Talbot Katz wrote: > I hope you'll indulge an ignorant outsider. I work at a financial > software firm, and the tool I currently use for my research is R, a > software environment for statistical computing and graphics. R is > designed with matrix manipulation in mind, and it's very easy to do > regression and time series modeling, and to plot the results and test > hypotheses. The kinds of functionality we rely on the most are standard > and robust versions of regression and principal component / factor > analysis, bayesian methods such as Gibbs sampling and shrinkage, and > optimization by linear, quadratic, newtonian / nonlinear, and genetic > programming; frequently used graphics include QQ plots and histograms. > In R, these procedures are all available as functions (some of them are > in auxiliary libraries that don't come with the standard distribution, > but are easily downloaded from a central repository).
I use both R and Python for my work. I think R is probably better for most of the stuff you are mentioning. I do any sort of heavy lifting--database queries/tabulation/aggregation in Python and load the resulting data frames into R for analysis and graphics. -- Michael Hoffman -- http://mail.python.org/mailman/listinfo/python-list