How to do stats on the surface of he earth with beautiful visualizations

includes lots of tasty goodies radiating out from the SVM core, such as generalised markovian model learning, HDF5, string kernels, multi-language bindings and an emphasis on scalability.

interactive exploratory data munging console for the more intuitive flavour of R user.

A gentle climb up a staircase, atop which is ARIMA.

"So, this post is a bit of a brain dump on rich data structures in Python and what needs to happen in the very near future. I care about them for statistical computing (I want to build a statistical computing environment that trounces R) and financial data analysis (all evidence leads me to believe that Python is the best all-around tool for the finance space). Other people in the scientific Python community want them for numerous other applications: geophysics, neuroscience, etc. It’s really hard to make everyone happy with a single solution. But the current state of affairs has me rather anxious. And I’d like to explain why..."

"pandas is a python package providing convenient data structures for time series, cross-sectional, or any other form of “labeled” data, with tools for building statistical and econometric models."

handle data in python intuitively. pass to R for fiddly bits.

Overview:

How to setup R on an EC2 instance of Ubuntu 11.04 (Natty Narwhal)
How to setup Apache Tomcat 6.0 web server and configuring it with basic authentication so that we can view our output from R on a password protected webpage
How to automate your R scripts to run as a daily cron job.

Like many people who crunch numbers frequently, I have increasingly been integrating Amazon’s cloud computing services into my daily workflow. In particular, I have been using their elastic cloud computing (EC2) on a regular basis. The service is an excellent way to offload computationally intensive work from your laptop for literally pennies on the dollar.

One drawback that I have found, however, is there are not any obvious pre-configured images, called AMIs, designed for scientific computing in the languages I use most: Python and R. ... Thus began the odyssey of modifying the StarCluster AMI to more fully support scientific computing in Python in R. I have now uploaded and made public the resulting image, which includes several hundred Python and R packages for scientific computing, statistics, machine learning, data mining and visualization.

Lately I’ve been doing some work with creating ad-hoc clusters of EC2 machines. My ultimate goal is to create a simple way to spin up a cluster of EC2 machines for use with Bryan Lewis’s very cool doRedis backend for the R foreach package. But that’s a whole other post. What I was scratching my head about today was that I’d really just like to, with a single command, spin up an EC2 instance, wait for it to come up, and then ssh into it. I do this iteration about 20 times a day when I’m testing things, so it seemed to make sense to shell script it.
To do this, one needs the EC2 command line tools installed on your workstation. In Ubuntu that’s as easy as `sudo apt-get ec2-api-tools`

categorical data has other Shannon-information-like estiamtors of "interestingness"

c graph library with r and python support

general instructions for upgrading r and keeping your packages magically installed despite its zany clunky library handling.

interesting bunch of courses on statistical data fiddling

storage search and interchange for large array data

best, simplest example of R<->python interaction i've seen

the case for replacing the language of R with somethig else that can access the same statistical power

how to find, in a statistically valid fashion, that your data fits a power law.

MI and H estimates

the python<->R interface is being rebooted

Tufte-compliant graphing tricks for R

This guy is reliably entertaining. in this case, it's a userful, arrogant rant about what you need to know about stats to push beyond the "mean request time or whatever. highlights:

>The next day we had IBM fixing the problem (turned out to be a single update index command) and we all kept our jobs. That’s what a proper analysis method can do for you.

>still I see software developers begging for gazillions of dollars to buy some crap tool that doesn’t even mention “standard deviation”, but throws “user” around like it’s Dr. Phil treating Robert Downey Jr. for heroin addiction.

network stats for R

R can haz wiki

O'reilly gives an overview of large dataset tools, including some nice machine learning ones

export from netlogo to R

learn r monkey-see-monkey-do style

interesting looking economics simulation person

how to use r plus some graph libraryies to do statistically useful social network analysis.

a noice-looking graph analysis library

Instant guid to getting started with machine learning

more free R docs.