I researched & prototyped (in R), and ultimately put into production (C++) a core set of spectral risk analysis analytics that replaced outdated VaR and vol forecasts for an extremely large asset management firm and left immediately afterwards. Technically speaking, guided risk mgmt/investment decisions at the trillion-dollar scale, ran as-is for 8-10 years before being recently re-implemented in some other language.

80s.nyc has a somewhat similar street view using archival images from the 80s

awesome, and yet the 1940s images are vastly better

Monte Carlo simulation is the most oft-cited example. This tends to be most relevant to quickly American pricing options, or anything that isn't easily done in closed-form.

-- closed 2011 Nov 28.

For anyone who doesn't want to roll their own FFT, take a look at this excellent pure C implementation: http://www.fftw.org/

This is a parallel agorithm...

I actually find base R excellent for data munging and manipulation, even without using additional packages. Here is a reproducible example that very easily accomplishes what you were trying to do (first two lines just set up a sample data frame)

  set.seed(123)
  dfrm <- data.frame(height=runif(20),
                     name=paste(sample(LETTERS[1:5],20,replace=TRUE),letters[1:20]))
  subset(dfrm, grepl('^D',name), sel=height)

Basic R functions like subset, transform, with(in), reshape, aggregate, (a,ma,ta,sa,va}pply, match, grep(l), by, split, table, etc. allow you to accomplish just about any data frame munging you might want. Add on the plyr, reshape2, data.table, xts/zoo packages and you're ready to tackle just about anything.

I'm not a big fan of sqldf because imo R is not supposed to act like SQL. Using sqldf in practice would require a lot of query string manipulation and takes away from the nice functional features of R.

Nevertheless, it is very easy to write incomprehensible R code. The best way to avoid this is to take one of the existing style guides (Google, Hadley Wickham's) and adopt it seriously.

One drawback with R is that in computations like this, several intermediate data structures with one dimension equal in length to nrow(dfrm) are allocated. Traversing an iterable of tuples is a simple way to think about it, is efficient, and ties in with other technologies e.g. relational databases. R is often people's first language (e.g. science graduates) and those people would be better off learning how to iterate over tuples than learning the obscure bestiary of data structure manipulators you point out.

I tried to convince the croupier that zero was an even number, but alas ...

this is pretty neat! one quick thing -- 40th and 8th ave -> grand central: suggests S grand central -> 53rd and park: suggests 6 40th and 8th -> 53rd and park: some crazy sequence of bus routes.

http://sandytransit.alastair.is/opentripplanner-webapp/index... Nov 01 2012 21:26:37 GMT-0400 (Eastern Daylight Time)&arriveBy=false&itinID=1&wheelchair=false&preferredRoutes=&unpreferredRoutes=

no love for J?

J is more of a general-purpose array language than one specialised for numerical work.

they don't mention NSPR. is it dead? http://www.mozilla.org/projects/nspr/

It doesn't need quite the same attention as the other stuff, nor does it have the same development pace. It's using CVS for crying out loud.

Also, it's shared with other projects (Google uses it).