometric
programming environments: Gauss, Ox and S-PLUS,
Francisco Cribari-Neto.
J. of Applied Econometrics, 12(1):77-89, 1997

Ox can not be used interactively, and has a C-style syntax (it even requires users to pre-declare variables!). Its only advantage is speed. S-Plus has tons of features and good documentation, but is slow. Gauss is somewhere in between. - MATLAB as an econometric
programming environment,
Francisco Cribari-Neto and Mark J. Jensen.
J. of Applied Econometrics, 12(6):735-432, 1997.

The basic conclusion is that Matlab has excellent graphics and sparse-matrix facilities, but is slower than Gauss/Ox (especially on code with loops), and has few statistical routines built-in (one must buy the stats toolbox). - R: Yet another econometric
programming environment,
Francisco Cribari-Neto and S. Zarkos.
J. of Applied Econometrics, 14(3):319-329, 1999.

The basic conclusion is that R is much faster than Splus on code with loops, but a little bit slower on vectorized code. (Gauss/ Ox is much faster than both; in my experience, R and Matlab have about the same speed.) However, R has much better memory management than Splus, and R is free. Otherwise, R/S/Splus are essentially the same. - Scilab, an open source alternative to Matlab.
- Octave, an open source version of matlab.
- Lush, Yann Le Cun's lisp-like Matlab replacement. It seems to meet many of the desiderata above (although it does not work on windows), and has proven adequate for real time computer vision and large-scale machine learning experiments.
- PVwave, described by John Fisher as "Matlab on steroids". It is designed for data analysis and visualization.
- R, an open-source version of S. Click here for a list of pros and cons for rewriting BNT in R. Click here for a new project to implement a graphical models library in R.